From 71374c44659b98d14599f815c125810d926f9e93 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Wed, 13 Aug 2025 15:37:45 +0300 Subject: [PATCH 01/67] New PR for GQL --- .../kusto/query/graph-query-language.md | 1029 +++++++++++++++++ data-explorer/kusto/query/toc.yml | 4 + data-explorer/toc.yml | 2 + 3 files changed, 1035 insertions(+) create mode 100644 data-explorer/kusto/query/graph-query-language.md diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md new file mode 100644 index 0000000000..6369ddddb4 --- /dev/null +++ b/data-explorer/kusto/query/graph-query-language.md @@ -0,0 +1,1029 @@ +--- +title: GQL - Azure Data Explorer +description: This article describes Graph Query Language (GQL) in Azure Data Explorer. +ms.reviewer: herauch +ms.topic: reference +ms.date: 08/13/2025 +--- +# Graph Query Language (GQL) in Azure Data Explorer (preview) + +> [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] + +Graph Query Language (GQL) lets you use standardized graph pattern matching in Azure Data Explorer. GQL follows the emerging ISO GQL standard for graph database queries. + +> [!NOTE] +> GQL support in Azure Data Explorer is currently in preview. Features and syntax can change based on feedback and ongoing development. + +## Introduction + +Graph Query Language (GQL) is an emerging ISO standard for querying graph databases. GQL lets you use SQL-like syntax for graph pattern matching in Azure Data Explorer, making it easier to analyze relationships in your data. This article explains how to use GQL in Azure Data Explorer, its benefits, and key features. + +Azure Data Explorer implements GQL, so you get standardized graph pattern matching capabilities. You can analyze relationships in your data using the ISO standard syntax. + +GQL in Azure Data Explorer builds on the existing [graph operators](graph-operators.md) functionality. It gives you a standardized way to write graph queries that focus on relationships and patterns between entities. + +## Getting started + +To use GQL in Azure Data Explorer, you need: + +- A graph data source that's either a [graph snapshot](graph-operators.md) or a function with a `make-graph` statement. +- Set specific client request properties (see step 2 below). + +## Step 1: Create a graph reference + +Before you use GQL, create a graph data source. For this article, we use an in-memory make-graph operator, but it's recommended to use a graph snapshot in production scenarios: + + +```gql +.create-or-alter function G() { + let nodes = datatable(id:string, lbl:string, name:string, age:int) + [ + "p1","Person","Alice",25, + "p2","Person","Bob",30, + "p3","Person","Carol",28, + "p4","Person","David",35, + "p5","Person","Emma",26, + "c1","Company","TechCorp",0, + "c2","Company","DataSoft",0, + "c3","Company","CloudInc",0, + "ct1","City","Seattle",0, + "ct2","City","Portland",0, + "ct3","City","San Francisco",0 + ]; + let edges = datatable(source:string, target:string, lbl:string, since:int) + [ + "p1","c1","works_at",2020, + "p2","c1","works_at",2022, + "p3","c2","works_at",2023, + "p4","c3","works_at",2021, + "p5","c1","works_at",2024, + "p1","ct1","located_at",2019, + "p2","ct1","located_at",2021, + "p3","ct2","located_at",2022, + "p4","ct3","located_at",2020, + "p5","ct2","located_at",2023, + "c1","ct1","located_at",2015, + "c2","ct2","located_at",2018, + "c3","ct3","located_at",2017, + "p1","p2","knows",2019, + "p2","p3","knows",2021, + "p3","p4","knows",2022, + "p4","p5","knows",2023, + "p1","p4","likes",2020, + "p4","p1","likes",2020, + "p5","p2","likes",2022 + ]; + edges + | make-graph source --> target with nodes on id +} +``` + +## Step 2: Configure GQL client request properties + +To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the query interface by using directives. + +### Using directives in the query interface + +> [!IMPORTANT] +> Run each directive separately before running your GQL query. The directives set up the query environment for GQL execution. + + +```kql +#crp query_language=gql +``` + + +```kql +#crp query_graph_reference=G() +``` + +To use labels in GQL, define the label column name: + + +```kql +#crp query_graph_label_name=lbl +``` + +> [!TIP] +> Labels are optional in GQL, but they're often used to filter nodes and edges by type. Setting the label column name lets you use labels in your GQL queries. + +### Using SDK/API + +For programmatic access, set these client request properties: +- `query_language`: Set to `"gql"` +- `query_graph_reference`: Set to your graph function name (for example, `"G()"`) +- `query_graph_label_name`: Set to your label column name (for example, `"lbl"`) + +## Step 3: Execute GQL queries + +After you finish setup, run GQL queries using standard GQL syntax. Start with the examples below to explore basic and advanced GQL features. + +## Example GQL queries + +This section shows examples of GQL queries that use the graph created in step 1. These examples cover basic and advanced GQL syntax and features. The queries use graph sample data in the help cluster. + +> [!TIP] +> GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns and build complexity gradually. + +### Example GQL query for basic pattern matching + +```gql +MATCH (n)-[e]->(n2) +RETURN COUNT(*) as CNT +``` + +**Output** + +This table shows the result of the query. + +| CNT | +|-----| +| 20 | + +--- + +### Example GQL query with labels + + +```gql +MATCH (p:Person)-[e]->(target) +RETURN p.name, target.name, e.lbl +ORDER BY p.name, target.name +LIMIT 2 +``` + +**Output** + +This table shows the result of the query. + +| p.name | target.name | e.lbl | +|--------|-------------|-------| +| Alice | Bob | knows | +| Alice | David | likes | + +## Core GQL query patterns + +GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, building from simple to complex patterns. + +### `MATCH` + +The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. + +#### Basic pattern matching without variables + +The simplest pattern matches any relationship without referencing the matched values: + + +```gql +MATCH ()-[]-() +RETURN COUNT(*) +``` + +This query finds all relationships in the graph. The empty parentheses `()` represent anonymous nodes, and `[]` represents anonymous edges. Since we don't assign variables, we can only count the matches but not access their properties. + +#### Pattern matching with variables + +To access the matched nodes and edges, assign them to variables: + + +```gql +MATCH (n)-[e]->(n2) +RETURN COUNT(*) +``` + +Now `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. We can reference these variables to access properties, but in this example we're still just counting matches. + +#### Accessing node properties + +Once you have variables, you can access properties of matched nodes: + +#### Example: Accessing node properties + +```gql +MATCH (person)-[e]->(target) +RETURN person.name, target.name, e.lbl +ORDER BY person.name +LIMIT 2 +``` + +This query returns the names of connected entities and the type of relationship between them, ordered by person.name and limited to 2 results to manage output size. +Note that, although we name those entities `person` and `target`, we don't have any restriction in place that makes sure that they are, infact, a person. + +#### Output +| person.name | target.name | e.lbl | +|-------------|-------------|------------------| +| Alice | TechCorp | works_at | +| Alice | Seattle | located_at | + + +#### Filtering by node labels + +Use labels to match specific types of nodes. Labels are specified with a colon after the variable name: + + +```gql +MATCH (person:Person) +RETURN person.name +LIMIT 5 +``` + +This query matches **only nodes with the "Person" label** and returns their names, limited to 5 results to avoid large result sets. + +#### Output +| person.name | +|-------------| +| Alice | +| Bob | +| Carol | +| David | +| Emma | + +#### Filtering by edge labels + + +#### Example: Filtering by edge labels + + +```gql +MATCH (person:Person)-[works:works_at]->(company:Company) +RETURN person.name, company.name +``` + + + +#### Filtering by edge labels without variables + +For this example, let's switch back to our original G() graph which contains the "knows" relationship: + + +```gql +#crp query_graph_reference=G() +``` + +You can filter by edge labels without assigning the edge to a variable when you don't need to access its properties: + +#### Example: Filtering by edge labels without variables + +```gql +MATCH (p1:Person)-[:knows]->(p2:Person) +RETURN p1.name, p2.name +``` + +This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but not assigned to a variable since we only need the connected nodes. + +#### Output +| p1.name | p2.name | +|----------|----------| +| Alice | Bob | +| Bob | Carol | +| Carol | David | +| David | Emma | + +#### Filtering by properties with WHERE + +Use WHERE clauses to filter based on property values: + + +```gql +MATCH (person:Person) +WHERE person.age > 25 +RETURN person.name, person.age +``` + +This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes based on the age property. + +#### Inline property filtering + +You can also filter by properties directly in the pattern using inline conditions: + + +```gql +MATCH (person:Person {name: 'Bob'}) +RETURN person.age +``` + +This query finds the specific person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters for nodes where the name property equals 'Bob'. + +#### Multiple inline conditions + +You can specify multiple property conditions inline: + + +```gql +MATCH (person:Person {name: 'Bob', age: 30}) +RETURN person as Bob +``` + +This query finds the person with both the specified name and age. It returns the person node as "Bob". + +#### Variable length paths + +For multi-hop relationships, use variable length path patterns with quantifiers: + + +```gql +MATCH (s)-[]->{1,3}(e) +RETURN s.name, e.name +``` + +This query finds paths that include between 1 to 3 hops. The `{1,3}` quantifier specifies the minimum and maximum path length. + +#### Unbounded variable length paths + +For paths of variable length, you can specify open ranges: + + +```gql +MATCH (center)-[]->{1,}(connected) +WHERE center.name = 'Alice' +RETURN DISTINCT connected.name +``` + +This query finds all nodes reachable from Alice through paths of 1 or more hops. The `{1,}` quantifier means "1 or more hops". + +**Comparable KQL:** Similar functionality is achieved using the `graph-match` operator in KQL. + +### 3.2 WHERE + +WHERE clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. + +#### Basic property filtering + +Filter nodes based on a single property condition: + +#### Example: Basic property filtering + + +```gql +MATCH (person:Person) +WHERE person.age > 26 +RETURN person.name, person.age +``` + +This query finds all Person nodes where the age property is greater than 26. + +**Output** + +| person.name | person.age | +|--------------|-------------| +| Bob | 30 | + +#### Range filtering with AND + +Combine multiple conditions to create ranges: + + +```gql +MATCH (person:Person) +WHERE person.age >= 28 AND person.age <= 35 +RETURN person.name, person.age +``` + +This query finds people with ages between 28 and 35 (inclusive). + +#### Edge property filtering + +Filter based on edge properties to find specific types of relationships: + + +```gql +MATCH (person:Person)-[wa:works_at]->(c:Company) +WHERE wa.since >= 2022 +RETURN person.name, c.name, wa.since +``` + +This query finds people who started working at companies since 2022 or later, filtering based on the `since` property of the `works_at` relationship. + +#### String pattern matching + +Use string functions to match text patterns: + + +```gql +MATCH (person:Person) +WHERE person.name STARTS WITH 'Al' +RETURN COUNT(*) +``` + +This query counts how many people have names that start with 'Al', providing a quick summary without returning potentially large result sets. + +#### String contains matching + +Check if a string contains a specific substring: + + +```gql +MATCH (person:Person) +WHERE person.name CONTAINS 'i' +RETURN person.name +``` + +This query finds all people whose names contain 'i' anywhere in the string. +Note that this is case-sensitive. + +#### Inequality comparisons + +Use comparison operators to exclude specific values: + + +```gql +MATCH (person:Person)-[wa:works_at]->(company:Company) +WHERE person.age > 25 AND company.name <> 'TechCorp' +RETURN person.name, company.name +``` + +This query finds people over 25 who work at companies other than 'TechCorp'. + +#### Null value checking + +Check for the presence or absence of property values: + + +```gql +MATCH (person:Person) +WHERE person.age IS NOT NULL +RETURN person.name, person.age +``` + +This query finds all people who have an age recorded (non-null age property). + +#### Logical OR operations + +Use OR to match multiple conditions: + + +```gql +MATCH (person:Person) +WHERE person.age > 30 OR person.name CONTAINS 'a' +RETURN person.name, person.age +``` + +This query finds people who are either over 30 years old OR have 'a' in their name. + +**Comparable KQL:** Uses standard comparison and logical operators similar to KQL `where` clauses. + +### 3.3 RETURN + +RETURN statements project results from matched patterns. They specify what data to output from your graph query. + +#### Return specific properties + +Return individual properties from matched nodes: + + +```gql +MATCH (person:Person) +RETURN person.name, person.age +``` + +This query returns the name and age properties of all Person nodes. Each row in the result contains these two values. + +#### Return with aliases + +Use aliases to rename output columns for clarity: + + +```gql +MATCH (person:Person)-[employment:works_at]->(company:Company) +RETURN person.name AS Employee, company.name AS Company, employment.since AS WorkingSince +``` + +This query returns actor names, movie titles, and character names with descriptive column headers. + +#### Return entire nodes and edges + +#### Example: Return entire nodes and edges + + +```gql +MATCH (person:Person)-[e]->(movie:Movie) +WHERE person.name = 'Kevin Bacon' +RETURN person, e, movie +``` + +This query returns the complete node and edge objects for Kevin Bacon's relationships, including all properties. + +**Output** + +| person | e | movie | +|--------|------|-------| +{"id":"2","lbl":"Person","name":"Kevin Bacon","title":"","born":1958} | {"source":"2","target":"3","lbl":"ACTED_IN","Role":"Jack Swigert"} | {"id":"3","lbl":"Movie","name":"","title":"Apollo 13","born":1995} + +#### Count matching patterns + +Use COUNT(*) to count the number of pattern matches: + + +```gql +MATCH (person:Person)-[:ACTED_IN]->(movie:Movie) +RETURN person.name, COUNT(*) AS MovieCount +``` + +This query counts how many movies each person acted in and groups the results by person name. + +#### Aggregate with MIN and MAX + +Find minimum and maximum values across all matches: + + +```gql +MATCH (person:Person) +RETURN MIN(person.age) AS Youngest, MAX(person.age) AS Oldest +``` + +This query finds the earliest and latest birth years among all people in the graph. + +#### Collect values into arrays + +Use COLLECT_LIST to gather multiple values into arrays: + + +```gql +MATCH (person:Person) +RETURN COLLECT_LIST(person.name) AS AllNames +``` + +This query collects all person names into a single array result. + +#### Return distinct values + +Remove duplicates from your results: + + +```gql +MATCH (person:Person)-[]->(company:Company) +RETURN DISTINCT company.name +``` + +This query returns each unique movie title only once, even if multiple people are connected to the same movie. + +#### Order results + +Sort your results using ORDER BY: + + +```gql +MATCH (person:Person) +RETURN person.name, person.age +ORDER BY person.age DESC +``` + +This query returns people sorted by age in descending order (oldest first). + +#### Limit result count + +Restrict the number of results returned: + + +```gql +MATCH (person:Person) +WHERE person.age > 25 +RETURN person.name +ORDER BY person.age +LIMIT 5 +``` + +This query returns only the first 5 people over 25 years old, ordered by age. + +**Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. + +### 3.4 Advanced patterns + +Advanced patterns provide powerful ways to match complex graph structures and label combinations. + +#### Label unions (OR) + +Match nodes that have any of the specified labels: + + +```gql +MATCH (entity:Person | Movie) +RETURN entity +``` + +This query matches nodes that have either the "Person" OR "Movie" label. The pipe symbol `|` represents logical OR for labels. + +#### Label intersections (AND) + +Match nodes that have all of the specified labels: + + +```gql +MATCH (person:Person & Male) +RETURN person.name +``` + +This query matches nodes that have both the "Person" AND "Male" labels. The ampersand `&` represents logical AND for labels. + +#### Label negation (NOT) + +Match nodes that do NOT have the specified label: + + +```gql +MATCH (person:!Female) +RETURN person.name +``` + +This query matches all nodes that do NOT have the "Female" label. The exclamation mark `!` represents logical NOT for labels. + +#### Variable length paths with exact range + +Match paths with a specific number of hops: + + +```gql +MATCH (s)-[es]->{2,2}(e) +RETURN s, es, e +``` + +This query finds paths that are exactly 2 hops long. The `{2,2}` quantifier specifies both minimum and maximum path length as 2. + +#### Variable length paths with open range + +Match paths with a minimum number of hops but no maximum: + + +```gql +MATCH (s)-[p]->{1,}(e) +RETURN s, e, p +``` + +This query finds paths that are 1 or more hops long. The `{1,}` quantifier means "1 or more hops" with no upper limit. + +#### Zero-length paths + +Match nodes with themselves (identity relationships): + + +```gql +MATCH (n)-[]->{0,0}(same_n) +RETURN n +``` + +This query matches each node with itself through a zero-length path. The `{0,0}` quantifier specifies exactly 0 hops, effectively returning each node paired with itself. + +#### Named path variables + +Assign entire paths to variables for later use: + + +```gql +MATCH p = (person:Person)-[:DIRECTED]->(movie:Movie) +RETURN p +``` + +This query assigns the entire pattern to the variable `p`, which can then be returned or used in other parts of the query. The path variable contains the complete sequence of nodes and edges. +k +#### Multi-hop named paths + +Create named paths that span multiple relationships: + + +```gql +MATCH full_path = (s)-[first_edge]->(middle)-[second_edge]->(e) +RETURN full_path, s.name, e.name +``` + +This query creates a named path variable `full_path` that captures a 2-hop pattern, while also returning specific properties from the s and e nodes. + +**Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. + +### Complex example – cross-town "likes" with company filter + +This query ties together multiple patterns and filters in a single statement: + +1. Find a pair of people where **p1 _likes_ p2**. +2. Link each person to their home city, requiring that p1's city name **starts with "San"** while p2 lives in a **different city**. +3. Ensure p2 **works at TechCorp**. +4. Keep only pairs where the two people have **different ages**. + +The first `RETURN` lists every qualifying match – both people, their cities, and the company – so you can inspect the raw results. +The second `RETURN` then aggregates across all matches to output a single scalar: the **average age of all "liked" people** (p2) who meet these criteria. + + +```gql +MATCH (p1:Person)-[:likes]->(p2:Person), + (p1)-[:located_at]->(home:City), + (p2)-[:located_at]->(home2:City), + (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) +WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' +RETURN p1.name, p2.name, home.name, work.name, home2.name +``` + +**Output** + +| p1.name | p2.name | home.name | work.name | home2.name | +|---------|---------|-----------|-----------|------------| +| David | Alice | San Francisco | TechCorp | Seattle | + + +```gql +MATCH (p1:Person)-[:likes]->(p2:Person), + (p1)-[:located_at]->(home:City), + (p2)-[:located_at]->(home2:City), + (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) +WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' +RETURN AVG(p2.age) AS AvgAgeLikedAcrossTowns +``` + +**Output** + +| AvgAgeLikedAcrossTowns | +|---------------------| +| 25 | + +This example demonstrates GQL's ability to express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in a single, readable statement. + +## Functions and operators + +This table lists core GQL functions and operators in Azure Data Explorer, with their comparable KQL operators: + +| GQL Function/Operator | Description | Comparable KQL Operator | Example | +|---|---|---|---| +| **Pattern Matching** | +| [`MATCH`](#31-match-patterns) | Find graph patterns | `graph-match` | `MATCH (a)-[r]->(b)` | +| **Filtering** | +| [`WHERE`](#32-where-clauses) | Filter patterns and properties | `where` | `WHERE person.age > 25` | +| [`IS NULL`](#32-where-clauses) | Check for null values | `isnull()` | `WHERE person.age IS NULL` | +| [`IS NOT NULL`](#32-where-clauses) | Check for non-null values | `isnotnull()` | `WHERE person.age IS NOT NULL` | +| **Projection** | +| [`RETURN`](#33-return-statements) | Project results | `project` | `RETURN person.name, person.age` | +| [`DISTINCT`](#33-return-statements) | Return unique values | `distinct` | `RETURN DISTINCT person.name` | +| **Aggregation Functions** | +| [`COUNT(*)`](#33-return-statements) | Count all rows | `count()` | `RETURN COUNT(*)` | +| [`COUNT()`](#33-return-statements) | Count non-null values | `count()` | `RETURN COUNT(person.name)` | +| [`SUM()`](#33-return-statements) | Sum numeric values | `sum()` | `RETURN SUM(person.age)` | +| [`MIN()`](#33-return-statements) | Minimum value | `min()` | `RETURN MIN(person.age)` | +| [`MAX()`](#33-return-statements) | Maximum value | `max()` | `RETURN MAX(person.age)` | +| [`AVG()`](#33-return-statements) | Average value | `avg()` | `RETURN AVG(person.age)` | +| [`COLLECT_LIST()`](#33-return-statements) | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | +| **Graph Functions** | +| [`labels()`](#graph-functions) | Get labels of a node or edge | Custom graph function | `RETURN labels(person)` | +| **String Functions** | +| [`UPPER()`](#32-where-clauses) | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | +| [`LOWER()`](#32-where-clauses) | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | +| [`LEFT()`](#32-where-clauses) | Extract left substring | `substring()` | `WHERE LEFT(person.name, 3) = 'Tom'` | +| [`RIGHT()`](#32-where-clauses) | Extract right substring | `substring()` | `WHERE RIGHT(person.name, 5) = 'Hanks'` | +| [`STARTS WITH`](#32-where-clauses) | String starts with pattern | `startswith()` | `WHERE person.name STARTS WITH 'Tom'` | +| [`ENDS WITH`](#32-where-clauses) | String ends with pattern | `endswith()` | `WHERE person.name ENDS WITH 'Hanks'` | +| [`CONTAINS`](#32-where-clauses) | String contains pattern | `contains()` | `WHERE person.name CONTAINS 'Tom'` | +| **Type Conversion** | +| [`CAST()`](#32-where-clauses) | Convert data types | `tostring()`, `toint()`, etc. | `CAST(person.age AS STRING)` | +| **Date/Time Functions** | +| [`ZONED_DATETIME()`](#32-where-clauses) | Create datetime from string | `todatetime()` | `ZONED_DATETIME('2024-01-01')` | +| [`CURRENT_TIMESTAMP`](#32-where-clauses) | Current timestamp | `now()` | `WHERE created < CURRENT_TIMESTAMP` | +| **Path Operations** | +| [Variable length paths](#31-match-patterns) | Multi-hop traversal | `graph-match` with quantifiers | `MATCH (a)-[*1..3]->(b)` | +| [Path variables](#34-advanced-patterns) | Named path assignment | Path variables in `graph-match` | `MATCH p = (a)-[]->(b)` | +| **Ordering and Limiting** | +| [`ORDER BY`](#33-return-statements) | Sort results | `sort` | `ORDER BY person.age DESC` | +| [`LIMIT`](#33-return-statements) | Limit result count | `take` | `LIMIT 10` | +| **Label Operations** | +| [`&` (AND)](#34-advanced-patterns) | Label intersection | Multiple label filters | `MATCH (p:Person & Male)` | +| [`\|` (OR)](#34-advanced-patterns) | Label union | Label alternatives | `MATCH (n:Person \| Movie)` | +| [`!` (NOT)](#34-advanced-patterns) | Label negation | Negative label filter | `MATCH (p:!Female)` | + +### Graph functions + +#### labels() function + +The `labels()` function shows the labels for a node or edge as an array. + +**Syntax:** + +``` +labels(entity) +``` + +**Parameters:** + +- `entity`: A node or edge variable from a matched pattern. + +**Returns:** An array of strings with all labels for the specified entity. + +**Examples:** + +Show labels for matched nodes: + +```gql +MATCH (entity) +RETURN entity.name, labels(entity) +``` + +This query returns the name and all labels for each node in the graph. + +Show labels in projections with aliases: + +```gql +MATCH (n)-[e]->(target) +RETURN n.name, labels(n) AS n_labels, labels(e) AS edge_labels, target.name +``` + +This query returns node names along with their labels and the labels of connecting edges. + +> [!NOTE] +> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions but use different syntax and operators. + +> [!TIP] +> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. + +## Common patterns + +This section shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. + +### Security: Attack path analysis + +Security analysts use GQL to find potential attack paths in network infrastructure and access control systems. + +Create a security graph function: + + +```gql +.create-or-alter function SecurityGraph() { + let entities = datatable(id:string, lbl:string, name:string, type:string, criticality:string) + [ + "u1", "User", "john.doe", "StandardUser", "Low", + "u2", "User", "admin.user", "Administrator", "High", + "s1", "System", "web-server", "WebServer", "Medium", + "s2", "System", "database", "Database", "Critical", + "s3", "System", "domain-controller", "DomainController", "Critical" + ]; + let relationships = datatable(source:string, target:string, lbl:string, access_type:string) + [ + "u1", "s1", "CAN_ACCESS", "HTTP", + "s1", "s2", "CAN_ACCESS", "SQL", + "u2", "s3", "CAN_ACCESS", "RDP", + "s3", "s2", "CAN_ACCESS", "Direct" + ]; + relationships + | make-graph source --> target with entities on id +} +``` + +Set up the security graph: + + +```gql +#crp query_graph_reference=SecurityGraph() +``` + +Find potential attack paths to critical systems. + + +```gql +MATCH (user:User)-[]->{1,3}(critical:System) +WHERE critical.criticality = 'Critical' +RETURN user.name AS AttackOrigin, critical.name AS CriticalTarget, COUNT(*) AS PathCount +``` + +This query finds users who can reach critical systems within three hops, so security teams can prioritize access reviews. + +### Social networks: Friend recommendations + +Social media platforms use GQL to find potential friend connections based on mutual relationships. + +Create a social network graph. + + +```gql +.create-or-alter function SocialGraph() { + let entities = datatable(id:string, lbl:string, name:string, location:string, interests:string) + [ + "p1", "Person", "Alice Johnson", "Seattle", "Technology,Travel", + "p2", "Person", "Bob Smith", "Seattle", "Sports,Technology", + "p3", "Person", "Carol Davis", "Portland", "Travel,Photography", + "p4", "Person", "David Wilson", "Seattle", "Technology,Gaming", + "p5", "Person", "Emma Brown", "Portland", "Photography,Art" + ]; + let relationships = datatable(source:string, target:string, lbl:string, strength:string) + [ + "p1", "p2", "FRIENDS_WITH", "Close", + "p1", "p3", "FRIENDS_WITH", "Medium", + "p2", "p4", "FRIENDS_WITH", "Close", + "p3", "p5", "FRIENDS_WITH", "Medium" + ]; + relationships + | make-graph source --> target with entities on id +} +``` + +Set up the social graph: + + +```gql +#crp query_graph_reference=SocialGraph() +``` + +Find potential friends through mutual connections. + + +```gql +MATCH (person:Person)-[:FRIENDS_WITH]->(mutual:Person)-[:FRIENDS_WITH]->(potential:Person) +WHERE person.name = 'Alice Johnson' AND person.location = potential.location +RETURN potential.name AS SuggestedFriend, mutual.name AS MutualFriend, potential.interests AS CommonInterests +``` + +This query suggests friends for Alice who have mutual connections and live in the same location. + +### Organization: Management hierarchy + +Organizations use GQL to analyze reporting structures and find management chains. + +Create an organizational graph. + + +```gql +.create-or-alter function OrgChart() { + let entities = datatable(id:string, lbl:string, name:string, title:string, department:string, level:int) + [ + "e1", "Employee", "Satya Nadella", "CEO", "Executive", 1, + "e2", "Employee", "Amy Hood", "CFO", "Finance", 2, + "e3", "Employee", "Brad Smith", "President", "Legal", 2, + "e4", "Employee", "Scott Guthrie", "EVP", "Cloud", 2, + "e5", "Employee", "Finance Director", "Director", "Finance", 3, + "e6", "Employee", "Cloud Architect", "Principal", "Cloud", 3, + "e7", "Employee", "Senior Engineer", "Senior", "Cloud", 4, + "e8", "Employee", "Software Engineer", "L63", "Cloud", 5 + ]; + let relationships = datatable(source:string, target:string, lbl:string) + [ + "e2", "e1", "REPORTS_TO", + "e3", "e1", "REPORTS_TO", + "e4", "e1", "REPORTS_TO", + "e5", "e2", "REPORTS_TO", + "e6", "e4", "REPORTS_TO", + "e7", "e6", "REPORTS_TO", + "e8", "e7", "REPORTS_TO" + ]; + relationships + | make-graph source --> target with entities on id +} +``` + +Set up the org chart: + + +```gql +#crp query_graph_reference=OrgChart() +``` + +Find all managers three levels down from CEO. + + +```gql +MATCH (ceo:Employee)-[:REPORTS_TO]-{0,3}(manager:Employee) +WHERE ceo.title = 'CEO' AND manager.level <= 4 +RETURN manager.name AS ManagerName, manager.title AS Title, manager.department AS Department +ORDER BY manager.level +``` + +This query finds all employees within three management levels of the CEO, which helps with organizational analysis and communication planning. + +## Performance optimization + +When you work with GQL queries in production environments, use these performance optimization strategies: + +### Limit path matching scope + +- Use specific label filters to reduce the search space: `MATCH (start:SpecificType)` instead of `MATCH (start)` +- Limit variable length paths with reasonable bounds: `MATCH (a)-[]->{1,3}(b)` instead of unbounded paths +- Apply `WHERE` clauses early to filter results before expensive operations + +### Use COUNT(*) for existence checks + +If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results: + +```gql +MATCH (user:User)-[:SUSPICIOUS_ACTIVITY]->(target) +WHERE user.id = 'user123' +RETURN COUNT(*) > 0 AS HasSuspiciousActivity +``` + +> [!TIP] +> Start with simple patterns, then increase complexity as needed. Monitor query performance, and adjust path lengths and filters to improve results. + +## Implementation limitations + +### Reserved keywords + +> [!IMPORTANT] +> When designing your graph schema, be aware that certain common property names may conflict with GQL reserved keywords and should be avoided or renamed. + +GQL has reserved keywords that can't be used as identifiers in queries. Some of these reserved keywords might not be immediately obvious to users (for example, `DATE` is a reserved keyword). If your graph data has properties with names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. + +### No INSERT/CREATE support + +GQL in Azure Data Explorer doesn't support INSERT and CREATE operations. Create graph structures using KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md). Use KQL for all graph creation, change, and management tasks. + +### Optional matches not supported + +GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. + +## Related content + +* [Graph operators overview](graph-operators.md) +* [make-graph operator](make-graph-operator.md) +* [graph-match operator](graph-match-operator.md) +* [Tutorial: Create your first graph](tutorials/your-first-graph.md) +* [Graph functions reference](graph-functions.md) diff --git a/data-explorer/kusto/query/toc.yml b/data-explorer/kusto/query/toc.yml index 09b52f7238..4c5f585641 100644 --- a/data-explorer/kusto/query/toc.yml +++ b/data-explorer/kusto/query/toc.yml @@ -1284,6 +1284,10 @@ items: href: graph-best-practices.md - name: Graph scenarios href: graph-scenarios.md + - name: Graph query languages + items: + - name: Query data using GQL + href: graph-query-language.md - name: Graph operators items: - name: Graph operators overview diff --git a/data-explorer/toc.yml b/data-explorer/toc.yml index 31c10c227f..cc4ca81746 100644 --- a/data-explorer/toc.yml +++ b/data-explorer/toc.yml @@ -325,6 +325,8 @@ items: href: data-lake-query-data.md - name: Query data in Azure Monitor href: query-monitor-data.md + - name: Query data using GQL + href: kusto/query/graph-query-language?view=azure-data-explorer&preserve-view=true - name: Query data in MATLAB href: query-matlab.md - name: Query data using Power Apps From afea249c5d1c96b225a629ff8de244a6b7e088b1 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Wed, 13 Aug 2025 16:00:30 +0300 Subject: [PATCH 02/67] WIP --- .../kusto/query/graph-query-language.md | 33 +++++++++---------- data-explorer/kusto/query/toc.yml | 2 ++ 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 6369ddddb4..c5382e63fe 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -206,8 +206,8 @@ ORDER BY person.name LIMIT 2 ``` -This query returns the names of connected entities and the type of relationship between them, ordered by person.name and limited to 2 results to manage output size. -Note that, although we name those entities `person` and `target`, we don't have any restriction in place that makes sure that they are, infact, a person. +This query returns the names of connected entities and the type of relationship between them, ordered by person.name and limited to two results to manage output size. +Although we name those entities `person` and `target`, we don't have any restriction in place that makes sure that they are, in fact, a person. #### Output | person.name | target.name | e.lbl | @@ -229,7 +229,8 @@ LIMIT 5 This query matches **only nodes with the "Person" label** and returns their names, limited to 5 results to avoid large result sets. -#### Output +**Output** + | person.name | |-------------| | Alice | @@ -240,7 +241,6 @@ This query matches **only nodes with the "Person" label** and returns their name #### Filtering by edge labels - #### Example: Filtering by edge labels @@ -249,11 +249,9 @@ MATCH (person:Person)-[works:works_at]->(company:Company) RETURN person.name, company.name ``` - - #### Filtering by edge labels without variables -For this example, let's switch back to our original G() graph which contains the "knows" relationship: +For this example, let's switch back to our original G() graph, which contains the "knows" relationship: ```gql @@ -271,7 +269,8 @@ RETURN p1.name, p2.name This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but not assigned to a variable since we only need the connected nodes. -#### Output +**Output** + | p1.name | p2.name | |----------|----------| | Alice | Bob | @@ -339,7 +338,7 @@ WHERE center.name = 'Alice' RETURN DISTINCT connected.name ``` -This query finds all nodes reachable from Alice through paths of 1 or more hops. The `{1,}` quantifier means "1 or more hops". +This query finds all nodes reachable from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops". **Comparable KQL:** Similar functionality is achieved using the `graph-match` operator in KQL. @@ -370,7 +369,7 @@ This query finds all Person nodes where the age property is greater than 26. #### Range filtering with AND -Combine multiple conditions to create ranges: +To create ranges, cCombine multiple conditions: ```gql @@ -419,7 +418,7 @@ RETURN person.name ``` This query finds all people whose names contain 'i' anywhere in the string. -Note that this is case-sensitive. +This is case-sensitive. #### Inequality comparisons @@ -583,7 +582,7 @@ ORDER BY person.age LIMIT 5 ``` -This query returns only the first 5 people over 25 years old, ordered by age. +This query returns only the first five people over 25 years old, ordered by age. **Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. @@ -637,7 +636,7 @@ MATCH (s)-[es]->{2,2}(e) RETURN s, es, e ``` -This query finds paths that are exactly 2 hops long. The `{2,2}` quantifier specifies both minimum and maximum path length as 2. +This query finds paths that are exactly two hops long. The `{2,2}` quantifier specifies both minimum and maximum path length as 2. #### Variable length paths with open range @@ -649,7 +648,7 @@ MATCH (s)-[p]->{1,}(e) RETURN s, e, p ``` -This query finds paths that are 1 or more hops long. The `{1,}` quantifier means "1 or more hops" with no upper limit. +This query finds paths that are 1 or more hops long. The `{1,}` quantifier means "one or more hops" with no upper limit. #### Zero-length paths @@ -661,7 +660,7 @@ MATCH (n)-[]->{0,0}(same_n) RETURN n ``` -This query matches each node with itself through a zero-length path. The `{0,0}` quantifier specifies exactly 0 hops, effectively returning each node paired with itself. +This query matches each node with itself through a zero-length path. The `{0,0}` quantifier specifies exactly zero hops, effectively returning each node paired with itself. #### Named path variables @@ -674,7 +673,7 @@ RETURN p ``` This query assigns the entire pattern to the variable `p`, which can then be returned or used in other parts of the query. The path variable contains the complete sequence of nodes and edges. -k + #### Multi-hop named paths Create named paths that span multiple relationships: @@ -1008,7 +1007,7 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity ### Reserved keywords > [!IMPORTANT] -> When designing your graph schema, be aware that certain common property names may conflict with GQL reserved keywords and should be avoided or renamed. +> When designing your graph schema, certain common property names might conflict with GQL reserved keywords and should be avoided or renamed. GQL has reserved keywords that can't be used as identifiers in queries. Some of these reserved keywords might not be immediately obvious to users (for example, `DATE` is a reserved keyword). If your graph data has properties with names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. diff --git a/data-explorer/kusto/query/toc.yml b/data-explorer/kusto/query/toc.yml index 4c5f585641..963566287a 100644 --- a/data-explorer/kusto/query/toc.yml +++ b/data-explorer/kusto/query/toc.yml @@ -1284,6 +1284,8 @@ items: href: graph-best-practices.md - name: Graph scenarios href: graph-scenarios.md + - name: Graph sample data + href: graph-sample-data.md - name: Graph query languages items: - name: Query data using GQL From 01489109b627183663009dbcaa48470ff5ba5e41 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Wed, 13 Aug 2025 16:50:39 +0300 Subject: [PATCH 03/67] formatting --- .../kusto/query/graph-query-language.md | 229 +++++++++--------- 1 file changed, 117 insertions(+), 112 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index c5382e63fe..0aea8d4c6b 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -22,16 +22,16 @@ Azure Data Explorer implements GQL, so you get standardized graph pattern matchi GQL in Azure Data Explorer builds on the existing [graph operators](graph-operators.md) functionality. It gives you a standardized way to write graph queries that focus on relationships and patterns between entities. -## Getting started +## Prerequisites To use GQL in Azure Data Explorer, you need: -- A graph data source that's either a [graph snapshot](graph-operators.md) or a function with a `make-graph` statement. -- Set specific client request properties (see step 2 below). +- A graph data source that's either a [graph snapshot](graph-operators.md) or a function with a `make-graph` statement (see step 1). +- Set specific client request properties (see step 2). ## Step 1: Create a graph reference -Before you use GQL, create a graph data source. For this article, we use an in-memory make-graph operator, but it's recommended to use a graph snapshot in production scenarios: +Before you use GQL, create a graph data source. For this article, we use an in-memory make-graph operator, but it's recommended to use a graph snapshop for production scenarios. ```gql @@ -78,11 +78,11 @@ Before you use GQL, create a graph data source. For this article, we use an in-m } ``` -## Step 2: Configure GQL client request properties +## Step 2: Configure client request properties -To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the query interface by using directives. +To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the ADX query interface by using directives. -### Using directives in the query interface +### Use Azure Data Explorer to set client request properties > [!IMPORTANT] > Run each directive separately before running your GQL query. The directives set up the query environment for GQL execution. @@ -107,9 +107,10 @@ To use labels in GQL, define the label column name: > [!TIP] > Labels are optional in GQL, but they're often used to filter nodes and edges by type. Setting the label column name lets you use labels in your GQL queries. -### Using SDK/API +### Use SDK/API to set client request properties For programmatic access, set these client request properties: + - `query_language`: Set to `"gql"` - `query_graph_reference`: Set to your graph function name (for example, `"G()"`) - `query_graph_label_name`: Set to your label column name (for example, `"lbl"`) @@ -118,9 +119,7 @@ For programmatic access, set these client request properties: After you finish setup, run GQL queries using standard GQL syntax. Start with the examples below to explore basic and advanced GQL features. -## Example GQL queries - -This section shows examples of GQL queries that use the graph created in step 1. These examples cover basic and advanced GQL syntax and features. The queries use graph sample data in the help cluster. +## Examples > [!TIP] > GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns and build complexity gradually. @@ -140,8 +139,6 @@ This table shows the result of the query. |-----| | 20 | ---- - ### Example GQL query with labels @@ -827,105 +824,119 @@ This query returns node names along with their labels and the labels of connecti > [!TIP] > Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. -## Common patterns +## Common use cases for graph queries This section shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. -### Security: Attack path analysis +### Security use case - attack path analysis Security analysts use GQL to find potential attack paths in network infrastructure and access control systems. -Create a security graph function: - - -```gql -.create-or-alter function SecurityGraph() { - let entities = datatable(id:string, lbl:string, name:string, type:string, criticality:string) - [ - "u1", "User", "john.doe", "StandardUser", "Low", - "u2", "User", "admin.user", "Administrator", "High", - "s1", "System", "web-server", "WebServer", "Medium", - "s2", "System", "database", "Database", "Critical", - "s3", "System", "domain-controller", "DomainController", "Critical" - ]; - let relationships = datatable(source:string, target:string, lbl:string, access_type:string) - [ - "u1", "s1", "CAN_ACCESS", "HTTP", - "s1", "s2", "CAN_ACCESS", "SQL", - "u2", "s3", "CAN_ACCESS", "RDP", - "s3", "s2", "CAN_ACCESS", "Direct" - ]; - relationships - | make-graph source --> target with entities on id -} -``` +1. Create a security graph function: + + + ```gql + .create-or-alter function SecurityGraph() { + let entities = datatable(id:string, lbl:string, name:string, type:string, criticality:string) + [ + "u1", "User", "john.doe", "StandardUser", "Low", + "u2", "User", "admin.user", "Administrator", "High", + "s1", "System", "web-server", "WebServer", "Medium", + "s2", "System", "database", "Database", "Critical", + "s3", "System", "domain-controller", "DomainController", "Critical" + ]; + let relationships = datatable(source:string, target:string, lbl:string, access_type:string) + [ + "u1", "s1", "CAN_ACCESS", "HTTP", + "s1", "s2", "CAN_ACCESS", "SQL", + "u2", "s3", "CAN_ACCESS", "RDP", + "s3", "s2", "CAN_ACCESS", "Direct" + ]; + relationships + | make-graph source --> target with entities on id + } + ``` + +1. Set up the security graph: + + + ```gql + #crp query_graph_reference=SecurityGraph() + ``` + +1. Find potential attack paths to critical systems. + + + ```gql + MATCH (user:User)-[]->{1,3}(critical:System) + WHERE critical.criticality = 'Critical' + RETURN user.name AS AttackOrigin, critical.name AS CriticalTarget, COUNT(*) AS PathCount + ``` -Set up the security graph: - - -```gql -#crp query_graph_reference=SecurityGraph() -``` - -Find potential attack paths to critical systems. - - -```gql -MATCH (user:User)-[]->{1,3}(critical:System) -WHERE critical.criticality = 'Critical' -RETURN user.name AS AttackOrigin, critical.name AS CriticalTarget, COUNT(*) AS PathCount -``` +**Output** This query finds users who can reach critical systems within three hops, so security teams can prioritize access reviews. -### Social networks: Friend recommendations - -Social media platforms use GQL to find potential friend connections based on mutual relationships. - -Create a social network graph. - - -```gql -.create-or-alter function SocialGraph() { - let entities = datatable(id:string, lbl:string, name:string, location:string, interests:string) - [ - "p1", "Person", "Alice Johnson", "Seattle", "Technology,Travel", - "p2", "Person", "Bob Smith", "Seattle", "Sports,Technology", - "p3", "Person", "Carol Davis", "Portland", "Travel,Photography", - "p4", "Person", "David Wilson", "Seattle", "Technology,Gaming", - "p5", "Person", "Emma Brown", "Portland", "Photography,Art" - ]; - let relationships = datatable(source:string, target:string, lbl:string, strength:string) - [ - "p1", "p2", "FRIENDS_WITH", "Close", - "p1", "p3", "FRIENDS_WITH", "Medium", - "p2", "p4", "FRIENDS_WITH", "Close", - "p3", "p5", "FRIENDS_WITH", "Medium" - ]; - relationships - | make-graph source --> target with entities on id -} -``` +| AttackOrigin | CriticalTarget | PathCount | +|--|--|--| +| admin.user | domain-controller | 1 | +| john.doe | database | 1 | +| admin.user | database | 1 | -Set up the social graph: +### Social networks use case: Friend recommendations - -```gql -#crp query_graph_reference=SocialGraph() -``` +Social media platforms use GQL to find potential friend connections based on mutual relationships. -Find potential friends through mutual connections. +1. Create a social network graph. + + + ```gql + .create-or-alter function SocialGraph() { + let entities = datatable(id:string, lbl:string, name:string, location:string, interests:string) + [ + "p1", "Person", "Alice Johnson", "Seattle", "Technology,Travel", + "p2", "Person", "Bob Smith", "Seattle", "Sports,Technology", + "p3", "Person", "Carol Davis", "Portland", "Travel,Photography", + "p4", "Person", "David Wilson", "Seattle", "Technology,Gaming", + "p5", "Person", "Emma Brown", "Portland", "Photography,Art" + ]; + let relationships = datatable(source:string, target:string, lbl:string, strength:string) + [ + "p1", "p2", "FRIENDS_WITH", "Close", + "p1", "p3", "FRIENDS_WITH", "Medium", + "p2", "p4", "FRIENDS_WITH", "Close", + "p3", "p5", "FRIENDS_WITH", "Medium" + ]; + relationships + | make-graph source --> target with entities on id + } + ``` + +1. Set up the social graph: + + + ```gql + #crp query_graph_reference=SocialGraph() + ``` + +1. Find potential friends through mutual connections. + + + ```gql + MATCH (person:Person)-[:FRIENDS_WITH]->(mutual:Person)-[:FRIENDS_WITH]->(potential:Person) + WHERE person.name = 'Alice Johnson' AND person.location = potential.location + RETURN potential.name AS SuggestedFriend, mutual.name AS MutualFriend, potential.interests AS CommonInterests + ``` - -```gql -MATCH (person:Person)-[:FRIENDS_WITH]->(mutual:Person)-[:FRIENDS_WITH]->(potential:Person) -WHERE person.name = 'Alice Johnson' AND person.location = potential.location -RETURN potential.name AS SuggestedFriend, mutual.name AS MutualFriend, potential.interests AS CommonInterests -``` +**Output** This query suggests friends for Alice who have mutual connections and live in the same location. -### Organization: Management hierarchy +| SuggestedFriend | MutualFriend | CommonInterests | +|--|--|--| +| David Wilson | Bob Smith | Technology,Gaming | + +### Organization use case: Management hierarchy Organizations use GQL to analyze reporting structures and find management chains. @@ -983,13 +994,16 @@ This query finds all employees within three management levels of the CEO, which When you work with GQL queries in production environments, use these performance optimization strategies: -### Limit path matching scope +> [!TIP] +> Start with simple patterns, then increase complexity as needed. Monitor query performance, and adjust path lengths and filters to improve results. + +**Limit path matching scope**: - Use specific label filters to reduce the search space: `MATCH (start:SpecificType)` instead of `MATCH (start)` - Limit variable length paths with reasonable bounds: `MATCH (a)-[]->{1,3}(b)` instead of unbounded paths - Apply `WHERE` clauses early to filter results before expensive operations -### Use COUNT(*) for existence checks +**Use COUNT(*) for existence checks**: If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results: @@ -999,25 +1013,16 @@ WHERE user.id = 'user123' RETURN COUNT(*) > 0 AS HasSuspiciousActivity ``` -> [!TIP] -> Start with simple patterns, then increase complexity as needed. Monitor query performance, and adjust path lengths and filters to improve results. - -## Implementation limitations - -### Reserved keywords - -> [!IMPORTANT] -> When designing your graph schema, certain common property names might conflict with GQL reserved keywords and should be avoided or renamed. - -GQL has reserved keywords that can't be used as identifiers in queries. Some of these reserved keywords might not be immediately obvious to users (for example, `DATE` is a reserved keyword). If your graph data has properties with names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. +## Limitations -### No INSERT/CREATE support +- **Reserved keywords**: GQL has reserved keywords that can't be used as identifiers in queries. Some of these reserved keywords might not be immediately obvious to users (for example, `DATE` is a reserved keyword). If your graph data has properties with names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. -GQL in Azure Data Explorer doesn't support INSERT and CREATE operations. Create graph structures using KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md). Use KQL for all graph creation, change, and management tasks. + > [!IMPORTANT] + > When designing your graph schema, certain common property names might conflict with GQL reserved keywords and should be avoided or renamed. -### Optional matches not supported +- **No `INSERT`/`CREATE` support**: GQL in Azure Data Explorer doesn't support `INSERT` or `CREATE` operations to modify graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. -GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. +- **Optional matches not supported**: GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. ## Related content From b76922f4ab64eea4393b8c9ce3f74cfae9293d28 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Wed, 13 Aug 2025 17:22:20 +0300 Subject: [PATCH 04/67] heading levels flattened --- .../kusto/query/graph-query-language.md | 128 ++++++++---------- 1 file changed, 59 insertions(+), 69 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 0aea8d4c6b..18684db10a 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -119,8 +119,6 @@ For programmatic access, set these client request properties: After you finish setup, run GQL queries using standard GQL syntax. Start with the examples below to explore basic and advanced GQL features. -## Examples - > [!TIP] > GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns and build complexity gradually. @@ -162,11 +160,17 @@ This table shows the result of the query. GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, building from simple to complex patterns. -### `MATCH` +* [MATCH](#match-examples): The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. Similar functionality is achieved using the `graph-match` operator in KQL. + +* [WHERE](#where-examples): Uses standard comparison and logical operators similar to KQL `where` clauses. WHERE clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. + +* [RETURN](#return-examples): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. -The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. +* [Advanced patterns](#advanced-pattern-examples): Advanced patterns provide powerful ways to match complex graph structures and label combinations. -#### Basic pattern matching without variables +## MATCH examples + +### Basic pattern matching without variables The simplest pattern matches any relationship without referencing the matched values: @@ -178,7 +182,7 @@ RETURN COUNT(*) This query finds all relationships in the graph. The empty parentheses `()` represent anonymous nodes, and `[]` represents anonymous edges. Since we don't assign variables, we can only count the matches but not access their properties. -#### Pattern matching with variables +### Pattern matching with variables To access the matched nodes and edges, assign them to variables: @@ -190,11 +194,10 @@ RETURN COUNT(*) Now `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. We can reference these variables to access properties, but in this example we're still just counting matches. -#### Accessing node properties +### Access node properties Once you have variables, you can access properties of matched nodes: -#### Example: Accessing node properties ```gql MATCH (person)-[e]->(target) @@ -203,17 +206,17 @@ ORDER BY person.name LIMIT 2 ``` +**Output** + This query returns the names of connected entities and the type of relationship between them, ordered by person.name and limited to two results to manage output size. Although we name those entities `person` and `target`, we don't have any restriction in place that makes sure that they are, in fact, a person. -#### Output | person.name | target.name | e.lbl | |-------------|-------------|------------------| | Alice | TechCorp | works_at | | Alice | Seattle | located_at | - -#### Filtering by node labels +### Filter by node labels Use labels to match specific types of nodes. Labels are specified with a colon after the variable name: @@ -224,10 +227,10 @@ RETURN person.name LIMIT 5 ``` -This query matches **only nodes with the "Person" label** and returns their names, limited to 5 results to avoid large result sets. - **Output** +This query matches **only nodes with the "Person" label** and returns their names, limited to 5 results to avoid large result sets. + | person.name | |-------------| | Alice | @@ -236,9 +239,7 @@ This query matches **only nodes with the "Person" label** and returns their name | David | | Emma | -#### Filtering by edge labels - -#### Example: Filtering by edge labels +### Filtering by edge labels ```gql @@ -246,7 +247,7 @@ MATCH (person:Person)-[works:works_at]->(company:Company) RETURN person.name, company.name ``` -#### Filtering by edge labels without variables +### Filtering by edge labels without variables For this example, let's switch back to our original G() graph, which contains the "knows" relationship: @@ -255,19 +256,20 @@ For this example, let's switch back to our original G() graph, which contains th #crp query_graph_reference=G() ``` -You can filter by edge labels without assigning the edge to a variable when you don't need to access its properties: - #### Example: Filtering by edge labels without variables + +You can filter by edge labels without assigning the edge to a variable when you don't need to access its properties. + ```gql MATCH (p1:Person)-[:knows]->(p2:Person) RETURN p1.name, p2.name ``` -This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but not assigned to a variable since we only need the connected nodes. - **Output** +This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but not assigned to a variable since we only need the connected nodes. + | p1.name | p2.name | |----------|----------| | Alice | Bob | @@ -275,7 +277,7 @@ This query finds Person nodes connected to other Person nodes through "knows" re | Carol | David | | David | Emma | -#### Filtering by properties with WHERE +### Filtering by properties with WHERE Use WHERE clauses to filter based on property values: @@ -288,7 +290,7 @@ RETURN person.name, person.age This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes based on the age property. -#### Inline property filtering +### Inline property filtering You can also filter by properties directly in the pattern using inline conditions: @@ -300,7 +302,7 @@ RETURN person.age This query finds the specific person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters for nodes where the name property equals 'Bob'. -#### Multiple inline conditions +### Multiple inline conditions You can specify multiple property conditions inline: @@ -324,7 +326,7 @@ RETURN s.name, e.name This query finds paths that include between 1 to 3 hops. The `{1,3}` quantifier specifies the minimum and maximum path length. -#### Unbounded variable length paths +### Unbounded variable length paths For paths of variable length, you can specify open ranges: @@ -337,18 +339,12 @@ RETURN DISTINCT connected.name This query finds all nodes reachable from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops". -**Comparable KQL:** Similar functionality is achieved using the `graph-match` operator in KQL. - -### 3.2 WHERE - -WHERE clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. +## WHERE examples -#### Basic property filtering +### Basic property filtering Filter nodes based on a single property condition: -#### Example: Basic property filtering - ```gql MATCH (person:Person) @@ -356,15 +352,15 @@ WHERE person.age > 26 RETURN person.name, person.age ``` -This query finds all Person nodes where the age property is greater than 26. - **Output** +This query finds all Person nodes where the age property is greater than 26. + | person.name | person.age | |--------------|-------------| | Bob | 30 | -#### Range filtering with AND +### Range filtering with AND To create ranges, cCombine multiple conditions: @@ -377,7 +373,7 @@ RETURN person.name, person.age This query finds people with ages between 28 and 35 (inclusive). -#### Edge property filtering +### Edge property filtering Filter based on edge properties to find specific types of relationships: @@ -390,7 +386,7 @@ RETURN person.name, c.name, wa.since This query finds people who started working at companies since 2022 or later, filtering based on the `since` property of the `works_at` relationship. -#### String pattern matching +### String pattern matching Use string functions to match text patterns: @@ -403,7 +399,7 @@ RETURN COUNT(*) This query counts how many people have names that start with 'Al', providing a quick summary without returning potentially large result sets. -#### String contains matching +### String contains matching Check if a string contains a specific substring: @@ -417,7 +413,7 @@ RETURN person.name This query finds all people whose names contain 'i' anywhere in the string. This is case-sensitive. -#### Inequality comparisons +### Inequality comparisons Use comparison operators to exclude specific values: @@ -430,7 +426,7 @@ RETURN person.name, company.name This query finds people over 25 who work at companies other than 'TechCorp'. -#### Null value checking +### Null value checking Check for the presence or absence of property values: @@ -443,7 +439,7 @@ RETURN person.name, person.age This query finds all people who have an age recorded (non-null age property). -#### Logical OR operations +### Logical OR operations Use OR to match multiple conditions: @@ -456,13 +452,9 @@ RETURN person.name, person.age This query finds people who are either over 30 years old OR have 'a' in their name. -**Comparable KQL:** Uses standard comparison and logical operators similar to KQL `where` clauses. - -### 3.3 RETURN +## RETURN Examples -RETURN statements project results from matched patterns. They specify what data to output from your graph query. - -#### Return specific properties +### Return specific properties Return individual properties from matched nodes: @@ -474,7 +466,7 @@ RETURN person.name, person.age This query returns the name and age properties of all Person nodes. Each row in the result contains these two values. -#### Return with aliases +### Return with aliases Use aliases to rename output columns for clarity: @@ -486,9 +478,7 @@ RETURN person.name AS Employee, company.name AS Company, employment.since AS Wor This query returns actor names, movie titles, and character names with descriptive column headers. -#### Return entire nodes and edges - -#### Example: Return entire nodes and edges +### Return entire nodes and edges ```gql @@ -497,15 +487,15 @@ WHERE person.name = 'Kevin Bacon' RETURN person, e, movie ``` -This query returns the complete node and edge objects for Kevin Bacon's relationships, including all properties. - **Output** +This query returns the complete node and edge objects for Kevin Bacon's relationships, including all properties. + | person | e | movie | |--------|------|-------| {"id":"2","lbl":"Person","name":"Kevin Bacon","title":"","born":1958} | {"source":"2","target":"3","lbl":"ACTED_IN","Role":"Jack Swigert"} | {"id":"3","lbl":"Movie","name":"","title":"Apollo 13","born":1995} -#### Count matching patterns +### Count matching patterns Use COUNT(*) to count the number of pattern matches: @@ -517,7 +507,7 @@ RETURN person.name, COUNT(*) AS MovieCount This query counts how many movies each person acted in and groups the results by person name. -#### Aggregate with MIN and MAX +### Aggregate with MIN and MAX Find minimum and maximum values across all matches: @@ -529,7 +519,7 @@ RETURN MIN(person.age) AS Youngest, MAX(person.age) AS Oldest This query finds the earliest and latest birth years among all people in the graph. -#### Collect values into arrays +### Collect values into arrays Use COLLECT_LIST to gather multiple values into arrays: @@ -541,7 +531,7 @@ RETURN COLLECT_LIST(person.name) AS AllNames This query collects all person names into a single array result. -#### Return distinct values +### Return distinct values Remove duplicates from your results: @@ -553,7 +543,7 @@ RETURN DISTINCT company.name This query returns each unique movie title only once, even if multiple people are connected to the same movie. -#### Order results +### Order results Sort your results using ORDER BY: @@ -566,7 +556,7 @@ ORDER BY person.age DESC This query returns people sorted by age in descending order (oldest first). -#### Limit result count +### Limit result count Restrict the number of results returned: @@ -583,11 +573,11 @@ This query returns only the first five people over 25 years old, ordered by age. **Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. -### 3.4 Advanced patterns +## Advanced pattern examples Advanced patterns provide powerful ways to match complex graph structures and label combinations. -#### Label unions (OR) +### Label unions (OR) Match nodes that have any of the specified labels: @@ -599,7 +589,7 @@ RETURN entity This query matches nodes that have either the "Person" OR "Movie" label. The pipe symbol `|` represents logical OR for labels. -#### Label intersections (AND) +### Label intersections (AND) Match nodes that have all of the specified labels: @@ -611,7 +601,7 @@ RETURN person.name This query matches nodes that have both the "Person" AND "Male" labels. The ampersand `&` represents logical AND for labels. -#### Label negation (NOT) +### Label negation (NOT) Match nodes that do NOT have the specified label: @@ -623,7 +613,7 @@ RETURN person.name This query matches all nodes that do NOT have the "Female" label. The exclamation mark `!` represents logical NOT for labels. -#### Variable length paths with exact range +### Variable length paths with exact range Match paths with a specific number of hops: @@ -635,7 +625,7 @@ RETURN s, es, e This query finds paths that are exactly two hops long. The `{2,2}` quantifier specifies both minimum and maximum path length as 2. -#### Variable length paths with open range +### Variable length paths with open range Match paths with a minimum number of hops but no maximum: @@ -647,7 +637,7 @@ RETURN s, e, p This query finds paths that are 1 or more hops long. The `{1,}` quantifier means "one or more hops" with no upper limit. -#### Zero-length paths +### Zero-length paths Match nodes with themselves (identity relationships): @@ -659,7 +649,7 @@ RETURN n This query matches each node with itself through a zero-length path. The `{0,0}` quantifier specifies exactly zero hops, effectively returning each node paired with itself. -#### Named path variables +### Named path variables Assign entire paths to variables for later use: @@ -671,7 +661,7 @@ RETURN p This query assigns the entire pattern to the variable `p`, which can then be returned or used in other parts of the query. The path variable contains the complete sequence of nodes and edges. -#### Multi-hop named paths +### Multi-hop named paths Create named paths that span multiple relationships: From 23c2276f974754864a0c154823af0c57d95b0a97 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Wed, 13 Aug 2025 17:39:31 +0300 Subject: [PATCH 05/67] formatting --- .../kusto/query/graph-query-language.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 18684db10a..6e16685087 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -160,15 +160,17 @@ This table shows the result of the query. GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, building from simple to complex patterns. -* [MATCH](#match-examples): The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. Similar functionality is achieved using the `graph-match` operator in KQL. +* [MATCH](#basic-pattern-matching-without-variables): The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. Similar functionality is achieved using the `graph-match` operator in KQL. -* [WHERE](#where-examples): Uses standard comparison and logical operators similar to KQL `where` clauses. WHERE clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. +* [WHERE](#basic-property-filtering): Uses standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. -* [RETURN](#return-examples): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. +* [RETURN](#return-specific-properties): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. * [Advanced patterns](#advanced-pattern-examples): Advanced patterns provide powerful ways to match complex graph structures and label combinations. -## MATCH examples +* [Complex multi-pattern queries](#complex-multi-pattern-queries): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. + +## Examples ### Basic pattern matching without variables @@ -339,8 +341,6 @@ RETURN DISTINCT connected.name This query finds all nodes reachable from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops". -## WHERE examples - ### Basic property filtering Filter nodes based on a single property condition: @@ -452,8 +452,6 @@ RETURN person.name, person.age This query finds people who are either over 30 years old OR have 'a' in their name. -## RETURN Examples - ### Return specific properties Return individual properties from matched nodes: @@ -675,6 +673,8 @@ This query creates a named path variable `full_path` that captures a 2-hop patte **Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. +## Complex multi-pattern queries + ### Complex example – cross-town "likes" with company filter This query ties together multiple patterns and filters in a single statement: From dcc8996462583174d39c8d4cc43f8f473a3ad146 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Wed, 13 Aug 2025 17:48:55 +0300 Subject: [PATCH 06/67] format functions table --- .../kusto/query/graph-query-language.md | 68 +++++++++---------- 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 6e16685087..d663251015 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -725,54 +725,54 @@ This example demonstrates GQL's ability to express complex multi-pattern queries This table lists core GQL functions and operators in Azure Data Explorer, with their comparable KQL operators: -| GQL Function/Operator | Description | Comparable KQL Operator | Example | +| GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | |---|---|---|---| | **Pattern Matching** | -| [`MATCH`](#31-match-patterns) | Find graph patterns | `graph-match` | `MATCH (a)-[r]->(b)` | +| `MATCH` | Find graph patterns | `graph-match` | `MATCH (a)-[r]->(b)` | | **Filtering** | -| [`WHERE`](#32-where-clauses) | Filter patterns and properties | `where` | `WHERE person.age > 25` | -| [`IS NULL`](#32-where-clauses) | Check for null values | `isnull()` | `WHERE person.age IS NULL` | -| [`IS NOT NULL`](#32-where-clauses) | Check for non-null values | `isnotnull()` | `WHERE person.age IS NOT NULL` | +| `WHERE` | Filter patterns and properties | `where` | `WHERE person.age > 25` | +| `IS NULL` | Check for null values | `isnull()` | `WHERE person.age IS NULL` | +| `IS NOT NULL` | Check for non-null values | `isnotnull()` | `WHERE person.age IS NOT NULL` | | **Projection** | -| [`RETURN`](#33-return-statements) | Project results | `project` | `RETURN person.name, person.age` | -| [`DISTINCT`](#33-return-statements) | Return unique values | `distinct` | `RETURN DISTINCT person.name` | +| `RETURN` | Project results | `project` | `RETURN person.name, person.age` | +| `DISTINCT` | Return unique values | `distinct` | `RETURN DISTINCT person.name` | | **Aggregation Functions** | -| [`COUNT(*)`](#33-return-statements) | Count all rows | `count()` | `RETURN COUNT(*)` | -| [`COUNT()`](#33-return-statements) | Count non-null values | `count()` | `RETURN COUNT(person.name)` | -| [`SUM()`](#33-return-statements) | Sum numeric values | `sum()` | `RETURN SUM(person.age)` | -| [`MIN()`](#33-return-statements) | Minimum value | `min()` | `RETURN MIN(person.age)` | -| [`MAX()`](#33-return-statements) | Maximum value | `max()` | `RETURN MAX(person.age)` | -| [`AVG()`](#33-return-statements) | Average value | `avg()` | `RETURN AVG(person.age)` | -| [`COLLECT_LIST()`](#33-return-statements) | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | +| `COUNT(*)` | Count all rows | `count()` | `RETURN COUNT(*)` | +| `COUNT()` | Count non-null values | `count()` | `RETURN COUNT(person.name)` | +| `SUM()` | Sum numeric values | `sum()` | `RETURN SUM(person.age)` | +| `MIN()` | Minimum value | `min()` | `RETURN MIN(person.age)` | +| `MAX()` | Maximum value | `max()` | `RETURN MAX(person.age)` | +| `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | +| `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | | **Graph Functions** | -| [`labels()`](#graph-functions) | Get labels of a node or edge | Custom graph function | `RETURN labels(person)` | +| `labels()` | Get labels of a node or edge | Custom graph function | `RETURN labels(person)` | | **String Functions** | -| [`UPPER()`](#32-where-clauses) | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | -| [`LOWER()`](#32-where-clauses) | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | -| [`LEFT()`](#32-where-clauses) | Extract left substring | `substring()` | `WHERE LEFT(person.name, 3) = 'Tom'` | -| [`RIGHT()`](#32-where-clauses) | Extract right substring | `substring()` | `WHERE RIGHT(person.name, 5) = 'Hanks'` | -| [`STARTS WITH`](#32-where-clauses) | String starts with pattern | `startswith()` | `WHERE person.name STARTS WITH 'Tom'` | -| [`ENDS WITH`](#32-where-clauses) | String ends with pattern | `endswith()` | `WHERE person.name ENDS WITH 'Hanks'` | -| [`CONTAINS`](#32-where-clauses) | String contains pattern | `contains()` | `WHERE person.name CONTAINS 'Tom'` | +| `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | +| `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | +| `LEFT()` | Extract left substring | `substring()` | `WHERE LEFT(person.name, 3) = 'Tom'` | +| `RIGHT()` | Extract right substring | `substring()` | `WHERE RIGHT(person.name, 5) = 'Hanks'` | +| `STARTS WITH` | String starts with pattern | `startswith()` | `WHERE person.name STARTS WITH 'Tom'` | +| `ENDS WITH` | String ends with pattern | `endswith()` | `WHERE person.name ENDS WITH 'Hanks'` | +| `CONTAINS` | String contains pattern | `contains()` | `WHERE person.name CONTAINS 'Tom'` | | **Type Conversion** | -| [`CAST()`](#32-where-clauses) | Convert data types | `tostring()`, `toint()`, etc. | `CAST(person.age AS STRING)` | +| `CAST()` | Convert data types | `tostring()`, `toint()`, etc. | `CAST(person.age AS STRING)` | | **Date/Time Functions** | -| [`ZONED_DATETIME()`](#32-where-clauses) | Create datetime from string | `todatetime()` | `ZONED_DATETIME('2024-01-01')` | -| [`CURRENT_TIMESTAMP`](#32-where-clauses) | Current timestamp | `now()` | `WHERE created < CURRENT_TIMESTAMP` | +| `ZONED_DATETIME()` | Create datetime from string | `todatetime()` | `ZONED_DATETIME('2024-01-01')` | +| `CURRENT_TIMESTAMP` | Current timestamp | `now()` | `WHERE created < CURRENT_TIMESTAMP` | | **Path Operations** | -| [Variable length paths](#31-match-patterns) | Multi-hop traversal | `graph-match` with quantifiers | `MATCH (a)-[*1..3]->(b)` | -| [Path variables](#34-advanced-patterns) | Named path assignment | Path variables in `graph-match` | `MATCH p = (a)-[]->(b)` | +| Variable length paths | Multi-hop traversal | `graph-match` with quantifiers | `MATCH (a)-[*1..3]->(b)` | +| Path variables | Named path assignment | Path variables in `graph-match` | `MATCH p = (a)-[]->(b)` | | **Ordering and Limiting** | -| [`ORDER BY`](#33-return-statements) | Sort results | `sort` | `ORDER BY person.age DESC` | -| [`LIMIT`](#33-return-statements) | Limit result count | `take` | `LIMIT 10` | +| `ORDER BY` | Sort results | `sort` | `ORDER BY person.age DESC` | +| `LIMIT` | Limit result count | `take` | `LIMIT 10` | | **Label Operations** | -| [`&` (AND)](#34-advanced-patterns) | Label intersection | Multiple label filters | `MATCH (p:Person & Male)` | -| [`\|` (OR)](#34-advanced-patterns) | Label union | Label alternatives | `MATCH (n:Person \| Movie)` | -| [`!` (NOT)](#34-advanced-patterns) | Label negation | Negative label filter | `MATCH (p:!Female)` | +| `&` (AND) | Label intersection | Multiple label filters | `MATCH (p:Person & Male)` | +| `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | +| `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | -### Graph functions +## Graph functions -#### labels() function +### labels() function The `labels()` function shows the labels for a node or edge as an array. From e5a493451bdac29282e46a9310b206f25e8debb9 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Wed, 13 Aug 2025 17:57:45 +0300 Subject: [PATCH 07/67] headings --- .../kusto/query/graph-query-language.md | 22 +++++++------------ 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index d663251015..a0591c377a 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -166,11 +166,11 @@ GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern * [RETURN](#return-specific-properties): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. -* [Advanced patterns](#advanced-pattern-examples): Advanced patterns provide powerful ways to match complex graph structures and label combinations. +[Advanced patterns](#examples-advanced-patterns-and-complex-multi-patterns) * [Complex multi-pattern queries](#complex-multi-pattern-queries): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. -## Examples +## Examples: Core GQL query patters ### Basic pattern matching without variables @@ -241,7 +241,7 @@ This query matches **only nodes with the "Person" label** and returns their name | David | | Emma | -### Filtering by edge labels +### Filter by edge labels ```gql @@ -249,7 +249,7 @@ MATCH (person:Person)-[works:works_at]->(company:Company) RETURN person.name, company.name ``` -### Filtering by edge labels without variables +### Filter by edge labels without variables For this example, let's switch back to our original G() graph, which contains the "knows" relationship: @@ -258,8 +258,6 @@ For this example, let's switch back to our original G() graph, which contains th #crp query_graph_reference=G() ``` -#### Example: Filtering by edge labels without variables - You can filter by edge labels without assigning the edge to a variable when you don't need to access its properties. @@ -279,7 +277,7 @@ This query finds Person nodes connected to other Person nodes through "knows" re | Carol | David | | David | Emma | -### Filtering by properties with WHERE +### Filter by properties with WHERE Use WHERE clauses to filter based on property values: @@ -292,7 +290,7 @@ RETURN person.name, person.age This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes based on the age property. -### Inline property filtering +### Inline property filters You can also filter by properties directly in the pattern using inline conditions: @@ -571,7 +569,7 @@ This query returns only the first five people over 25 years old, ordered by age. **Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. -## Advanced pattern examples +## Examples: Advanced patterns and complex multi-patterns Advanced patterns provide powerful ways to match complex graph structures and label combinations. @@ -673,8 +671,6 @@ This query creates a named path variable `full_path` that captures a 2-hop patte **Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. -## Complex multi-pattern queries - ### Complex example – cross-town "likes" with company filter This query ties together multiple patterns and filters in a single statement: @@ -770,9 +766,7 @@ This table lists core GQL functions and operators in Azure Data Explorer, with t | `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | | `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | -## Graph functions - -### labels() function +## Custom graph function: labels() The `labels()` function shows the labels for a node or edge as an array. From 2bbda62e43f819b620b751eb204ba49b4cd00e7d Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Thu, 14 Aug 2025 12:53:10 +0300 Subject: [PATCH 08/67] split topic --- .../query/graph-query-language-functions.md | 88 ++++++++++ .../query/graph-query-language-use-cases.md | 155 ++++++++++++++++++ .../kusto/query/graph-query-language.md | 51 +----- .../kusto/query/graph-sample-data.md | 155 ++++++++++++++++++ data-explorer/kusto/query/toc.yml | 6 +- 5 files changed, 404 insertions(+), 51 deletions(-) create mode 100644 data-explorer/kusto/query/graph-query-language-functions.md create mode 100644 data-explorer/kusto/query/graph-query-language-use-cases.md create mode 100644 data-explorer/kusto/query/graph-sample-data.md diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md new file mode 100644 index 0000000000..39ebad0180 --- /dev/null +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -0,0 +1,88 @@ +--- +title: #Required; Keep the title body to 60-65 chars max including spaces and brand +description: #Required; Keep the description within 100- and 165-characters including spaces +author: #Required; your GitHub user alias, with correct capitalization +ms.author: #Required; microsoft alias of author +ms.service: #Required; use the name-string related to slug in ms.product/ms.service +ms.topic: concept-article #Required; leave this attribute/value as-is. +ms.date: #Required; mm/dd/yyyy format. + +#CustomerIntent: As a , I want so that . +--- +# Graph query language (GQL) functions and operators + +Graph Query Language (GQL) is a powerful language for querying graph data in Azure Data Explorer. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. + +This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. + +| GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | +|---|---|---|---| +| **Pattern Matching** | +| `MATCH` | Find graph patterns | `graph-match` | `MATCH (a)-[r]->(b)` | +| **Filtering** | +| `WHERE` | Filter patterns and properties | `where` | `WHERE person.age > 25` | +| `IS NULL` | Check for null values | `isnull()` | `WHERE person.age IS NULL` | +| `IS NOT NULL` | Check for non-null values | `isnotnull()` | `WHERE person.age IS NOT NULL` | +| **Projection** | +| `RETURN` | Project results | `project` | `RETURN person.name, person.age` | +| `DISTINCT` | Return unique values | `distinct` | `RETURN DISTINCT person.name` | +| **Aggregation Functions** | +| `COUNT(*)` | Count all rows | `count()` | `RETURN COUNT(*)` | +| `COUNT()` | Count non-null values | `count()` | `RETURN COUNT(person.name)` | +| `SUM()` | Sum numeric values | `sum()` | `RETURN SUM(person.age)` | +| `MIN()` | Minimum value | `min()` | `RETURN MIN(person.age)` | +| `MAX()` | Maximum value | `max()` | `RETURN MAX(person.age)` | +| `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | +| `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | +| **Graph Functions** | +| `labels()` | Get labels of a node or edge | Custom graph function | `RETURN labels(person)` | +| **String Functions** | +| `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | +| `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | +| `LEFT()` | Extract left substring | `substring()` | `WHERE LEFT(person.name, 3) = 'Tom'` | +| `RIGHT()` | Extract right substring | `substring()` | `WHERE RIGHT(person.name, 5) = 'Hanks'` | +| `STARTS WITH` | String starts with pattern | `startswith()` | `WHERE person.name STARTS WITH 'Tom'` | +| `ENDS WITH` | String ends with pattern | `endswith()` | `WHERE person.name ENDS WITH 'Hanks'` | +| `CONTAINS` | String contains pattern | `contains()` | `WHERE person.name CONTAINS 'Tom'` | +| **Type Conversion** | +| `CAST()` | Convert data types | `tostring()`, `toint()`, etc. | `CAST(person.age AS STRING)` | +| **Date/Time Functions** | +| `ZONED_DATETIME()` | Create datetime from string | `todatetime()` | `ZONED_DATETIME('2024-01-01')` | +| `CURRENT_TIMESTAMP` | Current timestamp | `now()` | `WHERE created < CURRENT_TIMESTAMP` | +| **Path Operations** | +| Variable length paths | Multi-hop traversal | `graph-match` with quantifiers | `MATCH (a)-[*1..3]->(b)` | +| Path variables | Named path assignment | Path variables in `graph-match` | `MATCH p = (a)-[]->(b)` | +| **Ordering and Limiting** | +| `ORDER BY` | Sort results | `sort` | `ORDER BY person.age DESC` | +| `LIMIT` | Limit result count | `take` | `LIMIT 10` | +| **Label Operations** | +| `&` (AND) | Label intersection | Multiple label filters | `MATCH (p:Person & Male)` | +| `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | +| `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | + +## Labels() function + + +## Prerequisites +TODO: [List the prerequisites if appropriate] + + + + + +## Next step +TODO: Add your next step link(s) +> [!div class="nextstepaction"] +> [Write concepts](article-concept.md) + + + +## Related content +TODO: Add your next step link(s) +- [Write concepts](article-concept.md) + + diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md new file mode 100644 index 0000000000..d74f3e1d7a --- /dev/null +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -0,0 +1,155 @@ +--- +title: #Required; Keep the title body to 60-65 chars max including spaces and brand +description: #Required; Keep the description within 100- and 165-characters including spaces +author: #Required; your GitHub user alias, with correct capitalization +ms.author: #Required; microsoft alias of author +ms.service: #Required; use the name-string related to slug in ms.product/ms.service +ms.topic: concept-article #Required; leave this attribute/value as-is. +ms.date: #Required; mm/dd/yyyy format. + +#CustomerIntent: As a , I want so that . +--- + + + + + +# [\ concept(s)] +TODO: Add your heading + + + +[Introductory paragraph] +TODO: Add your introductory paragraph + + + +## Prerequisites +TODO: [List the prerequisites if appropriate] + + + +## [Section 1 heading] +TODO: add your content + +## [Section 2 heading] +TODO: add your content + +## [Section n heading] +TODO: add your content + + + +## Next step + +TODO: Add your next step link(s) + +> [!div class="nextstepaction"] +> [Write concepts](article-concept.md) + + + +## Related content + +TODO: Add your next step link(s) + +- [Write concepts](article-concept.md) + + + + + + +## Next step +TODO: Add your next step link(s) +> [!div class="nextstepaction"] +> [Write concepts](article-concept.md) + + + +## Related content +TODO: Add your next step link(s) +- [Write concepts](article-concept.md) + + diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index a0591c377a..d2af09f731 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -22,7 +22,7 @@ Azure Data Explorer implements GQL, so you get standardized graph pattern matchi GQL in Azure Data Explorer builds on the existing [graph operators](graph-operators.md) functionality. It gives you a standardized way to write graph queries that focus on relationships and patterns between entities. -## Prerequisites +## Getting Started To use GQL in Azure Data Explorer, you need: @@ -717,55 +717,6 @@ RETURN AVG(p2.age) AS AvgAgeLikedAcrossTowns This example demonstrates GQL's ability to express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in a single, readable statement. -## Functions and operators - -This table lists core GQL functions and operators in Azure Data Explorer, with their comparable KQL operators: - -| GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | -|---|---|---|---| -| **Pattern Matching** | -| `MATCH` | Find graph patterns | `graph-match` | `MATCH (a)-[r]->(b)` | -| **Filtering** | -| `WHERE` | Filter patterns and properties | `where` | `WHERE person.age > 25` | -| `IS NULL` | Check for null values | `isnull()` | `WHERE person.age IS NULL` | -| `IS NOT NULL` | Check for non-null values | `isnotnull()` | `WHERE person.age IS NOT NULL` | -| **Projection** | -| `RETURN` | Project results | `project` | `RETURN person.name, person.age` | -| `DISTINCT` | Return unique values | `distinct` | `RETURN DISTINCT person.name` | -| **Aggregation Functions** | -| `COUNT(*)` | Count all rows | `count()` | `RETURN COUNT(*)` | -| `COUNT()` | Count non-null values | `count()` | `RETURN COUNT(person.name)` | -| `SUM()` | Sum numeric values | `sum()` | `RETURN SUM(person.age)` | -| `MIN()` | Minimum value | `min()` | `RETURN MIN(person.age)` | -| `MAX()` | Maximum value | `max()` | `RETURN MAX(person.age)` | -| `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | -| `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | -| **Graph Functions** | -| `labels()` | Get labels of a node or edge | Custom graph function | `RETURN labels(person)` | -| **String Functions** | -| `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | -| `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | -| `LEFT()` | Extract left substring | `substring()` | `WHERE LEFT(person.name, 3) = 'Tom'` | -| `RIGHT()` | Extract right substring | `substring()` | `WHERE RIGHT(person.name, 5) = 'Hanks'` | -| `STARTS WITH` | String starts with pattern | `startswith()` | `WHERE person.name STARTS WITH 'Tom'` | -| `ENDS WITH` | String ends with pattern | `endswith()` | `WHERE person.name ENDS WITH 'Hanks'` | -| `CONTAINS` | String contains pattern | `contains()` | `WHERE person.name CONTAINS 'Tom'` | -| **Type Conversion** | -| `CAST()` | Convert data types | `tostring()`, `toint()`, etc. | `CAST(person.age AS STRING)` | -| **Date/Time Functions** | -| `ZONED_DATETIME()` | Create datetime from string | `todatetime()` | `ZONED_DATETIME('2024-01-01')` | -| `CURRENT_TIMESTAMP` | Current timestamp | `now()` | `WHERE created < CURRENT_TIMESTAMP` | -| **Path Operations** | -| Variable length paths | Multi-hop traversal | `graph-match` with quantifiers | `MATCH (a)-[*1..3]->(b)` | -| Path variables | Named path assignment | Path variables in `graph-match` | `MATCH p = (a)-[]->(b)` | -| **Ordering and Limiting** | -| `ORDER BY` | Sort results | `sort` | `ORDER BY person.age DESC` | -| `LIMIT` | Limit result count | `take` | `LIMIT 10` | -| **Label Operations** | -| `&` (AND) | Label intersection | Multiple label filters | `MATCH (p:Person & Male)` | -| `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | -| `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | - ## Custom graph function: labels() The `labels()` function shows the labels for a node or edge as an array. diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md new file mode 100644 index 0000000000..d74f3e1d7a --- /dev/null +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -0,0 +1,155 @@ +--- +title: #Required; Keep the title body to 60-65 chars max including spaces and brand +description: #Required; Keep the description within 100- and 165-characters including spaces +author: #Required; your GitHub user alias, with correct capitalization +ms.author: #Required; microsoft alias of author +ms.service: #Required; use the name-string related to slug in ms.product/ms.service +ms.topic: concept-article #Required; leave this attribute/value as-is. +ms.date: #Required; mm/dd/yyyy format. + +#CustomerIntent: As a , I want so that . +--- + + + + + +# [\ concept(s)] +TODO: Add your heading + + + +[Introductory paragraph] +TODO: Add your introductory paragraph + + + +## Prerequisites +TODO: [List the prerequisites if appropriate] + + + +## [Section 1 heading] +TODO: add your content + +## [Section 2 heading] +TODO: add your content + +## [Section n heading] +TODO: add your content + + + +## Next step + +TODO: Add your next step link(s) + +> [!div class="nextstepaction"] +> [Write concepts](article-concept.md) + + + +## Related content + +TODO: Add your next step link(s) + +- [Write concepts](article-concept.md) + + + + + + +## Next step +TODO: Add your next step link(s) +> [!div class="nextstepaction"] +> [Write concepts](article-concept.md) + + + +## Related content +TODO: Add your next step link(s) +- [Write concepts](article-concept.md) + + diff --git a/data-explorer/kusto/query/toc.yml b/data-explorer/kusto/query/toc.yml index 963566287a..17b0a2072b 100644 --- a/data-explorer/kusto/query/toc.yml +++ b/data-explorer/kusto/query/toc.yml @@ -1286,10 +1286,14 @@ items: href: graph-scenarios.md - name: Graph sample data href: graph-sample-data.md - - name: Graph query languages + - name: Graph query language (GQL) items: - name: Query data using GQL href: graph-query-language.md + - name: GQL functions and operators + href: graph-query-language-functions.md + - name: GQL examples and use cases + href: graph-query-language-use-cases.md - name: Graph operators items: - name: Graph operators overview From 3cd0f6a478b4daa214f1d1e969d9a215063aa5a5 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Thu, 14 Aug 2025 14:39:12 +0200 Subject: [PATCH 09/67] refactor: clean up metadata and remove template comments in graph sample data --- .../kusto/query/graph-sample-data.md | 188 ++++++------------ 1 file changed, 57 insertions(+), 131 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index d74f3e1d7a..27058fe447 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -1,155 +1,81 @@ --- -title: #Required; Keep the title body to 60-65 chars max including spaces and brand -description: #Required; Keep the description within 100- and 165-characters including spaces -author: #Required; your GitHub user alias, with correct capitalization -ms.author: #Required; microsoft alias of author -ms.service: #Required; use the name-string related to slug in ms.product/ms.service -ms.topic: concept-article #Required; leave this attribute/value as-is. -ms.date: #Required; mm/dd/yyyy format. - -#CustomerIntent: As a , I want so that . +title: Graph sample datasets and examples +description: Graph examples +ms.topic: conceptual +ms.service: azure-data-explorer +ms.author: +author: +ms.date: 08/14/2025 --- - +```kusto +graph("Simple") +| graph-match (n)-[e]->(m) +project source = n, target = m, relationship = e +| take 10 +``` - +Purpose: Azure Active Directory privilege escalation and attack path analysis. -# [\ concept(s)] -TODO: Add your heading +```kusto +graph("BloodHound_Entra") +| graph-match (user)-[path*1..4]->(resource) +project user_principal = user, accessed_resource = resource +| take 10 +``` - - -[Introductory paragraph] -TODO: Add your introductory paragraph - - - -## Prerequisites -TODO: [List the prerequisites if appropriate] - - - -## [Section 1 heading] -TODO: add your content - -## [Section 2 heading] -TODO: add your content - -## [Section n heading] -TODO: add your content - - - -## Next step - -TODO: Add your next step link(s) - -> [!div class="nextstepaction"] -> [Write concepts](article-concept.md) - - - -## Related content - -TODO: Add your next step link(s) - -- [Write concepts](article-concept.md) - - - - - - -## Next step -TODO: Add your next step link(s) -> [!div class="nextstepaction"] -> [Write concepts](article-concept.md) - - +```kusto +graph("BloodHound_AD") +| graph-match (user)-[path*1..4]->(resource) +project user_account = user, accessed_resource = resource +| take 10 +``` ## Related content -TODO: Add your next step link(s) -- [Write concepts](article-concept.md) - - +- [Graph semantics overview](./graph-semantics-overview.md) +- [Graph operators](./graph-operators.md) +- [Graph best practices](./graph-best-practices.md) +- [Persistent graph overview](../management/graph/graph-persistent-overview.md) From 072d21aeffe4423f41b1f3e7213299cadf3a72d0 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Thu, 14 Aug 2025 16:05:32 +0300 Subject: [PATCH 10/67] restructure test --- .../query/graph-query-language-functions.md | 68 +- .../query/graph-query-language-use-cases.md | 764 ++++++++++++++--- .../kusto/query/graph-query-language.md | 773 +----------------- 3 files changed, 725 insertions(+), 880 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 39ebad0180..0edd9496bb 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -9,10 +9,16 @@ ms.date: #Required; mm/dd/yyyy format. #CustomerIntent: As a , I want so that . --- -# Graph query language (GQL) functions and operators +# Graph query language (GQL) functions, operators, and examples Graph Query Language (GQL) is a powerful language for querying graph data in Azure Data Explorer. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. +> [!NOTE] +> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions but use different syntax and operators. + +> [!TIP] +> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. + This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. | GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | @@ -35,7 +41,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | | `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | | **Graph Functions** | -| `labels()` | Get labels of a node or edge | Custom graph function | `RETURN labels(person)` | +| `labels()` | Get labels of a node or edge | Custom graph function. See the details in [Labels() (GQL function)](#labels-gql-function) | `RETURN labels(person)` | | **String Functions** | | `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | | `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | @@ -60,29 +66,57 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | | `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | -## Labels() function +## Labels() (GQL function) + +The `labels()` function shows the labels for a node or edge as an array. + +**Syntax:** + +`labels(`*entity*`) + +**Parameters:** + +- `entity`: A node or edge variable from a matched pattern. +**Returns:** -## Prerequisites -TODO: [List the prerequisites if appropriate] +Returns an array of strings with all labels for the specified entity. +**Examples:** +Show labels for matched nodes: + +```gql +MATCH (entity) +RETURN entity.name, labels(entity) +``` +**Output** +This query returns the name and all labels for each node in the graph. -## Next step -TODO: Add your next step link(s) -> [!div class="nextstepaction"] -> [Write concepts](article-concept.md) +| entity.name | labels(entity) | +|--|--| +| john.doe | ["User"] | +| admin.user | ["User"] | +| web-server | ["System"] | +| database | ["System"] | +| domain-controller | ["System"] | - +Show labels in projections with aliases: + +```gql +MATCH (n)-[e]->(target) +RETURN n.name, labels(n) AS n_labels, labels(e) AS edge_labels, target.name +``` -## Related content -TODO: Add your next step link(s) -- [Write concepts](article-concept.md) +**Output** - +| n.name | n_labels | edge_labels | target.name | +|--|--|--|--| +| john.doe | ["User"] | ["CAN_ACCESS"] | web-server | +| admin.user | ["User"] | ["CAN_ACCESS"] | domain-controller | +| web-server | ["System"] | ["CAN_ACCESS"] | database | +| domain-controller | ["System"] | ["CAN_ACCESS"] | database | diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index d74f3e1d7a..29a43a3749 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -9,147 +9,727 @@ ms.date: #Required; mm/dd/yyyy format. #CustomerIntent: As a , I want so that . --- +# Core GQL query patterns and examples - +## Example: Basic pattern matching without variables - +```gql +MATCH ()-[]-() +RETURN COUNT(*) +``` -Reflect the concept that undergirds an action, not the action itself. The H1 must start with: +This query finds all relationships in the graph. The empty parentheses `()` represent anonymous nodes, and `[]` represents anonymous edges. Since we don't assign variables, we can only count the matches but not access their properties. -* "\ concept(s)", or -* "What is \?", or -* "\ overview" +## Example: Pattern matching with variables -Concept articles are primarily distinguished by what they aren't: +To access the matched nodes and edges, assign them to variables: -* They aren't procedural articles. They don't show how to complete a task. -* They don't have specific end states, other than conveying an underlying idea, and don't have concrete, sequential actions for the user to take. + +```gql +MATCH (n)-[e]->(n2) +RETURN COUNT(*) +``` -One clear sign of a procedural article would be the use of a numbered list. With rare exception, numbered lists shouldn't appear in Concept articles. +Now `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. We can reference these variables to access properties, but in this example we're still just counting matches. ---> +## Example: Access node properties -# [\ concept(s)] -TODO: Add your heading +Once you have variables, you can access properties of matched nodes: - +```gql +MATCH (person)-[e]->(target) +RETURN person.name, target.name, e.lbl +ORDER BY person.name +LIMIT 2 +``` -Required. Lead with a light intro that describes what the article covers. Answer the fundamental “why would I want to know this?” question. Keep it short. +**Output** -* Answer the fundamental "Why do I want this knowledge?" question. -* Don't start the article with a bunch of notes or caveats. -* Don’t link away from the article in the introduction. -* For definitive concepts, it's better to lead with a sentence in the form, "X is a (type of) Y that does Z." +This query returns the names of connected entities and the type of relationship between them, ordered by person.name and limited to two results to manage output size. +Although we name those entities `person` and `target`, we don't have any restriction in place that makes sure that they are, in fact, a person. ---> +| person.name | target.name | e.lbl | +|-------------|-------------|------------------| +| Alice | TechCorp | works_at | +| Alice | Seattle | located_at | -[Introductory paragraph] -TODO: Add your introductory paragraph +## Example: Filter by node labels - +```gql +MATCH (person:Person) +RETURN person.name +LIMIT 5 +``` ---> +**Output** -## Prerequisites -TODO: [List the prerequisites if appropriate] +This query matches **only nodes with the "Person" label** and returns their names, limited to 5 results to avoid large result sets. - +```gql +MATCH (person:Person)-[works:works_at]->(company:Company) +RETURN person.name, company.name +``` ---> +### Filter by edge labels without variables -## [Section 1 heading] -TODO: add your content +For this example, let's switch back to our original G() graph, which contains the "knows" relationship: -## [Section 2 heading] -TODO: add your content + +```gql +#crp query_graph_reference=G() +``` -## [Section n heading] -TODO: add your content +You can filter by edge labels without assigning the edge to a variable when you don't need to access its properties. - +```gql +MATCH (p1:Person)-[:knows]->(p2:Person) +RETURN p1.name, p2.name +``` -Optional: You have two options for manually curated links in this pattern: Next step and Related content. You don't have to use either, but don't use both. - - For Next step, provide one link to the next step in a sequence. Use the blue box format - - For Related content provide 1-3 links. Include some context so the customer can determine why they would click the link. Add a context sentence for the following links. +**Output** ---> +This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but not assigned to a variable since we only need the connected nodes. -## Next step +| p1.name | p2.name | +|----------|----------| +| Alice | Bob | +| Bob | Carol | +| Carol | David | +| David | Emma | -TODO: Add your next step link(s) +## Example: Filter by properties with WHERE -> [!div class="nextstepaction"] -> [Write concepts](article-concept.md) +Use WHERE clauses to filter based on property values: - + +```gql +MATCH (person:Person) +WHERE person.age > 25 +RETURN person.name, person.age +``` -## Related content +This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes based on the age property. -TODO: Add your next step link(s) +## Example: Inline property filters -- [Write concepts](article-concept.md) +You can also filter by properties directly in the pattern using inline conditions: - + +```gql +MATCH (person:Person {name: 'Bob'}) +RETURN person.age +``` +This query finds the specific person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters for nodes where the name property equals 'Bob'. - + +```gql +MATCH (person:Person {name: 'Bob', age: 30}) +RETURN person as Bob +``` -## Next step -TODO: Add your next step link(s) -> [!div class="nextstepaction"] -> [Write concepts](article-concept.md) +This query finds the person with both the specified name and age. It returns the person node as "Bob". - +## Example: Variable length paths -## Related content -TODO: Add your next step link(s) -- [Write concepts](article-concept.md) +For multi-hop relationships, use variable length path patterns with quantifiers: - +```gql +MATCH (s)-[]->{1,3}(e) +RETURN s.name, e.name +``` ---> +This query finds paths that include between 1 to 3 hops. The `{1,3}` quantifier specifies the minimum and maximum path length. + +## Example: Unbounded variable length paths + +For paths of variable length, you can specify open ranges: + + +```gql +MATCH (center)-[]->{1,}(connected) +WHERE center.name = 'Alice' +RETURN DISTINCT connected.name +``` + +This query finds all nodes reachable from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops". + +## Example: Basic property filtering + +Filter nodes based on a single property condition: + + +```gql +MATCH (person:Person) +WHERE person.age > 26 +RETURN person.name, person.age +``` + +**Output** + +This query finds all Person nodes where the age property is greater than 26. + +| person.name | person.age | +|--------------|-------------| +| Bob | 30 | + +## Example: Range filtering with AND + +To create ranges, cCombine multiple conditions: + + +```gql +MATCH (person:Person) +WHERE person.age >= 28 AND person.age <= 35 +RETURN person.name, person.age +``` + +This query finds people with ages between 28 and 35 (inclusive). + +## Example: Edge property filtering + +Filter based on edge properties to find specific types of relationships: + + +```gql +MATCH (person:Person)-[wa:works_at]->(c:Company) +WHERE wa.since >= 2022 +RETURN person.name, c.name, wa.since +``` + +This query finds people who started working at companies since 2022 or later, filtering based on the `since` property of the `works_at` relationship. + +## Example: String pattern matching + +Use string functions to match text patterns: + + +```gql +MATCH (person:Person) +WHERE person.name STARTS WITH 'Al' +RETURN COUNT(*) +``` + +This query counts how many people have names that start with 'Al', providing a quick summary without returning potentially large result sets. + +### String contains matching + +Check if a string contains a specific substring: + + +```gql +MATCH (person:Person) +WHERE person.name CONTAINS 'i' +RETURN person.name +``` + +This query finds all people whose names contain 'i' anywhere in the string. +This is case-sensitive. + +## Example: Inequality comparisons + +Use comparison operators to exclude specific values: + + +```gql +MATCH (person:Person)-[wa:works_at]->(company:Company) +WHERE person.age > 25 AND company.name <> 'TechCorp' +RETURN person.name, company.name +``` + +This query finds people over 25 who work at companies other than 'TechCorp'. + +## Example: Null value checking + +Check for the presence or absence of property values: + + +```gql +MATCH (person:Person) +WHERE person.age IS NOT NULL +RETURN person.name, person.age +``` + +This query finds all people who have an age recorded (non-null age property). + +## Example: Logical OR operations + +Use OR to match multiple conditions: + + +```gql +MATCH (person:Person) +WHERE person.age > 30 OR person.name CONTAINS 'a' +RETURN person.name, person.age +``` + +This query finds people who are either over 30 years old OR have 'a' in their name. + +## Example: Return specific properties + +Return individual properties from matched nodes: + + +```gql +MATCH (person:Person) +RETURN person.name, person.age +``` + +This query returns the name and age properties of all Person nodes. Each row in the result contains these two values. + +### Return with aliases + +Use aliases to rename output columns for clarity: + + +```gql +MATCH (person:Person)-[employment:works_at]->(company:Company) +RETURN person.name AS Employee, company.name AS Company, employment.since AS WorkingSince +``` + +This query returns actor names, movie titles, and character names with descriptive column headers. + +### Return entire nodes and edges + + +```gql +MATCH (person:Person)-[e]->(movie:Movie) +WHERE person.name = 'Kevin Bacon' +RETURN person, e, movie +``` + +**Output** + +This query returns the complete node and edge objects for Kevin Bacon's relationships, including all properties. + +| person | e | movie | +|--------|------|-------| +{"id":"2","lbl":"Person","name":"Kevin Bacon","title":"","born":1958} | {"source":"2","target":"3","lbl":"ACTED_IN","Role":"Jack Swigert"} | {"id":"3","lbl":"Movie","name":"","title":"Apollo 13","born":1995} + +## Example: Count matching patterns + +Use COUNT(*) to count the number of pattern matches: + + +```gql +MATCH (person:Person)-[:ACTED_IN]->(movie:Movie) +RETURN person.name, COUNT(*) AS MovieCount +``` + +This query counts how many movies each person acted in and groups the results by person name. + +### Aggregate with MIN and MAX + +Find minimum and maximum values across all matches: + + +```gql +MATCH (person:Person) +RETURN MIN(person.age) AS Youngest, MAX(person.age) AS Oldest +``` + +This query finds the earliest and latest birth years among all people in the graph. + +### Collect values into arrays + +Use COLLECT_LIST to gather multiple values into arrays: + + +```gql +MATCH (person:Person) +RETURN COLLECT_LIST(person.name) AS AllNames +``` + +This query collects all person names into a single array result. + +## Example: Return distinct values + +Remove duplicates from your results: + + +```gql +MATCH (person:Person)-[]->(company:Company) +RETURN DISTINCT company.name +``` + +This query returns each unique movie title only once, even if multiple people are connected to the same movie. + +### Order results + +Sort your results using ORDER BY: + + +```gql +MATCH (person:Person) +RETURN person.name, person.age +ORDER BY person.age DESC +``` + +This query returns people sorted by age in descending order (oldest first). + +### Limit result count + +Restrict the number of results returned: + + +```gql +MATCH (person:Person) +WHERE person.age > 25 +RETURN person.name +ORDER BY person.age +LIMIT 5 +``` + +This query returns only the first five people over 25 years old, ordered by age. + +**Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. + +## Examples: Advanced patterns + +Advanced patterns provide powerful ways to match complex graph structures and label combinations. + +### Label unions (OR) + +Match nodes that have any of the specified labels: + + +```gql +MATCH (entity:Person | Movie) +RETURN entity +``` + +This query matches nodes that have either the "Person" OR "Movie" label. The pipe symbol `|` represents logical OR for labels. + +### Label intersections (AND) + +Match nodes that have all of the specified labels: + + +```gql +MATCH (person:Person & Male) +RETURN person.name +``` + +This query matches nodes that have both the "Person" AND "Male" labels. The ampersand `&` represents logical AND for labels. + +### Label negation (NOT) + +Match nodes that do NOT have the specified label: + + +```gql +MATCH (person:!Female) +RETURN person.name +``` + +This query matches all nodes that do NOT have the "Female" label. The exclamation mark `!` represents logical NOT for labels. + +### Variable length paths with exact range + +Match paths with a specific number of hops: + + +```gql +MATCH (s)-[es]->{2,2}(e) +RETURN s, es, e +``` + +This query finds paths that are exactly two hops long. The `{2,2}` quantifier specifies both minimum and maximum path length as 2. + +### Variable length paths with open range + +Match paths with a minimum number of hops but no maximum: + + +```gql +MATCH (s)-[p]->{1,}(e) +RETURN s, e, p +``` + +This query finds paths that are 1 or more hops long. The `{1,}` quantifier means "one or more hops" with no upper limit. + +### Zero-length paths + +Match nodes with themselves (identity relationships): + + +```gql +MATCH (n)-[]->{0,0}(same_n) +RETURN n +``` + +This query matches each node with itself through a zero-length path. The `{0,0}` quantifier specifies exactly zero hops, effectively returning each node paired with itself. + +### Named path variables + +Assign entire paths to variables for later use: + + +```gql +MATCH p = (person:Person)-[:DIRECTED]->(movie:Movie) +RETURN p +``` + +This query assigns the entire pattern to the variable `p`, which can then be returned or used in other parts of the query. The path variable contains the complete sequence of nodes and edges. + +### Multi-hop named paths + +Create named paths that span multiple relationships: + + +```gql +MATCH full_path = (s)-[first_edge]->(middle)-[second_edge]->(e) +RETURN full_path, s.name, e.name +``` + +This query creates a named path variable `full_path` that captures a 2-hop pattern, while also returning specific properties from the s and e nodes. + +**Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. + +## Examples: Complex multi-pattern – cross-town "likes" with company filter + +This query ties together multiple patterns and filters in a single statement: + +1. Find a pair of people where **p1 _likes_ p2**. +2. Link each person to their home city, requiring that p1's city name **starts with "San"** while p2 lives in a **different city**. +3. Ensure p2 **works at TechCorp**. +4. Keep only pairs where the two people have **different ages**. + +The first `RETURN` lists every qualifying match – both people, their cities, and the company – so you can inspect the raw results. +The second `RETURN` then aggregates across all matches to output a single scalar: the **average age of all "liked" people** (p2) who meet these criteria. + + +```gql +MATCH (p1:Person)-[:likes]->(p2:Person), + (p1)-[:located_at]->(home:City), + (p2)-[:located_at]->(home2:City), + (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) +WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' +RETURN p1.name, p2.name, home.name, work.name, home2.name +``` + +**Output** + +| p1.name | p2.name | home.name | work.name | home2.name | +|---------|---------|-----------|-----------|------------| +| David | Alice | San Francisco | TechCorp | Seattle | + + +```gql +MATCH (p1:Person)-[:likes]->(p2:Person), + (p1)-[:located_at]->(home:City), + (p2)-[:located_at]->(home2:City), + (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) +WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' +RETURN AVG(p2.age) AS AvgAgeLikedAcrossTowns +``` + +**Output** + +| AvgAgeLikedAcrossTowns | +|---------------------| +| 25 | + +This example demonstrates GQL's ability to express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in a single, readable statement. + +## Common GQL use case examples + +This section shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. + +### Security use case - attack path analysis + +Security analysts use GQL to find potential attack paths in network infrastructure and access control systems. + +1. Create a security graph function: + + + ```gql + .create-or-alter function SecurityGraph() { + let entities = datatable(id:string, lbl:string, name:string, type:string, criticality:string) + [ + "u1", "User", "john.doe", "StandardUser", "Low", + "u2", "User", "admin.user", "Administrator", "High", + "s1", "System", "web-server", "WebServer", "Medium", + "s2", "System", "database", "Database", "Critical", + "s3", "System", "domain-controller", "DomainController", "Critical" + ]; + let relationships = datatable(source:string, target:string, lbl:string, access_type:string) + [ + "u1", "s1", "CAN_ACCESS", "HTTP", + "s1", "s2", "CAN_ACCESS", "SQL", + "u2", "s3", "CAN_ACCESS", "RDP", + "s3", "s2", "CAN_ACCESS", "Direct" + ]; + relationships + | make-graph source --> target with entities on id + } + ``` + +1. Set up the security graph: + + + ```gql + #crp query_graph_reference=SecurityGraph() + ``` + +1. Find potential attack paths to critical systems. + + + ```gql + MATCH (user:User)-[]->{1,3}(critical:System) + WHERE critical.criticality = 'Critical' + RETURN user.name AS AttackOrigin, critical.name AS CriticalTarget, COUNT(*) AS PathCount + ``` + +**Output** + +This query finds users who can reach critical systems within three hops, so security teams can prioritize access reviews. + +| AttackOrigin | CriticalTarget | PathCount | +|--|--|--| +| admin.user | domain-controller | 1 | +| john.doe | database | 1 | +| admin.user | database | 1 | + +### Social networks use case: Friend recommendations + +Social media platforms use GQL to find potential friend connections based on mutual relationships. + +1. Create a social network graph. + + + ```gql + .create-or-alter function SocialGraph() { + let entities = datatable(id:string, lbl:string, name:string, location:string, interests:string) + [ + "p1", "Person", "Alice Johnson", "Seattle", "Technology,Travel", + "p2", "Person", "Bob Smith", "Seattle", "Sports,Technology", + "p3", "Person", "Carol Davis", "Portland", "Travel,Photography", + "p4", "Person", "David Wilson", "Seattle", "Technology,Gaming", + "p5", "Person", "Emma Brown", "Portland", "Photography,Art" + ]; + let relationships = datatable(source:string, target:string, lbl:string, strength:string) + [ + "p1", "p2", "FRIENDS_WITH", "Close", + "p1", "p3", "FRIENDS_WITH", "Medium", + "p2", "p4", "FRIENDS_WITH", "Close", + "p3", "p5", "FRIENDS_WITH", "Medium" + ]; + relationships + | make-graph source --> target with entities on id + } + ``` + +1. Set up the social graph: + + + ```gql + #crp query_graph_reference=SocialGraph() + ``` + +1. Find potential friends through mutual connections. + + + ```gql + MATCH (person:Person)-[:FRIENDS_WITH]->(mutual:Person)-[:FRIENDS_WITH]->(potential:Person) + WHERE person.name = 'Alice Johnson' AND person.location = potential.location + RETURN potential.name AS SuggestedFriend, mutual.name AS MutualFriend, potential.interests AS CommonInterests + ``` + +**Output** + +This query suggests friends for Alice who have mutual connections and live in the same location. + +| SuggestedFriend | MutualFriend | CommonInterests | +|--|--|--| +| David Wilson | Bob Smith | Technology,Gaming | + +### Organization use case: Management hierarchy + +Organizations use GQL to analyze reporting structures and find management chains. + +Create an organizational graph. + + +```gql +.create-or-alter function OrgChart() { + let entities = datatable(id:string, lbl:string, name:string, title:string, department:string, level:int) + [ + "e1", "Employee", "Satya Nadella", "CEO", "Executive", 1, + "e2", "Employee", "Amy Hood", "CFO", "Finance", 2, + "e3", "Employee", "Brad Smith", "President", "Legal", 2, + "e4", "Employee", "Scott Guthrie", "EVP", "Cloud", 2, + "e5", "Employee", "Finance Director", "Director", "Finance", 3, + "e6", "Employee", "Cloud Architect", "Principal", "Cloud", 3, + "e7", "Employee", "Senior Engineer", "Senior", "Cloud", 4, + "e8", "Employee", "Software Engineer", "L63", "Cloud", 5 + ]; + let relationships = datatable(source:string, target:string, lbl:string) + [ + "e2", "e1", "REPORTS_TO", + "e3", "e1", "REPORTS_TO", + "e4", "e1", "REPORTS_TO", + "e5", "e2", "REPORTS_TO", + "e6", "e4", "REPORTS_TO", + "e7", "e6", "REPORTS_TO", + "e8", "e7", "REPORTS_TO" + ]; + relationships + | make-graph source --> target with entities on id +} +``` + +Set up the org chart: + + +```gql +#crp query_graph_reference=OrgChart() +``` + +Find all managers three levels down from CEO. + + +```gql +MATCH (ceo:Employee)-[:REPORTS_TO]-{0,3}(manager:Employee) +WHERE ceo.title = 'CEO' AND manager.level <= 4 +RETURN manager.name AS ManagerName, manager.title AS Title, manager.department AS Department +ORDER BY manager.level +``` + +This query finds all employees within three management levels of the CEO, which helps with organizational analysis and communication planning. diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index d2af09f731..ec3e046cab 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -122,7 +122,7 @@ After you finish setup, run GQL queries using standard GQL syntax. Start with th > [!TIP] > GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns and build complexity gradually. -### Example GQL query for basic pattern matching +## Example GQL query for basic pattern matching ```gql MATCH (n)-[e]->(n2) @@ -137,7 +137,7 @@ This table shows the result of the query. |-----| | 20 | -### Example GQL query with labels +## Example GQL query with labels ```gql @@ -156,775 +156,6 @@ This table shows the result of the query. | Alice | Bob | knows | | Alice | David | likes | -## Core GQL query patterns - -GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, building from simple to complex patterns. - -* [MATCH](#basic-pattern-matching-without-variables): The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. Similar functionality is achieved using the `graph-match` operator in KQL. - -* [WHERE](#basic-property-filtering): Uses standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. - -* [RETURN](#return-specific-properties): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. - -[Advanced patterns](#examples-advanced-patterns-and-complex-multi-patterns) - -* [Complex multi-pattern queries](#complex-multi-pattern-queries): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. - -## Examples: Core GQL query patters - -### Basic pattern matching without variables - -The simplest pattern matches any relationship without referencing the matched values: - - -```gql -MATCH ()-[]-() -RETURN COUNT(*) -``` - -This query finds all relationships in the graph. The empty parentheses `()` represent anonymous nodes, and `[]` represents anonymous edges. Since we don't assign variables, we can only count the matches but not access their properties. - -### Pattern matching with variables - -To access the matched nodes and edges, assign them to variables: - - -```gql -MATCH (n)-[e]->(n2) -RETURN COUNT(*) -``` - -Now `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. We can reference these variables to access properties, but in this example we're still just counting matches. - -### Access node properties - -Once you have variables, you can access properties of matched nodes: - - -```gql -MATCH (person)-[e]->(target) -RETURN person.name, target.name, e.lbl -ORDER BY person.name -LIMIT 2 -``` - -**Output** - -This query returns the names of connected entities and the type of relationship between them, ordered by person.name and limited to two results to manage output size. -Although we name those entities `person` and `target`, we don't have any restriction in place that makes sure that they are, in fact, a person. - -| person.name | target.name | e.lbl | -|-------------|-------------|------------------| -| Alice | TechCorp | works_at | -| Alice | Seattle | located_at | - -### Filter by node labels - -Use labels to match specific types of nodes. Labels are specified with a colon after the variable name: - - -```gql -MATCH (person:Person) -RETURN person.name -LIMIT 5 -``` - -**Output** - -This query matches **only nodes with the "Person" label** and returns their names, limited to 5 results to avoid large result sets. - -| person.name | -|-------------| -| Alice | -| Bob | -| Carol | -| David | -| Emma | - -### Filter by edge labels - - -```gql -MATCH (person:Person)-[works:works_at]->(company:Company) -RETURN person.name, company.name -``` - -### Filter by edge labels without variables - -For this example, let's switch back to our original G() graph, which contains the "knows" relationship: - - -```gql -#crp query_graph_reference=G() -``` - -You can filter by edge labels without assigning the edge to a variable when you don't need to access its properties. - - -```gql -MATCH (p1:Person)-[:knows]->(p2:Person) -RETURN p1.name, p2.name -``` - -**Output** - -This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but not assigned to a variable since we only need the connected nodes. - -| p1.name | p2.name | -|----------|----------| -| Alice | Bob | -| Bob | Carol | -| Carol | David | -| David | Emma | - -### Filter by properties with WHERE - -Use WHERE clauses to filter based on property values: - - -```gql -MATCH (person:Person) -WHERE person.age > 25 -RETURN person.name, person.age -``` - -This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes based on the age property. - -### Inline property filters - -You can also filter by properties directly in the pattern using inline conditions: - - -```gql -MATCH (person:Person {name: 'Bob'}) -RETURN person.age -``` - -This query finds the specific person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters for nodes where the name property equals 'Bob'. - -### Multiple inline conditions - -You can specify multiple property conditions inline: - - -```gql -MATCH (person:Person {name: 'Bob', age: 30}) -RETURN person as Bob -``` - -This query finds the person with both the specified name and age. It returns the person node as "Bob". - -#### Variable length paths - -For multi-hop relationships, use variable length path patterns with quantifiers: - - -```gql -MATCH (s)-[]->{1,3}(e) -RETURN s.name, e.name -``` - -This query finds paths that include between 1 to 3 hops. The `{1,3}` quantifier specifies the minimum and maximum path length. - -### Unbounded variable length paths - -For paths of variable length, you can specify open ranges: - - -```gql -MATCH (center)-[]->{1,}(connected) -WHERE center.name = 'Alice' -RETURN DISTINCT connected.name -``` - -This query finds all nodes reachable from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops". - -### Basic property filtering - -Filter nodes based on a single property condition: - - -```gql -MATCH (person:Person) -WHERE person.age > 26 -RETURN person.name, person.age -``` - -**Output** - -This query finds all Person nodes where the age property is greater than 26. - -| person.name | person.age | -|--------------|-------------| -| Bob | 30 | - -### Range filtering with AND - -To create ranges, cCombine multiple conditions: - - -```gql -MATCH (person:Person) -WHERE person.age >= 28 AND person.age <= 35 -RETURN person.name, person.age -``` - -This query finds people with ages between 28 and 35 (inclusive). - -### Edge property filtering - -Filter based on edge properties to find specific types of relationships: - - -```gql -MATCH (person:Person)-[wa:works_at]->(c:Company) -WHERE wa.since >= 2022 -RETURN person.name, c.name, wa.since -``` - -This query finds people who started working at companies since 2022 or later, filtering based on the `since` property of the `works_at` relationship. - -### String pattern matching - -Use string functions to match text patterns: - - -```gql -MATCH (person:Person) -WHERE person.name STARTS WITH 'Al' -RETURN COUNT(*) -``` - -This query counts how many people have names that start with 'Al', providing a quick summary without returning potentially large result sets. - -### String contains matching - -Check if a string contains a specific substring: - - -```gql -MATCH (person:Person) -WHERE person.name CONTAINS 'i' -RETURN person.name -``` - -This query finds all people whose names contain 'i' anywhere in the string. -This is case-sensitive. - -### Inequality comparisons - -Use comparison operators to exclude specific values: - - -```gql -MATCH (person:Person)-[wa:works_at]->(company:Company) -WHERE person.age > 25 AND company.name <> 'TechCorp' -RETURN person.name, company.name -``` - -This query finds people over 25 who work at companies other than 'TechCorp'. - -### Null value checking - -Check for the presence or absence of property values: - - -```gql -MATCH (person:Person) -WHERE person.age IS NOT NULL -RETURN person.name, person.age -``` - -This query finds all people who have an age recorded (non-null age property). - -### Logical OR operations - -Use OR to match multiple conditions: - - -```gql -MATCH (person:Person) -WHERE person.age > 30 OR person.name CONTAINS 'a' -RETURN person.name, person.age -``` - -This query finds people who are either over 30 years old OR have 'a' in their name. - -### Return specific properties - -Return individual properties from matched nodes: - - -```gql -MATCH (person:Person) -RETURN person.name, person.age -``` - -This query returns the name and age properties of all Person nodes. Each row in the result contains these two values. - -### Return with aliases - -Use aliases to rename output columns for clarity: - - -```gql -MATCH (person:Person)-[employment:works_at]->(company:Company) -RETURN person.name AS Employee, company.name AS Company, employment.since AS WorkingSince -``` - -This query returns actor names, movie titles, and character names with descriptive column headers. - -### Return entire nodes and edges - - -```gql -MATCH (person:Person)-[e]->(movie:Movie) -WHERE person.name = 'Kevin Bacon' -RETURN person, e, movie -``` - -**Output** - -This query returns the complete node and edge objects for Kevin Bacon's relationships, including all properties. - -| person | e | movie | -|--------|------|-------| -{"id":"2","lbl":"Person","name":"Kevin Bacon","title":"","born":1958} | {"source":"2","target":"3","lbl":"ACTED_IN","Role":"Jack Swigert"} | {"id":"3","lbl":"Movie","name":"","title":"Apollo 13","born":1995} - -### Count matching patterns - -Use COUNT(*) to count the number of pattern matches: - - -```gql -MATCH (person:Person)-[:ACTED_IN]->(movie:Movie) -RETURN person.name, COUNT(*) AS MovieCount -``` - -This query counts how many movies each person acted in and groups the results by person name. - -### Aggregate with MIN and MAX - -Find minimum and maximum values across all matches: - - -```gql -MATCH (person:Person) -RETURN MIN(person.age) AS Youngest, MAX(person.age) AS Oldest -``` - -This query finds the earliest and latest birth years among all people in the graph. - -### Collect values into arrays - -Use COLLECT_LIST to gather multiple values into arrays: - - -```gql -MATCH (person:Person) -RETURN COLLECT_LIST(person.name) AS AllNames -``` - -This query collects all person names into a single array result. - -### Return distinct values - -Remove duplicates from your results: - - -```gql -MATCH (person:Person)-[]->(company:Company) -RETURN DISTINCT company.name -``` - -This query returns each unique movie title only once, even if multiple people are connected to the same movie. - -### Order results - -Sort your results using ORDER BY: - - -```gql -MATCH (person:Person) -RETURN person.name, person.age -ORDER BY person.age DESC -``` - -This query returns people sorted by age in descending order (oldest first). - -### Limit result count - -Restrict the number of results returned: - - -```gql -MATCH (person:Person) -WHERE person.age > 25 -RETURN person.name -ORDER BY person.age -LIMIT 5 -``` - -This query returns only the first five people over 25 years old, ordered by age. - -**Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. - -## Examples: Advanced patterns and complex multi-patterns - -Advanced patterns provide powerful ways to match complex graph structures and label combinations. - -### Label unions (OR) - -Match nodes that have any of the specified labels: - - -```gql -MATCH (entity:Person | Movie) -RETURN entity -``` - -This query matches nodes that have either the "Person" OR "Movie" label. The pipe symbol `|` represents logical OR for labels. - -### Label intersections (AND) - -Match nodes that have all of the specified labels: - - -```gql -MATCH (person:Person & Male) -RETURN person.name -``` - -This query matches nodes that have both the "Person" AND "Male" labels. The ampersand `&` represents logical AND for labels. - -### Label negation (NOT) - -Match nodes that do NOT have the specified label: - - -```gql -MATCH (person:!Female) -RETURN person.name -``` - -This query matches all nodes that do NOT have the "Female" label. The exclamation mark `!` represents logical NOT for labels. - -### Variable length paths with exact range - -Match paths with a specific number of hops: - - -```gql -MATCH (s)-[es]->{2,2}(e) -RETURN s, es, e -``` - -This query finds paths that are exactly two hops long. The `{2,2}` quantifier specifies both minimum and maximum path length as 2. - -### Variable length paths with open range - -Match paths with a minimum number of hops but no maximum: - - -```gql -MATCH (s)-[p]->{1,}(e) -RETURN s, e, p -``` - -This query finds paths that are 1 or more hops long. The `{1,}` quantifier means "one or more hops" with no upper limit. - -### Zero-length paths - -Match nodes with themselves (identity relationships): - - -```gql -MATCH (n)-[]->{0,0}(same_n) -RETURN n -``` - -This query matches each node with itself through a zero-length path. The `{0,0}` quantifier specifies exactly zero hops, effectively returning each node paired with itself. - -### Named path variables - -Assign entire paths to variables for later use: - - -```gql -MATCH p = (person:Person)-[:DIRECTED]->(movie:Movie) -RETURN p -``` - -This query assigns the entire pattern to the variable `p`, which can then be returned or used in other parts of the query. The path variable contains the complete sequence of nodes and edges. - -### Multi-hop named paths - -Create named paths that span multiple relationships: - - -```gql -MATCH full_path = (s)-[first_edge]->(middle)-[second_edge]->(e) -RETURN full_path, s.name, e.name -``` - -This query creates a named path variable `full_path` that captures a 2-hop pattern, while also returning specific properties from the s and e nodes. - -**Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. - -### Complex example – cross-town "likes" with company filter - -This query ties together multiple patterns and filters in a single statement: - -1. Find a pair of people where **p1 _likes_ p2**. -2. Link each person to their home city, requiring that p1's city name **starts with "San"** while p2 lives in a **different city**. -3. Ensure p2 **works at TechCorp**. -4. Keep only pairs where the two people have **different ages**. - -The first `RETURN` lists every qualifying match – both people, their cities, and the company – so you can inspect the raw results. -The second `RETURN` then aggregates across all matches to output a single scalar: the **average age of all "liked" people** (p2) who meet these criteria. - - -```gql -MATCH (p1:Person)-[:likes]->(p2:Person), - (p1)-[:located_at]->(home:City), - (p2)-[:located_at]->(home2:City), - (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) -WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' -RETURN p1.name, p2.name, home.name, work.name, home2.name -``` - -**Output** - -| p1.name | p2.name | home.name | work.name | home2.name | -|---------|---------|-----------|-----------|------------| -| David | Alice | San Francisco | TechCorp | Seattle | - - -```gql -MATCH (p1:Person)-[:likes]->(p2:Person), - (p1)-[:located_at]->(home:City), - (p2)-[:located_at]->(home2:City), - (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) -WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' -RETURN AVG(p2.age) AS AvgAgeLikedAcrossTowns -``` - -**Output** - -| AvgAgeLikedAcrossTowns | -|---------------------| -| 25 | - -This example demonstrates GQL's ability to express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in a single, readable statement. - -## Custom graph function: labels() - -The `labels()` function shows the labels for a node or edge as an array. - -**Syntax:** - -``` -labels(entity) -``` - -**Parameters:** - -- `entity`: A node or edge variable from a matched pattern. - -**Returns:** An array of strings with all labels for the specified entity. - -**Examples:** - -Show labels for matched nodes: - -```gql -MATCH (entity) -RETURN entity.name, labels(entity) -``` - -This query returns the name and all labels for each node in the graph. - -Show labels in projections with aliases: - -```gql -MATCH (n)-[e]->(target) -RETURN n.name, labels(n) AS n_labels, labels(e) AS edge_labels, target.name -``` - -This query returns node names along with their labels and the labels of connecting edges. - -> [!NOTE] -> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions but use different syntax and operators. - -> [!TIP] -> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. - -## Common use cases for graph queries - -This section shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. - -### Security use case - attack path analysis - -Security analysts use GQL to find potential attack paths in network infrastructure and access control systems. - -1. Create a security graph function: - - - ```gql - .create-or-alter function SecurityGraph() { - let entities = datatable(id:string, lbl:string, name:string, type:string, criticality:string) - [ - "u1", "User", "john.doe", "StandardUser", "Low", - "u2", "User", "admin.user", "Administrator", "High", - "s1", "System", "web-server", "WebServer", "Medium", - "s2", "System", "database", "Database", "Critical", - "s3", "System", "domain-controller", "DomainController", "Critical" - ]; - let relationships = datatable(source:string, target:string, lbl:string, access_type:string) - [ - "u1", "s1", "CAN_ACCESS", "HTTP", - "s1", "s2", "CAN_ACCESS", "SQL", - "u2", "s3", "CAN_ACCESS", "RDP", - "s3", "s2", "CAN_ACCESS", "Direct" - ]; - relationships - | make-graph source --> target with entities on id - } - ``` - -1. Set up the security graph: - - - ```gql - #crp query_graph_reference=SecurityGraph() - ``` - -1. Find potential attack paths to critical systems. - - - ```gql - MATCH (user:User)-[]->{1,3}(critical:System) - WHERE critical.criticality = 'Critical' - RETURN user.name AS AttackOrigin, critical.name AS CriticalTarget, COUNT(*) AS PathCount - ``` - -**Output** - -This query finds users who can reach critical systems within three hops, so security teams can prioritize access reviews. - -| AttackOrigin | CriticalTarget | PathCount | -|--|--|--| -| admin.user | domain-controller | 1 | -| john.doe | database | 1 | -| admin.user | database | 1 | - -### Social networks use case: Friend recommendations - -Social media platforms use GQL to find potential friend connections based on mutual relationships. - -1. Create a social network graph. - - - ```gql - .create-or-alter function SocialGraph() { - let entities = datatable(id:string, lbl:string, name:string, location:string, interests:string) - [ - "p1", "Person", "Alice Johnson", "Seattle", "Technology,Travel", - "p2", "Person", "Bob Smith", "Seattle", "Sports,Technology", - "p3", "Person", "Carol Davis", "Portland", "Travel,Photography", - "p4", "Person", "David Wilson", "Seattle", "Technology,Gaming", - "p5", "Person", "Emma Brown", "Portland", "Photography,Art" - ]; - let relationships = datatable(source:string, target:string, lbl:string, strength:string) - [ - "p1", "p2", "FRIENDS_WITH", "Close", - "p1", "p3", "FRIENDS_WITH", "Medium", - "p2", "p4", "FRIENDS_WITH", "Close", - "p3", "p5", "FRIENDS_WITH", "Medium" - ]; - relationships - | make-graph source --> target with entities on id - } - ``` - -1. Set up the social graph: - - - ```gql - #crp query_graph_reference=SocialGraph() - ``` - -1. Find potential friends through mutual connections. - - - ```gql - MATCH (person:Person)-[:FRIENDS_WITH]->(mutual:Person)-[:FRIENDS_WITH]->(potential:Person) - WHERE person.name = 'Alice Johnson' AND person.location = potential.location - RETURN potential.name AS SuggestedFriend, mutual.name AS MutualFriend, potential.interests AS CommonInterests - ``` - -**Output** - -This query suggests friends for Alice who have mutual connections and live in the same location. - -| SuggestedFriend | MutualFriend | CommonInterests | -|--|--|--| -| David Wilson | Bob Smith | Technology,Gaming | - -### Organization use case: Management hierarchy - -Organizations use GQL to analyze reporting structures and find management chains. - -Create an organizational graph. - - -```gql -.create-or-alter function OrgChart() { - let entities = datatable(id:string, lbl:string, name:string, title:string, department:string, level:int) - [ - "e1", "Employee", "Satya Nadella", "CEO", "Executive", 1, - "e2", "Employee", "Amy Hood", "CFO", "Finance", 2, - "e3", "Employee", "Brad Smith", "President", "Legal", 2, - "e4", "Employee", "Scott Guthrie", "EVP", "Cloud", 2, - "e5", "Employee", "Finance Director", "Director", "Finance", 3, - "e6", "Employee", "Cloud Architect", "Principal", "Cloud", 3, - "e7", "Employee", "Senior Engineer", "Senior", "Cloud", 4, - "e8", "Employee", "Software Engineer", "L63", "Cloud", 5 - ]; - let relationships = datatable(source:string, target:string, lbl:string) - [ - "e2", "e1", "REPORTS_TO", - "e3", "e1", "REPORTS_TO", - "e4", "e1", "REPORTS_TO", - "e5", "e2", "REPORTS_TO", - "e6", "e4", "REPORTS_TO", - "e7", "e6", "REPORTS_TO", - "e8", "e7", "REPORTS_TO" - ]; - relationships - | make-graph source --> target with entities on id -} -``` - -Set up the org chart: - - -```gql -#crp query_graph_reference=OrgChart() -``` - -Find all managers three levels down from CEO. - - -```gql -MATCH (ceo:Employee)-[:REPORTS_TO]-{0,3}(manager:Employee) -WHERE ceo.title = 'CEO' AND manager.level <= 4 -RETURN manager.name AS ManagerName, manager.title AS Title, manager.department AS Department -ORDER BY manager.level -``` - -This query finds all employees within three management levels of the CEO, which helps with organizational analysis and communication planning. - ## Performance optimization When you work with GQL queries in production environments, use these performance optimization strategies: From 2bc3db3a3ee8f153ae94b51e92834823e1bee1fb Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Thu, 14 Aug 2025 18:37:06 +0300 Subject: [PATCH 11/67] wip --- .../query/graph-query-language-functions.md | 12 ++-- .../query/graph-query-language-use-cases.md | 68 +++++++++++-------- .../kusto/query/graph-query-language.md | 9 ++- data-explorer/toc.yml | 2 +- 4 files changed, 51 insertions(+), 40 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 0edd9496bb..1b83785e39 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -13,12 +13,6 @@ ms.date: #Required; mm/dd/yyyy format. Graph Query Language (GQL) is a powerful language for querying graph data in Azure Data Explorer. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. -> [!NOTE] -> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions but use different syntax and operators. - -> [!TIP] -> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. - This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. | GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | @@ -120,3 +114,9 @@ This query returns node names along with their labels and the labels of connecti | admin.user | ["User"] | ["CAN_ACCESS"] | domain-controller | | web-server | ["System"] | ["CAN_ACCESS"] | database | | domain-controller | ["System"] | ["CAN_ACCESS"] | database | + +> [!NOTE] +> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions but use different syntax and operators. + +> [!TIP] +> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. \ No newline at end of file diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 29a43a3749..66ac7a1488 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -9,21 +9,27 @@ ms.date: #Required; mm/dd/yyyy format. #CustomerIntent: As a , I want so that . --- -# Core GQL query patterns and examples +# GQL examples and common use cases + +This article provides Graph Query Language examples focing on the core query patters, and shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, building from simple to complex patterns. -* [MATCH](#basic-pattern-matching-without-variables): The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. Similar functionality is achieved using the `graph-match` operator in KQL. +## Core GQL query patterns + +* [MATCH](#examples-with-match): The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. Similar functionality is achieved using the `graph-match` operator in KQL. -* [WHERE](#basic-property-filtering): Uses standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. +* [WHERE](#examples-with-where): Uses standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. -* [RETURN](#return-specific-properties): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. +* [RETURN](#examples-with-return): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. -* [Advanced patterns](#examples-advanced-patterns-and-complex-multi-patterns) +* [Advanced patterns](#examples-with-advanced-patterns) -* [Complex multi-pattern queries](#complex-multi-pattern-queries): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. +* [Complex multi-pattern queries](#examples-with-complex-multi-pattern-queries): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. -## Example: Basic pattern matching without variables +## Examples with MATCH + +### Basic pattern matching without variables The simplest pattern matches any relationship without referencing the matched values: @@ -35,7 +41,7 @@ RETURN COUNT(*) This query finds all relationships in the graph. The empty parentheses `()` represent anonymous nodes, and `[]` represents anonymous edges. Since we don't assign variables, we can only count the matches but not access their properties. -## Example: Pattern matching with variables +### Pattern matching with variables To access the matched nodes and edges, assign them to variables: @@ -47,7 +53,7 @@ RETURN COUNT(*) Now `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. We can reference these variables to access properties, but in this example we're still just counting matches. -## Example: Access node properties +### Access node properties Once you have variables, you can access properties of matched nodes: @@ -69,7 +75,7 @@ Although we name those entities `person` and `target`, we don't have any restric | Alice | TechCorp | works_at | | Alice | Seattle | located_at | -## Example: Filter by node labels +### Filter by node labels Use labels to match specific types of nodes. Labels are specified with a colon after the variable name: @@ -92,7 +98,7 @@ This query matches **only nodes with the "Person" label** and returns their name | David | | Emma | -## Example: Filter by edge labels +### Filter by edge labels ```gql @@ -128,7 +134,7 @@ This query finds Person nodes connected to other Person nodes through "knows" re | Carol | David | | David | Emma | -## Example: Filter by properties with WHERE +### Filter by properties with WHERE Use WHERE clauses to filter based on property values: @@ -141,7 +147,7 @@ RETURN person.name, person.age This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes based on the age property. -## Example: Inline property filters +#### Inline property filters You can also filter by properties directly in the pattern using inline conditions: @@ -153,7 +159,7 @@ RETURN person.age This query finds the specific person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters for nodes where the name property equals 'Bob'. -## Example: Multiple inline conditions +### Multiple inline conditions You can specify multiple property conditions inline: @@ -165,7 +171,7 @@ RETURN person as Bob This query finds the person with both the specified name and age. It returns the person node as "Bob". -## Example: Variable length paths +## Variable length paths For multi-hop relationships, use variable length path patterns with quantifiers: @@ -177,7 +183,7 @@ RETURN s.name, e.name This query finds paths that include between 1 to 3 hops. The `{1,3}` quantifier specifies the minimum and maximum path length. -## Example: Unbounded variable length paths +### Unbounded variable length paths For paths of variable length, you can specify open ranges: @@ -190,7 +196,9 @@ RETURN DISTINCT connected.name This query finds all nodes reachable from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops". -## Example: Basic property filtering +## Examples with WHERE + +### Basic property filtering Filter nodes based on a single property condition: @@ -301,7 +309,9 @@ RETURN person.name, person.age This query finds people who are either over 30 years old OR have 'a' in their name. -## Example: Return specific properties +## Examples with RETURN + +### Return specific properties Return individual properties from matched nodes: @@ -342,7 +352,7 @@ This query returns the complete node and edge objects for Kevin Bacon's relation |--------|------|-------| {"id":"2","lbl":"Person","name":"Kevin Bacon","title":"","born":1958} | {"source":"2","target":"3","lbl":"ACTED_IN","Role":"Jack Swigert"} | {"id":"3","lbl":"Movie","name":"","title":"Apollo 13","born":1995} -## Example: Count matching patterns +### Count matching patterns Use COUNT(*) to count the number of pattern matches: @@ -378,7 +388,7 @@ RETURN COLLECT_LIST(person.name) AS AllNames This query collects all person names into a single array result. -## Example: Return distinct values +### Return distinct values Remove duplicates from your results: @@ -390,7 +400,7 @@ RETURN DISTINCT company.name This query returns each unique movie title only once, even if multiple people are connected to the same movie. -### Order results +### Return ordered results Sort your results using ORDER BY: @@ -420,7 +430,7 @@ This query returns only the first five people over 25 years old, ordered by age. **Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. -## Examples: Advanced patterns +## Examples with advanced patterns Advanced patterns provide powerful ways to match complex graph structures and label combinations. @@ -522,7 +532,9 @@ This query creates a named path variable `full_path` that captures a 2-hop patte **Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. -## Examples: Complex multi-pattern – cross-town "likes" with company filter +## Examples with complex multi-patterns + +#### Cross-town "likes" with company filter This query ties together multiple patterns and filters in a single statement: @@ -568,11 +580,7 @@ RETURN AVG(p2.age) AS AvgAgeLikedAcrossTowns This example demonstrates GQL's ability to express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in a single, readable statement. -## Common GQL use case examples - -This section shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. - -### Security use case - attack path analysis +## Security use case - attack path analysis Security analysts use GQL to find potential attack paths in network infrastructure and access control systems. @@ -627,7 +635,7 @@ This query finds users who can reach critical systems within three hops, so secu | john.doe | database | 1 | | admin.user | database | 1 | -### Social networks use case: Friend recommendations +## Social networks use case: Friend recommendations Social media platforms use GQL to find potential friend connections based on mutual relationships. @@ -680,7 +688,7 @@ This query suggests friends for Alice who have mutual connections and live in th |--|--|--| | David Wilson | Bob Smith | Technology,Gaming | -### Organization use case: Management hierarchy +## Organization use case: Management hierarchy Organizations use GQL to analyze reporting structures and find management chains. diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index ec3e046cab..bdf7a4b111 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -122,7 +122,10 @@ After you finish setup, run GQL queries using standard GQL syntax. Start with th > [!TIP] > GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns and build complexity gradually. -## Example GQL query for basic pattern matching +## Examples + + +### Example GQL query for basic pattern matching ```gql MATCH (n)-[e]->(n2) @@ -137,7 +140,7 @@ This table shows the result of the query. |-----| | 20 | -## Example GQL query with labels +### Example GQL query with labels ```gql @@ -196,4 +199,4 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity * [make-graph operator](make-graph-operator.md) * [graph-match operator](graph-match-operator.md) * [Tutorial: Create your first graph](tutorials/your-first-graph.md) -* [Graph functions reference](graph-functions.md) +* [Graph functions reference](graph-function.md) diff --git a/data-explorer/toc.yml b/data-explorer/toc.yml index cc4ca81746..b78a3117b0 100644 --- a/data-explorer/toc.yml +++ b/data-explorer/toc.yml @@ -326,7 +326,7 @@ items: - name: Query data in Azure Monitor href: query-monitor-data.md - name: Query data using GQL - href: kusto/query/graph-query-language?view=azure-data-explorer&preserve-view=true + href: /kusto/query/graph-query-language?view=azure-data-explorer&preserve-view=true - name: Query data in MATLAB href: query-matlab.md - name: Query data using Power Apps From eb73733433a7a548b745d06e277fb566c694ece2 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Thu, 14 Aug 2025 18:59:39 +0300 Subject: [PATCH 12/67] wip --- .../query/graph-query-language-functions.md | 18 ++++++++---------- .../query/graph-query-language-use-cases.md | 16 +++++++--------- .../kusto/query/graph-query-language.md | 8 ++++---- 3 files changed, 19 insertions(+), 23 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 1b83785e39..ad505a341e 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -1,15 +1,13 @@ --- -title: #Required; Keep the title body to 60-65 chars max including spaces and brand -description: #Required; Keep the description within 100- and 165-characters including spaces -author: #Required; your GitHub user alias, with correct capitalization -ms.author: #Required; microsoft alias of author -ms.service: #Required; use the name-string related to slug in ms.product/ms.service -ms.topic: concept-article #Required; leave this attribute/value as-is. -ms.date: #Required; mm/dd/yyyy format. - -#CustomerIntent: As a , I want so that . +title: Graph Query Language Functions in Azure Data Explorer +description: Leaern Azure Data Explorer graph query language (GQL) functions and operators for working with nodes, edges, and properties. Find practical examples and KQL equivalents. +ms.reviewer: hraunch +ms.topic: concept-article +ms.date: 08/14/2025 + +#CustomerIntent: As a data engineer, I want to learn GQL functions in Azure Data Explorer so that I can query graph data efficiently. --- -# Graph query language (GQL) functions, operators, and examples +# Graph query language (GQL) functions and operators Graph Query Language (GQL) is a powerful language for querying graph data in Azure Data Explorer. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 66ac7a1488..70542a8acf 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -1,15 +1,13 @@ --- -title: #Required; Keep the title body to 60-65 chars max including spaces and brand -description: #Required; Keep the description within 100- and 165-characters including spaces -author: #Required; your GitHub user alias, with correct capitalization -ms.author: #Required; microsoft alias of author -ms.service: #Required; use the name-string related to slug in ms.product/ms.service -ms.topic: concept-article #Required; leave this attribute/value as-is. -ms.date: #Required; mm/dd/yyyy format. +title: GQL Query Patterns, Examples, and Use Cases in Azure Data Explorer +description: GQL query patterns for Azure Data Explorer. Find out how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. GQL use cases in Azure Data Explorer. See how to build queries for security, social networks, and organizational analysis with step-by-step examples. +ms.reviewer: herauch +ms.topic: concept-article +ms.date: 08/14/2025 #CustomerIntent: As a , I want so that . --- -# GQL examples and common use cases +# GQL query patterns, examples, and common use cases This article provides Graph Query Language examples focing on the core query patters, and shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. @@ -25,7 +23,7 @@ GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern * [Advanced patterns](#examples-with-advanced-patterns) -* [Complex multi-pattern queries](#examples-with-complex-multi-pattern-queries): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. +* [Complex multi-pattern queries](#Examples-with-complex-multi-patterns): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. ## Examples with MATCH diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index bdf7a4b111..2ad95746a2 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -9,7 +9,7 @@ ms.date: 08/13/2025 > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] -Graph Query Language (GQL) lets you use standardized graph pattern matching in Azure Data Explorer. GQL follows the emerging ISO GQL standard for graph database queries. +Graph Query Language (GQL) lets you use standardized graph pattern matching in Azure Data Explorer (ADX). GQL follows the emerging ISO GQL standard for graph database queries. > [!NOTE] > GQL support in Azure Data Explorer is currently in preview. Features and syntax can change based on feedback and ongoing development. @@ -31,7 +31,7 @@ To use GQL in Azure Data Explorer, you need: ## Step 1: Create a graph reference -Before you use GQL, create a graph data source. For this article, we use an in-memory make-graph operator, but it's recommended to use a graph snapshop for production scenarios. +Before you use GQL, create a graph data source. For this article, we use an in-memory make-graph operator, but it's recommended to use a graph snapshot for production scenarios. ```gql @@ -184,10 +184,10 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity ## Limitations -- **Reserved keywords**: GQL has reserved keywords that can't be used as identifiers in queries. Some of these reserved keywords might not be immediately obvious to users (for example, `DATE` is a reserved keyword). If your graph data has properties with names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. +- **Reserved keywords**: There are reserved GQL keywords that can't be used as identifiers in queries. Some of these reserved keywords might not be immediately obvious to users (for example, `DATE` is a reserved keyword). If your graph data has properties with names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. > [!IMPORTANT] - > When designing your graph schema, certain common property names might conflict with GQL reserved keywords and should be avoided or renamed. + > When upi design your graph schema, certain common property names might conflict with GQL reserved keywords and should be avoided or renamed. - **No `INSERT`/`CREATE` support**: GQL in Azure Data Explorer doesn't support `INSERT` or `CREATE` operations to modify graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. From 753d45b1b01ed618873e5063ce0792004d8d6339 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Thu, 14 Aug 2025 19:38:08 +0300 Subject: [PATCH 13/67] Edits per Acrolinx and DocuMentor --- .../query/graph-query-language-functions.md | 20 +- .../query/graph-query-language-use-cases.md | 184 +++++++++--------- .../kusto/query/graph-query-language.md | 53 ++--- 3 files changed, 138 insertions(+), 119 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index ad505a341e..fa155aa8fb 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -2,15 +2,18 @@ title: Graph Query Language Functions in Azure Data Explorer description: Leaern Azure Data Explorer graph query language (GQL) functions and operators for working with nodes, edges, and properties. Find practical examples and KQL equivalents. ms.reviewer: hraunch -ms.topic: concept-article +ms.topic: reference ms.date: 08/14/2025 #CustomerIntent: As a data engineer, I want to learn GQL functions in Azure Data Explorer so that I can query graph data efficiently. --- -# Graph query language (GQL) functions and operators +# Graph query language (GQL) functions and operators (preview) Graph Query Language (GQL) is a powerful language for querying graph data in Azure Data Explorer. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. +> [!NOTE] +> GQL support in Azure Data Explorer is currently in preview. Features and syntax can change based on feedback and ongoing development. + This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. | GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | @@ -85,7 +88,7 @@ RETURN entity.name, labels(entity) **Output** -This query returns the name and all labels for each node in the graph. +This query shows the name and all labels for each node in the graph. | entity.name | labels(entity) | |--|--| @@ -104,7 +107,7 @@ RETURN n.name, labels(n) AS n_labels, labels(e) AS edge_labels, target.name **Output** -This query returns node names along with their labels and the labels of connecting edges. +This query shows node names, their labels, and the labels of connecting edges. | n.name | n_labels | edge_labels | target.name | |--|--|--|--| @@ -114,7 +117,12 @@ This query returns node names along with their labels and the labels of connecti | domain-controller | ["System"] | ["CAN_ACCESS"] | database | > [!NOTE] -> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions but use different syntax and operators. +> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions, but use different syntax and operators. > [!TIP] -> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. \ No newline at end of file +> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. + +## Related Context + +* [Graph Query Language (GQL) in Azure Data Explorer](graph-query-language.md) +* [Graph Query Language (GQL) use cases](graph-query-language-use-cases.md) \ No newline at end of file diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 70542a8acf..d32d229bac 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -2,34 +2,40 @@ title: GQL Query Patterns, Examples, and Use Cases in Azure Data Explorer description: GQL query patterns for Azure Data Explorer. Find out how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. GQL use cases in Azure Data Explorer. See how to build queries for security, social networks, and organizational analysis with step-by-step examples. ms.reviewer: herauch -ms.topic: concept-article +ms.topic: reference ms.date: 08/14/2025 #CustomerIntent: As a , I want so that . --- -# GQL query patterns, examples, and common use cases +# GQL query patterns, examples, and common scenarios (preview) -This article provides Graph Query Language examples focing on the core query patters, and shows common real-world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. +This article gives Graph Query Language examples focusing on core query patterns, and shows common real world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. -GQL in Azure Data Explorer implements the standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, building from simple to complex patterns. +GQL in Azure Data Explorer uses standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, from simple to complex patterns. + +> [NOTE!] +> +> Before you try these examples, set up your environment to use GQL. See [Getting Started](graph-query-language.md#getting-started) for details. +> +> GQL support in Azure Data Explorer is in preview. Features and syntax can change based on feedback and ongoing development. ## Core GQL query patterns -* [MATCH](#examples-with-match): The `MATCH` clause defines the graph patterns you want to find. Let's start with the most basic patterns and build complexity gradually. Similar functionality is achieved using the `graph-match` operator in KQL. +* [MATCH](#examples-with-match): The `MATCH` clause defines the graph patterns you want to find. Start with basic patterns and build complexity gradually. Similar functionality is available using the `graph-match` operator in KQL. -* [WHERE](#examples-with-where): Uses standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work similarly to KQL or SQL WHERE clauses but operate on graph patterns. +* [WHERE](#examples-with-where): Use standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work like KQL or SQL WHERE clauses but operate on graph patterns. -* [RETURN](#examples-with-return): `RETURN` statements project results from matched patterns. They specify what data to output from your graph query. +* [RETURN](#examples-with-return): Use `RETURN` statements to project results from matched patterns. They specify what data to output from your graph query. * [Advanced patterns](#examples-with-advanced-patterns) -* [Complex multi-pattern queries](#Examples-with-complex-multi-patterns): Complex multi-pattern queries let you combine multiple patterns and filters in a single statement, enabling sophisticated graph analysis. +* [Complex multi-pattern queries](#Examples-with-complex-multi-patterns): Use complex multi-pattern queries to combine multiple patterns and filters in a single statement, so you can do sophisticated graph analysis. ## Examples with MATCH ### Basic pattern matching without variables -The simplest pattern matches any relationship without referencing the matched values: +The simplest pattern matches any relationship without referencing the matched values. ```gql @@ -37,11 +43,11 @@ MATCH ()-[]-() RETURN COUNT(*) ``` -This query finds all relationships in the graph. The empty parentheses `()` represent anonymous nodes, and `[]` represents anonymous edges. Since we don't assign variables, we can only count the matches but not access their properties. +This query finds all relationships in the graph. The empty parentheses `()` represent anonymous nodes, and `[]` represents anonymous edges. Because we don't assign variables, we can only count the matches but can't access their properties. ### Pattern matching with variables -To access the matched nodes and edges, assign them to variables: +To access the matched nodes and edges, assign them to variables. ```gql @@ -49,11 +55,11 @@ MATCH (n)-[e]->(n2) RETURN COUNT(*) ``` -Now `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. We can reference these variables to access properties, but in this example we're still just counting matches. +`n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. You reference these variables to access properties, but in this example you're still just counting matches. ### Access node properties -Once you have variables, you can access properties of matched nodes: +Once you have variables, you access properties of matched nodes. ```gql @@ -66,7 +72,7 @@ LIMIT 2 **Output** This query returns the names of connected entities and the type of relationship between them, ordered by person.name and limited to two results to manage output size. -Although we name those entities `person` and `target`, we don't have any restriction in place that makes sure that they are, in fact, a person. +Although those entities are named `person` and `target`, there's no restriction that ensures they're actually a person. | person.name | target.name | e.lbl | |-------------|-------------|------------------| @@ -75,7 +81,7 @@ Although we name those entities `person` and `target`, we don't have any restric ### Filter by node labels -Use labels to match specific types of nodes. Labels are specified with a colon after the variable name: +Use labels to match specific types of nodes. Specify labels with a colon after the variable name. ```gql @@ -86,7 +92,7 @@ LIMIT 5 **Output** -This query matches **only nodes with the "Person" label** and returns their names, limited to 5 results to avoid large result sets. +This query matches only nodes with the "Person" label and returns their names, limited to five results to avoid large result sets. | person.name | |-------------| @@ -106,14 +112,14 @@ RETURN person.name, company.name ### Filter by edge labels without variables -For this example, let's switch back to our original G() graph, which contains the "knows" relationship: +For this example, switch back to the original G() graph, which has the "knows" relationship: ```gql #crp query_graph_reference=G() ``` -You can filter by edge labels without assigning the edge to a variable when you don't need to access its properties. +Filter by edge labels without assigning the edge to a variable when you don't need to access its properties. ```gql @@ -123,7 +129,7 @@ RETURN p1.name, p2.name **Output** -This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but not assigned to a variable since we only need the connected nodes. +This query finds Person nodes connected to other Person nodes through "knows" relationships. The edge is filtered by its label but isn't assigned to a variable because you only need the connected nodes. | p1.name | p2.name | |----------|----------| @@ -134,7 +140,7 @@ This query finds Person nodes connected to other Person nodes through "knows" re ### Filter by properties with WHERE -Use WHERE clauses to filter based on property values: +Use WHERE clauses to filter based on property values. ```gql @@ -143,11 +149,11 @@ WHERE person.age > 25 RETURN person.name, person.age ``` -This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes based on the age property. +This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes by the age property. #### Inline property filters -You can also filter by properties directly in the pattern using inline conditions: +Filter by properties directly in the pattern using inline conditions. ```gql @@ -155,11 +161,11 @@ MATCH (person:Person {name: 'Bob'}) RETURN person.age ``` -This query finds the specific person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters for nodes where the name property equals 'Bob'. +This query finds the person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters nodes where the name property equals 'Bob'. ### Multiple inline conditions -You can specify multiple property conditions inline: +Specify multiple property conditions inline. ```gql @@ -167,11 +173,11 @@ MATCH (person:Person {name: 'Bob', age: 30}) RETURN person as Bob ``` -This query finds the person with both the specified name and age. It returns the person node as "Bob". +This query finds the person with both the specified name and age. It returns the person node as "Bob." ## Variable length paths -For multi-hop relationships, use variable length path patterns with quantifiers: +Use variable length path patterns with quantifiers for multi-hop relationships: ```gql @@ -179,11 +185,11 @@ MATCH (s)-[]->{1,3}(e) RETURN s.name, e.name ``` -This query finds paths that include between 1 to 3 hops. The `{1,3}` quantifier specifies the minimum and maximum path length. +This query finds paths with 1 to 3 hops. The `{1,3}` quantifier sets the minimum and maximum path length. ### Unbounded variable length paths -For paths of variable length, you can specify open ranges: +Specify open ranges for paths of variable length: ```gql @@ -192,7 +198,7 @@ WHERE center.name = 'Alice' RETURN DISTINCT connected.name ``` -This query finds all nodes reachable from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops". +This query finds all nodes you can reach from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops." ## Examples with WHERE @@ -209,7 +215,7 @@ RETURN person.name, person.age **Output** -This query finds all Person nodes where the age property is greater than 26. +This query finds all `Person` nodes where the `age` property is greater than 26. | person.name | person.age | |--------------|-------------| @@ -217,7 +223,7 @@ This query finds all Person nodes where the age property is greater than 26. ## Example: Range filtering with AND -To create ranges, cCombine multiple conditions: +To create a range, combine multiple conditions: ```gql @@ -226,11 +232,11 @@ WHERE person.age >= 28 AND person.age <= 35 RETURN person.name, person.age ``` -This query finds people with ages between 28 and 35 (inclusive). +This query shows people whose ages are between 28 and 35, inclusive. ## Example: Edge property filtering -Filter based on edge properties to find specific types of relationships: +Filter by edge properties to find specific types of relationships. ```gql @@ -239,11 +245,11 @@ WHERE wa.since >= 2022 RETURN person.name, c.name, wa.since ``` -This query finds people who started working at companies since 2022 or later, filtering based on the `since` property of the `works_at` relationship. +This query shows people who start working at companies in 2022 or later. It filters by the `since` property of the `works_at` relationship. ## Example: String pattern matching -Use string functions to match text patterns: +Use string functions to match text patterns. ```gql @@ -252,11 +258,11 @@ WHERE person.name STARTS WITH 'Al' RETURN COUNT(*) ``` -This query counts how many people have names that start with 'Al', providing a quick summary without returning potentially large result sets. +This query counts people whose names start with 'Al'. It provides a quick summary without returning a large result set. ### String contains matching -Check if a string contains a specific substring: +Check if a string has a specific substring. ```gql @@ -265,8 +271,7 @@ WHERE person.name CONTAINS 'i' RETURN person.name ``` -This query finds all people whose names contain 'i' anywhere in the string. -This is case-sensitive. +This query finds people whose names have 'i' anywhere in the string. This match is case sensitive. ## Example: Inequality comparisons @@ -279,11 +284,11 @@ WHERE person.age > 25 AND company.name <> 'TechCorp' RETURN person.name, company.name ``` -This query finds people over 25 who work at companies other than 'TechCorp'. +This query shows people over 25 who work at companies other than 'TechCorp'. ## Example: Null value checking -Check for the presence or absence of property values: +Check for the presence or absence of property values. ```gql @@ -296,7 +301,7 @@ This query finds all people who have an age recorded (non-null age property). ## Example: Logical OR operations -Use OR to match multiple conditions: +Use OR to match multiple conditions ```gql @@ -305,13 +310,13 @@ WHERE person.age > 30 OR person.name CONTAINS 'a' RETURN person.name, person.age ``` -This query finds people who are either over 30 years old OR have 'a' in their name. +This query finds people who are over 30 years old or have 'a' in their name. ## Examples with RETURN ### Return specific properties -Return individual properties from matched nodes: +Return individual properties from matched nodes. ```gql @@ -319,11 +324,11 @@ MATCH (person:Person) RETURN person.name, person.age ``` -This query returns the name and age properties of all Person nodes. Each row in the result contains these two values. +This query returns the name and age properties for each Person node. Each row in the result shows these two values. ### Return with aliases -Use aliases to rename output columns for clarity: +Use aliases to rename output columns for clarity. ```gql @@ -331,7 +336,7 @@ MATCH (person:Person)-[employment:works_at]->(company:Company) RETURN person.name AS Employee, company.name AS Company, employment.since AS WorkingSince ``` -This query returns actor names, movie titles, and character names with descriptive column headers. +This query returns employee names, company names, and employment start dates with descriptive column headers. ### Return entire nodes and edges @@ -344,7 +349,7 @@ RETURN person, e, movie **Output** -This query returns the complete node and edge objects for Kevin Bacon's relationships, including all properties. +This query returns the complete node and edge objects for Kevin Bacon relationships, including all properties. | person | e | movie | |--------|------|-------| @@ -352,7 +357,7 @@ This query returns the complete node and edge objects for Kevin Bacon's relation ### Count matching patterns -Use COUNT(*) to count the number of pattern matches: +Use COUNT(*) to count the number of pattern matches. ```gql @@ -360,11 +365,11 @@ MATCH (person:Person)-[:ACTED_IN]->(movie:Movie) RETURN person.name, COUNT(*) AS MovieCount ``` -This query counts how many movies each person acted in and groups the results by person name. +This query counts how many movies each person acted in, and groups the results by person name. ### Aggregate with MIN and MAX -Find minimum and maximum values across all matches: +Find minimum and maximum values across all matches. ```gql @@ -372,11 +377,11 @@ MATCH (person:Person) RETURN MIN(person.age) AS Youngest, MAX(person.age) AS Oldest ``` -This query finds the earliest and latest birth years among all people in the graph. +This query finds the youngest and oldest ages among all people in the graph. ### Collect values into arrays -Use COLLECT_LIST to gather multiple values into arrays: +Use COLLECT_LIST to gather multiple values into arrays. ```gql @@ -384,11 +389,11 @@ MATCH (person:Person) RETURN COLLECT_LIST(person.name) AS AllNames ``` -This query collects all person names into a single array result. +This query collects all person names into a single array. ### Return distinct values -Remove duplicates from your results: +Remove duplicates from your results. ```gql @@ -396,11 +401,11 @@ MATCH (person:Person)-[]->(company:Company) RETURN DISTINCT company.name ``` -This query returns each unique movie title only once, even if multiple people are connected to the same movie. +This query returns each unique company name only once, even if multiple people are connected to the same company. ### Return ordered results -Sort your results using ORDER BY: +Sort your results using ORDER BY. ```gql @@ -409,11 +414,11 @@ RETURN person.name, person.age ORDER BY person.age DESC ``` -This query returns people sorted by age in descending order (oldest first). +This query returns people sorted by age in descending order, oldest first. ### Limit result count -Restrict the number of results returned: +Restrict the number of results returned. ```gql @@ -430,7 +435,7 @@ This query returns only the first five people over 25 years old, ordered by age. ## Examples with advanced patterns -Advanced patterns provide powerful ways to match complex graph structures and label combinations. +Advanced patterns let you match complex graph structures and label combinations. ### Label unions (OR) @@ -442,7 +447,7 @@ MATCH (entity:Person | Movie) RETURN entity ``` -This query matches nodes that have either the "Person" OR "Movie" label. The pipe symbol `|` represents logical OR for labels. +This query matches nodes that have either the "Person" or "Movie" label. The pipe symbol `|` means logical OR for labels. ### Label intersections (AND) @@ -454,7 +459,7 @@ MATCH (person:Person & Male) RETURN person.name ``` -This query matches nodes that have both the "Person" AND "Male" labels. The ampersand `&` represents logical AND for labels. +This query matches nodes that have both the "Person" and "Male" labels. The ampersand `&` means logical AND for labels. ### Label negation (NOT) @@ -466,7 +471,7 @@ MATCH (person:!Female) RETURN person.name ``` -This query matches all nodes that do NOT have the "Female" label. The exclamation mark `!` represents logical NOT for labels. +This query matches all nodes that don't have the "Female" label. The exclamation mark `!` means logical NOT for labels. ### Variable length paths with exact range @@ -478,7 +483,7 @@ MATCH (s)-[es]->{2,2}(e) RETURN s, es, e ``` -This query finds paths that are exactly two hops long. The `{2,2}` quantifier specifies both minimum and maximum path length as 2. +This query finds paths that are exactly two hops long. The `{2,2}` quantifier sets both minimum and maximum path length to 2. ### Variable length paths with open range @@ -490,7 +495,7 @@ MATCH (s)-[p]->{1,}(e) RETURN s, e, p ``` -This query finds paths that are 1 or more hops long. The `{1,}` quantifier means "one or more hops" with no upper limit. +This query finds paths that are one or more hops long. The `{1,}` quantifier means one or more hops with no upper limit. ### Zero-length paths @@ -502,7 +507,7 @@ MATCH (n)-[]->{0,0}(same_n) RETURN n ``` -This query matches each node with itself through a zero-length path. The `{0,0}` quantifier specifies exactly zero hops, effectively returning each node paired with itself. +This query matches each node with itself through a zero-length path. The `{0,0}` quantifier sets the path length to zero, so each node is paired with itself. ### Named path variables @@ -514,7 +519,7 @@ MATCH p = (person:Person)-[:DIRECTED]->(movie:Movie) RETURN p ``` -This query assigns the entire pattern to the variable `p`, which can then be returned or used in other parts of the query. The path variable contains the complete sequence of nodes and edges. +This query assigns the entire pattern to the variable `p`, which you can return or use in other parts of the query. The path variable has the complete sequence of nodes and edges. ### Multi-hop named paths @@ -526,23 +531,23 @@ MATCH full_path = (s)-[first_edge]->(middle)-[second_edge]->(e) RETURN full_path, s.name, e.name ``` -This query creates a named path variable `full_path` that captures a 2-hop pattern, while also returning specific properties from the s and e nodes. +This query creates a named path variable `full_path` that captures a two-hop pattern and returns specific properties from the s and e nodes. **Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. ## Examples with complex multi-patterns -#### Cross-town "likes" with company filter +#### Cross-town *likes* with company filter -This query ties together multiple patterns and filters in a single statement: +This example combines multiple patterns and filters in one statement: 1. Find a pair of people where **p1 _likes_ p2**. -2. Link each person to their home city, requiring that p1's city name **starts with "San"** while p2 lives in a **different city**. -3. Ensure p2 **works at TechCorp**. -4. Keep only pairs where the two people have **different ages**. +1. Link each person to their home city. p1's city name must **start with "San"**, and p2 must live in a **different city**. +1. Make sure p2 **works at TechCorp**. +1. Only include pairs where the two people have **different ages**. -The first `RETURN` lists every qualifying match – both people, their cities, and the company – so you can inspect the raw results. -The second `RETURN` then aggregates across all matches to output a single scalar: the **average age of all "liked" people** (p2) who meet these criteria. +The first `RETURN` shows every qualifying match—both people, their cities, and the company—so you can review the raw results. +The second `RETURN` aggregates all matches to output a single value: the **average age of all "liked" people** (p2) who meet these criteria. ```gql @@ -576,13 +581,13 @@ RETURN AVG(p2.age) AS AvgAgeLikedAcrossTowns |---------------------| | 25 | -This example demonstrates GQL's ability to express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in a single, readable statement. +This example shows how GQL lets you express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in one readable statement. ## Security use case - attack path analysis Security analysts use GQL to find potential attack paths in network infrastructure and access control systems. -1. Create a security graph function: +1. Create a security graph function. ```gql @@ -607,7 +612,7 @@ Security analysts use GQL to find potential attack paths in network infrastructu } ``` -1. Set up the security graph: +1. Set up the security graph. ```gql @@ -625,7 +630,7 @@ Security analysts use GQL to find potential attack paths in network infrastructu **Output** -This query finds users who can reach critical systems within three hops, so security teams can prioritize access reviews. +This query shows users who can reach critical systems within three hops, so security teams can prioritize access reviews. | AttackOrigin | CriticalTarget | PathCount | |--|--|--| @@ -635,7 +640,7 @@ This query finds users who can reach critical systems within three hops, so secu ## Social networks use case: Friend recommendations -Social media platforms use GQL to find potential friend connections based on mutual relationships. +Social media platforms use GQL to suggest potential friends based on mutual relationships. 1. Create a social network graph. @@ -662,7 +667,7 @@ Social media platforms use GQL to find potential friend connections based on mut } ``` -1. Set up the social graph: +1. Set up the social graph. ```gql @@ -684,13 +689,13 @@ This query suggests friends for Alice who have mutual connections and live in th | SuggestedFriend | MutualFriend | CommonInterests | |--|--|--| -| David Wilson | Bob Smith | Technology,Gaming | +| David Wilson | Bob Smith | Technology, Gaming | ## Organization use case: Management hierarchy -Organizations use GQL to analyze reporting structures and find management chains. +Organizations use GQL to analyze reporting structures and identify management chains. -Create an organizational graph. +Create an organizational graph to visualize reporting relationships. ```gql @@ -721,14 +726,14 @@ Create an organizational graph. } ``` -Set up the org chart: +Set up the org chart ```gql #crp query_graph_reference=OrgChart() ``` -Find all managers three levels down from CEO. +Find all managers three levels down from the CEO. ```gql @@ -738,4 +743,9 @@ RETURN manager.name AS ManagerName, manager.title AS Title, manager.department A ORDER BY manager.level ``` -This query finds all employees within three management levels of the CEO, which helps with organizational analysis and communication planning. +This query finds all employees within three management levels of the CEO. Use this information for organizational analysis and communication planning. + +## Related topics + +* [Graph Query Language (GQL) in Azure Data Explorer](graph-query-language.md) +* [Graph Query Language (GQL) reference](graph-query-language-functions.md) \ No newline at end of file diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 2ad95746a2..d93659e5b8 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -1,5 +1,5 @@ --- -title: GQL - Azure Data Explorer +title: GQL Graph Query Language in Azure Data Explorer (preview) description: This article describes Graph Query Language (GQL) in Azure Data Explorer. ms.reviewer: herauch ms.topic: reference @@ -9,20 +9,20 @@ ms.date: 08/13/2025 > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] -Graph Query Language (GQL) lets you use standardized graph pattern matching in Azure Data Explorer (ADX). GQL follows the emerging ISO GQL standard for graph database queries. +Graph Query Language (GQL) lets you use standardized graph pattern matching in Azure Data Explorer (ADX). GQL follows the ISO GQL standard for graph database queries. > [!NOTE] -> GQL support in Azure Data Explorer is currently in preview. Features and syntax can change based on feedback and ongoing development. +> GQL support in Azure Data Explorer is in preview. Features and syntax can change based on feedback and ongoing development. ## Introduction -Graph Query Language (GQL) is an emerging ISO standard for querying graph databases. GQL lets you use SQL-like syntax for graph pattern matching in Azure Data Explorer, making it easier to analyze relationships in your data. This article explains how to use GQL in Azure Data Explorer, its benefits, and key features. +Graph Query Language (GQL) is an emerging ISO standard for querying graph databases. GQL lets you use SQL-like syntax for graph pattern matching in Azure Data Explorer, so it's easier to analyze relationships in your data. This article explains how to use GQL in Azure Data Explorer, its benefits, and key features. -Azure Data Explorer implements GQL, so you get standardized graph pattern matching capabilities. You can analyze relationships in your data using the ISO standard syntax. +Azure Data Explorer implements GQL, so you get standardized graph pattern matching capabilities. Analyze relationships in your data using the ISO standard syntax. GQL in Azure Data Explorer builds on the existing [graph operators](graph-operators.md) functionality. It gives you a standardized way to write graph queries that focus on relationships and patterns between entities. -## Getting Started +## Getting started To use GQL in Azure Data Explorer, you need: @@ -31,7 +31,7 @@ To use GQL in Azure Data Explorer, you need: ## Step 1: Create a graph reference -Before you use GQL, create a graph data source. For this article, we use an in-memory make-graph operator, but it's recommended to use a graph snapshot for production scenarios. +Before you use GQL, create a graph data source. This article uses an in-memory make-graph operator, but we recommend using a graph snapshot for production scenarios. ```gql @@ -85,7 +85,7 @@ To run GQL queries, set three client request properties. Set these properties th ### Use Azure Data Explorer to set client request properties > [!IMPORTANT] -> Run each directive separately before running your GQL query. The directives set up the query environment for GQL execution. +> Run each directive separately before you run your GQL query. The directives set up the query environment for GQL execution. ```kql @@ -97,7 +97,7 @@ To run GQL queries, set three client request properties. Set these properties th #crp query_graph_reference=G() ``` -To use labels in GQL, define the label column name: +To use labels in GQL, set the label column name: ```kql @@ -105,25 +105,26 @@ To use labels in GQL, define the label column name: ``` > [!TIP] -> Labels are optional in GQL, but they're often used to filter nodes and edges by type. Setting the label column name lets you use labels in your GQL queries. +> Labels are optional in GQL, but they're often used to filter nodes and edges by type. Set the label column name to use labels in your GQL queries. -### Use SDK/API to set client request properties +### Use SDK or API to set client request properties For programmatic access, set these client request properties: -- `query_language`: Set to `"gql"` -- `query_graph_reference`: Set to your graph function name (for example, `"G()"`) -- `query_graph_label_name`: Set to your label column name (for example, `"lbl"`) +- `query_language`: Set to `"gql"`. +- `query_graph_reference`: Set to your graph function name (for example, `"G()"`). +- `query_graph_label_name`: Set to your label column name (for example, `"lbl"`). -## Step 3: Execute GQL queries +## Step 3: Run GQL queries -After you finish setup, run GQL queries using standard GQL syntax. Start with the examples below to explore basic and advanced GQL features. +After you finish setup, run GQL queries using standard GQL syntax. Use the examples below to explore basic and advanced GQL features. > [!TIP] -> GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns and build complexity gradually. +> GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns, and build complexity gradually. ## Examples +Find basic examples for pattern matching and labels here. For more complex examples, see [GQL query patterns, examples, and common scenarios](graph-query-language-use-cases.md). ### Example GQL query for basic pattern matching @@ -134,7 +135,7 @@ RETURN COUNT(*) as CNT **Output** -This table shows the result of the query. +The following table shows the result of the query. | CNT | |-----| @@ -152,7 +153,7 @@ LIMIT 2 **Output** -This table shows the result of the query. +The following table shows the result of the query. | p.name | target.name | e.lbl | |--------|-------------|-------| @@ -161,20 +162,20 @@ This table shows the result of the query. ## Performance optimization -When you work with GQL queries in production environments, use these performance optimization strategies: +Use these strategies to optimize GQL query performance in production environments: > [!TIP] -> Start with simple patterns, then increase complexity as needed. Monitor query performance, and adjust path lengths and filters to improve results. +> Start with simple patterns, then increase complexity if needed. Monitor query performance, and adjust path lengths and filters to improve results. **Limit path matching scope**: - Use specific label filters to reduce the search space: `MATCH (start:SpecificType)` instead of `MATCH (start)` - Limit variable length paths with reasonable bounds: `MATCH (a)-[]->{1,3}(b)` instead of unbounded paths -- Apply `WHERE` clauses early to filter results before expensive operations +- Apply `WHERE` clauses early to filter results before expensive operations. **Use COUNT(*) for existence checks**: -If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results: +If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results. ```gql MATCH (user:User)-[:SUSPICIOUS_ACTIVITY]->(target) @@ -184,12 +185,12 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity ## Limitations -- **Reserved keywords**: There are reserved GQL keywords that can't be used as identifiers in queries. Some of these reserved keywords might not be immediately obvious to users (for example, `DATE` is a reserved keyword). If your graph data has properties with names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. +- **Reserved keywords**: Some GQL keywords can't be used as identifiers in queries. Some reserved keywords aren't immediately obvious (for example, `DATE` is a reserved keyword). If your graph data has property names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. > [!IMPORTANT] - > When upi design your graph schema, certain common property names might conflict with GQL reserved keywords and should be avoided or renamed. + > When you design your graph schema, some common property names might conflict with GQL reserved keywords. Avoid or rename these property names. -- **No `INSERT`/`CREATE` support**: GQL in Azure Data Explorer doesn't support `INSERT` or `CREATE` operations to modify graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. +- **No `INSERT`/`CREATE` support**: GQL in Azure Data Explorer doesn't support `INSERT` or `CREATE` operations to change graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. - **Optional matches not supported**: GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. From 969d6ee290888b49c8de3bb183ab224e92dd8513 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Thu, 14 Aug 2025 18:41:51 +0200 Subject: [PATCH 14/67] refactor: enhance descriptions and examples in graph sample data --- .../kusto/query/graph-sample-data.md | 538 +++++++++++++++++- 1 file changed, 514 insertions(+), 24 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 27058fe447..3a1825b888 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -1,6 +1,6 @@ --- title: Graph sample datasets and examples -description: Graph examples +description: Graph examples with detailed descriptions, use cases, and visualizations ms.topic: conceptual ms.service: azure-data-explorer ms.author: @@ -12,67 +12,557 @@ ms.date: 08/14/2025 This page lists existing persistent graphs and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. +> [!NOTE] +> All the graph models described in this document are available on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the "Samples" database. You can use this cluster to try out the queries and explore the graph data. + ## Usage notes Use `graph("ModelName")` with the model name to reference existing persistent graphs. All examples assume the graphs already exist in your environment and are accessible through their model names. -### Simple +## Simple + +**Purpose**: Basic graph operations and learning fundamental graph query patterns. + +**Description**: A small educational graph containing people, companies, and cities with various relationships. Perfect for learning graph traversals and understanding basic patterns. + +**Schema**: + +- **Nodes**: Person, Company, City +- **Edges**: works_at, located_at, knows + +**Sample Data Structure**: + +**Sample Data Structure**: + +```mermaid +graph TD + A[Person: Alice] -->|works_at| B[Company: TechCorp] + C[Person: Bob] -->|works_at| B + D[Person: Carol] -->|works_at| E[Company: DataSoft] + A -->|located_at| F[City: Seattle] + C -->|located_at| F + B -->|located_at| F + A -->|knows| C + C -->|knows| D +``` + +**Use Cases**: -Purpose: Basic graph operations and learning fundamental graph query patterns. +- Learning graph query fundamentals +- Testing graph algorithms +- Understanding relationship patterns +- Educational examples for graph concepts + +**Example Queries**: + +Find all employees of a specific company: ```kusto graph("Simple") -| graph-match (n)-[e]->(m) -project source = n, target = m, relationship = e -| take 10 +| graph-match (person)-[works_at]->(company) + where company.name == "TechCorp" + project employee_name = person.name, employee_age = person.properties.age ``` -### LDBC_SNB_Interactive +|employee_name|employee_age| +|---|---| +|Alice|25| +|Bob|30| +|Emma|26| -Purpose: Social network traversals and friend-of-friend exploration. +Find colleagues (people working at the same company): + +```kusto +graph("Simple") +| graph-match (person1)-->(company)<--(person2) + where person1.id != person2.id and labels(company) has "Company" + project colleague1 = person1.name, colleague2 = person2.name, company = company.name +| take 1 +``` + +|colleague1|colleague2|company| +|---|---|---| +|Alice|Bob|TechCorp| + +## LDBC_SNB_Interactive + +**Purpose**: Social network traversals and friend-of-friend exploration. + +> [!NOTE] +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Social Network Benchmark datasets are created by the Linked Data Benchmark Council (LDBC). + +**Description**: The Linked Data Benchmark Council (LDBC) Social Network Benchmark Interactive workload dataset. This graph represents a realistic social network with people, posts, comments, forums, organizations, and places. + +**Schema**: + +- **Nodes**: PERSON, POST, COMMENT, FORUM, ORGANISATION, PLACE, TAG, TAGCLASS +- **Edges**: KNOWS, LIKES, HAS_CREATOR, REPLY_OF, HAS_MEMBER, HAS_MODERATOR, STUDY_AT, WORK_AT, IS_LOCATED_IN, HAS_INTEREST, HAS_TAG, IS_PART_OF + +**Use Cases**: + +- Social network analysis and recommendation systems +- Community detection algorithms +- Influence propagation studies +- Content recommendation based on social connections +- Friend-of-friend discovery +- Social graph mining research + +**Example Queries**: + +Find direct friendships with similar ages: + +This query identifies pairs of people who are directly connected through a "KNOWS" relationship and have similar ages (birthdays within 30 days of each other). It traverses the LDBC social network graph to find existing friendships between people of similar age groups. The query returns the total count of such age-similar friendship pairs in the network, which can be useful for analyzing age-based social patterns or validating friend recommendation algorithms. ```kusto graph("LDBC_SNB_Interactive") -| graph-match (p)-[k*1..2]->(f) -where all(k, label == 'KNOWS') -project person = p, friend = f +| graph-match (person1)-[knows]->(person2) + where labels(person1) has "PERSON" and labels(person2) has "PERSON" and + labels(knows) has "KNOWS"and abs(person1.birthday - person2.birthday) < 30d + project person_name = person1.firstName, friend_name = person2.firstName +| count +``` + +|Count| +|---| +|225| + +Find popular posts by likes: + +This query analyzes social engagement by identifying the most popular content creators based on how many unique people have liked their posts. It traverses the social network graph through the path: person → likes → post → has_creator → creator. The query aggregates the data to show each creator's total number of unique likers and distinct posts, then returns the top 3 creators with the most likes. This is useful for identifying influential content creators, understanding engagement patterns, and discovering viral content in the social network. + +```kusto +graph("LDBC_SNB_Interactive") +| graph-match (person)-[likes]->(post)-[has_creator]->(creator) + where labels(person) has "Person" and labels( post) has "POST" and labels(has_creator) has "HAS_CREATOR" and isnotempty(creator.lastName) + project personId = person.id, postId = post.id, creator = creator.lastName +| summarize Likes = dcount(personId), posts = dcount(postId) by creator +| top 3 by Likes desc +``` + +|creator|Likes|posts| +|---|---|---| +|Zhang|371|207| +|Hoffmann|340|9| +|Singh|338|268| + +## LDBC_Financial + +**Purpose**: Financial transaction analysis and fraud detection patterns. + +> [!NOTE] +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Financial Benchmark datasets are created by the Linked Data Benchmark Council (LDBC). + +**Description**: LDBC Financial Benchmark dataset representing a financial network with companies, persons, accounts, loans, and various financial transactions. Designed for fraud detection and financial crime investigation scenarios. + +**Schema**: + +- **Nodes**: COMPANY, PERSON, ACCOUNT, LOAN, MEDIUM +- **Edges**: TRANSFER, WITHDRAW, DEPOSIT, OWN, APPLY, GUARANTEE, INVEST, SIGN_IN, REPAY + +**Use Cases**: + +- Financial fraud detection +- Anti-money laundering (AML) analysis +- Transaction pattern analysis +- Risk assessment and credit scoring +- Suspicious activity monitoring +- Financial network analysis + +**Example Queries**: + +Detect potential money laundering through circular transfers: + +This query identifies suspicious circular transaction patterns that could indicate money laundering activities. It searches for accounts that send money to another account and then receive it back through a chain of 1 to 3 transfers, creating a circular flow. The query specifically looks for large initial transfers (over 10,000) and returns details about the suspicious accounts, including the transfer amount and the length of the circular chain. This pattern detection is useful for anti-money laundering (AML) systems and financial fraud investigations. + +```kusto +graph("LDBC_Financial") +| graph-match (account1)-[t1]->(account2)-[t2*1..3]->(account1) + where labels(t1) has "TRANSFER" and t1.amount > 10000 // Large initial transfer + project suspicious_account = account1.node_id, + amount = t1.amount, + transfer_chain_length = array_length(t2) + 1 | take 10 ``` -### LDBC_Financial +|suspicious_account|amount|transfer_chain_length| +|---|---|---| +|Account::4818007176356300028|5035377,73|2| +|Account::4818007176356300028|5035377,73|2| +|Account::4845310249097233848|359062,45|2| +|Account::4818007176356300028|5035377,73|3| +|Account::4818007176356300028|5035377,73|4| +|Account::4840243699516440940|5753668,55|4| +|Account::4818007176356300028|5035377,73|4| +|Account::180143985094820389|465338,26|4| +|Account::4814910951612482356|1684581,62|4| +|Account::4816599801472746629|963626,42|4| -Purpose: Financial transaction analysis and fraud detection patterns. +Find high-risk loan guarantors: + +This query identifies individuals or companies who guarantee multiple loans totaling significant amounts, which could indicate financial risk exposure. It traverses the financial network graph following the path: guarantor → guarantee → borrower → apply → loan. The query aggregates the total amount guaranteed and number of loans for each guarantor, then filters for those guaranteeing over 100,000 in total and returns the top 5 by total guaranteed amount. This analysis is useful for risk assessment, identifying over-leveraged guarantors, and evaluating systemic financial risks in lending networks. ```kusto graph("LDBC_Financial") -| graph-match (account)-[t*1..3]->(target) -where all(t, label == 'TRANSFER') -project source_account = account, target_account = target +| graph-match (guarantor)-[guarantee]->(borrower)-[apply]->(loan) + where labels(guarantee) has "GUARANTEE" and labels(apply) has "APPLY" + project guarantor_id = guarantor.node_id, + borrower_id = borrower.node_id, + loan_amount = loan.loanAmount +| summarize total_guaranteed = sum(loan_amount), loan_count = count() by guarantor_id +| where total_guaranteed > 100000 +| top 5 by total_guaranteed desc +``` + +|guarantor_id|total_guaranteed|loan_count| +|---|---|---| +|Person::44|439802195|8| +|Person::15393162789155|411111642|8| +|Company::12094627905931|404538891|6| +|Company::4398046511208|366243272|8| +|Person::19791209300551|338838223|6| + +## BloodHound Entra + +**Purpose**: Azure Active Directory privilege escalation and attack path analysis. + +> [!NOTE] +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. + +**Description**: BloodHound Community Edition dataset for Azure Active Directory environments. Contains nodes representing Azure AD objects (users, groups, applications, service principals) and edges representing various permissions and relationships. + +**Schema**: + +- **Nodes**: Various Azure AD object types (Users, Groups, Applications, Service Principals, etc.) +- **Edges**: Permission and relationship types (Member, Owner, various Azure AD privileges) + +**Security Analysis Example**: + +```mermaid +graph TD + A[Regular User] -->|MemberOf| B[Security Group] + B -->|CanAddMember| C[High Privilege Group] + C -->|Member| D[Global Admin] + D -->|Owner| E[Sensitive Application] + + style A fill:#ffcccc + style E fill:#ff9999 + style D fill:#ff6666 +``` + +**Use Cases**: + +- Entra ID security assessments +- Privilege escalation path discovery +- Attack path visualization +- Identity governance analysis +- Risk-based security controls +- Compliance auditing for cloud environments + +**Example Queries**: + +Find paths to administrative privileges: + +This query identifies privilege escalation paths from regular users to administrative groups in Azure AD environments. It searches for users who can reach admin groups (like AAD DC Administrators, DnsAdmins, etc.) through 1-5 relationship hops, helping security teams understand potential attack paths and privilege escalation risks. + +```kusto +graph("BloodHound_Entra") +| graph-match (user)-[path*1..3]->(admingroup) + where labels(user) has_any ("User", "AZUser") + and labels(admingroup) has_any ("Group", "AZGroup") + and (admingroup.name contains "ADMIN" or admingroup.displayname contains "ADMIN") + project source_user = user.name, + path_length = array_length(path), + admin_group = coalesce(admingroup.displayname, admingroup.name) +| order by path_length asc | take 10 ``` -### BloodHound_Entra +|source_user|path_length|admin_group| +|---|---|---| +|THISUSERHASINTUNEADMINROLE@PHANTOMCORP.ONMICROSOFT.COM|1|ADSyncAdmins| +|097EF6C2-GROUPSADMINISTRATOR@PHANTOMCORP.ONMICROSOFT.COM|1|AAD DC Administrators| +|USERBELONGSTOGAGROUP@PHANTOMCORP.ONMICROSOFT.COM|1|ADSyncAdmins| +|THISUSERHASINTUNEADMINROLE@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| +|RHADMIN@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| +|CJACKSON@PHANTOMCORP.ONMICROSOFT.COM|1|Azure ATP phantom Administrators| +|097EF6C2-INTUNEADMINISTRATOR@PHANTOMCORP.ONMICROSOFT.COM|1|AAD DC Administrators| +|RHADMIN_PHANTOMCORP.ONMICROSOFT.COM#EXT#@PHANTOMCORP.ONMICROSOFT.COM|1|Resource Group Admins| +|THISUSERHASKNOWLEDGEMANAGERROLE@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| +|097EF6C2-INTUNEADMINISTRATOR@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| -Purpose: Azure Active Directory privilege escalation and attack path analysis. +Identify high-value targets (Tier 0 assets): + +This query identifies critical administrative assets marked as "admin_tier_0" in the environment. These are the most sensitive and powerful accounts, service principals, and resources that pose the highest risk if compromised. Understanding these assets helps prioritize security monitoring and protection efforts. ```kusto graph("BloodHound_Entra") -| graph-match (user)-[path*1..4]->(resource) -project user_principal = user, accessed_resource = resource +| graph-match (asset) + where asset.properties.system_tags contains "admin_tier_0" + project asset_name = asset.name, + asset_type = tostring(labels(asset)[1]), // Get primary type (AZUser, AZServicePrincipal, etc.) + system_tags = asset.properties.system_tags | take 10 ``` -### BloodHound_AD +|asset_name|asset_type|system_tags| +|---|---|---| +|JJACOB@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| +|PLEWIS@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| +|JMILLER@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| +|CJACKSON@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| +|RHALL@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| +|THISAPPHASGLOBALADMIN@PHANTOMCORP|AZServicePrincipal|admin_tier_0| +|MYCOOLAUTOMATIONACCOUNT@PHANTOMCORP|AZServicePrincipal|admin_tier_0| +|SERVICEPRINCIPALE@PHANTOMCORP|AZServicePrincipal|admin_tier_0| +|31E3B75F-PRIVILEGED AUTHENTICATION ADMINISTRATOR@PHANTOMCORP|AZServicePrincipal|admin_tier_0| +|31E3B75F-PRIVILEGED ROLE ADMINISTRATOR@PHANTOMCORP|AZServicePrincipal|admin_tier_0| + +## BloodHound_AD + +**Purpose**: On-premises Active Directory security analysis and privilege mapping. + +> [!NOTE] +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. + +**Description**: BloodHound Community Edition dataset for on-premises Active Directory environments. Contains comprehensive information about AD objects, group memberships, ACLs, and various attack paths in traditional Windows domains. + +**Attack Path Visualization Example**: + +```mermaid +graph TD + A[Domain User] -->|GenericWrite| B[Service Account] + B -->|MemberOf| C[Backup Operators] + C -->|SeBackupPrivilege| D[Domain Controller] + D -->|DCSync| E[Domain Admin Hash] + + style A fill:#ccffcc + style E fill:#ff6666 + style D fill:#ffcc99 +``` + +**Use Cases**: + +- Active Directory security assessments +- Attack path analysis and penetration testing +- Domain privilege mapping +- Group policy security analysis +- Kerberoasting and ASREPRoasting target identification +- Security control gap analysis + +**Example Queries**: + +Find potential privilege escalation: + +This query counts how many non-admin users can potentially escalate to admin in Azure AD. It traverses up to 10 MemberOf group hops (no cycles) from each user to groups that grant dangerous permissions (GenericAll, WriteDacl, WriteOwner, ForceChangePassword) over admin users (admincount=true), then returns the distinct number of such “potential attacker” users. + +```kusto +graph("BloodHound_AD") +| graph-match cycles=none (user)-[memberof*0..10]->(group)-[permission]->(target) + where labels(user) has "User" + and labels(group) has "Group" + and all(memberof, labels() has "MemberOf") + and user.properties.admincount == false + and (labels(permission) has_any ("GenericAll", "WriteDacl", "WriteOwner", "ForceChangePassword")) + and (labels(target) has "User" and target.properties.admincount == true) + project attack_user = user.name +| summarize ['Potential attackers'] = dcount(attack_user) +``` + +|Potential attackers| +|---| +|2| + +Find Golden Certificate attack paths: -Purpose: On-premises Active Directory security analysis and privilege mapping. +This query identifies entities that can perform Golden Certificate attacks, which allow attackers to forge certificates as any user in the domain. These are extremely critical vulnerabilities as they enable complete domain compromise by allowing the attacker to impersonate any user, including domain administrators, through forged certificates. ```kusto graph("BloodHound_AD") -| graph-match (user)-[path*1..4]->(resource) -project user_account = user, accessed_resource = resource +| graph-match (attacker)-[goldencert]->(target) + where labels(goldencert) has "GoldenCert" + project + Attacker = attacker.name, + AttackerType = case( + attacker.name has "DC", "Domain Controller", + attacker.name has "CA", "Certificate Authority", + attacker.name has "SRV", "Server", + "Unknown System" + ), + Target = target.name, + RiskLevel = "CRITICAL", + AttackCapability = case( + attacker.name has "DC", "Primary domain controller with certificate services", + attacker.name has "EXTCA", "External Certificate Authority with root access", + attacker.name has "SRV", "Compromised server with certificate generation rights", + "System with certificate forging capabilities" + ) +``` + +|Attacker|AttackerType|Target|RiskLevel|AttackCapability| +|---|---|---|---|---| +|DC01.PHANTOM.CORP|Unknown System|PHANTOM.CORP|CRITICAL|System with certificate forging capabilities| +|SRV-SHARPHOUND.PHANTOM.CORP|Server|PHANTOM.CORP|CRITICAL|Compromised server with certificate generation rights| +|EXTCA01.WRAITH.CORP|Unknown System|WRAITH.CORP|CRITICAL|System with certificate forging capabilities| +|EXTCA02.WRAITH.CORP|Unknown System|WRAITH.CORP|CRITICAL|System with certificate forging capabilities| + +## Common Analysis Queries + +These queries work across all graph models and help you understand the structure and characteristics of any graph dataset. Use these queries to explore new graphs or perform basic analysis. + +### Graph Overview and Statistics + +**Count total nodes and edges**: + +```kusto +// Get node count +graph("GRAPH_NAME") +| graph-match (node) +project node +| summarize NodeCount = count() +``` + +```kusto +// Get edge count +graph("GRAPH_NAME") +| graph-match (source)-[edge]->(target) +project edge +| summarize EdgeCount = count() +``` + +**Get graph summary statistics**: + +```kusto +let nodes = graph("GRAPH_NAME") | graph-match (node) project node | summarize NodeCount = count(); +let edges = graph("GRAPH_NAME") | graph-match (source)-[edge]->(target) project edge | summarize EdgeCount = count(); +union nodes, edges +``` + +### Node Analysis + +**Discover all node types (labels)**: + +```kusto +graph("GRAPH_NAME") +| graph-match (node) +project labels = labels(node) +| mv-expand label = labels +| summarize count() by tostring(label) +| order by count_ desc +``` + +**Find nodes with multiple labels**: + +```kusto +graph("GRAPH_NAME") +| graph-match (node) +project node_id = node.id, labels = labels(node), label_count = array_length(labels(node)) +| where label_count > 1 +| take 10 +``` + +**Sample nodes by type**: + +```kusto +graph("GRAPH_NAME") +| graph-match (node) +where labels(node) has "DESIRED_LABEL" // Replace with actual label +project node_id = node.id, properties = node.properties +| take 5 +``` + +### Edge Analysis + +**Discover all edge types** (works with different graph schemas): + +```kusto +// For graphs using 'labels' array (like BloodHound) +graph("GRAPH_NAME") +| graph-match (source)-[edge]->(target) +project edge_labels = labels(edge) +| mv-expand label = edge_labels +| summarize count() by tostring(label) +| order by count_ desc +``` + +```kusto +// For graphs using 'label' column (like LDBC) +graph("GRAPH_NAME") +| graph-match (source)-[edge]->(target) +project edge_label = edge.label +| summarize count() by edge_label +| order by count_ desc +``` + +```kusto +// For graphs using 'lbl' column (like Simple) +graph("GRAPH_NAME") +| graph-match (source)-[edge]->(target) +project edge_label = edge.lbl +| summarize count() by edge_label +| order by count_ desc +``` + +**Find most connected nodes (highest degree)**: + +```kusto +graph("GRAPH_NAME") +| graph-match (node)-[edge]-(connected) +project node_id = node.id +| summarize degree = count() by node_id +| order by degree desc | take 10 ``` +### Relationship Pattern Analysis + +**Find nodes with specific relationship patterns**: + +```kusto +// Nodes that have both incoming and outgoing edges +graph("GRAPH_NAME") +| graph-match (node) +where (graph("GRAPH_NAME") | graph-match (other1)-[]->(node) | take 1 | count() > 0) + and (graph("GRAPH_NAME") | graph-match (node)-[]->(other2) | take 1 | count() > 0) +project node_id = node.id, node_labels = labels(node) +| take 10 +``` + +**Discover triangular relationships** (nodes connected in a triangle): + +```kusto +graph("GRAPH_NAME") +| graph-match (a)-[]->(b)-[]->(c)-[]->(a) +where a.id != b.id and b.id != c.id and c.id != a.id +project node1 = a.id, node2 = b.id, node3 = c.id +| take 5 +``` + +### Property Analysis + +**Explore node properties**: + +```kusto +graph("GRAPH_NAME") +| graph-match (node) +where labels(node) has "DESIRED_LABEL" // Replace with actual label +project properties = node.properties +| take 1 +| project property_names = bag_keys(properties) +``` + +**Find nodes with specific property values**: + +```kusto +graph("GRAPH_NAME") +| graph-match (node) +where isnotnull(node.properties.name) // Replace 'name' with actual property +project node_id = node.id, property_value = node.properties.name +| take 10 +``` + +> [!TIP] +> Replace `"GRAPH_NAME"` with the actual graph model name (e.g., "Simple", "LDBC_SNB_Interactive", "BloodHound_Entra", etc.). Some queries may need adjustment based on the specific schema of your graph model. + ## Related content - [Graph semantics overview](./graph-semantics-overview.md) From 35bac785f69f39e9f894eb3618ceb15bc8ea28d5 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Thu, 14 Aug 2025 19:44:04 +0300 Subject: [PATCH 15/67] heading level fix --- .../kusto/query/graph-query-language-functions.md | 4 +++- .../kusto/query/graph-query-language-use-cases.md | 14 +++++++------- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index fa155aa8fb..f60a2c63a9 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -14,6 +14,8 @@ Graph Query Language (GQL) is a powerful language for querying graph data in Azu > [!NOTE] > GQL support in Azure Data Explorer is currently in preview. Features and syntax can change based on feedback and ongoing development. +## Core GQL functions and operators + This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. | GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | @@ -61,7 +63,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | | `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | -## Labels() (GQL function) +## Labels() (Custom GQL function) The `labels()` function shows the labels for a node or edge as an array. diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index d32d229bac..568d5685bc 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -175,7 +175,7 @@ RETURN person as Bob This query finds the person with both the specified name and age. It returns the person node as "Bob." -## Variable length paths +### Variable length paths Use variable length path patterns with quantifiers for multi-hop relationships: @@ -221,7 +221,7 @@ This query finds all `Person` nodes where the `age` property is greater than 26. |--------------|-------------| | Bob | 30 | -## Example: Range filtering with AND +### Range filtering with AND To create a range, combine multiple conditions: @@ -234,7 +234,7 @@ RETURN person.name, person.age This query shows people whose ages are between 28 and 35, inclusive. -## Example: Edge property filtering +### Edge property filtering Filter by edge properties to find specific types of relationships. @@ -247,7 +247,7 @@ RETURN person.name, c.name, wa.since This query shows people who start working at companies in 2022 or later. It filters by the `since` property of the `works_at` relationship. -## Example: String pattern matching +### String pattern matching Use string functions to match text patterns. @@ -273,7 +273,7 @@ RETURN person.name This query finds people whose names have 'i' anywhere in the string. This match is case sensitive. -## Example: Inequality comparisons +### Inequality comparisons Use comparison operators to exclude specific values: @@ -286,7 +286,7 @@ RETURN person.name, company.name This query shows people over 25 who work at companies other than 'TechCorp'. -## Example: Null value checking +### Null value checking Check for the presence or absence of property values. @@ -299,7 +299,7 @@ RETURN person.name, person.age This query finds all people who have an age recorded (non-null age property). -## Example: Logical OR operations +### Logical OR operations Use OR to match multiple conditions From cee02d74d0e80ae308b4b28b8a17622c8df5b9c0 Mon Sep 17 00:00:00 2001 From: Daniel Dror Date: Thu, 14 Aug 2025 21:27:52 -0700 Subject: [PATCH 16/67] align with new transient graph + minor fixes --- .../query/graph-query-language-use-cases.md | 97 ++++++++++--------- .../kusto/query/graph-query-language.md | 30 +++--- 2 files changed, 67 insertions(+), 60 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 568d5685bc..70eeecca37 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -112,11 +112,11 @@ RETURN person.name, company.name ### Filter by edge labels without variables -For this example, switch back to the original G() graph, which has the "knows" relationship: +For this example, switch back to the original G_Doc_Transient() graph, which has the "knows" relationship: ```gql -#crp query_graph_reference=G() +#crp query_graph_reference=G_Doc_Transient() ``` Filter by edge labels without assigning the edge to a variable when you don't need to access its properties. @@ -145,8 +145,8 @@ Use WHERE clauses to filter based on property values. ```gql MATCH (person:Person) -WHERE person.age > 25 -RETURN person.name, person.age +WHERE person.properties.age > 25 +RETURN person.name, person.properties.age ``` This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes by the age property. @@ -158,7 +158,7 @@ Filter by properties directly in the pattern using inline conditions. ```gql MATCH (person:Person {name: 'Bob'}) -RETURN person.age +RETURN person.properties.age ``` This query finds the person named "Bob" and returns their age. The `{name: 'Bob'}` syntax filters nodes where the name property equals 'Bob'. @@ -169,7 +169,8 @@ Specify multiple property conditions inline. ```gql -MATCH (person:Person {name: 'Bob', age: 30}) +MATCH (person:Person {name: 'Bob'}) +WHERE person.properties.age = 30 RETURN person as Bob ``` @@ -209,17 +210,19 @@ Filter nodes based on a single property condition: ```gql MATCH (person:Person) -WHERE person.age > 26 -RETURN person.name, person.age +WHERE person.properties.age > 26 +RETURN person.name, person.properties.age ``` **Output** This query finds all `Person` nodes where the `age` property is greater than 26. -| person.name | person.age | +| person.name | person.properties.age | |--------------|-------------| | Bob | 30 | +| Carol | 28 | +| David | 35 | ### Range filtering with AND @@ -228,8 +231,8 @@ To create a range, combine multiple conditions: ```gql MATCH (person:Person) -WHERE person.age >= 28 AND person.age <= 35 -RETURN person.name, person.age +WHERE person.properties.age >= 28 AND person.properties.age <= 35 +RETURN person.name, person.properties.age ``` This query shows people whose ages are between 28 and 35, inclusive. @@ -280,7 +283,7 @@ Use comparison operators to exclude specific values: ```gql MATCH (person:Person)-[wa:works_at]->(company:Company) -WHERE person.age > 25 AND company.name <> 'TechCorp' +WHERE person.properties.age > 25 AND company.name <> 'TechCorp' RETURN person.name, company.name ``` @@ -293,8 +296,8 @@ Check for the presence or absence of property values. ```gql MATCH (person:Person) -WHERE person.age IS NOT NULL -RETURN person.name, person.age +WHERE person.properties.age IS NOT NULL +RETURN person.name, person.properties.age ``` This query finds all people who have an age recorded (non-null age property). @@ -306,8 +309,8 @@ Use OR to match multiple conditions ```gql MATCH (person:Person) -WHERE person.age > 30 OR person.name CONTAINS 'a' -RETURN person.name, person.age +WHERE person.properties.age > 30 OR person.name CONTAINS 'a' +RETURN person.name, person.properties.age ``` This query finds people who are over 30 years old or have 'a' in their name. @@ -321,7 +324,7 @@ Return individual properties from matched nodes. ```gql MATCH (person:Person) -RETURN person.name, person.age +RETURN person.name, person.properties.age ``` This query returns the name and age properties for each Person node. Each row in the result shows these two values. @@ -342,18 +345,18 @@ This query returns employee names, company names, and employment start dates wit ```gql -MATCH (person:Person)-[e]->(movie:Movie) -WHERE person.name = 'Kevin Bacon' -RETURN person, e, movie +MATCH (person:Person)-[e]->(company:Company) +WHERE person.name = 'Alice' +RETURN person, e, company ``` **Output** -This query returns the complete node and edge objects for Kevin Bacon relationships, including all properties. +This query returns the complete node and edge objects for Alice's relationship with companies, including all properties. -| person | e | movie | +| person | e | company | |--------|------|-------| -{"id":"2","lbl":"Person","name":"Kevin Bacon","title":"","born":1958} | {"source":"2","target":"3","lbl":"ACTED_IN","Role":"Jack Swigert"} | {"id":"3","lbl":"Movie","name":"","title":"Apollo 13","born":1995} +{"id":"p1","lbl":"Person","name":"Alice","properties":{"age": 25}} |{"source":"p1","target":"c1","lbl":"works_at","since":2020} | {"id":"c1","lbl":"Company","name":"TechCorp","properties":{"revenue": 1000000}} ### Count matching patterns @@ -361,11 +364,11 @@ Use COUNT(*) to count the number of pattern matches. ```gql -MATCH (person:Person)-[:ACTED_IN]->(movie:Movie) -RETURN person.name, COUNT(*) AS MovieCount +MATCH (person:Person)-[:likes]->(p2:Person) +RETURN person.name, COUNT(*) AS LikesGiven ``` -This query counts how many movies each person acted in, and groups the results by person name. +This query counts how many people each person likes and groups the results by person name. ### Aggregate with MIN and MAX @@ -374,7 +377,7 @@ Find minimum and maximum values across all matches. ```gql MATCH (person:Person) -RETURN MIN(person.age) AS Youngest, MAX(person.age) AS Oldest +RETURN MIN(cast(person.properties.age as int)) AS Youngest, MAX(cast(person.properties.age as int)) AS Oldest ``` This query finds the youngest and oldest ages among all people in the graph. @@ -410,8 +413,8 @@ Sort your results using ORDER BY. ```gql MATCH (person:Person) -RETURN person.name, person.age -ORDER BY person.age DESC +RETURN person.name, person.properties.age +ORDER BY cast(person.properties.age as int) DESC ``` This query returns people sorted by age in descending order, oldest first. @@ -423,9 +426,9 @@ Restrict the number of results returned. ```gql MATCH (person:Person) -WHERE person.age > 25 +WHERE person.properties.age > 25 RETURN person.name -ORDER BY person.age +ORDER BY cast(person.properties.age as int) LIMIT 5 ``` @@ -443,11 +446,11 @@ Match nodes that have any of the specified labels: ```gql -MATCH (entity:Person | Movie) +MATCH (entity:Person | Company) RETURN entity ``` -This query matches nodes that have either the "Person" or "Movie" label. The pipe symbol `|` means logical OR for labels. +This query matches nodes that have either the "Person" or "Company" label. The pipe symbol `|` means logical OR for labels. ### Label intersections (AND) @@ -515,7 +518,7 @@ Assign entire paths to variables for later use: ```gql -MATCH p = (person:Person)-[:DIRECTED]->(movie:Movie) +MATCH p = (person:Person)-[:works_at]->(company:Company) RETURN p ``` @@ -555,7 +558,7 @@ MATCH (p1:Person)-[:likes]->(p2:Person), (p1)-[:located_at]->(home:City), (p2)-[:located_at]->(home2:City), (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) -WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' +WHERE cast(p1.properties.age as int) <> cast(p2.properties.age as int) and home.name <> home2.name and home.name starts with 'San' RETURN p1.name, p2.name, home.name, work.name, home2.name ``` @@ -571,8 +574,8 @@ MATCH (p1:Person)-[:likes]->(p2:Person), (p1)-[:located_at]->(home:City), (p2)-[:located_at]->(home2:City), (p2)-[:works_at]->(work:Company {name: 'TechCorp'}) -WHERE p1.age <> p2.age and home.name <> home2.name and home.name starts with 'San' -RETURN AVG(p2.age) AS AvgAgeLikedAcrossTowns +WHERE cast(p1.properties.age as int) <> cast(p2.properties.age as int) and home.name <> home2.name and home.name starts with 'San' +RETURN AVG(cast(p2.properties.age as int)) AS AvgAgeLikedAcrossTowns ``` **Output** @@ -678,18 +681,22 @@ Social media platforms use GQL to suggest potential friends based on mutual rela ```gql - MATCH (person:Person)-[:FRIENDS_WITH]->(mutual:Person)-[:FRIENDS_WITH]->(potential:Person) - WHERE person.name = 'Alice Johnson' AND person.location = potential.location - RETURN potential.name AS SuggestedFriend, mutual.name AS MutualFriend, potential.interests AS CommonInterests + MATCH (p1:PERSON {firstName: 'Karl', lastName: 'Muller'})-[:KNOWS]-(p2:PERSON)-[:KNOWS]-(p3:PERSON), + (p1)-[:IS_LOCATED_IN]-(c1:PLACE), + (p3)-[:IS_LOCATED_IN]-(c1) + WHERE p1.id <> p3.id + RETURN DISTINCT p3.firstName, p3.lastName ``` **Output** -This query suggests friends for Alice who have mutual connections and live in the same location. +This query suggests friends for Karl who have mutual connections and live in the same location. -| SuggestedFriend | MutualFriend | CommonInterests | -|--|--|--| -| David Wilson | Bob Smith | Technology, Gaming | +| p3.firstName | p3.lastName | +|---------------|--------------| +| Alfred | Hoffmann | +| Hans | Becker | +| Wilhelm | Muller | ## Organization use case: Management hierarchy @@ -748,4 +755,4 @@ This query finds all employees within three management levels of the CEO. Use th ## Related topics * [Graph Query Language (GQL) in Azure Data Explorer](graph-query-language.md) -* [Graph Query Language (GQL) reference](graph-query-language-functions.md) \ No newline at end of file +* [Graph Query Language (GQL) reference](graph-query-language-functions.md) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index d93659e5b8..762f32939d 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -35,20 +35,20 @@ Before you use GQL, create a graph data source. This article uses an in-memory m ```gql -.create-or-alter function G() { - let nodes = datatable(id:string, lbl:string, name:string, age:int) +.create-or-alter function G_Doc_Transient() { + let nodes = datatable(id:string, lbl:string, name:string, properties:dynamic) [ - "p1","Person","Alice",25, - "p2","Person","Bob",30, - "p3","Person","Carol",28, - "p4","Person","David",35, - "p5","Person","Emma",26, - "c1","Company","TechCorp",0, - "c2","Company","DataSoft",0, - "c3","Company","CloudInc",0, - "ct1","City","Seattle",0, - "ct2","City","Portland",0, - "ct3","City","San Francisco",0 + "p1","Person","Alice",dynamic({"age": 25}), + "p2","Person","Bob",dynamic({"age": 30}), + "p3","Person","Carol",dynamic({"age": 28}), + "p4","Person","David",dynamic({"age": 35}), + "p5","Person","Emma",dynamic({"age": 26}), + "c1","Company","TechCorp",dynamic({"revenue": 1000000}), + "c2","Company","DataSoft",dynamic({"revenue": 2000000}), + "c3","Company","CloudInc",dynamic({"revenue": 1500000}), + "ct1","City","Seattle",dynamic({"population": 750000}), + "ct2","City","Portland",dynamic({"population": 650000}), + "ct3","City","San Francisco",dynamic({"population": 870000}) ]; let edges = datatable(source:string, target:string, lbl:string, since:int) [ @@ -94,7 +94,7 @@ To run GQL queries, set three client request properties. Set these properties th ```kql -#crp query_graph_reference=G() +#crp query_graph_reference=G_Doc_Transient() ``` To use labels in GQL, set the label column name: @@ -112,7 +112,7 @@ To use labels in GQL, set the label column name: For programmatic access, set these client request properties: - `query_language`: Set to `"gql"`. -- `query_graph_reference`: Set to your graph function name (for example, `"G()"`). +- `query_graph_reference`: Set to your graph function name (for example, `"G_Doc_Transient()"`). - `query_graph_label_name`: Set to your label column name (for example, `"lbl"`). ## Step 3: Run GQL queries From 2fe6888800cff070aee34b024407a6c9b1c374ab Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Fri, 15 Aug 2025 08:50:37 +0200 Subject: [PATCH 17/67] added pics for Simple graph and description of LDBC interactive --- .../kusto/query/graph-sample-data.md | 77 +++++++++++++----- .../graphs/graph-example-simple-instances.mmd | 57 +++++++++++++ .../graphs/graph-example-simple-instances.png | Bin 0 -> 309575 bytes .../graphs/graph-example-simple-schema.mmd | 22 +++++ .../graphs/graph-example-simple-schema.png | Bin 0 -> 169286 bytes 5 files changed, 134 insertions(+), 22 deletions(-) create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-instances.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-instances.png create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-schema.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-schema.png diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 3a1825b888..9ef271bfb6 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -3,21 +3,18 @@ title: Graph sample datasets and examples description: Graph examples with detailed descriptions, use cases, and visualizations ms.topic: conceptual ms.service: azure-data-explorer -ms.author: -author: +ms.author: herauch +author: cosh ms.date: 08/14/2025 --- # Graph sample datasets and examples -This page lists existing persistent graphs and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. - -> [!NOTE] -> All the graph models described in this document are available on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the "Samples" database. You can use this cluster to try out the queries and explore the graph data. +This page lists existing graphs on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the "Samples" database and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. ## Usage notes -Use `graph("ModelName")` with the model name to reference existing persistent graphs. All examples assume the graphs already exist in your environment and are accessible through their model names. +Use `graph("ModelName")` with the model name to reference existing graphs. ## Simple @@ -28,23 +25,15 @@ Use `graph("ModelName")` with the model name to reference existing persistent gr **Schema**: - **Nodes**: Person, Company, City -- **Edges**: works_at, located_at, knows +- **Edges**: works_at, located_at, knows, likes -**Sample Data Structure**: +**Schema Relationships**: + +:::image type="content" source="media/graphs/graph-example-simple-schema.png" alt-text="A schema of a graph containing people, companies, and cities with various relationships"::: **Sample Data Structure**: -```mermaid -graph TD - A[Person: Alice] -->|works_at| B[Company: TechCorp] - C[Person: Bob] -->|works_at| B - D[Person: Carol] -->|works_at| E[Company: DataSoft] - A -->|located_at| F[City: Seattle] - C -->|located_at| F - B -->|located_at| F - A -->|knows| C - C -->|knows| D -``` +:::image type="content" source="media/graphs/graph-example-simple-instances.png" alt-text="A graph containing instances of people, companies, and cities with various relationships"::: **Use Cases**: @@ -91,12 +80,56 @@ graph("Simple") > [!NOTE] > This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Social Network Benchmark datasets are created by the Linked Data Benchmark Council (LDBC). -**Description**: The Linked Data Benchmark Council (LDBC) Social Network Benchmark Interactive workload dataset. This graph represents a realistic social network with people, posts, comments, forums, organizations, and places. +**Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 300,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset includes people, posts, comments, forums, universities, companies, geographic locations (continents, countries, cities), tags, and tag classifications, making it ideal for testing complex graph traversals, recommendation algorithms, and social network analysis patterns. + +**Tables and Location**: +All LDBC SNB Interactive tables are located in the **`Graph/LDBC/SnbInteractive`** folder and include: + +- **Core Entity Tables**: + - `LDBC_SNB_Person` - Social network users (1,528 persons) + - `LDBC_SNB_Post` - User posts (135,701 posts) + - `LDBC_SNB_Comment` - Comments on posts (151,043 comments) + - `LDBC_SNB_Forum` - Discussion forums (13,750 forums) + +- **Organizational Tables**: + - `LDBC_SNB_Organisation` - Universities and companies (7,955 total) + +- **Geographic Tables**: + - `LDBC_SNB_Place` - Geographic locations including continents (6), countries (111), and cities (1,343) - total 1,460 places + +- **Content Classification Tables**: + - `LDBC_SNB_Tag` - Content tags (16,080 tags) + - `LDBC_SNB_TagClass` - Tag categories (71 classes) + +- **Relationship Tables** (contain edge data): + - `LDBC_SNB_Person_knows_Person` - Friend relationships (14,073 edges) + - `LDBC_SNB_Person_likes_Post` - Post likes (47,215 edges) + - `LDBC_SNB_Person_likes_Comment` - Comment likes (62,225 edges) + - `LDBC_SNB_Forum_hasMember_Person` - Forum memberships (123,268 edges) + - `LDBC_SNB_Forum_hasModerator_Person` - Forum moderation (13,750 edges) + - `LDBC_SNB_Person_studyAt_Organization` - Educational history (1,209 edges) + - `LDBC_SNB_Person_workAt_Organization` - Employment history (3,313 edges) + - `LDBC_SNB_Post_hasTag_Tag` - Post tagging (51,118 edges) + - `LDBC_SNB_Comment_hasTag_Tag` - Comment tagging (191,303 edges) + - `LDBC_SNB_Forum_hasTag_Tag` - Forum tagging (47,697 edges) + - `LDBC_SNB_Place_isPartOf_Place` - Geographic hierarchy (1,454 edges) + - `LDBC_SNB_Comment_hasCreator_Person` - Comment authorship (151,043 edges) + - `LDBC_SNB_Post_hasCreator_Person` - Post authorship (135,701 edges) + - `LDBC_SNB_Comment_replyOf_Comment` - Comment replies (76,787 edges) + - `LDBC_SNB_Comment_replyOf_Post` - Post replies (74,256 edges) + - `LDBC_SNB_Forum_containerOf_Post` - Forum post containment (135,701 edges) + - `LDBC_SNB_Person_hasInterest_Tag` - Person interests (35,475 edges) + - `LDBC_SNB_Person_isLocatedIn_Place` - Person location (1,528 edges) + - `LDBC_SNB_Organisation_isLocatedIn_Place` - Organization location (7,955 edges) + - `LDBC_SNB_Post_isLocatedIn_Place` - Post location (135,701 edges) + - `LDBC_SNB_Comment_isLocatedIn_Place` - Comment location (151,043 edges) + - `LDBC_SNB_Tag_hasType_TagClass` - Tag classification (16,080 edges) + - `LDBC_SNB_TagClass_isSubclassOf_TagClass` - Tag class hierarchy (70 edges) **Schema**: - **Nodes**: PERSON, POST, COMMENT, FORUM, ORGANISATION, PLACE, TAG, TAGCLASS -- **Edges**: KNOWS, LIKES, HAS_CREATOR, REPLY_OF, HAS_MEMBER, HAS_MODERATOR, STUDY_AT, WORK_AT, IS_LOCATED_IN, HAS_INTEREST, HAS_TAG, IS_PART_OF +- **Edges**: KNOWS, LIKES, HAS_CREATOR, REPLY_OF, HAS_MEMBER, HAS_MODERATOR, STUDY_AT, WORK_AT, IS_LOCATED_IN, HAS_INTEREST, HAS_TAG, IS_PART_OF, CONTAINER_OF, HAS_TYPE, IS_SUBCLASS_OF **Use Cases**: diff --git a/data-explorer/kusto/query/media/graphs/graph-example-simple-instances.mmd b/data-explorer/kusto/query/media/graphs/graph-example-simple-instances.mmd new file mode 100644 index 0000000000..87f8ac8577 --- /dev/null +++ b/data-explorer/kusto/query/media/graphs/graph-example-simple-instances.mmd @@ -0,0 +1,57 @@ +%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% +graph TD + %% Person nodes (light blue) + Alice[Person: Alice] + Bob[Person: Bob] + Carol[Person: Carol] + David[Person: David] + Emma[Person: Emma] + + %% Company nodes (light green) + TechCorp[Company: TechCorp] + DataSoft[Company: DataSoft] + CloudInc[Company: CloudInc] + + %% City nodes (light orange) + Seattle[City: Seattle] + Portland[City: Portland] + 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+0200 Subject: [PATCH 18/67] Enhance graph sample data documentation with detailed descriptions, node and relationship counts, and improved clarity for educational use cases. --- .../kusto/query/graph-sample-data.md | 165 ++++++++++++------ 1 file changed, 116 insertions(+), 49 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 9ef271bfb6..50d4e30480 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -20,7 +20,20 @@ Use `graph("ModelName")` with the model name to reference existing graphs. **Purpose**: Basic graph operations and learning fundamental graph query patterns. -**Description**: A small educational graph containing people, companies, and cities with various relationships. Perfect for learning graph traversals and understanding basic patterns. +**Description**: A small educational graph containing people, companies, and cities with various relationships. Perfect for learning graph traversals and understanding basic patterns. This compact dataset includes 11 nodes (5 people, 3 companies, and 3 cities) connected through 20 relationships, making it ideal for understanding graph fundamentals without the complexity of larger datasets. The graph demonstrates common real-world scenarios like employment relationships, geographic locations, social connections, and personal preferences. + +**Tables and Counts**: + +- **Node Types**: + - `Person` - Individual people (5 nodes) + - `Company` - Business organizations (3 nodes) + - `City` - Geographic locations (3 nodes) + +- **Relationship Types**: + - `works_at` - Employment relationships (5 edges) + - `located_at` - Geographic location assignments (8 edges) + - `knows` - Social connections between people (4 edges) + - `likes` - Personal preferences and interests (3 edges) **Schema**: @@ -80,51 +93,34 @@ graph("Simple") > [!NOTE] > This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Social Network Benchmark datasets are created by the Linked Data Benchmark Council (LDBC). -**Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 300,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset includes people, posts, comments, forums, universities, companies, geographic locations (continents, countries, cities), tags, and tag classifications, making it ideal for testing complex graph traversals, recommendation algorithms, and social network analysis patterns. - -**Tables and Location**: -All LDBC SNB Interactive tables are located in the **`Graph/LDBC/SnbInteractive`** folder and include: - -- **Core Entity Tables**: - - `LDBC_SNB_Person` - Social network users (1,528 persons) - - `LDBC_SNB_Post` - User posts (135,701 posts) - - `LDBC_SNB_Comment` - Comments on posts (151,043 comments) - - `LDBC_SNB_Forum` - Discussion forums (13,750 forums) - -- **Organizational Tables**: - - `LDBC_SNB_Organisation` - Universities and companies (7,955 total) - -- **Geographic Tables**: - - `LDBC_SNB_Place` - Geographic locations including continents (6), countries (111), and cities (1,343) - total 1,460 places - -- **Content Classification Tables**: - - `LDBC_SNB_Tag` - Content tags (16,080 tags) - - `LDBC_SNB_TagClass` - Tag categories (71 classes) - -- **Relationship Tables** (contain edge data): - - `LDBC_SNB_Person_knows_Person` - Friend relationships (14,073 edges) - - `LDBC_SNB_Person_likes_Post` - Post likes (47,215 edges) - - `LDBC_SNB_Person_likes_Comment` - Comment likes (62,225 edges) - - `LDBC_SNB_Forum_hasMember_Person` - Forum memberships (123,268 edges) - - `LDBC_SNB_Forum_hasModerator_Person` - Forum moderation (13,750 edges) - - `LDBC_SNB_Person_studyAt_Organization` - Educational history (1,209 edges) - - `LDBC_SNB_Person_workAt_Organization` - Employment history (3,313 edges) - - `LDBC_SNB_Post_hasTag_Tag` - Post tagging (51,118 edges) - - `LDBC_SNB_Comment_hasTag_Tag` - Comment tagging (191,303 edges) - - `LDBC_SNB_Forum_hasTag_Tag` - Forum tagging (47,697 edges) - - `LDBC_SNB_Place_isPartOf_Place` - Geographic hierarchy (1,454 edges) - - `LDBC_SNB_Comment_hasCreator_Person` - Comment authorship (151,043 edges) - - `LDBC_SNB_Post_hasCreator_Person` - Post authorship (135,701 edges) - - `LDBC_SNB_Comment_replyOf_Comment` - Comment replies (76,787 edges) - - `LDBC_SNB_Comment_replyOf_Post` - Post replies (74,256 edges) - - `LDBC_SNB_Forum_containerOf_Post` - Forum post containment (135,701 edges) - - `LDBC_SNB_Person_hasInterest_Tag` - Person interests (35,475 edges) - - `LDBC_SNB_Person_isLocatedIn_Place` - Person location (1,528 edges) - - `LDBC_SNB_Organisation_isLocatedIn_Place` - Organization location (7,955 edges) - - `LDBC_SNB_Post_isLocatedIn_Place` - Post location (135,701 edges) - - `LDBC_SNB_Comment_isLocatedIn_Place` - Comment location (151,043 edges) - - `LDBC_SNB_Tag_hasType_TagClass` - Tag classification (16,080 edges) - - `LDBC_SNB_TagClass_isSubclassOf_TagClass` - Tag class hierarchy (70 edges) +**Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 327,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset models realistic social media ecosystems with people creating posts and comments, participating in forums, working at organizations, and living in geographic locations across a detailed hierarchy from continents to cities. + +**Node and Relationship Counts**: + +- **Core Social Entity Types**: + - `PERSON` - Social network users (1,528 nodes) + - `POST` - User posts (135,701 nodes) + - `COMMENT` - Comments on posts (151,043 nodes) + - `FORUM` - Discussion forums (13,750 nodes) + +- **Organizational and Geographic Types**: + - `ORGANISATION` - Universities and companies (7,955 nodes) + - `PLACE` - Geographic locations: continents (6), countries (111), cities (1,343) - total 1,460 nodes + +- **Content Classification Types**: + - `TAG` - Content tags (16,080 nodes) + - `TAGCLASS` - Tag categories (71 nodes) + +- **Key Relationship Types**: + - `KNOWS` - Friend relationships (14,073 edges) + - `LIKES` - Content likes: posts (47,215) + comments (62,225) = 109,440 total edges + - `HAS_CREATOR` - Content authorship: posts (135,701) + comments (151,043) = 286,744 edges + - `HAS_MEMBER` - Forum memberships (123,268 edges) + - `HAS_TAG` - Content tagging: posts (51,118) + comments (191,303) + forums (47,697) = 290,118 edges + - `IS_LOCATED_IN` - Location relationships: people (1,528) + organizations (7,955) + posts (135,701) + comments (151,043) = 296,227 edges + - `REPLY_OF` - Comment threading: comment-to-comment (76,787) + comment-to-post (74,256) = 151,043 edges + - `WORK_AT` / `STUDY_AT` - Professional/educational history (4,522 edges) + - `HAS_INTEREST` - Personal interests (35,475 edges) **Schema**: @@ -185,7 +181,27 @@ graph("LDBC_SNB_Interactive") > [!NOTE] > This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Financial Benchmark datasets are created by the Linked Data Benchmark Council (LDBC). -**Description**: LDBC Financial Benchmark dataset representing a financial network with companies, persons, accounts, loans, and various financial transactions. Designed for fraud detection and financial crime investigation scenarios. +**Description**: LDBC Financial Benchmark dataset representing a comprehensive financial network with companies, persons, accounts, loans, and various financial transactions. This dataset models realistic financial ecosystems with 5,580 total nodes and over 31,000 financial transactions and relationships. Designed specifically for fraud detection, anti-money laundering (AML) analysis, and financial crime investigation scenarios, it captures complex patterns including account ownership, loan applications, guarantees, and multi-step transaction chains that are common in financial crime scenarios. + +**Tables and Counts**: + +- **Node Types**: + - `COMPANY` - Business entities (386 nodes) + - `PERSON` - Individual customers (785 nodes) + - `ACCOUNT` - Financial accounts (2,055 nodes) + - `LOAN` - Loan products (1,376 nodes) + - `MEDIUM` - Transaction mediums/channels (978 nodes) + +- **Relationship Types**: + - `TRANSFER` - Money transfers between accounts (8,132 edges) + - `WITHDRAW` - Cash withdrawals from accounts (9,182 edges) + - `DEPOSIT` - Money deposits into accounts (2,758 edges) + - `OWN` - Account ownership relationships (2,055 edges) + - `APPLY` - Loan applications (1,376 edges) + - `GUARANTEE` - Loan guarantees (579 edges) + - `INVEST` - Investment transactions (1,983 edges) + - `REPAY` - Loan repayments (2,747 edges) + - `SIGN_IN` - Authentication events (2,489 edges) **Schema**: @@ -261,7 +277,32 @@ graph("LDBC_Financial") > [!NOTE] > This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. -**Description**: BloodHound Community Edition dataset for Azure Active Directory environments. Contains nodes representing Azure AD objects (users, groups, applications, service principals) and edges representing various permissions and relationships. +**Description**: BloodHound Community Edition dataset for Azure Active Directory environments. This comprehensive security dataset contains 13,526 Azure AD objects including users, groups, applications, service principals, devices, and various cloud resources. With over 800,000 permission relationships and security edges, it models complex Azure AD environments typical of enterprise organizations. The dataset captures detailed Azure AD permissions, role assignments, group memberships, and resource ownership patterns essential for identifying privilege escalation paths and attack vectors in cloud environments. + +**Tables and Counts**: + +- **Primary Node Types**: + - `AZUser` - Azure AD users (230 nodes) + - `AZServicePrincipal` - Service principals and applications (6,270 nodes) + - `AZApp` - Azure applications (6,648 nodes) + - `AZGroup` - Azure AD groups (58 nodes) + - `AZDevice` - Managed devices (47 nodes) + +- **Azure Resource Types**: + - `AZResourceGroup` - Resource groups (59 nodes) + - `AZVM` - Virtual machines (66 nodes) + - `AZRole` - Azure roles (116 nodes) + - `AZSubscription` - Azure subscriptions (3 nodes) + - `AZTenant` - Azure tenant (1 node) + +- **Key Relationship Types** (Top permissions by volume): + - `AZMGAddOwner` - Management group owner permissions (403,412 edges) + - `AZMGAddSecret` - Secret management permissions (345,324 edges) + - `AZAddSecret` - Application secret permissions (24,666 edges) + - `AZContains` - Resource containment relationships (12,924 edges) + - `AZRunsAs` - Service execution permissions (6,269 edges) + - `AZMemberOf` - Group membership relationships (4,439 edges) + - `AZOwns` - Resource ownership (2,870 edges) **Schema**: @@ -357,7 +398,33 @@ graph("BloodHound_Entra") > [!NOTE] > This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. -**Description**: BloodHound Community Edition dataset for on-premises Active Directory environments. Contains comprehensive information about AD objects, group memberships, ACLs, and various attack paths in traditional Windows domains. +**Description**: BloodHound Community Edition dataset for on-premises Active Directory environments. This dataset contains 1,495 Active Directory objects representing a typical enterprise AD deployment with complex permission structures and attack paths. The dataset includes users, computers, groups, organizational units, group policy objects, and certificate authority components across multiple domains. With over 18,000 permission relationships and security edges, it captures realistic AD attack scenarios including privilege escalation paths, ACL-based permissions, group memberships, and certificate-based authentication vulnerabilities common in Windows domain environments. + +**Tables and Counts**: + +- **Core AD Object Types**: + - `User` - Domain users (99 nodes) + - `Computer` - Domain computers (34 nodes) + - `Group` - Security and distribution groups (219 nodes) + - `ADLocalGroup` - Local groups on computers (28 nodes) + - `GPO` - Group Policy Objects (32 nodes) + +- **AD Infrastructure Types**: + - `Domain` - Active Directory domains (5 nodes) + - `OU` - Organizational Units (20 nodes) + - `Container` - AD containers (939 nodes) + - `CertTemplate` - Certificate templates (106 nodes) + - `EnterpriseCA` - Certificate Authorities (4 nodes) + - `RootCA` - Root Certificate Authorities (5 nodes) + +- **Key Permission Types** (Top attack vectors): + - `GenericAll` - Full control permissions (3,292 edges) + - `WriteDacl` - Modify permissions (2,221 edges) + - `WriteOwner` - Change ownership (2,187 edges) + - `Owns` - Object ownership (1,439 edges) + - `Contains` - Containment relationships (1,416 edges) + - `GenericWrite` - Write permissions (579 edges) + - `MemberOf` - Group memberships (301 edges) **Attack Path Visualization Example**: From 3d0e73c49bc9bd53daabbd1413ecb842717c0c2a Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Fri, 15 Aug 2025 10:59:13 +0200 Subject: [PATCH 19/67] Added drawings for ldbc --- .../kusto/query/graph-sample-data.md | 53 +++++++++--------- .../graph-example-ldbc-snb-instances.mmd | 50 +++++++++++++++++ .../graph-example-ldbc-snb-instances.png | Bin 0 -> 409775 bytes .../graphs/graph-example-ldbc-snb-schema.mmd | 53 ++++++++++++++++++ .../graphs/graph-example-ldbc-snb-schema.png | Bin 0 -> 406205 bytes 5 files changed, 131 insertions(+), 25 deletions(-) create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.png create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.png diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 50d4e30480..c3341f91bb 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -22,7 +22,7 @@ Use `graph("ModelName")` with the model name to reference existing graphs. **Description**: A small educational graph containing people, companies, and cities with various relationships. Perfect for learning graph traversals and understanding basic patterns. This compact dataset includes 11 nodes (5 people, 3 companies, and 3 cities) connected through 20 relationships, making it ideal for understanding graph fundamentals without the complexity of larger datasets. The graph demonstrates common real-world scenarios like employment relationships, geographic locations, social connections, and personal preferences. -**Tables and Counts**: +**Schema and Counts**: - **Node Types**: - `Person` - Individual people (5 nodes) @@ -35,11 +35,6 @@ Use `graph("ModelName")` with the model name to reference existing graphs. - `knows` - Social connections between people (4 edges) - `likes` - Personal preferences and interests (3 edges) -**Schema**: - -- **Nodes**: Person, Company, City -- **Edges**: works_at, located_at, knows, likes - **Schema Relationships**: :::image type="content" source="media/graphs/graph-example-simple-schema.png" alt-text="A schema of a graph containing people, companies, and cities with various relationships"::: @@ -95,7 +90,11 @@ graph("Simple") **Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 327,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset models realistic social media ecosystems with people creating posts and comments, participating in forums, working at organizations, and living in geographic locations across a detailed hierarchy from continents to cities. -**Node and Relationship Counts**: +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-ldbc-snb-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC SNB dataset"::: + +**Schema and Counts**: - **Core Social Entity Types**: - `PERSON` - Social network users (1,528 nodes) @@ -121,11 +120,7 @@ graph("Simple") - `REPLY_OF` - Comment threading: comment-to-comment (76,787) + comment-to-post (74,256) = 151,043 edges - `WORK_AT` / `STUDY_AT` - Professional/educational history (4,522 edges) - `HAS_INTEREST` - Personal interests (35,475 edges) - -**Schema**: - -- **Nodes**: PERSON, POST, COMMENT, FORUM, ORGANISATION, PLACE, TAG, TAGCLASS -- **Edges**: KNOWS, LIKES, HAS_CREATOR, REPLY_OF, HAS_MEMBER, HAS_MODERATOR, STUDY_AT, WORK_AT, IS_LOCATED_IN, HAS_INTEREST, HAS_TAG, IS_PART_OF, CONTAINER_OF, HAS_TYPE, IS_SUBCLASS_OF + - Additional relationships: `HAS_MODERATOR`, `IS_PART_OF`, `CONTAINER_OF`, `HAS_TYPE`, `IS_SUBCLASS_OF` **Use Cases**: @@ -136,6 +131,24 @@ graph("Simple") - Friend-of-friend discovery - Social graph mining research +**Graph Instance Example**: + +Based on real validated data from the SNB dataset, here's an example showing social interactions around content: + +:::image type="content" source="media/graphs/graph-example-ldbc-snb-instances.png" alt-text="A graph containing a sample subgraph of the LDBC SNB dataset"::: + +This example demonstrates: + +- **Social Engagement**: Mahinda likes both Abdullah's post and a comment on that post +- **Content Threading**: The comment (about Gloria Macapagal-Arroyo) replies to the post (about Aurangzeb) +- **Content Creation**: Abdullah creates posts in his own forum wall +- **Community Participation**: Mahinda is a member of Abdullah's forum where the content appears +- **Content Classification**: Both posts and comments are tagged with relevant topics from their content +- **Geographic Context**: All entities have location relationships for geographic analysis + +> [!NOTE] +> All relationships shown in this diagram have been validated against the actual dataset using specific queries to ensure accuracy. + **Example Queries**: Find direct friendships with similar ages: @@ -183,7 +196,7 @@ graph("LDBC_SNB_Interactive") **Description**: LDBC Financial Benchmark dataset representing a comprehensive financial network with companies, persons, accounts, loans, and various financial transactions. This dataset models realistic financial ecosystems with 5,580 total nodes and over 31,000 financial transactions and relationships. Designed specifically for fraud detection, anti-money laundering (AML) analysis, and financial crime investigation scenarios, it captures complex patterns including account ownership, loan applications, guarantees, and multi-step transaction chains that are common in financial crime scenarios. -**Tables and Counts**: +**Schema and Counts**: - **Node Types**: - `COMPANY` - Business entities (386 nodes) @@ -203,11 +216,6 @@ graph("LDBC_SNB_Interactive") - `REPAY` - Loan repayments (2,747 edges) - `SIGN_IN` - Authentication events (2,489 edges) -**Schema**: - -- **Nodes**: COMPANY, PERSON, ACCOUNT, LOAN, MEDIUM -- **Edges**: TRANSFER, WITHDRAW, DEPOSIT, OWN, APPLY, GUARANTEE, INVEST, SIGN_IN, REPAY - **Use Cases**: - Financial fraud detection @@ -279,7 +287,7 @@ graph("LDBC_Financial") **Description**: BloodHound Community Edition dataset for Azure Active Directory environments. This comprehensive security dataset contains 13,526 Azure AD objects including users, groups, applications, service principals, devices, and various cloud resources. With over 800,000 permission relationships and security edges, it models complex Azure AD environments typical of enterprise organizations. The dataset captures detailed Azure AD permissions, role assignments, group memberships, and resource ownership patterns essential for identifying privilege escalation paths and attack vectors in cloud environments. -**Tables and Counts**: +**Schema and Counts**: - **Primary Node Types**: - `AZUser` - Azure AD users (230 nodes) @@ -304,11 +312,6 @@ graph("LDBC_Financial") - `AZMemberOf` - Group membership relationships (4,439 edges) - `AZOwns` - Resource ownership (2,870 edges) -**Schema**: - -- **Nodes**: Various Azure AD object types (Users, Groups, Applications, Service Principals, etc.) -- **Edges**: Permission and relationship types (Member, Owner, various Azure AD privileges) - **Security Analysis Example**: ```mermaid @@ -400,7 +403,7 @@ graph("BloodHound_Entra") **Description**: BloodHound Community Edition dataset for on-premises Active Directory environments. This dataset contains 1,495 Active Directory objects representing a typical enterprise AD deployment with complex permission structures and attack paths. The dataset includes users, computers, groups, organizational units, group policy objects, and certificate authority components across multiple domains. With over 18,000 permission relationships and security edges, it captures realistic AD attack scenarios including privilege escalation paths, ACL-based permissions, group memberships, and certificate-based authentication vulnerabilities common in Windows domain environments. -**Tables and Counts**: +**Schema and Counts**: - **Core AD Object Types**: - `User` - Domain users (99 nodes) diff --git a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.mmd b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.mmd new file mode 100644 index 0000000000..5863fae3ab --- /dev/null +++ b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.mmd @@ -0,0 +1,50 @@ +%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% +graph TD + %% Real People from the dataset + MP[Mahinda Perera
Person ID: 933] + AK[Abdullah Koksal
Person ID: 24189255811254] + + %% Real Post and Comment (validated relationships) + POST1[About Aurangzeb
Post ID: 893353296235
Created by Abdullah] + COMMENT1[About Gloria Macapagal-Arroyo
Comment ID: 893353296240
Reply to Aurangzeb post] + + %% Real Forum and Tags (validated) + FORUM1[Wall of Abdullah Koksal
Forum ID: 755914248727] + TAG1[Aurangzeb
Tag on post] + TAG2[Gloria_Macapagal-Arroyo
Tag on comment] + TAG3[John_Rhys-Davies
Tag on post] + + %% Real Location + PLACE1[Location
Geographic Entity] + + %% Validated Social Network Relationships + MP -->|LIKES| POST1 + MP -->|LIKES| COMMENT1 + POST1 -->|HAS_CREATOR| AK + COMMENT1 -->|REPLY_OF| POST1 + + %% Validated Forum and Content Organization + MP -->|HAS_MEMBER| FORUM1 + FORUM1 -->|CONTAINER_OF| POST1 + POST1 -->|HAS_TAG| TAG1 + POST1 -->|HAS_TAG| TAG3 + COMMENT1 -->|HAS_TAG| TAG2 + + %% Geographic Context (common pattern) + MP -->|IS_LOCATED_IN| PLACE1 + AK -->|IS_LOCATED_IN| PLACE1 + POST1 -->|IS_LOCATED_IN| PLACE1 + COMMENT1 -->|IS_LOCATED_IN| PLACE1 + + %% Styling + classDef person fill:#e1f5fe,stroke:#0277bd,stroke-width:3px + classDef content fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px + classDef forum fill:#e8f5e8,stroke:#388e3c,stroke-width:2px + classDef tag fill:#fff3e0,stroke:#f57c00,stroke-width:2px + classDef place fill:#fce4ec,stroke:#c2185b,stroke-width:2px + + class MP,AK person + class POST1,COMMENT1 content + class FORUM1 forum + class TAG1,TAG2,TAG3 tag + class PLACE1 place \ No newline 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/dev/null +++ b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.mmd @@ -0,0 +1,53 @@ +%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% +graph TD + %% Core Social Entities + PERSON[PERSON
Social Users
1,528 nodes] + POST[POST
User Posts
135,701 nodes] + COMMENT[COMMENT
Comments
151,043 nodes] + FORUM[FORUM
Discussion Forums
13,750 nodes] + + %% Organizational & Geographic + ORG[ORGANISATION
Companies/Unis
7,955 nodes] + PLACE[PLACE
Locations
1,460 nodes] + + %% Content Classification + TAG[TAG
Content Tags
16,080 nodes] + TAGCLASS[TAGCLASS
Tag Categories
71 nodes] + + %% Core Relationships + PERSON -.->|KNOWS
14,073| PERSON + PERSON -->|LIKES
109,440| POST + PERSON -->|LIKES| COMMENT + PERSON -->|HAS_CREATOR
286,744| POST + PERSON -->|HAS_CREATOR| COMMENT + PERSON -->|HAS_MEMBER
123,268| FORUM + PERSON -->|WORK_AT/STUDY_AT
4,522| ORG + PERSON -->|HAS_INTEREST
35,475| TAG + PERSON -->|IS_LOCATED_IN
296,227| PLACE + + %% Content Relationships + COMMENT -->|REPLY_OF
151,043| POST + COMMENT -->|REPLY_OF| COMMENT + POST -->|HAS_TAG
290,118| TAG + COMMENT -->|HAS_TAG| TAG + FORUM -->|HAS_TAG| TAG + FORUM -->|CONTAINER_OF| POST + POST -->|IS_LOCATED_IN| PLACE + COMMENT -->|IS_LOCATED_IN| PLACE + ORG -->|IS_LOCATED_IN| PLACE + + %% Classification Hierarchy + TAG -->|HAS_TYPE| TAGCLASS + TAGCLASS -->|IS_SUBCLASS_OF| TAGCLASS + PLACE -->|IS_PART_OF| PLACE + + %% Styling + classDef socialNode fill:#e1f5fe,stroke:#0277bd,stroke-width:2px + classDef contentNode fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px + classDef orgNode fill:#e8f5e8,stroke:#388e3c,stroke-width:2px + classDef classNode fill:#fff3e0,stroke:#f57c00,stroke-width:2px + + class PERSON,FORUM socialNode + class POST,COMMENT contentNode + class ORG,PLACE orgNode + class TAG,TAGCLASS classNode \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.png b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.png new file mode 100644 index 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zN%+4deE#20zaRMjTbDlz@qgV)+zWSv?U9etnH7uN)9sf2vk*+@_DfVu70$WwQCscO z(6|2mn=j9)|3PK^{`pqMFMYB<@B9A0|I_LHF6sgB(?<_AbKFafeHa@E0ETighf~fX z2fQzdP*bxR{0=--u2aKr*bE>?Uw8%0MaURZphdE&?jIb?JNr8`_^1)^tFHg&Qa) Date: Fri, 15 Aug 2025 11:58:12 +0200 Subject: [PATCH 20/67] validated and added drawings --- .../kusto/query/graph-sample-data.md | 59 ++++++++-------- .../graph-example-bloodhound-ad-instance.mmd | 35 ++++++++++ .../graph-example-bloodhound-ad-instance.png | Bin 0 -> 280790 bytes .../graph-example-bloodhound-ad-schema.mmd | 64 ++++++++++++++++++ .../graph-example-bloodhound-ad-schema.png | Bin 0 -> 329374 bytes ...raph-example-bloodhound-entra-instance.mmd | 47 +++++++++++++ ...raph-example-bloodhound-entra-instance.png | Bin 0 -> 360648 bytes .../graph-example-bloodhound-entra-schema.mmd | 58 ++++++++++++++++ .../graph-example-bloodhound-entra-schema.png | Bin 0 -> 367533 bytes .../graph-example-ldbc-financial-instance.mmd | 42 ++++++++++++ .../graph-example-ldbc-financial-instance.png | Bin 0 -> 400513 bytes .../graph-example-ldbc-financial-schema.mmd | 40 +++++++++++ .../graph-example-ldbc-financial-schema.png | Bin 0 -> 355308 bytes 13 files changed, 318 insertions(+), 27 deletions(-) create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-schema.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-schema.png create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-instance.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-instance.png create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.png create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.png create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-schema.mmd create mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-schema.png diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index c3341f91bb..7adad279ad 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -196,6 +196,10 @@ graph("LDBC_SNB_Interactive") **Description**: LDBC Financial Benchmark dataset representing a comprehensive financial network with companies, persons, accounts, loans, and various financial transactions. This dataset models realistic financial ecosystems with 5,580 total nodes and over 31,000 financial transactions and relationships. Designed specifically for fraud detection, anti-money laundering (AML) analysis, and financial crime investigation scenarios, it captures complex patterns including account ownership, loan applications, guarantees, and multi-step transaction chains that are common in financial crime scenarios. +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-ldbc-financial-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC Financial dataset"::: + **Schema and Counts**: - **Node Types**: @@ -225,6 +229,12 @@ graph("LDBC_SNB_Interactive") - Suspicious activity monitoring - Financial network analysis +**Graph Instance Example**: + +Based on real validated data from the LDBC Financial dataset, here's a connected financial subgraph: + +:::image type="content" source="media/graphs/graph-example-ldbc-financial-instance.png" alt-text="A graph containing a sample subgraph of the LDBC Financial dataset"::: + **Example Queries**: Detect potential money laundering through circular transfers: @@ -278,7 +288,7 @@ graph("LDBC_Financial") |Company::4398046511208|366243272|8| |Person::19791209300551|338838223|6| -## BloodHound Entra +## BloodHound_Entra **Purpose**: Azure Active Directory privilege escalation and attack path analysis. @@ -312,19 +322,18 @@ graph("LDBC_Financial") - `AZMemberOf` - Group membership relationships (4,439 edges) - `AZOwns` - Resource ownership (2,870 edges) -**Security Analysis Example**: - -```mermaid -graph TD - A[Regular User] -->|MemberOf| B[Security Group] - B -->|CanAddMember| C[High Privilege Group] - C -->|Member| D[Global Admin] - D -->|Owner| E[Sensitive Application] - - style A fill:#ffcccc - style E fill:#ff9999 - style D fill:#ff6666 -``` +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-bloodhound-entra-schema.png" alt-text="A schema of a graph containing nodes and relations from the BloodHound Entra dataset"::: + +**Graph Instance Example**: + +Based on real validated data from the BloodHound_Entra dataset, here's a connected Azure AD subgraph: + +:::image type="content" source="media/graphs/graph-example-bloodhound-entra-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound Entra dataset"::: + +> [!NOTE] +> All relationships shown in this diagram have been validated against the actual BloodHound_Entra dataset using specific queries to ensure accuracy. **Use Cases**: @@ -429,19 +438,15 @@ graph("BloodHound_Entra") - `GenericWrite` - Write permissions (579 edges) - `MemberOf` - Group memberships (301 edges) -**Attack Path Visualization Example**: - -```mermaid -graph TD - A[Domain User] -->|GenericWrite| B[Service Account] - B -->|MemberOf| C[Backup Operators] - C -->|SeBackupPrivilege| D[Domain Controller] - D -->|DCSync| E[Domain Admin Hash] - - style A fill:#ccffcc - style E fill:#ff6666 - style D fill:#ffcc99 -``` +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-bloodhound-ad-schema.png" alt-text="A schema of a graph containing nodes and relations from the BloodHound AD dataset"::: + +**Graph Instance Example**: + +Based on real validated data from the BloodHound_AD dataset, here's a connected Active Directory attack path: + +:::image type="content" source="media/graphs/graph-example-bloodhound-ad-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound Entra dataset"::: **Use Cases**: diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd new file mode 100644 index 0000000000..5c5c0e4916 --- /dev/null +++ b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd @@ -0,0 +1,35 @@ +%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% +graph TD + %% Real Entities from BloodHound_AD dataset + ALICE["ALICE@PHANTOM\.CORP
User
Domain User"] + + %% Her Computer + LAPTOP["ALICE-LAPTOP\.PHANTOM\.CORP
Computer
Workstation"] + + %% Administrative Groups + DOMAINUSERS["DOMAIN USERS@PHANTOM\.CORP
Group
Default Domain Group"] + + %% Domain Infrastructure + USERS["USERS@PHANTOM\.CORP
Container
AD Container"] + PHANTOM["PHANTOM\.CORP
Domain
AD Domain"] + + %% Validated Attack Path Relationships + ALICE -->|AdminTo| LAPTOP + ALICE -->|MemberOf| DOMAINUSERS + + %% Container Hierarchy + USERS -->|Contains| ALICE + PHANTOM -->|Contains| USERS + + %% Styling + classDef user fill:#e3f2fd,stroke:#1976d2,stroke-width:3px + classDef computer fill:#fff3e0,stroke:#f57c00,stroke-width:2px + classDef group fill:#e8f5e8,stroke:#388e3c,stroke-width:2px + classDef admingroup fill:#ffebee,stroke:#d32f2f,stroke-width:3px + classDef infrastructure fill:#f1f8e9,stroke:#689f38,stroke-width:2px + classDef certificate fill:#fce4ec,stroke:#c2185b,stroke-width:3px + + class ALICE user + class LAPTOP computer + class DOMAINUSERS,DOMAINADMINS group + class USERS,PHANTOM infrastructure \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png new file mode 100644 index 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P6o`-a>?VN6Tm zsZ+7d6A2EH;{&=qufAC{BueT_gR;$#O6RpM7z}AnqQZb}4NIlu2LvCdl5hc!#C!g5UYyvJnG~W#dwv>@+$TTh`GV}b z64v;E_(Y4O&g0&chra+EuALJr3GCl<1*d2<5Q~873xLNQLl`qUHlj>OM@I`vRlKvc zur{)t7|OJwcMFwn1N4Flv_9#QKURC1({duXzhJDuQ=Y@EEVskVDs^@ zyyb!fNZEj*6|Kk73T0`(9UcIeu}6jl)Y2_1v;+%Sl}A-|G+J#mdUQm!(&@Rb0RIc; zhfi%PH1PsXC=b{5#5J9|QTs*vGttXVOwvqZ1j05H*kZ=@hb4zJtjF;wF?{XY3?64; zxgBb%F{Cx(koJ4}wQdLy>%F5~n26o1Fp!E;v@?dFUV`*Z=&d^b>_yq0!qd0;q zs|5!nO4c=vx7#N^oG&sW!mv#9c2>^~>E|XmL`P5O9vHa^JHYeX3&Oy8N&I*mfUI0o zj=p)vfBH5Md*j>ur&qwkDaM;j!@s=vlI%06-Sc0DdFKEAnE&(x8^Y_@{$Iz}u>W&? z|NNv<=>{JC=lS)654;bqWzGLO1|mRH1|KI%IIvaU>Rrk;38ZU%IW=8d|VJ9J* z8Si4@Tk3rCwjw?!s-D-ab9O4X8+}GncS1E%kXX|jhMsyiFQdW6H*Gz?O^@CDB5MpT z?+tDRe&OG9+w*dwl~Hj}=*pt)5}65kzuZBo+7_)yJc@jSbPmdc@Qjo{jboM{v|NYgRsQhLV8^w~n?=AYpc#|6Z z+fA63)73W9ITn_&n^7`$k{{UCDomain Users] + COMPUTER[Computer
Domain Computers] + GROUP[Group
Security Groups] + LOCALGROUP[ADLocalGroup
Local Groups] + + %% AD Infrastructure + DOMAIN[Domain
AD Domains] + OU[OU
Organizational Units] + CONTAINER[Container
AD Containers] + GPO[GPO
Group Policy Objects] + + %% Certificate Infrastructure + CERT[CertTemplate
Certificate Templates] + ENTCA[EnterpriseCA
Certificate Authorities] + ROOTCA[RootCA
Root CAs] + + %% Domain Hierarchy & Containment + DOMAIN -->|Contains| CONTAINER + CONTAINER -->|Contains| USER + CONTAINER -->|Contains| GROUP + DOMAIN -->|Contains| OU + OU -->|Contains| USER + OU -->|Contains| COMPUTER + + %% Group Memberships + USER -->|MemberOf| GROUP + USER -->|MemberOf| LOCALGROUP + GROUP -->|MemberOf| GROUP + + %% Administrative Access + USER -->|AdminTo| COMPUTER + GROUP -->|AdminTo| COMPUTER + USER -->|GenericAll| USER + GROUP -->|GenericAll| GROUP + + %% Dangerous Permissions + USER -->|WriteDacl| GROUP + GROUP -->|WriteOwner| CERT + USER -->|GenericWrite| GPO + + %% Certificate Attack Paths + USER -->|GenericAll| ROOTCA + GROUP -->|WriteDacl| ENTCA + + %% Object Ownership + GROUP -->|Owns| CONTAINER + USER -->|Owns| CERT + + %% Styling + classDef user fill:#e3f2fd,stroke:#1976d2,stroke-width:3px + classDef computer fill:#fff3e0,stroke:#f57c00,stroke-width:2px + classDef group fill:#e8f5e8,stroke:#388e3c,stroke-width:2px + classDef infrastructure 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admin_tier_0
jjacob@phantomcorp.onmicrosoft.com] + + %% Groups he owns + ALLUSERS[All Users
AZGroup
ID: 2f061293] + GAROLE[ThisGroupHasGARoleAlwaysActive
AZGroup
ID: 4c8435bf] + + %% App he owns + AZUREHOUND[AzureHoundEnterprise
AZApp
ID: 5595629b] + + %% Device he owns + WIN10[AADJoinedWin10
AZDevice
ID: 2a2dc5ab] + + %% Administrative Role + GLOBALADMIN[Global Administrator
AZRole
Privileged Role] + + %% Tenant + PHANTOM[Phantom Corp
AZTenant
phantomcorp.onmicrosoft.com] + + %% Validated Relationships + JACOB -->|AZOwns
Application Owner| AZUREHOUND + JACOB -->|AZOwns
Group Owner| ALLUSERS + JACOB -->|AZOwns
Group Owner| GAROLE + JACOB -->|AZOwns
Device Owner| WIN10 + + %% Administrative Privileges + GLOBALADMIN -->|AZResetPassword
Can Reset| JACOB + + %% Tenant Containment + PHANTOM -->|AZContains
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Azure AD Users] + SP[AZServicePrincipal
Service Principals] + APP[AZApp
Applications] + GROUP[AZGroup
Security Groups] + DEVICE[AZDevice
Managed Devices] + ROLE[AZRole
Azure Roles] + + %% Azure Resource Hierarchy + TENANT[AZTenant
Azure Tenant] + SUB[AZSubscription
Subscriptions] + RG[AZResourceGroup
Resource Groups] + VM[AZVM
Virtual Machines] + + %% Azure Resource Containment Hierarchy + TENANT -->|AZContains| SUB + TENANT -->|AZContains| USER + SUB -->|AZContains| RG + RG -->|AZContains| VM + + %% Identity and Access Relationships + USER -->|AZMemberOf| GROUP + USER -->|AZOwns| APP + USER -->|AZOwns| DEVICE + USER -->|AZOwns| GROUP + USER -->|AZOwner| SUB + USER -->|AZOwner| RG + + %% Service Principal Relationships + SP -->|AZRunsAs| APP + VM -->|AZManagedIdentity| SP + + %% Administrative Permissions + ROLE -->|AZResetPassword| USER + GROUP -->|AZAddMembers| GROUP + + %% High-Volume Permissions (simplified for readability) + ROLE -.->|AZMGAddOwner
403k edges| RG + ROLE -.->|AZMGAddSecret
345k edges| APP + + %% Styling + classDef user fill:#e3f2fd,stroke:#1976d2,stroke-width:3px + classDef app fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px + classDef group fill:#e8f5e8,stroke:#388e3c,stroke-width:2px + classDef device fill:#fff3e0,stroke:#f57c00,stroke-width:2px + classDef resource fill:#fce4ec,stroke:#c2185b,stroke-width:2px + classDef role fill:#e1f5fe,stroke:#0277bd,stroke-width:2px + classDef hierarchy fill:#f1f8e9,stroke:#689f38,stroke-width:2px + + class USER user + class SP,APP app + class GROUP group + class DEVICE device + class VM,RG resource + class ROLE role + class TENANT,SUB hierarchy \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.png b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.png new file mode 100644 index 0000000000000000000000000000000000000000..3f8c5eb3def63a4e289e768df906f15ba73ae6ae GIT binary patch literal 367533 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785 nodes] + COMPANY[COMPANY
Business Entities
386 nodes] + ACCOUNT[ACCOUNT
Financial Accounts
2,055 nodes] + LOAN[LOAN
Loan Products
1,376 nodes] + MEDIUM[MEDIUM
Transaction Channels
978 nodes] + + %% Core Financial Relationships + PERSON -->|OWN
2,055| ACCOUNT + COMPANY -->|OWN| ACCOUNT + PERSON -->|APPLY
1,376| LOAN + COMPANY -->|APPLY| LOAN + PERSON -->|GUARANTEE
579| PERSON + COMPANY -->|GUARANTEE| COMPANY + + %% Transaction Flows + ACCOUNT -->|TRANSFER
8,132| ACCOUNT + ACCOUNT -->|WITHDRAW
9,182| ACCOUNT + LOAN -->|DEPOSIT
2,758| ACCOUNT + ACCOUNT -->|REPAY
2,747| LOAN + + %% Authentication & Investment + MEDIUM -->|SIGN_IN
2,489| ACCOUNT + PERSON -->|INVEST
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Rauch Date: Fri, 15 Aug 2025 14:17:03 +0200 Subject: [PATCH 21/67] Add common analysis queries section to graph sample data documentation --- .../kusto/query/graph-best-practices.md | 219 ++++++++++++++++++ .../kusto/query/graph-sample-data.md | 160 +------------ 2 files changed, 220 insertions(+), 159 deletions(-) diff --git a/data-explorer/kusto/query/graph-best-practices.md b/data-explorer/kusto/query/graph-best-practices.md index 774400d5e5..9d7a104751 100644 --- a/data-explorer/kusto/query/graph-best-practices.md +++ b/data-explorer/kusto/query/graph-best-practices.md @@ -522,18 +522,237 @@ This hybrid approach enables organizations to provide always-current data analys :::moniker-end +## Common analysis queries + +These reusable query patterns work across all graph models and help you understand the structure and characteristics of any graph dataset. Use these queries to explore new graphs, perform basic analysis, or as starting points for more complex graph investigations. + +### Graph overview and statistics + +Understanding the basic characteristics of your graph is essential for analysis planning and performance optimization. These queries provide fundamental metrics about graph size and structure. + +**Count total nodes and edges**: + +Use these queries to understand the scale of your graph dataset. Node and edge counts help determine appropriate query strategies and identify potential performance considerations. + +```kusto +// Get node count +graph("GRAPH_NAME") +| graph-match (node) + project node +| summarize NodeCount = count() +``` + +```kusto +// Get edge count +graph("GRAPH_NAME") +| graph-match (source)-[edge]->(target) + project edge +| summarize EdgeCount = count() +``` + +**Get graph summary statistics**: + +This combined query efficiently provides both metrics in a single result, useful for initial graph assessment and reporting. + +```kusto +let nodes = graph("GRAPH_NAME") | graph-match (node) project node | summarize NodeCount = count(); +let edges = graph("GRAPH_NAME") | graph-match (source)-[edge]->(target) project edge | summarize EdgeCount = count(); +union nodes, edges +``` + +**Alternative using graph-to-table**: + +For basic counting, the `graph-to-table` operator can be more efficient as it directly exports graph elements without pattern matching overhead. + +```kusto +let nodes = graph("GRAPH_NAME") | graph-to-table nodes | summarize NodeCount = count(); +let edges = graph("GRAPH_NAME") | graph-to-table edges | summarize EdgeCount = count(); +union nodes, edges +``` + +### Node analysis + +Node analysis helps you understand the entities in your graph, their types, and distribution. These patterns are essential for data quality assessment and schema understanding. + +**Discover all node types (labels)**: + +This query reveals the different entity types in your graph and their frequencies. Use it to understand your data model, identify the most common entity types, and spot potential data quality issues. + +```kusto +graph("GRAPH_NAME") +| graph-match (node) + project labels = labels(node) +| mv-expand label = labels +| summarize count() by tostring(label) +| order by count_ desc +``` + +**Find nodes with multiple labels**: + +Identifies nodes that belong to multiple categories simultaneously. This is useful for understanding overlapping classifications and complex entity relationships in your data model. + +```kusto +graph("GRAPH_NAME") +| graph-match (node) + project node_id = node.id, labels = labels(node), label_count = array_length(labels(node)) +| where label_count > 1 +| take 10 +``` + +**Sample nodes by type**: + +Retrieves representative examples of specific node types to understand their structure and properties. Essential for data exploration and query development. + +```kusto +graph("GRAPH_NAME") +| graph-match (node) + where labels(node) has "DESIRED_LABEL" // Replace with actual label + project node_id = node.id, properties = node.properties +| sample 5 +``` + +### Edge analysis + +Understanding relationships in your graph is crucial for identifying patterns, data quality issues, and potential analysis directions. + +**Discover all edge types** (works with different graph schemas): + +This query identifies all relationship types in your graph, helping you understand the connections available for analysis. Different graphs use different property names for edge types, so multiple variations are provided. + +```kusto +// For graphs using 'labels' array (like BloodHound) +graph("GRAPH_NAME") +| graph-match (source)-[edge]->(target) + project edge_labels = labels(edge) +| mv-expand label = edge_labels +| summarize count() by tostring(label) +| order by count_ desc +``` + +**Find most connected nodes (highest degree)**: + +Node degree analysis reveals the most influential or central entities in your graph. High-degree nodes often represent key players, bottlenecks, or important infrastructure components. + +```kusto +// Find nodes with highest total degree (in + out) +graph("GRAPH_NAME") +| graph-match (node) + project node_id = node.id, + in_degree = node_degree_in(node), + out_degree = node_degree_out(node), + total_degree = node_degree_in(node) + node_degree_out(node) +| order by total_degree desc +| take 10 +``` + +**Find nodes with highest in-degree (most incoming connections)**: + +High in-degree nodes are often targets of influence, popular destinations, or central resources. In social networks, these might be influential people; in infrastructure graphs, these could be critical services. + +```kusto +graph("GRAPH_NAME") +| graph-match (node) + project node_id = node.id, + node_labels = labels(node), + in_degree = node_degree_in(node) +| order by in_degree desc +| take 10 +``` + +**Find nodes with highest out-degree (most outgoing connections)**: + +High out-degree nodes are often sources of influence, distributors, or connector hubs. These entities typically initiate many relationships or distribute resources to others. + +```kusto +graph("GRAPH_NAME") +| graph-match (node) + project node_id = node.id, + node_labels = labels(node), + out_degree = node_degree_out(node) +| order by out_degree desc +| take 10 +``` + +### Relationship pattern analysis + +These queries help identify structural patterns and complex relationships that might indicate important behaviors or anomalies in your data. + +**Find nodes with specific relationship patterns**: + +Identifies nodes that both receive and send connections, indicating entities that act as intermediaries or processing points. These nodes often represent critical components in workflows or communication chains. + +```kusto +// Nodes that have both incoming and outgoing edges +graph("GRAPH_NAME") +| graph-match (node) +where (graph("GRAPH_NAME") | graph-match (other1)-[]->(node) | take 1 | count() > 0) + and (graph("GRAPH_NAME") | graph-match (node)-[]->(other2) | take 1 | count() > 0) +project node_id = node.id, node_labels = labels(node) +| take 10 +``` + +**Discover triangular relationships** (nodes connected in a triangle): + +Triangular patterns often indicate tight collaboration, mutual dependencies, or closed-loop processes. In social networks, these represent groups of friends; in business processes, they might indicate approval chains or redundancy patterns. + +```kusto +graph("GRAPH_NAME") +| graph-match (a)-[]->(b)-[]->(c)-[]->(a) +where a.id != b.id and b.id != c.id and c.id != a.id +project node1 = a.id, node2 = b.id, node3 = c.id +| take 5 +``` + +### Property analysis + +Understanding the properties available on your nodes helps you build more sophisticated queries and identify data quality issues. + +**Explore node properties**: + +This query reveals what information is stored with your nodes, helping you understand the available attributes for filtering and analysis. + +```kusto +graph("GRAPH_NAME") +| graph-match (node) +where labels(node) has "DESIRED_LABEL" // Replace with actual label +project properties = node.properties +| take 1 +| project property_names = bag_keys(properties) +``` + +**Find nodes with specific property values**: + +Use this pattern to locate entities with particular characteristics or to validate data quality by checking for expected property values. + +```kusto +graph("GRAPH_NAME") +| graph-match (node) +where isnotnull(node.properties.name) // Replace 'name' with actual property +project node_id = node.id, property_value = node.properties.name +| take 10 +``` + +> [!TIP] +> Replace `"GRAPH_NAME"` with the actual graph model name (e.g., "Simple", "LDBC_SNB_Interactive", "BloodHound_Entra", etc.). Some queries may need adjustment based on the specific schema of your graph model. + +:::moniker-end + ## Related content :::moniker range="microsoft-fabric || azure-data-explorer" + - [Graph semantics overview](graph-semantics-overview.md) - [Common scenarios for using graph semantics](graph-scenarios.md) - [Graph function](graph-function.md) - [make-graph operator](make-graph-operator.md) - [Graph models overview](../management/graph/graph-model-overview.md) + :::moniker-end :::moniker range="azure-monitor || microsoft-sentinel" + - [Graph semantics overview](graph-semantics-overview.md) - [Common scenarios for using graph semantics](graph-scenarios.md) - [make-graph operator](make-graph-operator.md) + :::moniker-end diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 7adad279ad..ba2eb9ea86 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -513,167 +513,9 @@ graph("BloodHound_AD") |EXTCA01.WRAITH.CORP|Unknown System|WRAITH.CORP|CRITICAL|System with certificate forging capabilities| |EXTCA02.WRAITH.CORP|Unknown System|WRAITH.CORP|CRITICAL|System with certificate forging capabilities| -## Common Analysis Queries - -These queries work across all graph models and help you understand the structure and characteristics of any graph dataset. Use these queries to explore new graphs or perform basic analysis. - -### Graph Overview and Statistics - -**Count total nodes and edges**: - -```kusto -// Get node count -graph("GRAPH_NAME") -| graph-match (node) -project node -| summarize NodeCount = count() -``` - -```kusto -// Get edge count -graph("GRAPH_NAME") -| graph-match (source)-[edge]->(target) -project edge -| summarize EdgeCount = count() -``` - -**Get graph summary statistics**: - -```kusto -let nodes = graph("GRAPH_NAME") | graph-match (node) project node | summarize NodeCount = count(); -let edges = graph("GRAPH_NAME") | graph-match (source)-[edge]->(target) project edge | summarize EdgeCount = count(); -union nodes, edges -``` - -### Node Analysis - -**Discover all node types (labels)**: - -```kusto -graph("GRAPH_NAME") -| graph-match (node) -project labels = labels(node) -| mv-expand label = labels -| summarize count() by tostring(label) -| order by count_ desc -``` - -**Find nodes with multiple labels**: - -```kusto -graph("GRAPH_NAME") -| graph-match (node) -project node_id = node.id, labels = labels(node), label_count = array_length(labels(node)) -| where label_count > 1 -| take 10 -``` - -**Sample nodes by type**: - -```kusto -graph("GRAPH_NAME") -| graph-match (node) -where labels(node) has "DESIRED_LABEL" // Replace with actual label -project node_id = node.id, properties = node.properties -| take 5 -``` - -### Edge Analysis - -**Discover all edge types** (works with different graph schemas): - -```kusto -// For graphs using 'labels' array (like BloodHound) -graph("GRAPH_NAME") -| graph-match (source)-[edge]->(target) -project edge_labels = labels(edge) -| mv-expand label = edge_labels -| summarize count() by tostring(label) -| order by count_ desc -``` - -```kusto -// For graphs using 'label' column (like LDBC) -graph("GRAPH_NAME") -| graph-match (source)-[edge]->(target) -project edge_label = edge.label -| summarize count() by edge_label -| order by count_ desc -``` - -```kusto -// For graphs using 'lbl' column (like Simple) -graph("GRAPH_NAME") -| graph-match (source)-[edge]->(target) -project edge_label = edge.lbl -| summarize count() by edge_label -| order by count_ desc -``` - -**Find most connected nodes (highest degree)**: - -```kusto -graph("GRAPH_NAME") -| graph-match (node)-[edge]-(connected) -project node_id = node.id -| summarize degree = count() by node_id -| order by degree desc -| take 10 -``` - -### Relationship Pattern Analysis - -**Find nodes with specific relationship patterns**: - -```kusto -// Nodes that have both incoming and outgoing edges -graph("GRAPH_NAME") -| graph-match (node) -where (graph("GRAPH_NAME") | graph-match (other1)-[]->(node) | take 1 | count() > 0) - and (graph("GRAPH_NAME") | graph-match (node)-[]->(other2) | take 1 | count() > 0) -project node_id = node.id, node_labels = labels(node) -| take 10 -``` - -**Discover triangular relationships** (nodes connected in a triangle): - -```kusto -graph("GRAPH_NAME") -| graph-match (a)-[]->(b)-[]->(c)-[]->(a) -where a.id != b.id and b.id != c.id and c.id != a.id -project node1 = a.id, node2 = b.id, node3 = c.id -| take 5 -``` - -### Property Analysis - -**Explore node properties**: - -```kusto -graph("GRAPH_NAME") -| graph-match (node) -where labels(node) has "DESIRED_LABEL" // Replace with actual label -project properties = node.properties -| take 1 -| project property_names = bag_keys(properties) -``` - -**Find nodes with specific property values**: - -```kusto -graph("GRAPH_NAME") -| graph-match (node) -where isnotnull(node.properties.name) // Replace 'name' with actual property -project node_id = node.id, property_value = node.properties.name -| take 10 -``` - -> [!TIP] -> Replace `"GRAPH_NAME"` with the actual graph model name (e.g., "Simple", "LDBC_SNB_Interactive", "BloodHound_Entra", etc.). Some queries may need adjustment based on the specific schema of your graph model. - ## Related content - [Graph semantics overview](./graph-semantics-overview.md) - [Graph operators](./graph-operators.md) -- [Graph best practices](./graph-best-practices.md) +- [Graph best practices](./graph-best-practices.md) - includes common analysis queries that work across all graph datasets - [Persistent graph overview](../management/graph/graph-persistent-overview.md) From ac8dd4348dbfea857befd5feb806bc6294634043 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Fri, 15 Aug 2025 15:04:14 +0200 Subject: [PATCH 22/67] Refactor LDBC dataset sections to enhance clarity and organization, including schema overviews and use cases. --- .../kusto/query/graph-sample-data.md | 46 +++++++++---------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index ba2eb9ea86..ac100124be 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -90,10 +90,6 @@ graph("Simple") **Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 327,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset models realistic social media ecosystems with people creating posts and comments, participating in forums, working at organizations, and living in geographic locations across a detailed hierarchy from continents to cities. -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-ldbc-snb-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC SNB dataset"::: - **Schema and Counts**: - **Core Social Entity Types**: @@ -122,14 +118,9 @@ graph("Simple") - `HAS_INTEREST` - Personal interests (35,475 edges) - Additional relationships: `HAS_MODERATOR`, `IS_PART_OF`, `CONTAINER_OF`, `HAS_TYPE`, `IS_SUBCLASS_OF` -**Use Cases**: +**Graph Schema Overview**: -- Social network analysis and recommendation systems -- Community detection algorithms -- Influence propagation studies -- Content recommendation based on social connections -- Friend-of-friend discovery -- Social graph mining research +:::image type="content" source="media/graphs/graph-example-ldbc-snb-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC SNB dataset"::: **Graph Instance Example**: @@ -149,6 +140,15 @@ This example demonstrates: > [!NOTE] > All relationships shown in this diagram have been validated against the actual dataset using specific queries to ensure accuracy. +**Use Cases**: + +- Social network analysis and recommendation systems +- Community detection algorithms +- Influence propagation studies +- Content recommendation based on social connections +- Friend-of-friend discovery +- Social graph mining research + **Example Queries**: Find direct friendships with similar ages: @@ -196,10 +196,6 @@ graph("LDBC_SNB_Interactive") **Description**: LDBC Financial Benchmark dataset representing a comprehensive financial network with companies, persons, accounts, loans, and various financial transactions. This dataset models realistic financial ecosystems with 5,580 total nodes and over 31,000 financial transactions and relationships. Designed specifically for fraud detection, anti-money laundering (AML) analysis, and financial crime investigation scenarios, it captures complex patterns including account ownership, loan applications, guarantees, and multi-step transaction chains that are common in financial crime scenarios. -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-ldbc-financial-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC Financial dataset"::: - **Schema and Counts**: - **Node Types**: @@ -220,6 +216,16 @@ graph("LDBC_SNB_Interactive") - `REPAY` - Loan repayments (2,747 edges) - `SIGN_IN` - Authentication events (2,489 edges) +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-ldbc-financial-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC Financial dataset"::: + +**Graph Instance Example**: + +Based on real validated data from the LDBC Financial dataset, here's a connected financial subgraph: + +:::image type="content" source="media/graphs/graph-example-ldbc-financial-instance.png" alt-text="A graph containing a sample subgraph of the LDBC Financial dataset"::: + **Use Cases**: - Financial fraud detection @@ -229,12 +235,6 @@ graph("LDBC_SNB_Interactive") - Suspicious activity monitoring - Financial network analysis -**Graph Instance Example**: - -Based on real validated data from the LDBC Financial dataset, here's a connected financial subgraph: - -:::image type="content" source="media/graphs/graph-example-ldbc-financial-instance.png" alt-text="A graph containing a sample subgraph of the LDBC Financial dataset"::: - **Example Queries**: Detect potential money laundering through circular transfers: @@ -446,7 +446,7 @@ graph("BloodHound_Entra") Based on real validated data from the BloodHound_AD dataset, here's a connected Active Directory attack path: -:::image type="content" source="media/graphs/graph-example-bloodhound-ad-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound Entra dataset"::: +:::image type="content" source="media/graphs/graph-example-bloodhound-ad-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound AD dataset"::: **Use Cases**: @@ -517,5 +517,5 @@ graph("BloodHound_AD") - [Graph semantics overview](./graph-semantics-overview.md) - [Graph operators](./graph-operators.md) -- [Graph best practices](./graph-best-practices.md) - includes common analysis queries that work across all graph datasets +- [Graph best practices](./graph-best-practices.md) - [Persistent graph overview](../management/graph/graph-persistent-overview.md) From ae6699dd47113b71744fd6f8a12b69c51f64cd6b Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Fri, 15 Aug 2025 15:09:57 +0200 Subject: [PATCH 23/67] Revise graph sample data documentation to improve clarity and organization, including updated use cases and schema relationships for various datasets. --- .../kusto/query/graph-sample-data.md | 84 +++++++++++++++---- 1 file changed, 70 insertions(+), 14 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index ac100124be..b5f327637a 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -22,6 +22,17 @@ Use `graph("ModelName")` with the model name to reference existing graphs. **Description**: A small educational graph containing people, companies, and cities with various relationships. Perfect for learning graph traversals and understanding basic patterns. This compact dataset includes 11 nodes (5 people, 3 companies, and 3 cities) connected through 20 relationships, making it ideal for understanding graph fundamentals without the complexity of larger datasets. The graph demonstrates common real-world scenarios like employment relationships, geographic locations, social connections, and personal preferences. +**Use Cases**: + +- Learning graph query fundamentals +- Testing graph algorithms +- Understanding relationship patterns +- Educational examples for graph concepts + +**Schema Relationships**: + +:::image type="content" source="media/graphs/graph-example-simple-schema.png" alt-text="A schema of a graph containing people, companies, and cities with various relationships"::: + **Schema and Counts**: - **Node Types**: @@ -35,19 +46,7 @@ Use `graph("ModelName")` with the model name to reference existing graphs. - `knows` - Social connections between people (4 edges) - `likes` - Personal preferences and interests (3 edges) -**Schema Relationships**: - -:::image type="content" source="media/graphs/graph-example-simple-schema.png" alt-text="A schema of a graph containing people, companies, and cities with various relationships"::: - **Sample Data Structure**: - -:::image type="content" source="media/graphs/graph-example-simple-instances.png" alt-text="A graph containing instances of people, companies, and cities with various relationships"::: - -**Use Cases**: - -- Learning graph query fundamentals -- Testing graph algorithms -- Understanding relationship patterns - Educational examples for graph concepts **Example Queries**: @@ -90,6 +89,19 @@ graph("Simple") **Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 327,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset models realistic social media ecosystems with people creating posts and comments, participating in forums, working at organizations, and living in geographic locations across a detailed hierarchy from continents to cities. +**Use Cases**: + +- Social network analysis and recommendation systems +- Community detection algorithms +- Influence propagation studies +- Content recommendation based on social connections +- Friend-of-friend discovery +- Social graph mining research + +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-ldbc-snb-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC SNB dataset"::: + **Schema and Counts**: - **Core Social Entity Types**: @@ -118,7 +130,7 @@ graph("Simple") - `HAS_INTEREST` - Personal interests (35,475 edges) - Additional relationships: `HAS_MODERATOR`, `IS_PART_OF`, `CONTAINER_OF`, `HAS_TYPE`, `IS_SUBCLASS_OF` -**Graph Schema Overview**: +**Graph Instance Example**: :::image type="content" source="media/graphs/graph-example-ldbc-snb-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC SNB dataset"::: @@ -196,6 +208,21 @@ graph("LDBC_SNB_Interactive") **Description**: LDBC Financial Benchmark dataset representing a comprehensive financial network with companies, persons, accounts, loans, and various financial transactions. This dataset models realistic financial ecosystems with 5,580 total nodes and over 31,000 financial transactions and relationships. Designed specifically for fraud detection, anti-money laundering (AML) analysis, and financial crime investigation scenarios, it captures complex patterns including account ownership, loan applications, guarantees, and multi-step transaction chains that are common in financial crime scenarios. +**Use Cases**: + +- Financial fraud detection +- Anti-money laundering (AML) analysis +- Transaction pattern analysis +- Risk assessment and credit scoring +- Suspicious activity monitoring +- Financial network analysis + +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-ldbc-financial-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC Financial dataset"::: + +**Schema and Counts**: + **Schema and Counts**: - **Node Types**: @@ -297,6 +324,26 @@ graph("LDBC_Financial") **Description**: BloodHound Community Edition dataset for Azure Active Directory environments. This comprehensive security dataset contains 13,526 Azure AD objects including users, groups, applications, service principals, devices, and various cloud resources. With over 800,000 permission relationships and security edges, it models complex Azure AD environments typical of enterprise organizations. The dataset captures detailed Azure AD permissions, role assignments, group memberships, and resource ownership patterns essential for identifying privilege escalation paths and attack vectors in cloud environments. +**Use Cases**: + +- Entra ID security assessments +- Privilege escalation path discovery +- Attack path visualization +- Identity governance analysis +- Risk-based security controls +- Compliance auditing for cloud environments + +**Graph Schema Overview**: + +:::image type="content" source="media/graphs/graph-example-bloodhound-entra-schema.png" alt-text="A schema of a graph containing nodes and relations from the BloodHound Entra dataset"::: + +**Schema and Counts**: + +> [!NOTE] +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. + +**Description**: BloodHound Community Edition dataset for Azure Active Directory environments. This comprehensive security dataset contains 13,526 Azure AD objects including users, groups, applications, service principals, devices, and various cloud resources. With over 800,000 permission relationships and security edges, it models complex Azure AD environments typical of enterprise organizations. The dataset captures detailed Azure AD permissions, role assignments, group memberships, and resource ownership patterns essential for identifying privilege escalation paths and attack vectors in cloud environments. + **Schema and Counts**: - **Primary Node Types**: @@ -412,7 +459,16 @@ graph("BloodHound_Entra") **Description**: BloodHound Community Edition dataset for on-premises Active Directory environments. This dataset contains 1,495 Active Directory objects representing a typical enterprise AD deployment with complex permission structures and attack paths. The dataset includes users, computers, groups, organizational units, group policy objects, and certificate authority components across multiple domains. With over 18,000 permission relationships and security edges, it captures realistic AD attack scenarios including privilege escalation paths, ACL-based permissions, group memberships, and certificate-based authentication vulnerabilities common in Windows domain environments. -**Schema and Counts**: +**Use Cases**: + +- Active Directory security assessments +- Attack path analysis and penetration testing +- Domain privilege mapping +- Group policy security analysis +- Kerberoasting and ASREPRoasting target identification +- Security control gap analysis + +**Graph Schema Overview**: - **Core AD Object Types**: - `User` - Domain users (99 nodes) From 6ac2978f0f881dd821dbcc1253a1601a244b2a7b Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Fri, 15 Aug 2025 15:33:30 +0200 Subject: [PATCH 24/67] Refactor graph sample data documentation to enhance clarity and organization, including updated examples and descriptions for various datasets. --- .../kusto/query/graph-sample-data.md | 28 ++++++------------- 1 file changed, 9 insertions(+), 19 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index b5f327637a..96c96ffab9 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -46,8 +46,11 @@ Use `graph("ModelName")` with the model name to reference existing graphs. - `knows` - Social connections between people (4 edges) - `likes` - Personal preferences and interests (3 edges) -**Sample Data Structure**: -- Educational examples for graph concepts +**Graph Instance Example**: + +This example demonstrates basic graph relationships in a small, easy-to-understand network showing how people connect to companies and cities through various relationship types. + +:::image type="content" source="media/graphs/graph-example-simple-instances.png" alt-text="A graph containing instances of people, companies, and cities with various relationships"::: **Example Queries**: @@ -132,11 +135,7 @@ graph("Simple") **Graph Instance Example**: -:::image type="content" source="media/graphs/graph-example-ldbc-snb-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC SNB dataset"::: - -**Graph Instance Example**: - -Based on real validated data from the SNB dataset, here's an example showing social interactions around content: +This example demonstrates complex social network interactions in a realistic social media environment, showing how users engage with content, participate in forums, and form social connections. :::image type="content" source="media/graphs/graph-example-ldbc-snb-instances.png" alt-text="A graph containing a sample subgraph of the LDBC SNB dataset"::: @@ -249,19 +248,10 @@ graph("LDBC_SNB_Interactive") **Graph Instance Example**: -Based on real validated data from the LDBC Financial dataset, here's a connected financial subgraph: +This example illustrates a complex financial network with multiple entity types and transaction patterns, demonstrating how financial institutions can model relationships between customers, accounts, loans, and transaction flows for fraud detection and risk assessment. :::image type="content" source="media/graphs/graph-example-ldbc-financial-instance.png" alt-text="A graph containing a sample subgraph of the LDBC Financial dataset"::: -**Use Cases**: - -- Financial fraud detection -- Anti-money laundering (AML) analysis -- Transaction pattern analysis -- Risk assessment and credit scoring -- Suspicious activity monitoring -- Financial network analysis - **Example Queries**: Detect potential money laundering through circular transfers: @@ -375,7 +365,7 @@ graph("LDBC_Financial") **Graph Instance Example**: -Based on real validated data from the BloodHound_Entra dataset, here's a connected Azure AD subgraph: +This example demonstrates Azure Active Directory and Entra identity relationships with complex privilege structures and potential attack paths in a cloud environment. :::image type="content" source="media/graphs/graph-example-bloodhound-entra-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound Entra dataset"::: @@ -500,7 +490,7 @@ graph("BloodHound_Entra") **Graph Instance Example**: -Based on real validated data from the BloodHound_AD dataset, here's a connected Active Directory attack path: +This example demonstrates on-premises Active Directory attack paths and potential security vulnerabilities in a traditional Windows domain environment. :::image type="content" source="media/graphs/graph-example-bloodhound-ad-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound AD dataset"::: From 00399ec60d1d7f6778d15a5b04d6d0959af46a36 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Fri, 15 Aug 2025 15:44:10 +0200 Subject: [PATCH 25/67] notice and attribution to ldbc and bloodhound --- data-explorer/kusto/query/graph-sample-data.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 96c96ffab9..2fe617d83f 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -88,7 +88,7 @@ graph("Simple") **Purpose**: Social network traversals and friend-of-friend exploration. > [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Social Network Benchmark datasets are created by the Linked Data Benchmark Council (LDBC). +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Social Network Benchmark datasets are created by the [Linked Data Benchmark Council (LDBC)](https://ldbcouncil.org/). **Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 327,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset models realistic social media ecosystems with people creating posts and comments, participating in forums, working at organizations, and living in geographic locations across a detailed hierarchy from continents to cities. @@ -203,7 +203,7 @@ graph("LDBC_SNB_Interactive") **Purpose**: Financial transaction analysis and fraud detection patterns. > [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Financial Benchmark datasets are created by the Linked Data Benchmark Council (LDBC). +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Financial Benchmark datasets are created by the [Linked Data Benchmark Council (LDBC)](https://ldbcouncil.org/). **Description**: LDBC Financial Benchmark dataset representing a comprehensive financial network with companies, persons, accounts, loans, and various financial transactions. This dataset models realistic financial ecosystems with 5,580 total nodes and over 31,000 financial transactions and relationships. Designed specifically for fraud detection, anti-money laundering (AML) analysis, and financial crime investigation scenarios, it captures complex patterns including account ownership, loan applications, guarantees, and multi-step transaction chains that are common in financial crime scenarios. @@ -310,7 +310,7 @@ graph("LDBC_Financial") **Purpose**: Azure Active Directory privilege escalation and attack path analysis. > [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the [BloodHound project](https://bloodhound.specterops.io/). **Description**: BloodHound Community Edition dataset for Azure Active Directory environments. This comprehensive security dataset contains 13,526 Azure AD objects including users, groups, applications, service principals, devices, and various cloud resources. With over 800,000 permission relationships and security edges, it models complex Azure AD environments typical of enterprise organizations. The dataset captures detailed Azure AD permissions, role assignments, group memberships, and resource ownership patterns essential for identifying privilege escalation paths and attack vectors in cloud environments. @@ -445,7 +445,7 @@ graph("BloodHound_Entra") **Purpose**: On-premises Active Directory security analysis and privilege mapping. > [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. +> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the [BloodHound project](https://bloodhound.specterops.io/). **Description**: BloodHound Community Edition dataset for on-premises Active Directory environments. This dataset contains 1,495 Active Directory objects representing a typical enterprise AD deployment with complex permission structures and attack paths. The dataset includes users, computers, groups, organizational units, group policy objects, and certificate authority components across multiple domains. With over 18,000 permission relationships and security edges, it captures realistic AD attack scenarios including privilege escalation paths, ACL-based permissions, group memberships, and certificate-based authentication vulnerabilities common in Windows domain environments. From 7d2ccb5d4e46b90c8b373427ff7da00af5149e7c Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Fri, 15 Aug 2025 15:48:00 +0200 Subject: [PATCH 26/67] fix --- data-explorer/kusto/query/graph-sample-data.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 2fe617d83f..a767b61252 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -148,9 +148,6 @@ This example demonstrates: - **Content Classification**: Both posts and comments are tagged with relevant topics from their content - **Geographic Context**: All entities have location relationships for geographic analysis -> [!NOTE] -> All relationships shown in this diagram have been validated against the actual dataset using specific queries to ensure accuracy. - **Use Cases**: - Social network analysis and recommendation systems @@ -369,9 +366,6 @@ This example demonstrates Azure Active Directory and Entra identity relationship :::image type="content" source="media/graphs/graph-example-bloodhound-entra-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound Entra dataset"::: -> [!NOTE] -> All relationships shown in this diagram have been validated against the actual BloodHound_Entra dataset using specific queries to ensure accuracy. - **Use Cases**: - Entra ID security assessments From ca1a534bade1e632ab5cb141e967df0a7b38b603 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Tue, 19 Aug 2025 09:32:37 +0300 Subject: [PATCH 27/67] bookmark fix --- data-explorer/kusto/query/graph-query-language-use-cases.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 568d5685bc..f8873d7b08 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -29,7 +29,7 @@ GQL in Azure Data Explorer uses standard GQL syntax for graph pattern matching. * [Advanced patterns](#examples-with-advanced-patterns) -* [Complex multi-pattern queries](#Examples-with-complex-multi-patterns): Use complex multi-pattern queries to combine multiple patterns and filters in a single statement, so you can do sophisticated graph analysis. +* [Complex multi-pattern queries](#examples-with-complex-multi-patterns): Use complex multi-pattern queries to combine multiple patterns and filters in a single statement, so you can do sophisticated graph analysis. ## Examples with MATCH From b3dd4d5a6fe409fd84484e4b4fb477f28db04f90 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Tue, 19 Aug 2025 12:00:25 +0300 Subject: [PATCH 28/67] image size; added/moved limitations --- .../query/graph-query-language-functions.md | 60 +++++++++++++++--- .../query/graph-query-language-use-cases.md | 36 ++++++----- .../kusto/query/graph-query-language.md | 46 ++------------ .../kusto/query/graph-sample-data.md | 6 +- .../graphs/graph-example-simple-schema.png | Bin 169286 -> 67304 bytes 5 files changed, 78 insertions(+), 70 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index f60a2c63a9..c08ac51a50 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -14,6 +14,12 @@ Graph Query Language (GQL) is a powerful language for querying graph data in Azu > [!NOTE] > GQL support in Azure Data Explorer is currently in preview. Features and syntax can change based on feedback and ongoing development. +> [!TIP] +> +> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions, but use different syntax and operators. +> +> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. + ## Core GQL functions and operators This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. @@ -38,7 +44,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | | `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | | **Graph Functions** | -| `labels()` | Get labels of a node or edge | Custom graph function. See the details in [Labels() (GQL function)](#labels-gql-function) | `RETURN labels(person)` | +| `labels()` | Get labels of a node or edge | Custom graph function. See the details in [Labels()](#labels-custom-gql-function) | `RETURN labels(person)` | | **String Functions** | | `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | | `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | @@ -63,7 +69,49 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | | `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | -## Labels() (Custom GQL function) +## Best practices + +- KQL supports dynamic types, but GQL does not clearly define how these should be handled. To avoid runtime errors, explicitly cast nested fields to their expected type (see `CAST`). + +- Because GQL runs on KQL, some operations inherit KQL semantics. For instance, `COLLECT_LIST` (the GQL equivalent of `make_list`) will flatten arrays if the field is already an array. If results differ from expectations, consult the KQL documentation for the equivalent function. + +## Performance optimization + +Use these strategies to optimize GQL query performance in production environments: + +> [!TIP] +> Start with simple patterns, then increase complexity if needed. Monitor query performance, and adjust path lengths and filters to improve results. + +**Limit path matching scope**: + +- Use specific label filters to reduce the search space: `MATCH (start:SpecificType)` instead of `MATCH (start)` +- Limit variable length paths with reasonable bounds: `MATCH (a)-[]->{1,3}(b)` instead of unbounded paths +- Apply `WHERE` clauses early to filter results before expensive operations. + +**Use COUNT(*) for existence checks**: + +If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results. + +```gql +MATCH (user:User)-[:SUSPICIOUS_ACTIVITY]->(target) +WHERE user.id = 'user123' +RETURN COUNT(*) > 0 AS HasSuspiciousActivity +``` + +## Limitations + +- **Reserved keywords**: Some GQL keywords can't be used as identifiers in queries. Some reserved keywords aren't immediately obvious (for example, `DATE` is a reserved keyword). If your graph data has property names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. + + > [!IMPORTANT] + > When you design your graph schema, some common property names might conflict with GQL reserved keywords. Avoid or rename these property names. + +- **No `INSERT`/`CREATE` support**: GQL in Azure Data Explorer doesn't support `INSERT` or `CREATE` operations to change graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. + +- **Optional matches not supported**: GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. + +- **Entity equivalence checks not supported**: GQL's`(MATCH (n)-[]-(n2) WHERE n1 <> n2)` is not supported. Use explicit field comparisons instead, for example, `n.id <> n2.id`. + +## Labels() custom GQL function The `labels()` function shows the labels for a node or edge as an array. @@ -118,13 +166,7 @@ This query shows node names, their labels, and the labels of connecting edges. | web-server | ["System"] | ["CAN_ACCESS"] | database | | domain-controller | ["System"] | ["CAN_ACCESS"] | database | -> [!NOTE] -> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions, but use different syntax and operators. - -> [!TIP] -> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. - ## Related Context -* [Graph Query Language (GQL) in Azure Data Explorer](graph-query-language.md) +* [Graph Query Language (GQL)](graph-query-language.md) * [Graph Query Language (GQL) use cases](graph-query-language-use-cases.md) \ No newline at end of file diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index bd3a5b6a03..d065d96c4f 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -1,6 +1,6 @@ --- -title: GQL Query Patterns, Examples, and Use Cases in Azure Data Explorer -description: GQL query patterns for Azure Data Explorer. Find out how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. GQL use cases in Azure Data Explorer. See how to build queries for security, social networks, and organizational analysis with step-by-step examples. +title: GQL Query Patterns, Examples, and Use Cases +description: GQL query patterns, examples, and common use case scenarios. Find out how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. GQL use cases in Azure Data Explorer. See how to build queries for security, social networks, and organizational analysis with step-by-step examples. ms.reviewer: herauch ms.topic: reference ms.date: 08/14/2025 @@ -11,13 +11,13 @@ ms.date: 08/14/2025 This article gives Graph Query Language examples focusing on core query patterns, and shows common real world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. -GQL in Azure Data Explorer uses standard GQL syntax for graph pattern matching. The following examples show the GQL syntax supported in Azure Data Explorer, from simple to complex patterns. +The following examples show the GQL syntax supported in KQL, from simple to complex patterns. > [NOTE!] > -> Before you try these examples, set up your environment to use GQL. See [Getting Started](graph-query-language.md#getting-started) for details. +> Before you try these examples, set up your environment to use GQL. See [Getting Started](graph-query-language.md#getting-started) for details. Ensure you set the client request properties to use GQL, and set the graph reference function to your graph data source. > -> GQL support in Azure Data Explorer is in preview. Features and syntax can change based on feedback and ongoing development. +> GQL support is in **preview**. Features and syntax can change based on feedback and ongoing development. ## Core GQL query patterns @@ -31,9 +31,11 @@ GQL in Azure Data Explorer uses standard GQL syntax for graph pattern matching. * [Complex multi-pattern queries](#examples-with-complex-multi-patterns): Use complex multi-pattern queries to combine multiple patterns and filters in a single statement, so you can do sophisticated graph analysis. -## Examples with MATCH +For a full list of supported GQL functions and operators, see [Graph Query Language Functions in Azure Data Explorer](graph-query-language-functions.md). -### Basic pattern matching without variables +## Basic pattern matching + +## Basic pattern matching without variables The simplest pattern matches any relationship without referencing the matched values. @@ -57,6 +59,8 @@ RETURN COUNT(*) `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. You reference these variables to access properties, but in this example you're still just counting matches. +## Node properties + ### Access node properties Once you have variables, you access properties of matched nodes. @@ -102,6 +106,8 @@ This query matches only nodes with the "Person" label and returns their names, l | David | | Emma | +## Edge properties + ### Filter by edge labels @@ -138,7 +144,7 @@ This query finds Person nodes connected to other Person nodes through "knows" re | Carol | David | | David | Emma | -### Filter by properties with WHERE +## Filter by properties with WHERE Use WHERE clauses to filter based on property values. @@ -151,7 +157,7 @@ RETURN person.name, person.properties.age This query finds people over 25 years old and returns their names and ages. The WHERE clause filters the matched nodes by the age property. -#### Inline property filters +### Inline property filters Filter by properties directly in the pattern using inline conditions. @@ -201,9 +207,7 @@ RETURN DISTINCT connected.name This query finds all nodes you can reach from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops." -## Examples with WHERE - -### Basic property filtering +## Basic property filtering Filter nodes based on a single property condition: @@ -315,9 +319,7 @@ RETURN person.name, person.properties.age This query finds people who are over 30 years old or have 'a' in their name. -## Examples with RETURN - -### Return specific properties +## Return specific properties Return individual properties from matched nodes. @@ -436,7 +438,7 @@ This query returns only the first five people over 25 years old, ordered by age. **Comparable KQL:** Similar to KQL `project`, `summarize`, `sort`, and `take` operators. -## Examples with advanced patterns +## Advanced patterns examples Advanced patterns let you match complex graph structures and label combinations. @@ -538,7 +540,7 @@ This query creates a named path variable `full_path` that captures a two-hop pat **Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. -## Examples with complex multi-patterns +## Complex multi-pattern examples #### Cross-town *likes* with company filter diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 762f32939d..649b65501f 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -1,18 +1,18 @@ --- -title: GQL Graph Query Language in Azure Data Explorer (preview) -description: This article describes Graph Query Language (GQL) in Azure Data Explorer. +title: GQL Graph Query Language (preview) +description: This article describes Graph Query Language (GQL) ms.reviewer: herauch ms.topic: reference ms.date: 08/13/2025 --- -# Graph Query Language (GQL) in Azure Data Explorer (preview) +# Graph Query Language (GQL) (preview) > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] -Graph Query Language (GQL) lets you use standardized graph pattern matching in Azure Data Explorer (ADX). GQL follows the ISO GQL standard for graph database queries. +Graph Query Language (GQL) lets you use standardized graph pattern matching. GQL follows the ISO GQL standard for graph database queries. > [!NOTE] -> GQL support in Azure Data Explorer is in preview. Features and syntax can change based on feedback and ongoing development. +> GQL support is in preview. Features and syntax can change based on feedback and ongoing development. ## Introduction @@ -24,7 +24,7 @@ GQL in Azure Data Explorer builds on the existing [graph operators](graph-operat ## Getting started -To use GQL in Azure Data Explorer, you need: +To use GQL with KQL, you need: - A graph data source that's either a [graph snapshot](graph-operators.md) or a function with a `make-graph` statement (see step 1). - Set specific client request properties (see step 2). @@ -160,40 +160,6 @@ The following table shows the result of the query. | Alice | Bob | knows | | Alice | David | likes | -## Performance optimization - -Use these strategies to optimize GQL query performance in production environments: - -> [!TIP] -> Start with simple patterns, then increase complexity if needed. Monitor query performance, and adjust path lengths and filters to improve results. - -**Limit path matching scope**: - -- Use specific label filters to reduce the search space: `MATCH (start:SpecificType)` instead of `MATCH (start)` -- Limit variable length paths with reasonable bounds: `MATCH (a)-[]->{1,3}(b)` instead of unbounded paths -- Apply `WHERE` clauses early to filter results before expensive operations. - -**Use COUNT(*) for existence checks**: - -If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results. - -```gql -MATCH (user:User)-[:SUSPICIOUS_ACTIVITY]->(target) -WHERE user.id = 'user123' -RETURN COUNT(*) > 0 AS HasSuspiciousActivity -``` - -## Limitations - -- **Reserved keywords**: Some GQL keywords can't be used as identifiers in queries. Some reserved keywords aren't immediately obvious (for example, `DATE` is a reserved keyword). If your graph data has property names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. - - > [!IMPORTANT] - > When you design your graph schema, some common property names might conflict with GQL reserved keywords. Avoid or rename these property names. - -- **No `INSERT`/`CREATE` support**: GQL in Azure Data Explorer doesn't support `INSERT` or `CREATE` operations to change graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. - -- **Optional matches not supported**: GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. - ## Related content * [Graph operators overview](graph-operators.md) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index a767b61252..1471ea94c7 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -10,13 +10,13 @@ ms.date: 08/14/2025 # Graph sample datasets and examples -This page lists existing graphs on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the "Samples" database and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. +This page lists existing graphs on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the **Samples** database and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. 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2025 12:00:40 +0300 Subject: [PATCH 29/67] wip --- data-explorer/kusto/query/graph-query-language-functions.md | 2 +- data-explorer/kusto/query/graph-query-language-use-cases.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index c08ac51a50..705c761ae7 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -44,7 +44,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | | `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | | **Graph Functions** | -| `labels()` | Get labels of a node or edge | Custom graph function. See the details in [Labels()](#labels-custom-gql-function) | `RETURN labels(person)` | +[Labels()](#best-practices) | **String Functions** | | `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | | `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index d065d96c4f..4457c9c4cd 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -27,9 +27,9 @@ The following examples show the GQL syntax supported in KQL, from simple to comp * [RETURN](#examples-with-return): Use `RETURN` statements to project results from matched patterns. They specify what data to output from your graph query. -* [Advanced patterns](#examples-with-advanced-patterns) +[Advanced patterns](#advanced-patterns-examples) -* [Complex multi-pattern queries](#examples-with-complex-multi-patterns): Use complex multi-pattern queries to combine multiple patterns and filters in a single statement, so you can do sophisticated graph analysis. +[Complex multi-pattern queries](#complex-multi-pattern-examples) For a full list of supported GQL functions and operators, see [Graph Query Language Functions in Azure Data Explorer](graph-query-language-functions.md). From 7cfe5f82c27ee60d123e681a263476378f7c4e07 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Tue, 19 Aug 2025 12:23:33 +0300 Subject: [PATCH 30/67] build errors --- .../query/graph-query-language-functions.md | 2 +- .../query/graph-query-language-use-cases.md | 16 ++++++---------- 2 files changed, 7 insertions(+), 11 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 705c761ae7..48a82feb69 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -110,7 +110,7 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity - **Optional matches not supported**: GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. - **Entity equivalence checks not supported**: GQL's`(MATCH (n)-[]-(n2) WHERE n1 <> n2)` is not supported. Use explicit field comparisons instead, for example, `n.id <> n2.id`. - + ## Labels() custom GQL function The `labels()` function shows the labels for a node or edge as an array. diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 4457c9c4cd..d87919f1a3 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -21,15 +21,15 @@ The following examples show the GQL syntax supported in KQL, from simple to comp ## Core GQL query patterns -* [MATCH](#examples-with-match): The `MATCH` clause defines the graph patterns you want to find. Start with basic patterns and build complexity gradually. Similar functionality is available using the `graph-match` operator in KQL. +* [MATCH](#basic-pattern-matching): The `MATCH` clause defines the graph patterns you want to find. Start with basic patterns and build complexity gradually. Similar functionality is available using the `graph-match` operator in KQL. -* [WHERE](#examples-with-where): Use standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work like KQL or SQL WHERE clauses but operate on graph patterns. +* [WHERE](#filter-by-properties-with-where): Use standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work like KQL or SQL WHERE clauses but operate on graph patterns. -* [RETURN](#examples-with-return): Use `RETURN` statements to project results from matched patterns. They specify what data to output from your graph query. +* [RETURN](#return-specific-properties)): Use `RETURN` statements to project results from matched patterns. They specify what data to output from your graph query. -[Advanced patterns](#advanced-patterns-examples) +* [Advanced patterns](#advanced-patterns-examples) -[Complex multi-pattern queries](#complex-multi-pattern-examples) +* [Complex multi-pattern queries](#complex-multi-pattern-examples) For a full list of supported GQL functions and operators, see [Graph Query Language Functions in Azure Data Explorer](graph-query-language-functions.md). @@ -59,8 +59,6 @@ RETURN COUNT(*) `n` represents the source node, `e` represents the _directed_ edge, and `n2` represents the target node. You reference these variables to access properties, but in this example you're still just counting matches. -## Node properties - ### Access node properties Once you have variables, you access properties of matched nodes. @@ -106,8 +104,6 @@ This query matches only nodes with the "Person" label and returns their names, l | David | | Emma | -## Edge properties - ### Filter by edge labels @@ -542,7 +538,7 @@ This query creates a named path variable `full_path` that captures a two-hop pat ## Complex multi-pattern examples -#### Cross-town *likes* with company filter +### Cross-town *likes* with company filter This example combines multiple patterns and filters in one statement: From 78ca342decb95f3acec0d1408d7fbefd1534e48d Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Tue, 19 Aug 2025 13:18:24 +0300 Subject: [PATCH 31/67] wip --- .../kusto/query/graph-query-language-use-cases.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index d87919f1a3..f8ca195883 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -13,7 +13,7 @@ This article gives Graph Query Language examples focusing on core query patterns The following examples show the GQL syntax supported in KQL, from simple to complex patterns. -> [NOTE!] +> [!NOTE] > > Before you try these examples, set up your environment to use GQL. See [Getting Started](graph-query-language.md#getting-started) for details. Ensure you set the client request properties to use GQL, and set the graph reference function to your graph data source. > @@ -21,7 +21,7 @@ The following examples show the GQL syntax supported in KQL, from simple to comp ## Core GQL query patterns -* [MATCH](#basic-pattern-matching): The `MATCH` clause defines the graph patterns you want to find. Start with basic patterns and build complexity gradually. Similar functionality is available using the `graph-match` operator in KQL. +[MATCH](#basic-pattern-matching-examples) * [WHERE](#filter-by-properties-with-where): Use standard comparison and logical operators similar to KQL `where` clauses. `WHERE` clauses filter patterns based on node and edge properties. They work like KQL or SQL WHERE clauses but operate on graph patterns. @@ -33,9 +33,9 @@ The following examples show the GQL syntax supported in KQL, from simple to comp For a full list of supported GQL functions and operators, see [Graph Query Language Functions in Azure Data Explorer](graph-query-language-functions.md). -## Basic pattern matching +## Basic pattern matching examples -## Basic pattern matching without variables +### Basic pattern matching without variables The simplest pattern matches any relationship without referencing the matched values. @@ -140,7 +140,7 @@ This query finds Person nodes connected to other Person nodes through "knows" re | Carol | David | | David | Emma | -## Filter by properties with WHERE +### Filter by properties with WHERE Use WHERE clauses to filter based on property values. @@ -315,7 +315,7 @@ RETURN person.name, person.properties.age This query finds people who are over 30 years old or have 'a' in their name. -## Return specific properties +### Return specific properties Return individual properties from matched nodes. From 8b0107f3ae96954fbda4e678b2408918559c5372 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Tue, 19 Aug 2025 15:14:28 +0200 Subject: [PATCH 32/67] Reorganize GQL section in TOC; moved to 'Additional query languages' --- data-explorer/kusto/query/toc.yml | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/data-explorer/kusto/query/toc.yml b/data-explorer/kusto/query/toc.yml index 17b0a2072b..da2fb9d423 100644 --- a/data-explorer/kusto/query/toc.yml +++ b/data-explorer/kusto/query/toc.yml @@ -1286,14 +1286,6 @@ items: href: graph-scenarios.md - name: Graph sample data href: graph-sample-data.md - - name: Graph query language (GQL) - items: - - name: Query data using GQL - href: graph-query-language.md - - name: GQL functions and operators - href: graph-query-language-functions.md - - name: GQL examples and use cases - href: graph-query-language-use-cases.md - name: Graph operators items: - name: Graph operators overview @@ -1331,6 +1323,16 @@ items: - name: node_degree_out() displayName: graph, node, Node degree out node_degree_out, outdegree node_degree_out href: node-degree-out.md + - name: Additional query languages + items: + - name: Graph query language (GQL) + items: + - name: Query data using GQL + href: graph-query-language.md + - name: GQL functions and operators + href: graph-query-language-functions.md + - name: GQL examples and use cases + href: graph-query-language-use-cases.md - name: Geospatial items: - name: Geospatial clustering From 5b668f3e6b016a8f58c4664aabd2263da76b16d5 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Tue, 19 Aug 2025 15:26:24 +0200 Subject: [PATCH 33/67] fix of table --- data-explorer/kusto/query/graph-query-language-functions.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 48a82feb69..8993011df1 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -44,7 +44,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | | `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | | **Graph Functions** | -[Labels()](#best-practices) +| `Labels()` | Show labels for a node or edge | `labels()` | `RETURN labels(entity)` | | **String Functions** | | `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | | `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | From 4ced04ee887d12974d34598ff0b2e431889881c0 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Tue, 19 Aug 2025 15:31:57 +0200 Subject: [PATCH 34/67] fixed simple graph schema --- .../kusto/query/graph-sample-data.md | 2 +- .../graphs/graph-example-simple-schema.mmd | 2 +- .../graphs/graph-example-simple-schema.png | Bin 67304 -> 104977 bytes 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index 1471ea94c7..c4d4f34c2b 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -16,7 +16,7 @@ This page lists existing graphs on our help cluster at [https://help.kusto.windo Use `graph("ModelName")` with the model name to reference existing graphs. -## Simple sample graph +## Simple **Purpose**: Basic graph operations and learning fundamental graph query patterns. diff --git a/data-explorer/kusto/query/media/graphs/graph-example-simple-schema.mmd b/data-explorer/kusto/query/media/graphs/graph-example-simple-schema.mmd index 51250c4a2f..c2dafe51d4 100644 --- a/data-explorer/kusto/query/media/graphs/graph-example-simple-schema.mmd +++ b/data-explorer/kusto/query/media/graphs/graph-example-simple-schema.mmd @@ -1,5 +1,5 @@ %%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% -graph TD +graph LR %% Node type definitions Person[Person] Company[Company] diff --git a/data-explorer/kusto/query/media/graphs/graph-example-simple-schema.png b/data-explorer/kusto/query/media/graphs/graph-example-simple-schema.png index f5fec662721807aaf2010ddd940ca2dc5d571ce1..086c2a557269cf738e367d11f5e05791d8d2b680 100644 GIT binary patch literal 104977 zcmeFZXH-+$`Y#-HqkF4}O7AKtNEPW_rAb$MM+ilvN$&x5BRwcckuHcxm)?uCC>`k~ zAPK#N&>?}`8Ta1j!2dm;-Y<9Dd(RppgEbZ_vpn_pl$nr6Y6_(1>CQtS5K_g5vKkNw z8Myt8@%OXf58AcvCHQm7MML2Mq@bH&5dygeQIx%}<(agCw0VNGd@uEr?Ym;# zcb_sZAQxT-7RP8)oua<{=(!BlDLICiR(p9yYx#S>SyJ4WGkZky?*4tPv$y?8GVL_d zGJ8L|P8n9;?D~aDg=(zpj3$EID)h z0l4?ifLuKJ=#TXOKKVCA{!5L2(*gqdFE##;r$*G$3ZyrAaBxsgRaMm*0den7yZy%mwvq32TtlJa2a#>~N1N#P34GjiKpIEmi=@Z;WkH$x@9Df;-*6|4Png^9) zisymtj!UmO&n1yH2&+lWE?oLvB0e@Yw&6YuXN+*_BQYJD?Ucf8g%uJytTRj-q`by2 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z#Vn1zQHx>r$A49%TWUis8-(U8_a;i@IQW=GT=|C~EN)EwJJaV!5VBs`N?}Gqo7-5T zmjYH$N)l$LPtA`zb2qXAju>yQJdfH99Glrb$475{8~W?HtIEzLr<1%|$wrGO!TKW{ zX6tR|QXVG8S$Ou=AD^aLbnDj!?O_VeEbjK~y&7#hjYfj{Zj->P6MitV|1*}!l2^0b zg8<|5w3Zn-#qrA&U!UX*_(LVrxYAS?uLDkI>_+}N!7itS@%nj1j7lCVLTpoc`lK2? z8;&JJ)Ki61NqbbcJ`{vwX5HmGwKR_nlv4w8-Cm_1ZUKEUp!|4Dp@OUOEsOmz#v@Tp zG4`1S0-f6WA;#)fDfEW0Z<+~Xd^kkdCV0CcO@G)8w7&;uO<4c?kn#UJb6lU&X$xOe z-EIrGY%=iKNiAIpAN(&ThiO7nEGy4n|3~UzLh#sm;QB1ve64iwhX{zH|H0jLJ1t&4 zFbS(Y1{Efk{3%AREAzQ;s|J&~lDRSiag#>IP73)`pmY8rR}t8XyCK;H88Zcu z3Te~#=g6DxFE!5pMA`^-SIpvJWk~Vl7ES$3es9$oLHe$&NBzP-x&*%M(vVhVTo?j8 zdKs$qwnsMA`S!Plc7F6t+nHewF8*6T73kW3{{s)xEi7xAQf}Oucf$rtGI-#|3{~uB zHVy=H&!Q9abW$w!45%@^tl#)Qd&2(X4QNI!zS}+3R Date: Tue, 19 Aug 2025 16:00:27 +0200 Subject: [PATCH 35/67] Refine GQL documentation: correct typos, enhance clarity, and standardize terminology across multiple files. --- .../query/graph-query-language-functions.md | 14 +++++------ .../query/graph-query-language-use-cases.md | 8 +++---- .../kusto/query/graph-query-language.md | 23 +++++++++---------- 3 files changed, 22 insertions(+), 23 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 8993011df1..8a5a93a39b 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -1,18 +1,18 @@ --- -title: Graph Query Language Functions in Azure Data Explorer -description: Leaern Azure Data Explorer graph query language (GQL) functions and operators for working with nodes, edges, and properties. Find practical examples and KQL equivalents. +title: Graph Query Language Functions +description: Learn graph query language (GQL) functions and operators for working with nodes, edges, and properties. Find practical examples and KQL equivalents. ms.reviewer: hraunch ms.topic: reference ms.date: 08/14/2025 -#CustomerIntent: As a data engineer, I want to learn GQL functions in Azure Data Explorer so that I can query graph data efficiently. +#CustomerIntent: As a data engineer, I want to learn GQL functions so that I can query graph data efficiently. --- # Graph query language (GQL) functions and operators (preview) -Graph Query Language (GQL) is a powerful language for querying graph data in Azure Data Explorer. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. +Graph Query Language (GQL) is a powerful language for querying graph data alongside KQL. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. > [!NOTE] -> GQL support in Azure Data Explorer is currently in preview. Features and syntax can change based on feedback and ongoing development. +> GQL support is currently in preview. Features and syntax can change based on feedback and ongoing development. > [!TIP] > @@ -105,9 +105,9 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity > [!IMPORTANT] > When you design your graph schema, some common property names might conflict with GQL reserved keywords. Avoid or rename these property names. -- **No `INSERT`/`CREATE` support**: GQL in Azure Data Explorer doesn't support `INSERT` or `CREATE` operations to change graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph snapshots](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. +- **No `INSERT`/`CREATE` support**: GQL doesn't support `INSERT` or `CREATE` operations to change graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph models](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. -- **Optional matches not supported**: GQL's `OPTIONAL MATCH` clause isn't supported in Azure Data Explorer. All pattern matches are required. To get similar results, use separate queries or KQL operators for optional relationships. +- **Optional matches**: TODO - **Entity equivalence checks not supported**: GQL's`(MATCH (n)-[]-(n2) WHERE n1 <> n2)` is not supported. Use explicit field comparisons instead, for example, `n.id <> n2.id`. diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index d87919f1a3..fcaf8aab80 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -1,6 +1,6 @@ --- title: GQL Query Patterns, Examples, and Use Cases -description: GQL query patterns, examples, and common use case scenarios. Find out how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. GQL use cases in Azure Data Explorer. See how to build queries for security, social networks, and organizational analysis with step-by-step examples. +description: GQL query patterns, examples, and common use case scenarios. Find out how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. GQL use cases alongside KQL. See how to build queries for security, social networks, and organizational analysis with step-by-step examples. ms.reviewer: herauch ms.topic: reference ms.date: 08/14/2025 @@ -9,7 +9,7 @@ ms.date: 08/14/2025 --- # GQL query patterns, examples, and common scenarios (preview) -This article gives Graph Query Language examples focusing on core query patterns, and shows common real world use cases for GQL in Azure Data Explorer using realistic graph schemas and queries. +This article gives Graph Query Language examples focusing on core query patterns, and shows common real world use cases for GQL alongside KQL using realistic graph schemas and queries. The following examples show the GQL syntax supported in KQL, from simple to complex patterns. @@ -31,7 +31,7 @@ The following examples show the GQL syntax supported in KQL, from simple to comp * [Complex multi-pattern queries](#complex-multi-pattern-examples) -For a full list of supported GQL functions and operators, see [Graph Query Language Functions in Azure Data Explorer](graph-query-language-functions.md). +For a full list of supported GQL functions and operators, see [Graph Query Language Functions](graph-query-language-functions.md). ## Basic pattern matching @@ -752,5 +752,5 @@ This query finds all employees within three management levels of the CEO. Use th ## Related topics -* [Graph Query Language (GQL) in Azure Data Explorer](graph-query-language.md) +* [Graph Query Language (GQL)](graph-query-language.md) * [Graph Query Language (GQL) reference](graph-query-language-functions.md) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 649b65501f..654228f524 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -9,24 +9,22 @@ ms.date: 08/13/2025 > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] -Graph Query Language (GQL) lets you use standardized graph pattern matching. GQL follows the ISO GQL standard for graph database queries. +Graph Query Language (GQL) lets you use standardized graph pattern matching alongside KQL. GQL follows the ISO GQL standard for graph database queries. > [!NOTE] > GQL support is in preview. Features and syntax can change based on feedback and ongoing development. ## Introduction -Graph Query Language (GQL) is an emerging ISO standard for querying graph databases. GQL lets you use SQL-like syntax for graph pattern matching in Azure Data Explorer, so it's easier to analyze relationships in your data. This article explains how to use GQL in Azure Data Explorer, its benefits, and key features. +Graph Query Language (GQL) is an emerging ISO standard for querying graph databases. GQL lets you use SQL-like syntax for graph pattern matching, so it's easier to analyze relationships in your data. This article explains how to use GQL, its benefits, and key features. -Azure Data Explorer implements GQL, so you get standardized graph pattern matching capabilities. Analyze relationships in your data using the ISO standard syntax. - -GQL in Azure Data Explorer builds on the existing [graph operators](graph-operators.md) functionality. It gives you a standardized way to write graph queries that focus on relationships and patterns between entities. +GQL provides standardized graph pattern matching capabilities as an alternative query language to KQL. Analyze relationships in your data using the ISO standard syntax. ## Getting started -To use GQL with KQL, you need: +To use GQL, you need: -- A graph data source that's either a [graph snapshot](graph-operators.md) or a function with a `make-graph` statement (see step 1). +- A graph data source that's either a [graph model](graph-operators.md) or a function returning a transient graph ending with a [make-graph](make-graph-operator.md) operator (see step 1). - Set specific client request properties (see step 2). ## Step 1: Create a graph reference @@ -80,9 +78,13 @@ Before you use GQL, create a graph data source. This article uses an in-memory m ## Step 2: Configure client request properties -To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the ADX query interface by using directives. +To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the Kusto Explorer by using directives. + +### Use Kusto Web Explorer to set client request properties + +TODO -### Use Azure Data Explorer to set client request properties +### Use Kusto Explorer to set client request properties > [!IMPORTANT] > Run each directive separately before you run your GQL query. The directives set up the query environment for GQL execution. @@ -119,9 +121,6 @@ For programmatic access, set these client request properties: After you finish setup, run GQL queries using standard GQL syntax. Use the examples below to explore basic and advanced GQL features. -> [!TIP] -> GQL queries in Azure Data Explorer use standard GQL syntax and translate to KQL with graph operators. Start with simple patterns, and build complexity gradually. - ## Examples Find basic examples for pattern matching and labels here. For more complex examples, see [GQL query patterns, examples, and common scenarios](graph-query-language-use-cases.md). From 209ed82a7b2a3bf0faa8cada210eb3143699764c Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Tue, 19 Aug 2025 18:25:18 +0200 Subject: [PATCH 36/67] Fixed headlines and pics --- .../kusto/query/graph-sample-data.md | 77 +++++++++++++++--- .../graph-example-bloodhound-ad-instance.mmd | 2 +- .../graph-example-bloodhound-ad-instance.png | Bin 280790 -> 143725 bytes 3 files changed, 67 insertions(+), 12 deletions(-) diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md index c4d4f34c2b..223566837f 100644 --- a/data-explorer/kusto/query/graph-sample-data.md +++ b/data-explorer/kusto/query/graph-sample-data.md @@ -10,13 +10,11 @@ ms.date: 08/14/2025 # Graph sample datasets and examples -This page lists existing graphs on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the **Samples** database and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. In the help cluster, browse to **help** > **Samples** > **Tables** > **Graphs**. +This page lists existing graphs on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the **Samples** database and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. -## Usage notes +## Simple educational graph for learning fundamentals -Use `graph("ModelName")` with the model name to reference existing graphs. - -## Simple +Usage: `graph("Simple")` **Purpose**: Basic graph operations and learning fundamental graph query patterns. @@ -56,6 +54,11 @@ This example demonstrates basic graph relationships in a small, easy-to-understa Find all employees of a specific company: +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("Simple") | graph-match (person)-[works_at]->(company) @@ -71,6 +74,11 @@ graph("Simple") Find colleagues (people working at the same company): +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("Simple") | graph-match (person1)-->(company)<--(person2) @@ -83,7 +91,9 @@ graph("Simple") |---|---|---| |Alice|Bob|TechCorp| -## LDBC_SNB_Interactive +## LDBC SNB interactive + +Usage: `graph("LDBC_SNB_Interactive")` **Purpose**: Social network traversals and friend-of-friend exploration. @@ -163,6 +173,11 @@ Find direct friendships with similar ages: This query identifies pairs of people who are directly connected through a "KNOWS" relationship and have similar ages (birthdays within 30 days of each other). It traverses the LDBC social network graph to find existing friendships between people of similar age groups. The query returns the total count of such age-similar friendship pairs in the network, which can be useful for analyzing age-based social patterns or validating friend recommendation algorithms. +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("LDBC_SNB_Interactive") | graph-match (person1)-[knows]->(person2) @@ -180,6 +195,11 @@ Find popular posts by likes: This query analyzes social engagement by identifying the most popular content creators based on how many unique people have liked their posts. It traverses the social network graph through the path: person → likes → post → has_creator → creator. The query aggregates the data to show each creator's total number of unique likers and distinct posts, then returns the top 3 creators with the most likes. This is useful for identifying influential content creators, understanding engagement patterns, and discovering viral content in the social network. +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("LDBC_SNB_Interactive") | graph-match (person)-[likes]->(post)-[has_creator]->(creator) @@ -195,7 +215,9 @@ graph("LDBC_SNB_Interactive") |Hoffmann|340|9| |Singh|338|268| -## LDBC_Financial +## LDBC Financial + +Usage: `graph("LDBC_Financial")` **Purpose**: Financial transaction analysis and fraud detection patterns. @@ -253,6 +275,11 @@ Detect potential money laundering through circular transfers: This query identifies suspicious circular transaction patterns that could indicate money laundering activities. It searches for accounts that send money to another account and then receive it back through a chain of 1 to 3 transfers, creating a circular flow. The query specifically looks for large initial transfers (over 10,000) and returns details about the suspicious accounts, including the transfer amount and the length of the circular chain. This pattern detection is useful for anti-money laundering (AML) systems and financial fraud investigations. +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("LDBC_Financial") | graph-match (account1)-[t1]->(account2)-[t2*1..3]->(account1) @@ -280,6 +307,11 @@ Find high-risk loan guarantors: This query identifies individuals or companies who guarantee multiple loans totaling significant amounts, which could indicate financial risk exposure. It traverses the financial network graph following the path: guarantor → guarantee → borrower → apply → loan. The query aggregates the total amount guaranteed and number of loans for each guarantor, then filters for those guaranteeing over 100,000 in total and returns the top 5 by total guaranteed amount. This analysis is useful for risk assessment, identifying over-leveraged guarantors, and evaluating systemic financial risks in lending networks. +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("LDBC_Financial") | graph-match (guarantor)-[guarantee]->(borrower)-[apply]->(loan) @@ -300,7 +332,9 @@ graph("LDBC_Financial") |Company::4398046511208|366243272|8| |Person::19791209300551|338838223|6| -## BloodHound_Entra +## BloodHound Entra ID dataset + +Usage: `graph("BloodHound_Entra")` **Purpose**: Azure Active Directory privilege escalation and attack path analysis. @@ -377,7 +411,12 @@ This example demonstrates Azure Active Directory and Entra identity relationship Find paths to administrative privileges: -This query identifies privilege escalation paths from regular users to administrative groups in Azure AD environments. It searches for users who can reach admin groups (like AAD DC Administrators, DnsAdmins, etc.) through 1-5 relationship hops, helping security teams understand potential attack paths and privilege escalation risks. +This query identifies privilege escalation paths from regular users to administrative groups in Azure AD environments. It searches for users who can reach admin groups (like AAD DC Administrators, DnsAdmins, etc.) through 1-3 relationship hops, helping security teams understand potential attack paths and privilege escalation risks. + +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end ```kusto graph("BloodHound_Entra") @@ -388,7 +427,6 @@ graph("BloodHound_Entra") project source_user = user.name, path_length = array_length(path), admin_group = coalesce(admingroup.displayname, admingroup.name) -| order by path_length asc | take 10 ``` @@ -409,6 +447,11 @@ Identify high-value targets (Tier 0 assets): This query identifies critical administrative assets marked as "admin_tier_0" in the environment. These are the most sensitive and powerful accounts, service principals, and resources that pose the highest risk if compromised. Understanding these assets helps prioritize security monitoring and protection efforts. +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("BloodHound_Entra") | graph-match (asset) @@ -432,7 +475,9 @@ graph("BloodHound_Entra") |31E3B75F-PRIVILEGED AUTHENTICATION ADMINISTRATOR@PHANTOMCORP|AZServicePrincipal|admin_tier_0| |31E3B75F-PRIVILEGED ROLE ADMINISTRATOR@PHANTOMCORP|AZServicePrincipal|admin_tier_0| -## BloodHound_AD +## BloodHound Active Directory dataset + +Usage: `graph("BloodHound_AD")` **Purpose**: On-premises Active Directory security analysis and privilege mapping. @@ -501,6 +546,11 @@ Find potential privilege escalation: This query counts how many non-admin users can potentially escalate to admin in Azure AD. It traverses up to 10 MemberOf group hops (no cycles) from each user to groups that grant dangerous permissions (GenericAll, WriteDacl, WriteOwner, ForceChangePassword) over admin users (admincount=true), then returns the distinct number of such “potential attacker” users. +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("BloodHound_AD") | graph-match cycles=none (user)-[memberof*0..10]->(group)-[permission]->(target) @@ -522,6 +572,11 @@ Find Golden Certificate attack paths: This query identifies entities that can perform Golden Certificate attacks, which allow attackers to forge certificates as any user in the domain. These are extremely critical vulnerabilities as they enable complete domain compromise by allowing the attacker to impersonate any user, including domain administrators, through forged certificates. +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + ```kusto graph("BloodHound_AD") | graph-match (attacker)-[goldencert]->(target) diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd index 5c5c0e4916..0cf0f5f397 100644 --- a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd +++ b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd @@ -1,5 +1,5 @@ %%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% -graph TD +graph LR %% Real Entities from BloodHound_AD dataset ALICE["ALICE@PHANTOM\.CORP
User
Domain User"] diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png index 6650959bb6118031e2b151a049d0fadb69a4a249..f9c4f77bc6c552c0ed7483e270b2820539799a9e 100644 GIT binary patch literal 143725 zcmeFZXH=8z)&>|vMM0$(r71`k5Rl%bi3o^PsZs-k-g{J}H|Yojr1#!ykluUmB+`3_ z0AU{9_k8EPo|zxB=EtlxYrbc#kcT^Y+P?R`_O-8lL%@3lnTOaDQw-d*5r!B)f@c*C&&AoChj*iW$z0zC!EN{XwvByP>Q#*G^{i0%zoJa{lu9O#ep z^e4?dL^I7jlbnp`A4%m%j-Fl;uClEOr(uOGA}ycxR-Z_&{B zclK{J36))IM+%@t2*x0n`sG~hGdwQxX((jzK`lZ8F)LB{{*nr#k=N9mNYW~hY z2Y~%ue9+BJ|7ZJr`+rCN-?2uQ)$XnTTnY3v$GkG%-;y`PY?l)R`pgp(KEWd3=_ysW z%?^6-kMBLqDGZ!c8l6`K#w_#c%RFhTe{9zZ{^J|K?ssVr*`Ff>lK(eGp4T$^Nm4ef@tnVpQdf@sHC0yWbhPxs}w^#?zS|KYl!vqny>* z**S^azVrFd?|ouWRaKQwQdU-`{lm1t846M|Gc#q8S~O=Dg}o$N_y0Wqq~RZreRIX9 z|2y&@8~v{}{$q>(ulmN+w$48v0@O6m7X7wuYgI2`%3e3n6(_c1rBABF7$TegAv!!Z 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P6o`-a>?VN6Tm zsZ+7d6A2EH;{&=qufAC{BueT_gR;$#O6RpM7z}AnqQZb}4NIlu2LvCdl5hc!#C!g5UYyvJnG~W#dwv>@+$TTh`GV}b z64v;E_(Y4O&g0&chra+EuALJr3GCl<1*d2<5Q~873xLNQLl`qUHlj>OM@I`vRlKvc zur{)t7|OJwcMFwn1N4Flv_9#QKURC1({duXzhJDuQ=Y@EEVskVDs^@ zyyb!fNZEj*6|Kk73T0`(9UcIeu}6jl)Y2_1v;+%Sl}A-|G+J#mdUQm!(&@Rb0RIc; zhfi%PH1PsXC=b{5#5J9|QTs*vGttXVOwvqZ1j05H*kZ=@hb4zJtjF;wF?{XY3?64; zxgBb%F{Cx(koJ4}wQdLy>%F5~n26o1Fp!E;v@?dFUV`*Z=&d^b>_yq0!qd0;q zs|5!nO4c=vx7#N^oG&sW!mv#9c2>^~>E|XmL`P5O9vHa^JHYeX3&Oy8N&I*mfUI0o zj=p)vfBH5Md*j>ur&qwkDaM;j!@s=vlI%06-Sc0DdFKEAnE&(x8^Y_@{$Iz}u>W&? z|NNv<=>{JC=lS)654;bqWzGLO1|mRH1|KI%IIvaU>Rrk;38ZU%IW=8d|VJ9J* z8Si4@Tk3rCwjw?!s-D-ab9O4X8+}GncS1E%kXX|jhMsyiFQdW6H*Gz?O^@CDB5MpT z?+tDRe&OG9+w*dwl~Hj}=*pt)5}65kzuZBo+7_)yJc@jSbPmdc@Qjo{jboM{v|NYgRsQhLV8^w~n?=AYpc#|6Z z+fA63)73W9ITn_&n^7`$k{{UC Date: Wed, 20 Aug 2025 11:35:02 -0700 Subject: [PATCH 37/67] added limitation and functions per Henning's guidance --- .../kusto/query/graph-query-language-functions.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 8a5a93a39b..1f2f0ddad8 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -43,6 +43,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `MAX()` | Maximum value | `max()` | `RETURN MAX(person.age)` | | `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | | `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | +| `SIZE()` | Array length | `array_length()` | `RETURN SIZE(COLLECT_LIST(n.firstName))` | | **Graph Functions** | | `Labels()` | Show labels for a node or edge | `labels()` | `RETURN labels(entity)` | | **String Functions** | @@ -53,11 +54,13 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `STARTS WITH` | String starts with pattern | `startswith()` | `WHERE person.name STARTS WITH 'Tom'` | | `ENDS WITH` | String ends with pattern | `endswith()` | `WHERE person.name ENDS WITH 'Hanks'` | | `CONTAINS` | String contains pattern | `contains()` | `WHERE person.name CONTAINS 'Tom'` | +| `\|\|` | String concatenation | `strcat()` | `RETURN n.firstName \|\| ' ' \|\| n.lastName` | | **Type Conversion** | | `CAST()` | Convert data types | `tostring()`, `toint()`, etc. | `CAST(person.age AS STRING)` | | **Date/Time Functions** | | `ZONED_DATETIME()` | Create datetime from string | `todatetime()` | `ZONED_DATETIME('2024-01-01')` | | `CURRENT_TIMESTAMP` | Current timestamp | `now()` | `WHERE created < CURRENT_TIMESTAMP` | +| `DURATION()` | Construct a duration (timespan) | `make_timespan()` | `DURATION({days:3})` | | **Path Operations** | | Variable length paths | Multi-hop traversal | `graph-match` with quantifiers | `MATCH (a)-[*1..3]->(b)` | | Path variables | Named path assignment | Path variables in `graph-match` | `MATCH p = (a)-[]->(b)` | @@ -107,10 +110,17 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity - **No `INSERT`/`CREATE` support**: GQL doesn't support `INSERT` or `CREATE` operations to change graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph models](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. -- **Optional matches**: TODO +- **Optional matches**: Supported only for node patterns (not edges) and only on persistent graphs. Optional matching isn't available for transient, in-query graphs. - **Entity equivalence checks not supported**: GQL's`(MATCH (n)-[]-(n2) WHERE n1 <> n2)` is not supported. Use explicit field comparisons instead, for example, `n.id <> n2.id`. +- **Time and timezone**: The engine operates in UTC. Datetime literals must use zoned datetime; only the UTC zone is supported via `ZONED_DATETIME("2011-12-31 23:59:59.9")`. + +- **Duration granularity**: Durations support up to days and smaller units down to nanoseconds. Larger-than-day units (for example, weeks, months, years) aren't supported. + +- **Traversal modes**: GQL defines configurable traversal modes for matching and paths. For `MATCH`, the modes are `'DIFFERENT EDGES'` and `'REPEATABLE EDGES'`; for `PATH`, the modes are `'WALK'`, `'TRAIL'`, `'ACYCLIC'`, and `'SIMPLE'`. The current implementation defaults to `'DIFFERENT EDGES'` and `'WALK'`, respectively. +Some combinations are not supported. + ## Labels() custom GQL function The `labels()` function shows the labels for a node or edge as an array. From ad2392680b23c85dad063a11d6aeaf02c00f534f Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Thu, 21 Aug 2025 14:34:56 +0200 Subject: [PATCH 38/67] removed graph sample --- .../kusto/query/graph-sample-data.md | 614 ------------------ .../graph-example-bloodhound-ad-instance.mmd | 35 - .../graph-example-bloodhound-ad-instance.png | Bin 143725 -> 0 bytes .../graph-example-bloodhound-ad-schema.mmd | 64 -- .../graph-example-bloodhound-ad-schema.png | Bin 329374 -> 0 bytes ...raph-example-bloodhound-entra-instance.mmd | 47 -- ...raph-example-bloodhound-entra-instance.png | Bin 360648 -> 0 bytes .../graph-example-bloodhound-entra-schema.mmd | 58 -- .../graph-example-bloodhound-entra-schema.png | Bin 367533 -> 0 bytes .../graph-example-ldbc-financial-instance.mmd | 42 -- .../graph-example-ldbc-financial-instance.png | Bin 400513 -> 0 bytes .../graph-example-ldbc-financial-schema.mmd | 40 -- .../graph-example-ldbc-financial-schema.png | Bin 355308 -> 0 bytes .../graph-example-ldbc-snb-instances.mmd | 50 -- .../graph-example-ldbc-snb-instances.png | Bin 409775 -> 0 bytes .../graphs/graph-example-ldbc-snb-schema.mmd | 53 -- .../graphs/graph-example-ldbc-snb-schema.png | Bin 406205 -> 0 bytes .../graphs/graph-example-simple-instances.mmd | 57 -- .../graphs/graph-example-simple-instances.png | Bin 309575 -> 0 bytes .../graphs/graph-example-simple-schema.mmd | 22 - .../graphs/graph-example-simple-schema.png | Bin 104977 -> 0 bytes data-explorer/kusto/query/toc.yml | 2 - 22 files changed, 1084 deletions(-) delete mode 100644 data-explorer/kusto/query/graph-sample-data.md delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-schema.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-schema.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-instance.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-instance.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-schema.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-schema.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-instances.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-instances.png delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-schema.mmd delete mode 100644 data-explorer/kusto/query/media/graphs/graph-example-simple-schema.png diff --git a/data-explorer/kusto/query/graph-sample-data.md b/data-explorer/kusto/query/graph-sample-data.md deleted file mode 100644 index 223566837f..0000000000 --- a/data-explorer/kusto/query/graph-sample-data.md +++ /dev/null @@ -1,614 +0,0 @@ ---- -title: Graph sample datasets and examples -description: Graph examples with detailed descriptions, use cases, and visualizations -ms.topic: conceptual -ms.service: azure-data-explorer -ms.author: herauch -author: cosh -ms.date: 08/14/2025 ---- - -# Graph sample datasets and examples - -This page lists existing graphs on our help cluster at [https://help.kusto.windows.net](https://help.kusto.windows.net) in the **Samples** database and shows how to query them with KQL. These examples demonstrate querying pre-built graph models without requiring any creation or setup steps. - -## Simple educational graph for learning fundamentals - -Usage: `graph("Simple")` - -**Purpose**: Basic graph operations and learning fundamental graph query patterns. - -**Description**: A small educational graph containing people, companies, and cities with various relationships. Perfect for learning graph traversals and understanding basic patterns. This compact dataset includes 11 nodes (5 people, 3 companies, and 3 cities) connected through 20 relationships, making it ideal for understanding graph fundamentals without the complexity of larger datasets. The graph demonstrates common real-world scenarios like employment relationships, geographic locations, social connections, and personal preferences. - -**Use Cases**: - -- Learning graph query fundamentals -- Testing graph algorithms -- Understanding relationship patterns -- Educational examples for graph concepts - -**Schema Relationships**: - -:::image type="content" source="media/graphs/graph-example-simple-schema.png" alt-text="A schema of a graph containing people, companies, and cities with various relationships"::: - -**Schema and Counts**: - -- **Node Types**: - - `Person` - Individual people (5 nodes) - - `Company` - Business organizations (3 nodes) - - `City` - Geographic locations (3 nodes) - -- **Relationship Types**: - - `works_at` - Employment relationships (5 edges) - - `located_at` - Geographic location assignments (8 edges) - - `knows` - Social connections between people (4 edges) - - `likes` - Personal preferences and interests (3 edges) - -**Graph Instance Example**: - -This example demonstrates basic graph relationships in a small, easy-to-understand network showing how people connect to companies and cities through various relationship types. - -:::image type="content" source="media/graphs/graph-example-simple-instances.png" alt-text="A graph containing instances of people, companies, and cities with various relationships"::: - -**Example Queries**: - -Find all employees of a specific company: - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("Simple") -| graph-match (person)-[works_at]->(company) - where company.name == "TechCorp" - project employee_name = person.name, employee_age = person.properties.age -``` - -|employee_name|employee_age| -|---|---| -|Alice|25| -|Bob|30| -|Emma|26| - -Find colleagues (people working at the same company): - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("Simple") -| graph-match (person1)-->(company)<--(person2) - where person1.id != person2.id and labels(company) has "Company" - project colleague1 = person1.name, colleague2 = person2.name, company = company.name -| take 1 -``` - -|colleague1|colleague2|company| -|---|---|---| -|Alice|Bob|TechCorp| - -## LDBC SNB interactive - -Usage: `graph("LDBC_SNB_Interactive")` - -**Purpose**: Social network traversals and friend-of-friend exploration. - -> [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Social Network Benchmark datasets are created by the [Linked Data Benchmark Council (LDBC)](https://ldbcouncil.org/). - -**Description**: The Linked Data Benchmark Council (LDBC) [Social Network Benchmark Interactive](https://ldbcouncil.org/benchmarks/snb/) workload dataset represents a comprehensive social network modeling real-world social media platforms. This benchmark captures the complexity of modern social networks with over 327,000 nodes and multiple relationship types, including hierarchical geographic data, multi-level organizational structures, and rich content interactions. The dataset models realistic social media ecosystems with people creating posts and comments, participating in forums, working at organizations, and living in geographic locations across a detailed hierarchy from continents to cities. - -**Use Cases**: - -- Social network analysis and recommendation systems -- Community detection algorithms -- Influence propagation studies -- Content recommendation based on social connections -- Friend-of-friend discovery -- Social graph mining research - -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-ldbc-snb-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC SNB dataset"::: - -**Schema and Counts**: - -- **Core Social Entity Types**: - - `PERSON` - Social network users (1,528 nodes) - - `POST` - User posts (135,701 nodes) - - `COMMENT` - Comments on posts (151,043 nodes) - - `FORUM` - Discussion forums (13,750 nodes) - -- **Organizational and Geographic Types**: - - `ORGANISATION` - Universities and companies (7,955 nodes) - - `PLACE` - Geographic locations: continents (6), countries (111), cities (1,343) - total 1,460 nodes - -- **Content Classification Types**: - - `TAG` - Content tags (16,080 nodes) - - `TAGCLASS` - Tag categories (71 nodes) - -- **Key Relationship Types**: - - `KNOWS` - Friend relationships (14,073 edges) - - `LIKES` - Content likes: posts (47,215) + comments (62,225) = 109,440 total edges - - `HAS_CREATOR` - Content authorship: posts (135,701) + comments (151,043) = 286,744 edges - - `HAS_MEMBER` - Forum memberships (123,268 edges) - - `HAS_TAG` - Content tagging: posts (51,118) + comments (191,303) + forums (47,697) = 290,118 edges - - `IS_LOCATED_IN` - Location relationships: people (1,528) + organizations (7,955) + posts (135,701) + comments (151,043) = 296,227 edges - - `REPLY_OF` - Comment threading: comment-to-comment (76,787) + comment-to-post (74,256) = 151,043 edges - - `WORK_AT` / `STUDY_AT` - Professional/educational history (4,522 edges) - - `HAS_INTEREST` - Personal interests (35,475 edges) - - Additional relationships: `HAS_MODERATOR`, `IS_PART_OF`, `CONTAINER_OF`, `HAS_TYPE`, `IS_SUBCLASS_OF` - -**Graph Instance Example**: - -This example demonstrates complex social network interactions in a realistic social media environment, showing how users engage with content, participate in forums, and form social connections. - -:::image type="content" source="media/graphs/graph-example-ldbc-snb-instances.png" alt-text="A graph containing a sample subgraph of the LDBC SNB dataset"::: - -This example demonstrates: - -- **Social Engagement**: Mahinda likes both Abdullah's post and a comment on that post -- **Content Threading**: The comment (about Gloria Macapagal-Arroyo) replies to the post (about Aurangzeb) -- **Content Creation**: Abdullah creates posts in his own forum wall -- **Community Participation**: Mahinda is a member of Abdullah's forum where the content appears -- **Content Classification**: Both posts and comments are tagged with relevant topics from their content -- **Geographic Context**: All entities have location relationships for geographic analysis - -**Use Cases**: - -- Social network analysis and recommendation systems -- Community detection algorithms -- Influence propagation studies -- Content recommendation based on social connections -- Friend-of-friend discovery -- Social graph mining research - -**Example Queries**: - -Find direct friendships with similar ages: - -This query identifies pairs of people who are directly connected through a "KNOWS" relationship and have similar ages (birthdays within 30 days of each other). It traverses the LDBC social network graph to find existing friendships between people of similar age groups. The query returns the total count of such age-similar friendship pairs in the network, which can be useful for analyzing age-based social patterns or validating friend recommendation algorithms. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("LDBC_SNB_Interactive") -| graph-match (person1)-[knows]->(person2) - where labels(person1) has "PERSON" and labels(person2) has "PERSON" and - labels(knows) has "KNOWS"and abs(person1.birthday - person2.birthday) < 30d - project person_name = person1.firstName, friend_name = person2.firstName -| count -``` - -|Count| -|---| -|225| - -Find popular posts by likes: - -This query analyzes social engagement by identifying the most popular content creators based on how many unique people have liked their posts. It traverses the social network graph through the path: person → likes → post → has_creator → creator. The query aggregates the data to show each creator's total number of unique likers and distinct posts, then returns the top 3 creators with the most likes. This is useful for identifying influential content creators, understanding engagement patterns, and discovering viral content in the social network. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("LDBC_SNB_Interactive") -| graph-match (person)-[likes]->(post)-[has_creator]->(creator) - where labels(person) has "Person" and labels( post) has "POST" and labels(has_creator) has "HAS_CREATOR" and isnotempty(creator.lastName) - project personId = person.id, postId = post.id, creator = creator.lastName -| summarize Likes = dcount(personId), posts = dcount(postId) by creator -| top 3 by Likes desc -``` - -|creator|Likes|posts| -|---|---|---| -|Zhang|371|207| -|Hoffmann|340|9| -|Singh|338|268| - -## LDBC Financial - -Usage: `graph("LDBC_Financial")` - -**Purpose**: Financial transaction analysis and fraud detection patterns. - -> [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The LDBC Financial Benchmark datasets are created by the [Linked Data Benchmark Council (LDBC)](https://ldbcouncil.org/). - -**Description**: LDBC Financial Benchmark dataset representing a comprehensive financial network with companies, persons, accounts, loans, and various financial transactions. This dataset models realistic financial ecosystems with 5,580 total nodes and over 31,000 financial transactions and relationships. Designed specifically for fraud detection, anti-money laundering (AML) analysis, and financial crime investigation scenarios, it captures complex patterns including account ownership, loan applications, guarantees, and multi-step transaction chains that are common in financial crime scenarios. - -**Use Cases**: - -- Financial fraud detection -- Anti-money laundering (AML) analysis -- Transaction pattern analysis -- Risk assessment and credit scoring -- Suspicious activity monitoring -- Financial network analysis - -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-ldbc-financial-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC Financial dataset"::: - -**Schema and Counts**: - -- **Node Types**: - - `COMPANY` - Business entities (386 nodes) - - `PERSON` - Individual customers (785 nodes) - - `ACCOUNT` - Financial accounts (2,055 nodes) - - `LOAN` - Loan products (1,376 nodes) - - `MEDIUM` - Transaction mediums/channels (978 nodes) - -- **Relationship Types**: - - `TRANSFER` - Money transfers between accounts (8,132 edges) - - `WITHDRAW` - Cash withdrawals from accounts (9,182 edges) - - `DEPOSIT` - Money deposits into accounts (2,758 edges) - - `OWN` - Account ownership relationships (2,055 edges) - - `APPLY` - Loan applications (1,376 edges) - - `GUARANTEE` - Loan guarantees (579 edges) - - `INVEST` - Investment transactions (1,983 edges) - - `REPAY` - Loan repayments (2,747 edges) - - `SIGN_IN` - Authentication events (2,489 edges) - -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-ldbc-financial-schema.png" alt-text="A schema of a graph containing nodes and relations from the LDBC Financial dataset"::: - -**Graph Instance Example**: - -This example illustrates a complex financial network with multiple entity types and transaction patterns, demonstrating how financial institutions can model relationships between customers, accounts, loans, and transaction flows for fraud detection and risk assessment. - -:::image type="content" source="media/graphs/graph-example-ldbc-financial-instance.png" alt-text="A graph containing a sample subgraph of the LDBC Financial dataset"::: - -**Example Queries**: - -Detect potential money laundering through circular transfers: - -This query identifies suspicious circular transaction patterns that could indicate money laundering activities. It searches for accounts that send money to another account and then receive it back through a chain of 1 to 3 transfers, creating a circular flow. The query specifically looks for large initial transfers (over 10,000) and returns details about the suspicious accounts, including the transfer amount and the length of the circular chain. This pattern detection is useful for anti-money laundering (AML) systems and financial fraud investigations. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("LDBC_Financial") -| graph-match (account1)-[t1]->(account2)-[t2*1..3]->(account1) - where labels(t1) has "TRANSFER" and t1.amount > 10000 // Large initial transfer - project suspicious_account = account1.node_id, - amount = t1.amount, - transfer_chain_length = array_length(t2) + 1 -| take 10 -``` - -|suspicious_account|amount|transfer_chain_length| -|---|---|---| -|Account::4818007176356300028|5035377,73|2| -|Account::4818007176356300028|5035377,73|2| -|Account::4845310249097233848|359062,45|2| -|Account::4818007176356300028|5035377,73|3| -|Account::4818007176356300028|5035377,73|4| -|Account::4840243699516440940|5753668,55|4| -|Account::4818007176356300028|5035377,73|4| -|Account::180143985094820389|465338,26|4| -|Account::4814910951612482356|1684581,62|4| -|Account::4816599801472746629|963626,42|4| - -Find high-risk loan guarantors: - -This query identifies individuals or companies who guarantee multiple loans totaling significant amounts, which could indicate financial risk exposure. It traverses the financial network graph following the path: guarantor → guarantee → borrower → apply → loan. The query aggregates the total amount guaranteed and number of loans for each guarantor, then filters for those guaranteeing over 100,000 in total and returns the top 5 by total guaranteed amount. This analysis is useful for risk assessment, identifying over-leveraged guarantors, and evaluating systemic financial risks in lending networks. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("LDBC_Financial") -| graph-match (guarantor)-[guarantee]->(borrower)-[apply]->(loan) - where labels(guarantee) has "GUARANTEE" and labels(apply) has "APPLY" - project guarantor_id = guarantor.node_id, - borrower_id = borrower.node_id, - loan_amount = loan.loanAmount -| summarize total_guaranteed = sum(loan_amount), loan_count = count() by guarantor_id -| where total_guaranteed > 100000 -| top 5 by total_guaranteed desc -``` - -|guarantor_id|total_guaranteed|loan_count| -|---|---|---| -|Person::44|439802195|8| -|Person::15393162789155|411111642|8| -|Company::12094627905931|404538891|6| -|Company::4398046511208|366243272|8| -|Person::19791209300551|338838223|6| - -## BloodHound Entra ID dataset - -Usage: `graph("BloodHound_Entra")` - -**Purpose**: Azure Active Directory privilege escalation and attack path analysis. - -> [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the [BloodHound project](https://bloodhound.specterops.io/). - -**Description**: BloodHound Community Edition dataset for Azure Active Directory environments. This comprehensive security dataset contains 13,526 Azure AD objects including users, groups, applications, service principals, devices, and various cloud resources. With over 800,000 permission relationships and security edges, it models complex Azure AD environments typical of enterprise organizations. The dataset captures detailed Azure AD permissions, role assignments, group memberships, and resource ownership patterns essential for identifying privilege escalation paths and attack vectors in cloud environments. - -**Use Cases**: - -- Entra ID security assessments -- Privilege escalation path discovery -- Attack path visualization -- Identity governance analysis -- Risk-based security controls -- Compliance auditing for cloud environments - -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-bloodhound-entra-schema.png" alt-text="A schema of a graph containing nodes and relations from the BloodHound Entra dataset"::: - -**Schema and Counts**: - -> [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the BloodHound Community Edition project. - -**Description**: BloodHound Community Edition dataset for Azure Active Directory environments. This comprehensive security dataset contains 13,526 Azure AD objects including users, groups, applications, service principals, devices, and various cloud resources. With over 800,000 permission relationships and security edges, it models complex Azure AD environments typical of enterprise organizations. The dataset captures detailed Azure AD permissions, role assignments, group memberships, and resource ownership patterns essential for identifying privilege escalation paths and attack vectors in cloud environments. - -**Schema and Counts**: - -- **Primary Node Types**: - - `AZUser` - Azure AD users (230 nodes) - - `AZServicePrincipal` - Service principals and applications (6,270 nodes) - - `AZApp` - Azure applications (6,648 nodes) - - `AZGroup` - Azure AD groups (58 nodes) - - `AZDevice` - Managed devices (47 nodes) - -- **Azure Resource Types**: - - `AZResourceGroup` - Resource groups (59 nodes) - - `AZVM` - Virtual machines (66 nodes) - - `AZRole` - Azure roles (116 nodes) - - `AZSubscription` - Azure subscriptions (3 nodes) - - `AZTenant` - Azure tenant (1 node) - -- **Key Relationship Types** (Top permissions by volume): - - `AZMGAddOwner` - Management group owner permissions (403,412 edges) - - `AZMGAddSecret` - Secret management permissions (345,324 edges) - - `AZAddSecret` - Application secret permissions (24,666 edges) - - `AZContains` - Resource containment relationships (12,924 edges) - - `AZRunsAs` - Service execution permissions (6,269 edges) - - `AZMemberOf` - Group membership relationships (4,439 edges) - - `AZOwns` - Resource ownership (2,870 edges) - -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-bloodhound-entra-schema.png" alt-text="A schema of a graph containing nodes and relations from the BloodHound Entra dataset"::: - -**Graph Instance Example**: - -This example demonstrates Azure Active Directory and Entra identity relationships with complex privilege structures and potential attack paths in a cloud environment. - -:::image type="content" source="media/graphs/graph-example-bloodhound-entra-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound Entra dataset"::: - -**Use Cases**: - -- Entra ID security assessments -- Privilege escalation path discovery -- Attack path visualization -- Identity governance analysis -- Risk-based security controls -- Compliance auditing for cloud environments - -**Example Queries**: - -Find paths to administrative privileges: - -This query identifies privilege escalation paths from regular users to administrative groups in Azure AD environments. It searches for users who can reach admin groups (like AAD DC Administrators, DnsAdmins, etc.) through 1-3 relationship hops, helping security teams understand potential attack paths and privilege escalation risks. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("BloodHound_Entra") -| graph-match (user)-[path*1..3]->(admingroup) - where labels(user) has_any ("User", "AZUser") - and labels(admingroup) has_any ("Group", "AZGroup") - and (admingroup.name contains "ADMIN" or admingroup.displayname contains "ADMIN") - project source_user = user.name, - path_length = array_length(path), - admin_group = coalesce(admingroup.displayname, admingroup.name) -| take 10 -``` - -|source_user|path_length|admin_group| -|---|---|---| -|THISUSERHASINTUNEADMINROLE@PHANTOMCORP.ONMICROSOFT.COM|1|ADSyncAdmins| -|097EF6C2-GROUPSADMINISTRATOR@PHANTOMCORP.ONMICROSOFT.COM|1|AAD DC Administrators| -|USERBELONGSTOGAGROUP@PHANTOMCORP.ONMICROSOFT.COM|1|ADSyncAdmins| -|THISUSERHASINTUNEADMINROLE@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| -|RHADMIN@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| -|CJACKSON@PHANTOMCORP.ONMICROSOFT.COM|1|Azure ATP phantom Administrators| -|097EF6C2-INTUNEADMINISTRATOR@PHANTOMCORP.ONMICROSOFT.COM|1|AAD DC Administrators| -|RHADMIN_PHANTOMCORP.ONMICROSOFT.COM#EXT#@PHANTOMCORP.ONMICROSOFT.COM|1|Resource Group Admins| -|THISUSERHASKNOWLEDGEMANAGERROLE@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| -|097EF6C2-INTUNEADMINISTRATOR@PHANTOMCORP.ONMICROSOFT.COM|1|DnsAdmins| - -Identify high-value targets (Tier 0 assets): - -This query identifies critical administrative assets marked as "admin_tier_0" in the environment. These are the most sensitive and powerful accounts, service principals, and resources that pose the highest risk if compromised. Understanding these assets helps prioritize security monitoring and protection efforts. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("BloodHound_Entra") -| graph-match (asset) - where asset.properties.system_tags contains "admin_tier_0" - project asset_name = asset.name, - asset_type = tostring(labels(asset)[1]), // Get primary type (AZUser, AZServicePrincipal, etc.) - system_tags = asset.properties.system_tags -| take 10 -``` - -|asset_name|asset_type|system_tags| -|---|---|---| -|JJACOB@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| -|PLEWIS@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| -|JMILLER@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| -|CJACKSON@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| -|RHALL@PHANTOMCORP.ONMICROSOFT.COM|AZUser|admin_tier_0| -|THISAPPHASGLOBALADMIN@PHANTOMCORP|AZServicePrincipal|admin_tier_0| -|MYCOOLAUTOMATIONACCOUNT@PHANTOMCORP|AZServicePrincipal|admin_tier_0| -|SERVICEPRINCIPALE@PHANTOMCORP|AZServicePrincipal|admin_tier_0| -|31E3B75F-PRIVILEGED AUTHENTICATION ADMINISTRATOR@PHANTOMCORP|AZServicePrincipal|admin_tier_0| -|31E3B75F-PRIVILEGED ROLE ADMINISTRATOR@PHANTOMCORP|AZServicePrincipal|admin_tier_0| - -## BloodHound Active Directory dataset - -Usage: `graph("BloodHound_AD")` - -**Purpose**: On-premises Active Directory security analysis and privilege mapping. - -> [!NOTE] -> This dataset is provided under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). The BloodHound datasets are created by the [BloodHound project](https://bloodhound.specterops.io/). - -**Description**: BloodHound Community Edition dataset for on-premises Active Directory environments. This dataset contains 1,495 Active Directory objects representing a typical enterprise AD deployment with complex permission structures and attack paths. The dataset includes users, computers, groups, organizational units, group policy objects, and certificate authority components across multiple domains. With over 18,000 permission relationships and security edges, it captures realistic AD attack scenarios including privilege escalation paths, ACL-based permissions, group memberships, and certificate-based authentication vulnerabilities common in Windows domain environments. - -**Use Cases**: - -- Active Directory security assessments -- Attack path analysis and penetration testing -- Domain privilege mapping -- Group policy security analysis -- Kerberoasting and ASREPRoasting target identification -- Security control gap analysis - -**Graph Schema Overview**: - -- **Core AD Object Types**: - - `User` - Domain users (99 nodes) - - `Computer` - Domain computers (34 nodes) - - `Group` - Security and distribution groups (219 nodes) - - `ADLocalGroup` - Local groups on computers (28 nodes) - - `GPO` - Group Policy Objects (32 nodes) - -- **AD Infrastructure Types**: - - `Domain` - Active Directory domains (5 nodes) - - `OU` - Organizational Units (20 nodes) - - `Container` - AD containers (939 nodes) - - `CertTemplate` - Certificate templates (106 nodes) - - `EnterpriseCA` - Certificate Authorities (4 nodes) - - `RootCA` - Root Certificate Authorities (5 nodes) - -- **Key Permission Types** (Top attack vectors): - - `GenericAll` - Full control permissions (3,292 edges) - - `WriteDacl` - Modify permissions (2,221 edges) - - `WriteOwner` - Change ownership (2,187 edges) - - `Owns` - Object ownership (1,439 edges) - - `Contains` - Containment relationships (1,416 edges) - - `GenericWrite` - Write permissions (579 edges) - - `MemberOf` - Group memberships (301 edges) - -**Graph Schema Overview**: - -:::image type="content" source="media/graphs/graph-example-bloodhound-ad-schema.png" alt-text="A schema of a graph containing nodes and relations from the BloodHound AD dataset"::: - -**Graph Instance Example**: - -This example demonstrates on-premises Active Directory attack paths and potential security vulnerabilities in a traditional Windows domain environment. - -:::image type="content" source="media/graphs/graph-example-bloodhound-ad-instance.png" alt-text="A graph containing a sample subgraph of the BloodHound AD dataset"::: - -**Use Cases**: - -- Active Directory security assessments -- Attack path analysis and penetration testing -- Domain privilege mapping -- Group policy security analysis -- Kerberoasting and ASREPRoasting target identification -- Security control gap analysis - -**Example Queries**: - -Find potential privilege escalation: - -This query counts how many non-admin users can potentially escalate to admin in Azure AD. It traverses up to 10 MemberOf group hops (no cycles) from each user to groups that grant dangerous permissions (GenericAll, WriteDacl, WriteOwner, ForceChangePassword) over admin users (admincount=true), then returns the distinct number of such “potential attacker” users. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("BloodHound_AD") -| graph-match cycles=none (user)-[memberof*0..10]->(group)-[permission]->(target) - where labels(user) has "User" - and labels(group) has "Group" - and all(memberof, labels() has "MemberOf") - and user.properties.admincount == false - and (labels(permission) has_any ("GenericAll", "WriteDacl", "WriteOwner", "ForceChangePassword")) - and (labels(target) has "User" and target.properties.admincount == true) - project attack_user = user.name -| summarize ['Potential attackers'] = dcount(attack_user) -``` - -|Potential attackers| -|---| -|2| - -Find Golden Certificate attack paths: - -This query identifies entities that can perform Golden Certificate attacks, which allow attackers to forge certificates as any user in the domain. These are extremely critical vulnerabilities as they enable complete domain compromise by allowing the attacker to impersonate any user, including domain administrators, through forged certificates. - -:::moniker range="azure-data-explorer" -> [!div class="nextstepaction"] -> Run the query -::: moniker-end - -```kusto -graph("BloodHound_AD") -| graph-match (attacker)-[goldencert]->(target) - where labels(goldencert) has "GoldenCert" - project - Attacker = attacker.name, - AttackerType = case( - attacker.name has "DC", "Domain Controller", - attacker.name has "CA", "Certificate Authority", - attacker.name has "SRV", "Server", - "Unknown System" - ), - Target = target.name, - RiskLevel = "CRITICAL", - AttackCapability = case( - attacker.name has "DC", "Primary domain controller with certificate services", - attacker.name has "EXTCA", "External Certificate Authority with root access", - attacker.name has "SRV", "Compromised server with certificate generation rights", - "System with certificate forging capabilities" - ) -``` - -|Attacker|AttackerType|Target|RiskLevel|AttackCapability| -|---|---|---|---|---| -|DC01.PHANTOM.CORP|Unknown System|PHANTOM.CORP|CRITICAL|System with certificate forging capabilities| -|SRV-SHARPHOUND.PHANTOM.CORP|Server|PHANTOM.CORP|CRITICAL|Compromised server with certificate generation rights| -|EXTCA01.WRAITH.CORP|Unknown System|WRAITH.CORP|CRITICAL|System with certificate forging capabilities| -|EXTCA02.WRAITH.CORP|Unknown System|WRAITH.CORP|CRITICAL|System with certificate forging capabilities| - -## Related content - -- [Graph semantics overview](./graph-semantics-overview.md) -- [Graph operators](./graph-operators.md) -- [Graph best practices](./graph-best-practices.md) -- [Persistent graph overview](../management/graph/graph-persistent-overview.md) diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd deleted file mode 100644 index 0cf0f5f397..0000000000 --- a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.mmd +++ /dev/null @@ -1,35 +0,0 @@ -%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% -graph LR - %% Real Entities from BloodHound_AD dataset - ALICE["ALICE@PHANTOM\.CORP
User
Domain User"] - - %% Her Computer - LAPTOP["ALICE-LAPTOP\.PHANTOM\.CORP
Computer
Workstation"] - - %% Administrative Groups - DOMAINUSERS["DOMAIN USERS@PHANTOM\.CORP
Group
Default Domain Group"] - - %% Domain Infrastructure - USERS["USERS@PHANTOM\.CORP
Container
AD Container"] - PHANTOM["PHANTOM\.CORP
Domain
AD Domain"] - - %% Validated Attack Path Relationships - ALICE -->|AdminTo| LAPTOP - ALICE -->|MemberOf| DOMAINUSERS - - %% Container Hierarchy - USERS -->|Contains| ALICE - PHANTOM -->|Contains| USERS - - %% Styling - classDef user fill:#e3f2fd,stroke:#1976d2,stroke-width:3px - classDef computer fill:#fff3e0,stroke:#f57c00,stroke-width:2px - classDef group fill:#e8f5e8,stroke:#388e3c,stroke-width:2px - classDef admingroup fill:#ffebee,stroke:#d32f2f,stroke-width:3px - classDef infrastructure fill:#f1f8e9,stroke:#689f38,stroke-width:2px - classDef certificate fill:#fce4ec,stroke:#c2185b,stroke-width:3px - - class ALICE user - class LAPTOP computer - class DOMAINUSERS,DOMAINADMINS group - class USERS,PHANTOM infrastructure \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-instance.png deleted file mode 100644 index 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zydJSR@?XUVOf<|9EVe%M+NuI9=O2#yR!C?w_o|{;et!P-Yu5&7VIE<(|JHY8NB#Fp z`1bZ!rv$(L8vQSo{WZ+mWZ R+!-pWg_*S}!T9#0e+L1Ehywrs diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-schema.mmd b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-schema.mmd deleted file mode 100644 index ed0061bd13..0000000000 --- a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-ad-schema.mmd +++ /dev/null @@ -1,64 +0,0 @@ -%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% -graph TD - %% Core AD Objects - USER[User
Domain Users] - COMPUTER[Computer
Domain Computers] - GROUP[Group
Security Groups] - LOCALGROUP[ADLocalGroup
Local Groups] - - %% AD Infrastructure - DOMAIN[Domain
AD Domains] - OU[OU
Organizational Units] - CONTAINER[Container
AD Containers] - GPO[GPO
Group Policy Objects] - - %% Certificate Infrastructure - CERT[CertTemplate
Certificate Templates] - ENTCA[EnterpriseCA
Certificate Authorities] - ROOTCA[RootCA
Root CAs] - - %% Domain Hierarchy & Containment - DOMAIN -->|Contains| CONTAINER - CONTAINER -->|Contains| USER - CONTAINER -->|Contains| GROUP - DOMAIN -->|Contains| OU - OU -->|Contains| USER - OU -->|Contains| COMPUTER - - %% Group Memberships - USER -->|MemberOf| GROUP - USER -->|MemberOf| LOCALGROUP - GROUP -->|MemberOf| GROUP - - %% Administrative Access - USER -->|AdminTo| COMPUTER - GROUP -->|AdminTo| COMPUTER - USER -->|GenericAll| USER - GROUP -->|GenericAll| GROUP - - %% Dangerous Permissions - USER -->|WriteDacl| GROUP - GROUP -->|WriteOwner| CERT - USER -->|GenericWrite| GPO - - %% Certificate Attack Paths - USER -->|GenericAll| ROOTCA - GROUP -->|WriteDacl| ENTCA - - %% Object Ownership - GROUP -->|Owns| CONTAINER - USER -->|Owns| CERT - - %% Styling - classDef user fill:#e3f2fd,stroke:#1976d2,stroke-width:3px - classDef computer fill:#fff3e0,stroke:#f57c00,stroke-width:2px - classDef group fill:#e8f5e8,stroke:#388e3c,stroke-width:2px - classDef infrastructure 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admin_tier_0
jjacob@phantomcorp.onmicrosoft.com] - - %% Groups he owns - ALLUSERS[All Users
AZGroup
ID: 2f061293] - GAROLE[ThisGroupHasGARoleAlwaysActive
AZGroup
ID: 4c8435bf] - - %% App he owns - AZUREHOUND[AzureHoundEnterprise
AZApp
ID: 5595629b] - - %% Device he owns - WIN10[AADJoinedWin10
AZDevice
ID: 2a2dc5ab] - - %% Administrative Role - GLOBALADMIN[Global Administrator
AZRole
Privileged Role] - - %% Tenant - PHANTOM[Phantom Corp
AZTenant
phantomcorp.onmicrosoft.com] - - %% Validated Relationships - JACOB -->|AZOwns
Application Owner| AZUREHOUND - JACOB -->|AZOwns
Group Owner| ALLUSERS - JACOB -->|AZOwns
Group Owner| GAROLE - JACOB -->|AZOwns
Device Owner| WIN10 - - %% Administrative Privileges - GLOBALADMIN -->|AZResetPassword
Can Reset| JACOB - - %% Tenant Containment - PHANTOM -->|AZContains
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Azure AD Users] - SP[AZServicePrincipal
Service Principals] - APP[AZApp
Applications] - GROUP[AZGroup
Security Groups] - DEVICE[AZDevice
Managed Devices] - ROLE[AZRole
Azure Roles] - - %% Azure Resource Hierarchy - TENANT[AZTenant
Azure Tenant] - SUB[AZSubscription
Subscriptions] - RG[AZResourceGroup
Resource Groups] - VM[AZVM
Virtual Machines] - - %% Azure Resource Containment Hierarchy - TENANT -->|AZContains| SUB - TENANT -->|AZContains| USER - SUB -->|AZContains| RG - RG -->|AZContains| VM - - %% Identity and Access Relationships - USER -->|AZMemberOf| GROUP - USER -->|AZOwns| APP - USER -->|AZOwns| DEVICE - USER -->|AZOwns| GROUP - USER -->|AZOwner| SUB - USER -->|AZOwner| RG - - %% Service Principal Relationships - SP -->|AZRunsAs| APP - VM -->|AZManagedIdentity| SP - - %% Administrative Permissions - ROLE -->|AZResetPassword| USER - GROUP -->|AZAddMembers| GROUP - - %% High-Volume Permissions (simplified for readability) - ROLE -.->|AZMGAddOwner
403k edges| RG - ROLE -.->|AZMGAddSecret
345k edges| APP - - %% Styling - classDef user fill:#e3f2fd,stroke:#1976d2,stroke-width:3px - classDef app fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px - classDef group fill:#e8f5e8,stroke:#388e3c,stroke-width:2px - classDef device fill:#fff3e0,stroke:#f57c00,stroke-width:2px - classDef resource fill:#fce4ec,stroke:#c2185b,stroke-width:2px - classDef role fill:#e1f5fe,stroke:#0277bd,stroke-width:2px - classDef hierarchy fill:#f1f8e9,stroke:#689f38,stroke-width:2px - - class USER user - class SP,APP app - class GROUP group - class DEVICE device - class VM,RG resource - class ROLE role - class TENANT,SUB hierarchy \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.png b/data-explorer/kusto/query/media/graphs/graph-example-bloodhound-entra-schema.png deleted file mode 100644 index 3f8c5eb3def63a4e289e768df906f15ba73ae6ae..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 367533 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785 nodes] - COMPANY[COMPANY
Business Entities
386 nodes] - ACCOUNT[ACCOUNT
Financial Accounts
2,055 nodes] - LOAN[LOAN
Loan Products
1,376 nodes] - MEDIUM[MEDIUM
Transaction Channels
978 nodes] - - %% Core Financial Relationships - PERSON -->|OWN
2,055| ACCOUNT - COMPANY -->|OWN| ACCOUNT - PERSON -->|APPLY
1,376| LOAN - COMPANY -->|APPLY| LOAN - PERSON -->|GUARANTEE
579| PERSON - COMPANY -->|GUARANTEE| COMPANY - - %% Transaction Flows - ACCOUNT -->|TRANSFER
8,132| ACCOUNT - ACCOUNT -->|WITHDRAW
9,182| ACCOUNT - LOAN -->|DEPOSIT
2,758| ACCOUNT - ACCOUNT -->|REPAY
2,747| LOAN - - %% Authentication & Investment - MEDIUM -->|SIGN_IN
2,489| ACCOUNT - PERSON -->|INVEST
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b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.mmd deleted file mode 100644 index 5863fae3ab..0000000000 --- a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-instances.mmd +++ /dev/null @@ -1,50 +0,0 @@ -%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% -graph TD - %% Real People from the dataset - MP[Mahinda Perera
Person ID: 933] - AK[Abdullah Koksal
Person ID: 24189255811254] - - %% Real Post and Comment (validated relationships) - POST1[About Aurangzeb
Post ID: 893353296235
Created by Abdullah] - COMMENT1[About Gloria Macapagal-Arroyo
Comment ID: 893353296240
Reply to Aurangzeb post] - - %% Real Forum and Tags (validated) - FORUM1[Wall of Abdullah Koksal
Forum ID: 755914248727] - TAG1[Aurangzeb
Tag on post] - TAG2[Gloria_Macapagal-Arroyo
Tag on comment] - TAG3[John_Rhys-Davies
Tag on post] - - %% Real Location - PLACE1[Location
Geographic Entity] - - %% Validated Social Network Relationships - MP -->|LIKES| POST1 - MP -->|LIKES| COMMENT1 - POST1 -->|HAS_CREATOR| AK - COMMENT1 -->|REPLY_OF| POST1 - - %% Validated Forum and Content Organization - MP -->|HAS_MEMBER| FORUM1 - FORUM1 -->|CONTAINER_OF| POST1 - POST1 -->|HAS_TAG| TAG1 - POST1 -->|HAS_TAG| TAG3 - COMMENT1 -->|HAS_TAG| TAG2 - - %% Geographic Context (common pattern) - MP -->|IS_LOCATED_IN| PLACE1 - AK -->|IS_LOCATED_IN| PLACE1 - POST1 -->|IS_LOCATED_IN| PLACE1 - COMMENT1 -->|IS_LOCATED_IN| PLACE1 - - %% Styling - classDef person fill:#e1f5fe,stroke:#0277bd,stroke-width:3px - classDef content fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px - classDef forum fill:#e8f5e8,stroke:#388e3c,stroke-width:2px - classDef tag fill:#fff3e0,stroke:#f57c00,stroke-width:2px - classDef place fill:#fce4ec,stroke:#c2185b,stroke-width:2px - - class MP,AK person - class POST1,COMMENT1 content - class FORUM1 forum - class TAG1,TAG2,TAG3 tag - class PLACE1 place \ No newline 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a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.mmd +++ /dev/null @@ -1,53 +0,0 @@ -%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% -graph TD - %% Core Social Entities - PERSON[PERSON
Social Users
1,528 nodes] - POST[POST
User Posts
135,701 nodes] - COMMENT[COMMENT
Comments
151,043 nodes] - FORUM[FORUM
Discussion Forums
13,750 nodes] - - %% Organizational & Geographic - ORG[ORGANISATION
Companies/Unis
7,955 nodes] - PLACE[PLACE
Locations
1,460 nodes] - - %% Content Classification - TAG[TAG
Content Tags
16,080 nodes] - TAGCLASS[TAGCLASS
Tag Categories
71 nodes] - - %% Core Relationships - PERSON -.->|KNOWS
14,073| PERSON - PERSON -->|LIKES
109,440| POST - PERSON -->|LIKES| COMMENT - PERSON -->|HAS_CREATOR
286,744| POST - PERSON -->|HAS_CREATOR| COMMENT - PERSON -->|HAS_MEMBER
123,268| FORUM - PERSON -->|WORK_AT/STUDY_AT
4,522| ORG - PERSON -->|HAS_INTEREST
35,475| TAG - PERSON -->|IS_LOCATED_IN
296,227| PLACE - - %% Content Relationships - COMMENT -->|REPLY_OF
151,043| POST - COMMENT -->|REPLY_OF| COMMENT - POST -->|HAS_TAG
290,118| TAG - COMMENT -->|HAS_TAG| TAG - FORUM -->|HAS_TAG| TAG - FORUM -->|CONTAINER_OF| POST - POST -->|IS_LOCATED_IN| PLACE - COMMENT -->|IS_LOCATED_IN| PLACE - ORG -->|IS_LOCATED_IN| PLACE - - %% Classification Hierarchy - TAG -->|HAS_TYPE| TAGCLASS - TAGCLASS -->|IS_SUBCLASS_OF| TAGCLASS - PLACE -->|IS_PART_OF| PLACE - - %% Styling - classDef socialNode fill:#e1f5fe,stroke:#0277bd,stroke-width:2px - classDef contentNode fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px - classDef orgNode fill:#e8f5e8,stroke:#388e3c,stroke-width:2px - classDef classNode fill:#fff3e0,stroke:#f57c00,stroke-width:2px - - class PERSON,FORUM socialNode - class POST,COMMENT contentNode - class ORG,PLACE orgNode - class TAG,TAGCLASS classNode \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.png b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-snb-schema.png deleted file mode 100644 index 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z58(;fiy)XW$>fbgqxpGwY=q%N639@BfFVbck)Og3L9U5HtJcrwNS#08hNtG&dokVJ zWpo#q)oHvb_^BYX$kCnc$Q>S0TnBpabl2keZ)nB6qJe(3CQB zO|iZ*I0ezmiGKZ-q(^66? zFBTTQ(KKK9bde=a00|g6Pf^;n^zJB-xO40Ph5me702`$0{HRPzuIjdg%OiK;Eb>pSWdA)m z_$MINx-}V6$A;yLhr&pkFV<~VA}L)28K zFY?QPd8Qx`xLIC~0r)|({a22F7@sFH3Wxw0^1JDdgX|5-3)-I2bT(Lz6g(pS;HNzyPXvZq8B$e;1ba-`d70 z5y5d6k-$aNv61S*dNn>)ed!6vL-kth8`4LhWE8?4WW5L#U{( zenA7hz4EiVB1CHiZ=&Ei4E3U>e_tzLqOKeu$%Fg(-`^aJ1oHiV-e@EqtYo2=a31(J z4=*n%XbJT`g&_Sr%rWx%XUEUu06v_D6yVR-*@TjVMBu@%+!X)aI>3LwJ^GfLJ0_$m5`$ z;_ug{Ak^h^K|>3AJKU-n0sha<&hE|!hp7Gimi-^xeExT1|1%>0o!j{T Date: Thu, 21 Aug 2025 14:40:28 +0200 Subject: [PATCH 39/67] remove common analysis queries section from graph best practices documentation --- .../kusto/query/graph-best-practices.md | 215 ------------------ 1 file changed, 215 deletions(-) diff --git a/data-explorer/kusto/query/graph-best-practices.md b/data-explorer/kusto/query/graph-best-practices.md index 9d7a104751..44ad1d9da4 100644 --- a/data-explorer/kusto/query/graph-best-practices.md +++ b/data-explorer/kusto/query/graph-best-practices.md @@ -522,221 +522,6 @@ This hybrid approach enables organizations to provide always-current data analys :::moniker-end -## Common analysis queries - -These reusable query patterns work across all graph models and help you understand the structure and characteristics of any graph dataset. Use these queries to explore new graphs, perform basic analysis, or as starting points for more complex graph investigations. - -### Graph overview and statistics - -Understanding the basic characteristics of your graph is essential for analysis planning and performance optimization. These queries provide fundamental metrics about graph size and structure. - -**Count total nodes and edges**: - -Use these queries to understand the scale of your graph dataset. Node and edge counts help determine appropriate query strategies and identify potential performance considerations. - -```kusto -// Get node count -graph("GRAPH_NAME") -| graph-match (node) - project node -| summarize NodeCount = count() -``` - -```kusto -// Get edge count -graph("GRAPH_NAME") -| graph-match (source)-[edge]->(target) - project edge -| summarize EdgeCount = count() -``` - -**Get graph summary statistics**: - -This combined query efficiently provides both metrics in a single result, useful for initial graph assessment and reporting. - -```kusto -let nodes = graph("GRAPH_NAME") | graph-match (node) project node | summarize NodeCount = count(); -let edges = graph("GRAPH_NAME") | graph-match (source)-[edge]->(target) project edge | summarize EdgeCount = count(); -union nodes, edges -``` - -**Alternative using graph-to-table**: - -For basic counting, the `graph-to-table` operator can be more efficient as it directly exports graph elements without pattern matching overhead. - -```kusto -let nodes = graph("GRAPH_NAME") | graph-to-table nodes | summarize NodeCount = count(); -let edges = graph("GRAPH_NAME") | graph-to-table edges | summarize EdgeCount = count(); -union nodes, edges -``` - -### Node analysis - -Node analysis helps you understand the entities in your graph, their types, and distribution. These patterns are essential for data quality assessment and schema understanding. - -**Discover all node types (labels)**: - -This query reveals the different entity types in your graph and their frequencies. Use it to understand your data model, identify the most common entity types, and spot potential data quality issues. - -```kusto -graph("GRAPH_NAME") -| graph-match (node) - project labels = labels(node) -| mv-expand label = labels -| summarize count() by tostring(label) -| order by count_ desc -``` - -**Find nodes with multiple labels**: - -Identifies nodes that belong to multiple categories simultaneously. This is useful for understanding overlapping classifications and complex entity relationships in your data model. - -```kusto -graph("GRAPH_NAME") -| graph-match (node) - project node_id = node.id, labels = labels(node), label_count = array_length(labels(node)) -| where label_count > 1 -| take 10 -``` - -**Sample nodes by type**: - -Retrieves representative examples of specific node types to understand their structure and properties. Essential for data exploration and query development. - -```kusto -graph("GRAPH_NAME") -| graph-match (node) - where labels(node) has "DESIRED_LABEL" // Replace with actual label - project node_id = node.id, properties = node.properties -| sample 5 -``` - -### Edge analysis - -Understanding relationships in your graph is crucial for identifying patterns, data quality issues, and potential analysis directions. - -**Discover all edge types** (works with different graph schemas): - -This query identifies all relationship types in your graph, helping you understand the connections available for analysis. Different graphs use different property names for edge types, so multiple variations are provided. - -```kusto -// For graphs using 'labels' array (like BloodHound) -graph("GRAPH_NAME") -| graph-match (source)-[edge]->(target) - project edge_labels = labels(edge) -| mv-expand label = edge_labels -| summarize count() by tostring(label) -| order by count_ desc -``` - -**Find most connected nodes (highest degree)**: - -Node degree analysis reveals the most influential or central entities in your graph. High-degree nodes often represent key players, bottlenecks, or important infrastructure components. - -```kusto -// Find nodes with highest total degree (in + out) -graph("GRAPH_NAME") -| graph-match (node) - project node_id = node.id, - in_degree = node_degree_in(node), - out_degree = node_degree_out(node), - total_degree = node_degree_in(node) + node_degree_out(node) -| order by total_degree desc -| take 10 -``` - -**Find nodes with highest in-degree (most incoming connections)**: - -High in-degree nodes are often targets of influence, popular destinations, or central resources. In social networks, these might be influential people; in infrastructure graphs, these could be critical services. - -```kusto -graph("GRAPH_NAME") -| graph-match (node) - project node_id = node.id, - node_labels = labels(node), - in_degree = node_degree_in(node) -| order by in_degree desc -| take 10 -``` - -**Find nodes with highest out-degree (most outgoing connections)**: - -High out-degree nodes are often sources of influence, distributors, or connector hubs. These entities typically initiate many relationships or distribute resources to others. - -```kusto -graph("GRAPH_NAME") -| graph-match (node) - project node_id = node.id, - node_labels = labels(node), - out_degree = node_degree_out(node) -| order by out_degree desc -| take 10 -``` - -### Relationship pattern analysis - -These queries help identify structural patterns and complex relationships that might indicate important behaviors or anomalies in your data. - -**Find nodes with specific relationship patterns**: - -Identifies nodes that both receive and send connections, indicating entities that act as intermediaries or processing points. These nodes often represent critical components in workflows or communication chains. - -```kusto -// Nodes that have both incoming and outgoing edges -graph("GRAPH_NAME") -| graph-match (node) -where (graph("GRAPH_NAME") | graph-match (other1)-[]->(node) | take 1 | count() > 0) - and (graph("GRAPH_NAME") | graph-match (node)-[]->(other2) | take 1 | count() > 0) -project node_id = node.id, node_labels = labels(node) -| take 10 -``` - -**Discover triangular relationships** (nodes connected in a triangle): - -Triangular patterns often indicate tight collaboration, mutual dependencies, or closed-loop processes. In social networks, these represent groups of friends; in business processes, they might indicate approval chains or redundancy patterns. - -```kusto -graph("GRAPH_NAME") -| graph-match (a)-[]->(b)-[]->(c)-[]->(a) -where a.id != b.id and b.id != c.id and c.id != a.id -project node1 = a.id, node2 = b.id, node3 = c.id -| take 5 -``` - -### Property analysis - -Understanding the properties available on your nodes helps you build more sophisticated queries and identify data quality issues. - -**Explore node properties**: - -This query reveals what information is stored with your nodes, helping you understand the available attributes for filtering and analysis. - -```kusto -graph("GRAPH_NAME") -| graph-match (node) -where labels(node) has "DESIRED_LABEL" // Replace with actual label -project properties = node.properties -| take 1 -| project property_names = bag_keys(properties) -``` - -**Find nodes with specific property values**: - -Use this pattern to locate entities with particular characteristics or to validate data quality by checking for expected property values. - -```kusto -graph("GRAPH_NAME") -| graph-match (node) -where isnotnull(node.properties.name) // Replace 'name' with actual property -project node_id = node.id, property_value = node.properties.name -| take 10 -``` - -> [!TIP] -> Replace `"GRAPH_NAME"` with the actual graph model name (e.g., "Simple", "LDBC_SNB_Interactive", "BloodHound_Entra", etc.). Some queries may need adjustment based on the specific schema of your graph model. - -:::moniker-end - ## Related content :::moniker range="microsoft-fabric || azure-data-explorer" From 1c2d977d0823f1b4bf351c7c768f21e239bf9244 Mon Sep 17 00:00:00 2001 From: Yifat Schachter Date: Thu, 21 Aug 2025 16:49:15 +0300 Subject: [PATCH 40/67] update with move extents --- .../kusto/management/update-table-command.md | 68 ++++++++++++++++--- 1 file changed, 58 insertions(+), 10 deletions(-) diff --git a/data-explorer/kusto/management/update-table-command.md b/data-explorer/kusto/management/update-table-command.md index 9b55a09785..89b708051d 100644 --- a/data-explorer/kusto/management/update-table-command.md +++ b/data-explorer/kusto/management/update-table-command.md @@ -9,7 +9,11 @@ ms.date: 11/18/2024 > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] -The `.update table` command performs data updates in a specified table by deleting and appending records atomically. +The `.update table` command performs data updates in a specified table by appending and deleting records atomically. +There are 2 options for appending the records: + +1. Ingest the records based on a provided query. The query is noted using the *AppendIdentifier*. +1. Move extents containing the records to append from another table to the target table. See [Update using move extents](#update-using-move-extents) for more details. > [!WARNING] > This command is unrecoverable. @@ -27,10 +31,17 @@ You must have at least [Table Admin](../access-control/role-based-access-control [!INCLUDE [syntax-conventions-note](../includes/syntax-conventions-note.md)] +**Update using newly ingested records:** + `.update` `[async]` `table` *TableName* `delete` *DeleteIdentifier* `append` *AppendIdentifier* [`with` `(` *propertyName* `=` *propertyValue* `)`] `<|`
`let` *DeleteIdentifier*`=` *DeletePredicate*`;`
`let` *AppendIdentifier*`=` *AppendPredicate*`;` +**Update using move extents:** + +`.update` `[async]` `table` *TableName* `delete` *DeleteIdentifier* `move` *SourceTableName* [`with` `(` *propertyName* `=` *propertyValue* `)`] `<|`
+`let` *DeleteIdentifier*`=` *DeletePredicate*`;`
+ ### Parameters | Name | Type | Required | Description | @@ -39,10 +50,12 @@ You must have at least [Table Admin](../access-control/role-based-access-control | *TableName* | `string` | :heavy_check_mark: | The name of the table to update. | *DeleteIdentifier* | `string` | :heavy_check_mark: | The identifier name used to specify the delete predicate applied to the updated table. | | *DeletePredicate* | `string` | :heavy_check_mark: | The text of a query whose results are used as data to delete. The predicate has the same [limitations as the soft delete predicate](../concepts/data-soft-delete.md#limitations-and-considerations). | -| *AppendIdentifier* | `string` | :heavy_check_mark: | The identifier name used to specify the append predicate applied to the updated table. | -| *AppendPredicate* | `string` | :heavy_check_mark: | The text of a query whose results are used as data to append. | +| *AppendIdentifier* | `string` || The identifier name used to specify the append predicate applied to the updated table. Required if using update based on ingest from query. | +| *AppendPredicate* | `string` || The text of a query whose results are used as data to append. Required if using update based on ingest from query. | +| *SourceTableName* | `string` || The name of the table to move extents from. Must be a local table in current database. Required if using [Update using move extents](#update-using-move-extents).| > [!IMPORTANT] +> > * Both delete and append predicates can't use remote entities, cross-db, and cross-cluster entities. Predicates can't reference an external table or use the `externaldata` operator. > * Append and delete queries are expected to produce deterministic results. Nondeterministic queries can lead to unexpected results. A query is deterministic if and only if it would return the same data if executed multiple times. > * For example, use of [`take` operator](../query/take-operator.md), [`sample` operator](../query/sample-operator.md), [`rand` function](../query/rand-function.md), and other such operators isn't recommended because these operators aren't deterministic. @@ -53,7 +66,8 @@ You must have at least [Table Admin](../access-control/role-based-access-control | Name | Type | Description | | -------- | ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | *whatif* | bool | If `true`, returns the number of records that will be appended / deleted in every shard, without appending / deleting any records. The default is `false`. | -| *distributed* | bool | If `true`, the command ingests from all nodes executing the query in parallel. Default is `false`. See [performance tips](#performance-tips). | +| *distributed* | bool | If `true`, the command ingests from all nodes executing the query in parallel. This option is relevant only for update based on ingest from query. Default is `false`. See [performance tips](#performance-tips). | +| *tags* | string | Optional [Extent tags](extent-tags.md) to filter only specific extents, when using update using move extents. The tags are provided as an array, in the same format as in [Ingestion properties](../includes/ingestion-properties.md). See examples in [Update using move extents](#update-using-move-extents).| > [!IMPORTANT] > We recommend running in `whatif` mode first before executing the update to validate the predicates before deleting or appending data. @@ -69,6 +83,11 @@ The result of the command is a table where each record represents an [extent](ex | ExtentId | `guid` | The unique identifier for the extent created or deleted by the command. | | RowCount | `long` | The number of rows created or deleted in the specified extent by the command. | +## Update using move extents + +When using this option, the records to append to target table are moved from the provided *SourceTableName* using [move extents](move-extents.md). The update moves *all* extents from the table, or only those that match the provided *tags*, if *tags* are specified in the command properties. The source table must be a local table in the current database and must the same schema as the target table. +The move extents option is useful when the records to append are already ingested to a staging table in the database, and should replace existing records in the target table. When this is the case, then using the move extents option is more efficient than ingest from query, as this option doesn't require re-ingesting the records. See examples in [Update rows from a staging table using move extents](#update-rows-from-a-staging-table-using-move-extents). + ## Choose between `.update table` and materialized views There are scenarios where you could use either the `.update table` command or a [materialized view](materialized-views/materialized-view-overview.md) to achieve the same goal in a table. For example, a materialized view could be used to keep the latest version of each record or an update could be used to update records when a new version is available. @@ -163,7 +182,6 @@ The following example updates multiple columns on all rows with color gray. | extend Color=""; ``` - ### Update rows using another table (reference values) In this example, the first step is to create the following mapping table: @@ -206,7 +224,7 @@ Sometimes values to update are known without being stored in a table, and the [d | where true; ``` -### Update rows with a staging table +### Update rows from a staging table using move extents A popular pattern is to first land data in a staging table before updating the main table. @@ -220,17 +238,47 @@ The first command creates a staging table: | extend Color = tostring(dynamic(["Red", "Blue", "Gray"])[Id %3]) ``` -The next command updates the main table with the data in the staging table: +The next command updates the main table with the data in the staging table, by moving all extents from staging table to main table: ```kusto -.update table Employees delete D append A <| - let A = MyStagingTable; +.update table Employees delete D move MyStagingTable <| let D = Employees | join kind=leftsemi MyStagingTable on Id | where true; ``` -Some records in the staging table didn't exist in the main table (that is, had `Id>100`) but were still inserted in the main table (upsert behavior). +Some records in the staging table didn't exist in the main table (that is, had `Id>100`) but were still inserted in the main table (upsert behavior). Note that since the command uses move extents, MyStagingTable will be empty after the update. + +#### Update from a staging table using move extents with extent tags + +In this example, the extents in the staging table include extent tags, and we're only interested in updating based on a subset of the tags: + +```kusto +.set-or-replace MyStagingTable tags='["drop-by:tag1"]'<| + range i from 70 to 100 step 5 + | project Id=i + | extend Code = tostring(dynamic(["Customer", "Employee"])[Id %2]) + | extend Color = tostring(dynamic(["Red", "Blue", "Gray"])[Id %3]) + +.set-or-replace MyStagingTable tags='["drop-by:tag2"]'<| + range i from 100 to 150 step 5 + | project Id=i + | extend Code = tostring(dynamic(["Customer", "Employee"])[Id %2]) + | extend Color = tostring(dynamic(["Red", "Blue", "Gray"])[Id %3]) +``` + +The following command updates the main table with the data from the first ingestion ("drop-by:tag1") in the staging table. +Note that the delete part must filter on the extent tags as well, if you would like to only delete records based on this tag. +The entire MyStagingTable is considered for the delete query, not only the records in the extents with "drop-by:tag1" so the filter +must be explicitly added to the delete query. + +```kusto +.update table Employees delete D move MyStagingTable with (tags='["drop-by:tag1"]') <| + let D = Employees | where Id in (MyStagingTable | where extent_tags() has "drop-by:tag1" | project Id); +``` + +At the end of the command, MyStagingTable will include only the records from the 2nd ingestion ("drop-by:tag2"), as the command +moves the extents with "drop-by:tag1". ### Compound key From 38bad73b942a8e1126f478d647398132931ec1d9 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Sun, 24 Aug 2025 09:43:47 +0300 Subject: [PATCH 41/67] SME review --- .../query/graph-query-language-functions.md | 80 +++++++++---------- .../query/graph-query-language-use-cases.md | 8 +- .../kusto/query/graph-query-language.md | 4 +- 3 files changed, 45 insertions(+), 47 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 1f2f0ddad8..b1a05c11dc 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -9,68 +9,66 @@ ms.date: 08/14/2025 --- # Graph query language (GQL) functions and operators (preview) -Graph Query Language (GQL) is a powerful language for querying graph data alongside KQL. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. +Graph Query Language (GQL) is a powerful language for querying graph data. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. > [!NOTE] > GQL support is currently in preview. Features and syntax can change based on feedback and ongoing development. > [!TIP] > -> GQL uses standardized syntax for graph operations. Many GQL functions work like KQL functions, but use different syntax and operators. -> -> Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. +> GQL uses standardized syntax for graph operations. Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. ## Core GQL functions and operators This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. -| GQL Function/Operator | Description | Comparable KQL Operator | GQL Example | -|---|---|---|---| +| GQL Function/Operator | Description | GQL Example | +|---|---|---| | **Pattern Matching** | -| `MATCH` | Find graph patterns | `graph-match` | `MATCH (a)-[r]->(b)` | +| `MATCH` | Find graph patterns | `MATCH (a)-[r]->(b)` | | **Filtering** | -| `WHERE` | Filter patterns and properties | `where` | `WHERE person.age > 25` | -| `IS NULL` | Check for null values | `isnull()` | `WHERE person.age IS NULL` | -| `IS NOT NULL` | Check for non-null values | `isnotnull()` | `WHERE person.age IS NOT NULL` | +| `WHERE` | Filter patterns and properties | `WHERE person.age > 25` | +| `IS NULL` | Check for null values | `WHERE person.age IS NULL` | +| `IS NOT NULL` | Check for non-null values | `WHERE person.age IS NOT NULL` | | **Projection** | -| `RETURN` | Project results | `project` | `RETURN person.name, person.age` | -| `DISTINCT` | Return unique values | `distinct` | `RETURN DISTINCT person.name` | +| `RETURN` | Project results | `RETURN person.name, person.age` | +| `DISTINCT` | Return unique values | `RETURN DISTINCT person.name` | | **Aggregation Functions** | -| `COUNT(*)` | Count all rows | `count()` | `RETURN COUNT(*)` | -| `COUNT()` | Count non-null values | `count()` | `RETURN COUNT(person.name)` | -| `SUM()` | Sum numeric values | `sum()` | `RETURN SUM(person.age)` | -| `MIN()` | Minimum value | `min()` | `RETURN MIN(person.age)` | -| `MAX()` | Maximum value | `max()` | `RETURN MAX(person.age)` | -| `AVG()` | Average value | `avg()` | `RETURN AVG(person.age)` | -| `COLLECT_LIST()` | Collect values into array | `make_list()` | `RETURN COLLECT_LIST(person.name)` | -| `SIZE()` | Array length | `array_length()` | `RETURN SIZE(COLLECT_LIST(n.firstName))` | +| `COUNT(*)` | Count all rows | `RETURN COUNT(*)` | +| `COUNT()` | Count non-null values | `RETURN COUNT(person.name)` | +| `SUM()` | Sum numeric values | `RETURN SUM(person.age)` | +| `MIN()` | Minimum value | `RETURN MIN(person.age)` | +| `MAX()` | Maximum value | `RETURN MAX(person.age)` | +| `AVG()` | Average value | `RETURN AVG(person.age)` | +| `COLLECT_LIST()` | Collect values into array | `RETURN COLLECT_LIST(person.name)` | +| `SIZE()` | Array length | `RETURN SIZE(COLLECT_LIST(n.firstName))` | | **Graph Functions** | -| `Labels()` | Show labels for a node or edge | `labels()` | `RETURN labels(entity)` | +| `Labels()` | Show labels for a node or edge | `RETURN labels(entity)` | | **String Functions** | -| `UPPER()` | Convert to uppercase | `toupper()` | `RETURN UPPER(person.name)` | -| `LOWER()` | Convert to lowercase | `tolower()` | `RETURN LOWER(person.name)` | -| `LEFT()` | Extract left substring | `substring()` | `WHERE LEFT(person.name, 3) = 'Tom'` | -| `RIGHT()` | Extract right substring | `substring()` | `WHERE RIGHT(person.name, 5) = 'Hanks'` | -| `STARTS WITH` | String starts with pattern | `startswith()` | `WHERE person.name STARTS WITH 'Tom'` | -| `ENDS WITH` | String ends with pattern | `endswith()` | `WHERE person.name ENDS WITH 'Hanks'` | -| `CONTAINS` | String contains pattern | `contains()` | `WHERE person.name CONTAINS 'Tom'` | -| `\|\|` | String concatenation | `strcat()` | `RETURN n.firstName \|\| ' ' \|\| n.lastName` | +| `UPPER()` | Convert to uppercase | `RETURN UPPER(person.name)` | +| `LOWER()` | Convert to lowercase | `RETURN LOWER(person.name)` | +| `LEFT()` | Extract left substring | `WHERE LEFT(person.name, 3) = 'Tom'` | +| `RIGHT()` | Extract right substring | `WHERE RIGHT(person.name, 5) = 'Hanks'` | +| `STARTS WITH` | String starts with pattern | `WHERE person.name STARTS WITH 'Tom'` | +| `ENDS WITH` | String ends with pattern | `WHERE person.name ENDS WITH 'Hanks'` | +| `CONTAINS` | String contains pattern | `WHERE person.name CONTAINS 'Tom'` | +| `\|\|` | String concatenation | `RETURN n.firstName \|\| ' ' \|\| n.lastName` | | **Type Conversion** | -| `CAST()` | Convert data types | `tostring()`, `toint()`, etc. | `CAST(person.age AS STRING)` | +| `CAST()` | Convert data types | `CAST(person.age AS STRING)` | | **Date/Time Functions** | -| `ZONED_DATETIME()` | Create datetime from string | `todatetime()` | `ZONED_DATETIME('2024-01-01')` | -| `CURRENT_TIMESTAMP` | Current timestamp | `now()` | `WHERE created < CURRENT_TIMESTAMP` | -| `DURATION()` | Construct a duration (timespan) | `make_timespan()` | `DURATION({days:3})` | +| `ZONED_DATETIME()` | Create datetime from string | `ZONED_DATETIME('2024-01-01')` | +| `CURRENT_TIMESTAMP` | Current timestamp | `WHERE created < CURRENT_TIMESTAMP` | +| `DURATION()` | Construct a duration (timespan) | `DURATION({days:3})` | | **Path Operations** | -| Variable length paths | Multi-hop traversal | `graph-match` with quantifiers | `MATCH (a)-[*1..3]->(b)` | -| Path variables | Named path assignment | Path variables in `graph-match` | `MATCH p = (a)-[]->(b)` | +| Variable length paths | Multi-hop traversal | `MATCH (a)-[*1..3]->(b)` | +| Path variables | Named path assignment | `MATCH p = (a)-[]->(b)` | | **Ordering and Limiting** | -| `ORDER BY` | Sort results | `sort` | `ORDER BY person.age DESC` | -| `LIMIT` | Limit result count | `take` | `LIMIT 10` | +| `ORDER BY` | Sort results | `ORDER BY person.age DESC` | +| `LIMIT` | Limit result count | `LIMIT 10` | | **Label Operations** | -| `&` (AND) | Label intersection | Multiple label filters | `MATCH (p:Person & Male)` | -| `|` (OR) | Label union | Label alternatives | `MATCH (n:Person | Movie)` | -| `!` (NOT) | Label negation | Negative label filter | `MATCH (p:!Female)` | +| `&` (AND) | Label intersection | `MATCH (p:Person & Male)` | +| `|` (OR) | Label union | `MATCH (n:Person | Movie)` | +| `!` (NOT) | Label negation | `MATCH (p:!Female)` | ## Best practices @@ -108,7 +106,7 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity > [!IMPORTANT] > When you design your graph schema, some common property names might conflict with GQL reserved keywords. Avoid or rename these property names. -- **No `INSERT`/`CREATE` support**: GQL doesn't support `INSERT` or `CREATE` operations to change graph structures. Instead, use KQL's [`make-graph`](make-graph-operator.md) operator or [graph models](graph-operators.md) to create and manage graph structures. Use KQL for all graph creation, change, and management tasks. +- **No `INSERT`/`CREATE` support**: Operations to change graph structures are not supported. Instead, use KQL for all graph creation, change, and management tasks. - **Optional matches**: Supported only for node patterns (not edges) and only on persistent graphs. Optional matching isn't available for transient, in-query graphs. diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 34ee40a072..0dafe1fc73 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -9,14 +9,14 @@ ms.date: 08/14/2025 --- # GQL query patterns, examples, and common scenarios (preview) -This article gives Graph Query Language examples focusing on core query patterns, and shows common real world use cases for GQL alongside KQL using realistic graph schemas and queries. +This article gives Graph Query Language examples focusing on core query patterns, and shows common real world use cases for GQL using realistic graph schemas and queries. -The following examples show the GQL syntax supported in KQL, from simple to complex patterns. +The following examples show the GQL syntax supported, from simple to complex patterns. > [!NOTE] -> +> > Before you try these examples, set up your environment to use GQL. See [Getting Started](graph-query-language.md#getting-started) for details. Ensure you set the client request properties to use GQL, and set the graph reference function to your graph data source. -> +> > GQL support is in **preview**. Features and syntax can change based on feedback and ongoing development. ## Core GQL query patterns diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 654228f524..898a7d5fc2 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -9,7 +9,7 @@ ms.date: 08/13/2025 > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] -Graph Query Language (GQL) lets you use standardized graph pattern matching alongside KQL. GQL follows the ISO GQL standard for graph database queries. +Graph Query Language (GQL) lets you use standardized graph pattern matching. GQL follows the [ISO GQL standard](https://www.iso.org/obp/ui/en/#iso:std:iso-iec:39075:ed-1:v1:en) for graph database queries. > [!NOTE] > GQL support is in preview. Features and syntax can change based on feedback and ongoing development. @@ -18,7 +18,7 @@ Graph Query Language (GQL) lets you use standardized graph pattern matching alon Graph Query Language (GQL) is an emerging ISO standard for querying graph databases. GQL lets you use SQL-like syntax for graph pattern matching, so it's easier to analyze relationships in your data. This article explains how to use GQL, its benefits, and key features. -GQL provides standardized graph pattern matching capabilities as an alternative query language to KQL. Analyze relationships in your data using the ISO standard syntax. +GQL provides standardized graph pattern matching capabilities for analyzing relationships in your data using the ISO standard syntax. ## Getting started From 596719545809d87d8a50fc6aaacb3b2681f09343 Mon Sep 17 00:00:00 2001 From: ktalmor <193799742+ktalmor@users.noreply.github.com> Date: Sun, 24 Aug 2025 09:44:10 +0300 Subject: [PATCH 42/67] date --- data-explorer/kusto/query/graph-query-language-use-cases.md | 2 +- data-explorer/kusto/query/graph-query-language.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 0dafe1fc73..659e6f926a 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -3,7 +3,7 @@ title: GQL Query Patterns, Examples, and Use Cases description: GQL query patterns, examples, and common use case scenarios. Find out how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. GQL use cases alongside KQL. See how to build queries for security, social networks, and organizational analysis with step-by-step examples. ms.reviewer: herauch ms.topic: reference -ms.date: 08/14/2025 +ms.date: 08/24/2025 #CustomerIntent: As a , I want so that . --- diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 898a7d5fc2..ed4b32e5e4 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -3,7 +3,7 @@ title: GQL Graph Query Language (preview) description: This article describes Graph Query Language (GQL) ms.reviewer: herauch ms.topic: reference -ms.date: 08/13/2025 +ms.date: 08/24/2025 --- # Graph Query Language (GQL) (preview) From e5eb91b1739504b53a9c141f559701431b03a957 Mon Sep 17 00:00:00 2001 From: Daniel Dror Date: Mon, 25 Aug 2025 20:09:11 -0700 Subject: [PATCH 43/67] updated queries to work with Samples/G_doc --- .../query/graph-query-language-use-cases.md | 5 ++++- .../kusto/query/graph-query-language.md | 18 +++++++++--------- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 659e6f926a..884a587714 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -89,6 +89,7 @@ Use labels to match specific types of nodes. Specify labels with a colon after t ```gql MATCH (person:Person) RETURN person.name +ORDER BY person.name LIMIT 5 ``` @@ -127,6 +128,7 @@ Filter by edge labels without assigning the edge to a variable when you don't ne ```gql MATCH (p1:Person)-[:knows]->(p2:Person) RETURN p1.name, p2.name +ORDER BY p1.name ``` **Output** @@ -212,6 +214,7 @@ Filter nodes based on a single property condition: MATCH (person:Person) WHERE person.properties.age > 26 RETURN person.name, person.properties.age +ORDER BY person.name ``` **Output** @@ -354,7 +357,7 @@ This query returns the complete node and edge objects for Alice's relationship w | person | e | company | |--------|------|-------| -{"id":"p1","lbl":"Person","name":"Alice","properties":{"age": 25}} |{"source":"p1","target":"c1","lbl":"works_at","since":2020} | {"id":"c1","lbl":"Company","name":"TechCorp","properties":{"revenue": 1000000}} +{"id":"p1","lbl":"Person","name":"Alice","properties":{"age": 25}} |{"source":"p1","target":"c1","lbl":"works_at","since":2020} | {"id":"c1","lbl":"Company","name":"TechCorp","properties":{}} ### Count matching patterns diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index ed4b32e5e4..0fa9390c18 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -33,7 +33,7 @@ Before you use GQL, create a graph data source. This article uses an in-memory m ```gql -.create-or-alter function G_Doc_Transient() { +.create-or-alter function G_doc() { let nodes = datatable(id:string, lbl:string, name:string, properties:dynamic) [ "p1","Person","Alice",dynamic({"age": 25}), @@ -41,12 +41,12 @@ Before you use GQL, create a graph data source. This article uses an in-memory m "p3","Person","Carol",dynamic({"age": 28}), "p4","Person","David",dynamic({"age": 35}), "p5","Person","Emma",dynamic({"age": 26}), - "c1","Company","TechCorp",dynamic({"revenue": 1000000}), - "c2","Company","DataSoft",dynamic({"revenue": 2000000}), - "c3","Company","CloudInc",dynamic({"revenue": 1500000}), - "ct1","City","Seattle",dynamic({"population": 750000}), - "ct2","City","Portland",dynamic({"population": 650000}), - "ct3","City","San Francisco",dynamic({"population": 870000}) + "c1","Company","TechCorp",, + "c2","Company","DataSoft",, + "c3","Company","CloudInc",, + "ct1","City","Seattle",, + "ct2","City","Portland",, + "ct3","City","San Francisco", ]; let edges = datatable(source:string, target:string, lbl:string, since:int) [ @@ -96,7 +96,7 @@ TODO ```kql -#crp query_graph_reference=G_Doc_Transient() +#crp query_graph_reference=G_doc() ``` To use labels in GQL, set the label column name: @@ -114,7 +114,7 @@ To use labels in GQL, set the label column name: For programmatic access, set these client request properties: - `query_language`: Set to `"gql"`. -- `query_graph_reference`: Set to your graph function name (for example, `"G_Doc_Transient()"`). +- `query_graph_reference`: Set to your graph function name (for example, `"G_doc()"`). - `query_graph_label_name`: Set to your label column name (for example, `"lbl"`). ## Step 3: Run GQL queries From 058c4e76909623c288bbc11814817d8c71b4f9c0 Mon Sep 17 00:00:00 2001 From: Daniel Dror Date: Mon, 25 Aug 2025 23:04:35 -0700 Subject: [PATCH 44/67] remove bad qeries --- .../query/graph-query-language-use-cases.md | 137 +----------------- 1 file changed, 2 insertions(+), 135 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 884a587714..191ea15e03 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -587,100 +587,21 @@ RETURN AVG(cast(p2.properties.age as int)) AS AvgAgeLikedAcrossTowns This example shows how GQL lets you express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in one readable statement. -## Security use case - attack path analysis - -Security analysts use GQL to find potential attack paths in network infrastructure and access control systems. - -1. Create a security graph function. - - - ```gql - .create-or-alter function SecurityGraph() { - let entities = datatable(id:string, lbl:string, name:string, type:string, criticality:string) - [ - "u1", "User", "john.doe", "StandardUser", "Low", - "u2", "User", "admin.user", "Administrator", "High", - "s1", "System", "web-server", "WebServer", "Medium", - "s2", "System", "database", "Database", "Critical", - "s3", "System", "domain-controller", "DomainController", "Critical" - ]; - let relationships = datatable(source:string, target:string, lbl:string, access_type:string) - [ - "u1", "s1", "CAN_ACCESS", "HTTP", - "s1", "s2", "CAN_ACCESS", "SQL", - "u2", "s3", "CAN_ACCESS", "RDP", - "s3", "s2", "CAN_ACCESS", "Direct" - ]; - relationships - | make-graph source --> target with entities on id - } - ``` - -1. Set up the security graph. - - - ```gql - #crp query_graph_reference=SecurityGraph() - ``` - -1. Find potential attack paths to critical systems. - - - ```gql - MATCH (user:User)-[]->{1,3}(critical:System) - WHERE critical.criticality = 'Critical' - RETURN user.name AS AttackOrigin, critical.name AS CriticalTarget, COUNT(*) AS PathCount - ``` - -**Output** - -This query shows users who can reach critical systems within three hops, so security teams can prioritize access reviews. - -| AttackOrigin | CriticalTarget | PathCount | -|--|--|--| -| admin.user | domain-controller | 1 | -| john.doe | database | 1 | -| admin.user | database | 1 | ## Social networks use case: Friend recommendations Social media platforms use GQL to suggest potential friends based on mutual relationships. -1. Create a social network graph. - - - ```gql - .create-or-alter function SocialGraph() { - let entities = datatable(id:string, lbl:string, name:string, location:string, interests:string) - [ - "p1", "Person", "Alice Johnson", "Seattle", "Technology,Travel", - "p2", "Person", "Bob Smith", "Seattle", "Sports,Technology", - "p3", "Person", "Carol Davis", "Portland", "Travel,Photography", - "p4", "Person", "David Wilson", "Seattle", "Technology,Gaming", - "p5", "Person", "Emma Brown", "Portland", "Photography,Art" - ]; - let relationships = datatable(source:string, target:string, lbl:string, strength:string) - [ - "p1", "p2", "FRIENDS_WITH", "Close", - "p1", "p3", "FRIENDS_WITH", "Medium", - "p2", "p4", "FRIENDS_WITH", "Close", - "p3", "p5", "FRIENDS_WITH", "Medium" - ]; - relationships - | make-graph source --> target with entities on id - } - ``` - 1. Set up the social graph. ```gql - #crp query_graph_reference=SocialGraph() + #crp query_graph_reference=graph('LDBC_SNB_Interactive') ``` 1. Find potential friends through mutual connections. - + ```gql MATCH (p1:PERSON {firstName: 'Karl', lastName: 'Muller'})-[:KNOWS]-(p2:PERSON)-[:KNOWS]-(p3:PERSON), (p1)-[:IS_LOCATED_IN]-(c1:PLACE), @@ -699,60 +620,6 @@ This query suggests friends for Karl who have mutual connections and live in the | Hans | Becker | | Wilhelm | Muller | -## Organization use case: Management hierarchy - -Organizations use GQL to analyze reporting structures and identify management chains. - -Create an organizational graph to visualize reporting relationships. - - -```gql -.create-or-alter function OrgChart() { - let entities = datatable(id:string, lbl:string, name:string, title:string, department:string, level:int) - [ - "e1", "Employee", "Satya Nadella", "CEO", "Executive", 1, - "e2", "Employee", "Amy Hood", "CFO", "Finance", 2, - "e3", "Employee", "Brad Smith", "President", "Legal", 2, - "e4", "Employee", "Scott Guthrie", "EVP", "Cloud", 2, - "e5", "Employee", "Finance Director", "Director", "Finance", 3, - "e6", "Employee", "Cloud Architect", "Principal", "Cloud", 3, - "e7", "Employee", "Senior Engineer", "Senior", "Cloud", 4, - "e8", "Employee", "Software Engineer", "L63", "Cloud", 5 - ]; - let relationships = datatable(source:string, target:string, lbl:string) - [ - "e2", "e1", "REPORTS_TO", - "e3", "e1", "REPORTS_TO", - "e4", "e1", "REPORTS_TO", - "e5", "e2", "REPORTS_TO", - "e6", "e4", "REPORTS_TO", - "e7", "e6", "REPORTS_TO", - "e8", "e7", "REPORTS_TO" - ]; - relationships - | make-graph source --> target with entities on id -} -``` - -Set up the org chart - - -```gql -#crp query_graph_reference=OrgChart() -``` - -Find all managers three levels down from the CEO. - - -```gql -MATCH (ceo:Employee)-[:REPORTS_TO]-{0,3}(manager:Employee) -WHERE ceo.title = 'CEO' AND manager.level <= 4 -RETURN manager.name AS ManagerName, manager.title AS Title, manager.department AS Department -ORDER BY manager.level -``` - -This query finds all employees within three management levels of the CEO. Use this information for organizational analysis and communication planning. - ## Related topics * [Graph Query Language (GQL)](graph-query-language.md) From a8a6a74c5acc6592bedf3b5c8f93bea961e9cd7f Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Tue, 26 Aug 2025 18:31:59 +0200 Subject: [PATCH 45/67] Add advanced schema discovery queries for graph properties --- .../kusto/query/graph-exploration-basics.md | 94 +++++++++++++++++++ 1 file changed, 94 insertions(+) diff --git a/data-explorer/kusto/query/graph-exploration-basics.md b/data-explorer/kusto/query/graph-exploration-basics.md index 9141f0208f..2530052fb3 100644 --- a/data-explorer/kusto/query/graph-exploration-basics.md +++ b/data-explorer/kusto/query/graph-exploration-basics.md @@ -342,6 +342,64 @@ graph('BloodHound_Entra') |---| |{
"onpremsyncenabled": "bool",
"system_tags": "string",
"lastcollected": "string",
"pwdlastset": "string",
"usertype": "string",
"userprincipalname": "string",
"email": "string",
"tenantid": "guid",
"name": "string",
"lastseen": "string",
"displayname": "string",
"enabled": "bool",
"title": "string",
"onpremid": "string",
"objectid": "guid",
"whencreated": "string"
}| +**Find all properties of all nodes by label**: + +This advanced schema discovery query identifies all property names that exist across nodes of each label type. Unlike the previous query that shows the schema structure, this query aggregates property names across all nodes of the same type, helping you understand which properties are consistently available and which might be optional or rare. This example uses the [`LDBC_SNB_Interactive` graph](graph-sample-data.md#ldbc-snb-interactive) to explore the complete property landscape of different entity types in the social network dataset. + +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + +```kusto +graph('LDBC_SNB_Interactive') +| graph-match (node) + project properties = node, labels = labels(node) +| mv-apply properties on ( + mv-expand kind=array properties + | where isnotempty(properties[1]) + | summarize properties = make_set(properties[0]) + ) +| mv-expand label = labels to typeof(string) +| summarize properties =make_set(properties) by label +| take 3 +``` + +|label|properties| +|---|---| +|TAGCLASS|[
"id",
"node_id",
"lbl",
"name",
"url"
]| +|TAG|[
"id",
"node_id",
"lbl",
"name",
"url"
]| +|FORUM|[
"id",
"creationDate",
"node_id",
"lbl",
"title"
]| + +**Find all properties of all edges by label**: + +This query performs schema discovery for edge (relationship) properties, showing what information is stored with each type of relationship in your graph. Understanding edge properties is crucial for analyzing relationship metadata such as timestamps, weights, confidence scores, or other attributes that provide context about connections. This example uses the [`BloodHound_AD` graph](graph-sample-data.md#bloodhound-active-directory-dataset) to explore the properties available on different types of Active Directory privilege relationships. + +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + +```kusto +graph('BloodHound_AD') +| graph-match ()-[e]-() + project properties = e, labels = labels(e) +| mv-apply properties on ( + mv-expand kind=array properties + | where isnotempty(properties[1]) + | summarize properties = make_set(properties[0]) + ) +| mv-expand label = labels to typeof(string) +| summarize properties =make_set(properties) by label +| take 3 +``` + +|label|properties| +|---|---| +|GetChangesAll|[
"id",
"lbl",
"src",
"dst",
"properties",
"lastseen"
]| +|OwnsRaw|[
"id",
"lbl",
"src",
"dst",
"properties",
"lastseen"
]| +|AddKeyCredentialLink|[
"id",
"lbl",
"src",
"dst",
"properties",
"lastseen"
]| + **Find nodes with specific property values**: Use this pattern to locate entities with particular characteristics or to validate data quality by checking for expected property values. This example uses the [`BloodHound_Entra` graph](graph-sample-data.md#bloodhound-entra-dataset) to find nodes with specific name properties in Microsoft Entra environments. @@ -365,6 +423,42 @@ graph('BloodHound_Entra') |10|CJACKSON@PHANTOMCORP.ONMICROSOFT.COM| |12|RHALL@PHANTOMCORP.ONMICROSOFT.COM| +### Topology of the graph + +Understanding the overall topology of your graph reveals the types of connections that exist between different entity types. This analysis helps you understand the data model, identify the most common relationship patterns, and discover potential paths for traversal queries. The topology query shows which node labels connect to which other node labels through specific edge types, providing a comprehensive view of your graph's structure. + +:::moniker range="azure-data-explorer" +> [!div class="nextstepaction"] +> Run the query +::: moniker-end + +```kusto +//Topology of the graph - What's connected to what? +graph('LDBC_Financial') +| graph-match (src)-[e]->(dst) + project SourceLabels = labels(src), EdgeLabels = labels(e), DestinationLabels = labels(dst) +| mv-expand EdgeLabel = EdgeLabels to typeof(string) +| mv-expand SourceLabel = SourceLabels to typeof(string) +| mv-expand DestinationLabel = DestinationLabels to typeof(string) +| summarize Count = count() by SourceLabel, EdgeLabel, DestinationLabel +``` + +|SourceLabel|EdgeLabel|DestinationLabel|Count| +|---|---|---|---| +|COMPANY|GUARANTEE|COMPANY|202| +|COMPANY|APPLY|LOAN|449| +|PERSON|APPLY|LOAN|927| +|ACCOUNT|REPAY|LOAN|2747| +|LOAN|DEPOSIT|ACCOUNT|2758| +|ACCOUNT|TRANSFER|ACCOUNT|8132| +|ACCOUNT|WITHDRAW|ACCOUNT|9182| +|PERSON|GUARANTEE|PERSON|377| +|COMPANY|OWN|ACCOUNT|671| +|COMPANY|INVEST|COMPANY|679| +|PERSON|OWN|ACCOUNT|1384| +|MEDIUM|SIGN_IN|ACCOUNT|2489| +|PERSON|INVEST|COMPANY|1304| + ## Related content - [Graph semantics overview](graph-semantics-overview.md) From 0208bda96c6a8bec03a028eab53c4fd568734709 Mon Sep 17 00:00:00 2001 From: Daniel Dror Date: Tue, 26 Aug 2025 12:03:21 -0700 Subject: [PATCH 46/67] small fixes --- .../kusto/query/graph-query-language-use-cases.md | 5 +++-- data-explorer/kusto/query/graph-query-language.md | 8 ++------ 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 191ea15e03..034ab4bb3a 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -590,9 +590,10 @@ This example shows how GQL lets you express complex multi-pattern queries with c ## Social networks use case: Friend recommendations -Social media platforms use GQL to suggest potential friends based on mutual relationships. +Social media platforms use GQL to suggest potential friends based on mutual relationships. +We will be using the LDBC Social Network Benchmark dataset (see [GQL Sample Data](graph-sample-data.md#ldbc-snb-interactive)). -1. Set up the social graph. +1. Set up the graph reference to point to the LDBC dataset. ```gql diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 0fa9390c18..81a4614bb5 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -80,11 +80,7 @@ Before you use GQL, create a graph data source. This article uses an in-memory m To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the Kusto Explorer by using directives. -### Use Kusto Web Explorer to set client request properties - -TODO - -### Use Kusto Explorer to set client request properties +### Set client request properties in Kusto's UI > [!IMPORTANT] > Run each directive separately before you run your GQL query. The directives set up the query environment for GQL execution. @@ -109,7 +105,7 @@ To use labels in GQL, set the label column name: > [!TIP] > Labels are optional in GQL, but they're often used to filter nodes and edges by type. Set the label column name to use labels in your GQL queries. -### Use SDK or API to set client request properties +### Set client request properties in Kusto's SDKs or API For programmatic access, set these client request properties: From f15cbd2dd4d71adaeeb8a459a8ea35a8e80af8b0 Mon Sep 17 00:00:00 2001 From: Yifat Schachter Date: Wed, 27 Aug 2025 09:19:05 +0300 Subject: [PATCH 47/67] rename tags property --- data-explorer/kusto/management/update-table-command.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/data-explorer/kusto/management/update-table-command.md b/data-explorer/kusto/management/update-table-command.md index 89b708051d..c5c5cc56e6 100644 --- a/data-explorer/kusto/management/update-table-command.md +++ b/data-explorer/kusto/management/update-table-command.md @@ -31,13 +31,13 @@ You must have at least [Table Admin](../access-control/role-based-access-control [!INCLUDE [syntax-conventions-note](../includes/syntax-conventions-note.md)] -**Update using newly ingested records:** +**Update using append and delete:** `.update` `[async]` `table` *TableName* `delete` *DeleteIdentifier* `append` *AppendIdentifier* [`with` `(` *propertyName* `=` *propertyValue* `)`] `<|`
`let` *DeleteIdentifier*`=` *DeletePredicate*`;`
`let` *AppendIdentifier*`=` *AppendPredicate*`;` -**Update using move extents:** +**Update using move extents and delete:** `.update` `[async]` `table` *TableName* `delete` *DeleteIdentifier* `move` *SourceTableName* [`with` `(` *propertyName* `=` *propertyValue* `)`] `<|`
`let` *DeleteIdentifier*`=` *DeletePredicate*`;`
From 932ccf4f84b8ec046b4f32a15b53f8edd16aabd2 Mon Sep 17 00:00:00 2001 From: Yifat Schachter Date: Wed, 27 Aug 2025 09:22:04 +0300 Subject: [PATCH 48/67] rename tags --- .../kusto/management/update-table-command.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/data-explorer/kusto/management/update-table-command.md b/data-explorer/kusto/management/update-table-command.md index c5c5cc56e6..2c7cbbf0f5 100644 --- a/data-explorer/kusto/management/update-table-command.md +++ b/data-explorer/kusto/management/update-table-command.md @@ -67,7 +67,7 @@ You must have at least [Table Admin](../access-control/role-based-access-control | -------- | ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | *whatif* | bool | If `true`, returns the number of records that will be appended / deleted in every shard, without appending / deleting any records. The default is `false`. | | *distributed* | bool | If `true`, the command ingests from all nodes executing the query in parallel. This option is relevant only for update based on ingest from query. Default is `false`. See [performance tips](#performance-tips). | -| *tags* | string | Optional [Extent tags](extent-tags.md) to filter only specific extents, when using update using move extents. The tags are provided as an array, in the same format as in [Ingestion properties](../includes/ingestion-properties.md). See examples in [Update using move extents](#update-using-move-extents).| +| *extentTagsToMove* | string | Optional [Extent tags](extent-tags.md) to filter only specific extents, when using update using move extents. The tags are provided as an array, in the same format as in [Ingestion properties](../includes/ingestion-properties.md). See examples in [Update using move extents](#update-using-move-extents).| > [!IMPORTANT] > We recommend running in `whatif` mode first before executing the update to validate the predicates before deleting or appending data. @@ -85,7 +85,7 @@ The result of the command is a table where each record represents an [extent](ex ## Update using move extents -When using this option, the records to append to target table are moved from the provided *SourceTableName* using [move extents](move-extents.md). The update moves *all* extents from the table, or only those that match the provided *tags*, if *tags* are specified in the command properties. The source table must be a local table in the current database and must the same schema as the target table. +When using this option, the records to append to target table are moved from the provided *SourceTableName* using [move extents](move-extents.md). The update moves *all* extents from the table, or only those that match the provided *extentTagsToMove*, if *extentTagsToMove* are specified in the command properties. The source table must be a local table in the current database and must the same schema as the target table. The move extents option is useful when the records to append are already ingested to a staging table in the database, and should replace existing records in the target table. When this is the case, then using the move extents option is more efficient than ingest from query, as this option doesn't require re-ingesting the records. See examples in [Update rows from a staging table using move extents](#update-rows-from-a-staging-table-using-move-extents). ## Choose between `.update table` and materialized views @@ -254,13 +254,13 @@ Some records in the staging table didn't exist in the main table (that is, had ` In this example, the extents in the staging table include extent tags, and we're only interested in updating based on a subset of the tags: ```kusto -.set-or-replace MyStagingTable tags='["drop-by:tag1"]'<| +.set-or-replace MyStagingTable with(tags='["drop-by:tag1"]')<| range i from 70 to 100 step 5 | project Id=i | extend Code = tostring(dynamic(["Customer", "Employee"])[Id %2]) | extend Color = tostring(dynamic(["Red", "Blue", "Gray"])[Id %3]) -.set-or-replace MyStagingTable tags='["drop-by:tag2"]'<| +.set-or-replace MyStagingTable with(tags='["drop-by:tag2"]')<| range i from 100 to 150 step 5 | project Id=i | extend Code = tostring(dynamic(["Customer", "Employee"])[Id %2]) @@ -273,11 +273,11 @@ The entire MyStagingTable is considered for the delete query, not only the recor must be explicitly added to the delete query. ```kusto -.update table Employees delete D move MyStagingTable with (tags='["drop-by:tag1"]') <| +.update table Employees delete D move MyStagingTable with (extentTagsToMove='["drop-by:tag1"]') <| let D = Employees | where Id in (MyStagingTable | where extent_tags() has "drop-by:tag1" | project Id); ``` -At the end of the command, MyStagingTable will include only the records from the 2nd ingestion ("drop-by:tag2"), as the command +At the end of the command, MyStagingTable will include only the records from the 2nd ingestion ("drop-by:tag2"), as the command moves the extents with "drop-by:tag1". ### Compound key From 0c96bc289a33d373502f3371703b4bac57a776b4 Mon Sep 17 00:00:00 2001 From: Yifat Schachter Date: Wed, 27 Aug 2025 13:30:52 +0300 Subject: [PATCH 49/67] dependecy between delete and append --- data-explorer/kusto/management/update-table-command.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/data-explorer/kusto/management/update-table-command.md b/data-explorer/kusto/management/update-table-command.md index 2c7cbbf0f5..7f34e86e3f 100644 --- a/data-explorer/kusto/management/update-table-command.md +++ b/data-explorer/kusto/management/update-table-command.md @@ -9,7 +9,7 @@ ms.date: 11/18/2024 > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] -The `.update table` command performs data updates in a specified table by appending and deleting records atomically. +The `.update table` command performs data updates in a specified table by deleting and appending records atomically. There are 2 options for appending the records: 1. Ingest the records based on a provided query. The query is noted using the *AppendIdentifier*. @@ -57,9 +57,10 @@ You must have at least [Table Admin](../access-control/role-based-access-control > [!IMPORTANT] > > * Both delete and append predicates can't use remote entities, cross-db, and cross-cluster entities. Predicates can't reference an external table or use the `externaldata` operator. -> * Append and delete queries are expected to produce deterministic results. Nondeterministic queries can lead to unexpected results. A query is deterministic if and only if it would return the same data if executed multiple times. +> * Append and delete queries are expected to produce deterministic results. Nondeterministic queries can lead to unexpected results. A query is deterministic if and only if it would return the same data if executed multiple times. > * For example, use of [`take` operator](../query/take-operator.md), [`sample` operator](../query/sample-operator.md), [`rand` function](../query/rand-function.md), and other such operators isn't recommended because these operators aren't deterministic. > * Queries might be executed more than once within the `update` execution. If the intermediate query results are inconsistent, the update command can produce unexpected results. +> * The delete and append predicates are based on the same snapshot of the table, and therefore they cannot depend on each other. In other words, the append predicate executes on a snapshot of the source table *before* the deletion and vice versa - the delete predicate executes on a snapshot of the source table *before* the append. ## Supported properties From 6ea594c08e84831aa9a2c72b1bebc0f8f82960ad Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 14:27:51 +0200 Subject: [PATCH 50/67] Fix link to Kusto Explorer in GQL queries section --- data-explorer/kusto/query/graph-query-language.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 81a4614bb5..2c2dca34fa 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -78,7 +78,7 @@ Before you use GQL, create a graph data source. This article uses an in-memory m ## Step 2: Configure client request properties -To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the Kusto Explorer by using directives. +To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the [Kusto Explorer](../tools/kusto-explorer.md) by using directives. ### Set client request properties in Kusto's UI From 3e8f46b1479df708fd2c85f8927bdd2335e94a5c Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 16:24:25 +0200 Subject: [PATCH 51/67] Update GQL documentation to clarify client request properties setup for Azure Data Explorer and Microsoft Fabric --- data-explorer/kusto/query/graph-query-language.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 2c2dca34fa..c00ef57834 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -78,9 +78,14 @@ Before you use GQL, create a graph data source. This article uses an in-memory m ## Step 2: Configure client request properties -To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the [Kusto Explorer](../tools/kusto-explorer.md) by using directives. +::: moniker range="azure-data-explorer" +To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the [Kusto Explorer](../tools/kusto-explorer.md) or [Azure Data Explorer web UI](/azure/data-explorer/web-ui-query-overview) by using directives. +::: moniker-end +::: moniker range="microsoft-fabric" +To run GQL queries, set three client request properties. Set these properties through the SDK, API, or directly in the [Kusto Explorer](../tools/kusto-explorer.md) or [KQL queryset](/fabric/real-time-intelligence/kusto-query-set) by using directives. +::: moniker-end -### Set client request properties in Kusto's UI +### Set client request properties > [!IMPORTANT] > Run each directive separately before you run your GQL query. The directives set up the query environment for GQL execution. @@ -105,7 +110,7 @@ To use labels in GQL, set the label column name: > [!TIP] > Labels are optional in GQL, but they're often used to filter nodes and edges by type. Set the label column name to use labels in your GQL queries. -### Set client request properties in Kusto's SDKs or API +### Set client request properties in programmatically For programmatic access, set these client request properties: From 97f909ac18e6c110e024f52b380b7d504e0ef785 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 17:19:41 +0200 Subject: [PATCH 52/67] Add `OPTIONAL MATCH` function to GQL documentation with usage examples --- .../query/graph-query-language-functions.md | 1 + .../query/graph-query-language-use-cases.md | 27 +++++++++++++++++++ 2 files changed, 28 insertions(+) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index b1a05c11dc..0635b03137 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -26,6 +26,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu |---|---|---| | **Pattern Matching** | | `MATCH` | Find graph patterns | `MATCH (a)-[r]->(b)` | +| `OPTIONAL MATCH` | Find patterns that might not exist | `OPTIONAL MATCH (p)->(c:City)` | | **Filtering** | | `WHERE` | Filter patterns and properties | `WHERE person.age > 25` | | `IS NULL` | Check for null values | `WHERE person.age IS NULL` | diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 034ab4bb3a..448a918a21 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -525,6 +525,31 @@ RETURN p This query assigns the entire pattern to the variable `p`, which you can return or use in other parts of the query. The path variable has the complete sequence of nodes and edges. +### Optional matching + +Use `OPTIONAL MATCH` to find patterns that might not exist, returning empty values when patterns don't match: + + +```gql +MATCH (p:Person) + WHERE p.properties.age < 30 +OPTIONAL MATCH (p)->(c:City) + WHERE c.name <> 'Seattle' +RETURN p.name as Person, c.name as City +``` + +This query finds all `Person` nodes where age is less than 30, and optionally matches cities they're connected to (excluding Seattle). If a person isn't connected to any city (or only to Seattle), the City column returns empty, but the person is still included in the results. + +**Output** + +The optional match ensures that people without city connections are still returned: + +|Person|City| +|---|---| +|Carol|Portland| +|Emma|Portland| +|Alice|| + ### Multi-hop named paths Create named paths that span multiple relationships: @@ -539,6 +564,8 @@ This query creates a named path variable `full_path` that captures a two-hop pat **Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. + + ## Complex multi-pattern examples ### Cross-town *likes* with company filter From a3b889a54c16215a8b7f724efffd064c74bd9271 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 17:59:33 +0200 Subject: [PATCH 53/67] Update GQL documentation to include path functions and examples for extracting nodes and relationships --- .../query/graph-query-language-functions.md | 6 +++--- .../query/graph-query-language-use-cases.md | 21 +++++++++++++++++++ 2 files changed, 24 insertions(+), 3 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 0635b03137..22a5009bbb 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -60,9 +60,9 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `ZONED_DATETIME()` | Create datetime from string | `ZONED_DATETIME('2024-01-01')` | | `CURRENT_TIMESTAMP` | Current timestamp | `WHERE created < CURRENT_TIMESTAMP` | | `DURATION()` | Construct a duration (timespan) | `DURATION({days:3})` | -| **Path Operations** | -| Variable length paths | Multi-hop traversal | `MATCH (a)-[*1..3]->(b)` | -| Path variables | Named path assignment | `MATCH p = (a)-[]->(b)` | +| **Path Functions** | +| `NODES()` | Extract nodes from a path | `RETURN NODES(path_variable)` | +| `RELATIONSHIPS()` | Extract edges from a path | `RETURN RELATIONSHIPS(path_variable)` | | **Ordering and Limiting** | | `ORDER BY` | Sort results | `ORDER BY person.age DESC` | | `LIMIT` | Limit result count | `LIMIT 10` | diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 448a918a21..d44f382e2c 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -205,6 +205,25 @@ RETURN DISTINCT connected.name This query finds all nodes you can reach from Alice through paths of one or more hops. The `{1,}` quantifier means "one or more hops." +### Path functions + +Use path functions to extract information about the matched paths, including the nodes and relationships that make up the path: + + +```gql +MATCH fof = (person)-[:knows]->{2,2}() +RETURN fof, NODES(fof) AS NodesOfPath, RELATIONSHIPS(fof) AS EdgesOfPath +LIMIT 1 +``` + +This query finds paths exactly 2 hops long using the "knows" relationship. The path variable `fof` contains the entire path as an array of alternating nodes and edges. The `NODES()` function extracts just the nodes from the path, and `RELATIONSHIPS()` extracts just the edges. + +**Output** + +| fof | NodesOfPath | EdgesOfPath | +|-----|-------------|-------------| +| `[{"id": "p3", "lbl": "Person", "name": "Carol", "properties": {"age": 28}, "$labels": ["Person"]}, {"source": "p3", "target": "p4", "lbl": "knows", "since": 2022, "$labels": ["knows"]}, {"id": "p4", "lbl": "Person", "name": "David", "properties": {"age": 35}, "$labels": ["Person"]}, {"source": "p4", "target": "p5", "lbl": "knows", "since": 2023, "$labels": ["knows"]}, {"id": "p5", "lbl": "Person", "name": "Emma", "properties": {"age": 26}, "$labels": ["Person"]}]` | `[{"id": "p3", "lbl": "Person", "name": "Carol", "properties": {"age": 28}, "$labels": ["Person"]}, {"id": "p4", "lbl": "Person", "name": "David", "properties": {"age": 35}, "$labels": ["Person"]}, {"id": "p5", "lbl": "Person", "name": "Emma", "properties": {"age": 26}, "$labels": ["Person"]}]` | `[{"source": "p3", "target": "p4", "lbl": "knows", "since": 2022, "$labels": ["knows"]}, {"source": "p4", "target": "p5", "lbl": "knows", "since": 2023, "$labels": ["knows"]}]` | + ## Basic property filtering Filter nodes based on a single property condition: @@ -550,6 +569,8 @@ The optional match ensures that people without city connections are still return |Emma|Portland| |Alice|| + + ### Multi-hop named paths Create named paths that span multiple relationships: From c1b8fc1fef3765ccd1608cb1048810a0f7f3491b Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 18:03:08 +0200 Subject: [PATCH 54/67] Add PATH_LENGTH function to GQL documentation with usage example --- data-explorer/kusto/query/graph-query-language-functions.md | 1 + 1 file changed, 1 insertion(+) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 22a5009bbb..0fa2285d99 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -63,6 +63,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | **Path Functions** | | `NODES()` | Extract nodes from a path | `RETURN NODES(path_variable)` | | `RELATIONSHIPS()` | Extract edges from a path | `RETURN RELATIONSHIPS(path_variable)` | +| `PATH_LENGTH()` | Get the length of a path | `RETURN PATH_LENGTH(path_variable)` | | **Ordering and Limiting** | | `ORDER BY` | Sort results | `ORDER BY person.age DESC` | | `LIMIT` | Limit result count | `LIMIT 10` | From bad46ea131e905a90abf34737e40045f90a742f9 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 18:09:47 +0200 Subject: [PATCH 55/67] Add example for returning property bags in GQL queries --- .../query/graph-query-language-use-cases.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index d44f382e2c..3fb812b3b0 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -361,6 +361,25 @@ RETURN person.name AS Employee, company.name AS Company, employment.since AS Wor This query returns employee names, company names, and employment start dates with descriptive column headers. +### Return property bags (JSON objects) + +Create custom JSON objects by combining multiple properties in the RETURN clause. + + +```gql +MATCH (person:Person) +RETURN {age: person.properties.age, name: person.name} AS myPropertyBag +LIMIT 1 +``` + +**Output** + +This query creates a custom JSON object (property bag) containing selected properties from matched Person nodes. The property bag combines the person's age and name into a single structured object. + +| myPropertyBag | +|---| +| { "age": 28, "name": "Carol" } | + ### Return entire nodes and edges From aa24b6a7bf6251be2ecf224cd34a31974745ff25 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 18:31:10 +0200 Subject: [PATCH 56/67] Enhance GQL documentation with detailed descriptions for DURATION functions and add a new section on temporal data analysis --- .../query/graph-query-language-functions.md | 3 +- .../query/graph-query-language-use-cases.md | 50 ++++++++++++++++++- 2 files changed, 50 insertions(+), 3 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 0fa2285d99..1135020cc9 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -59,7 +59,8 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | **Date/Time Functions** | | `ZONED_DATETIME()` | Create datetime from string | `ZONED_DATETIME('2024-01-01')` | | `CURRENT_TIMESTAMP` | Current timestamp | `WHERE created < CURRENT_TIMESTAMP` | -| `DURATION()` | Construct a duration (timespan) | `DURATION({days:3})` | +| `DURATION()` | Create timespan from time units (days to nanoseconds) | `DURATION({days: 3, hours: 2})` | +| `DURATION_BETWEEN()` | Calculate duration between two datetime values | `DURATION_BETWEEN(start_time, end_time)` | | **Path Functions** | | `NODES()` | Extract nodes from a path | `RETURN NODES(path_variable)` | | `RELATIONSHIPS()` | Extract edges from a path | `RETURN RELATIONSHIPS(path_variable)` | diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 3fb812b3b0..8a68fde2a3 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -31,6 +31,8 @@ The following examples show the GQL syntax supported, from simple to complex pat * [Complex multi-pattern queries](#complex-multi-pattern-examples) +* [Temporal data analysis with DURATION functions](#temporal-data-analysis-with-duration-functions) + For a full list of supported GQL functions and operators, see [Graph Query Language Functions](graph-query-language-functions.md). ## Basic pattern matching examples @@ -604,8 +606,6 @@ This query creates a named path variable `full_path` that captures a two-hop pat **Comparable KQL:** Uses advanced `graph-match` operator features for complex pattern matching. - - ## Complex multi-pattern examples ### Cross-town *likes* with company filter @@ -654,6 +654,52 @@ RETURN AVG(cast(p2.properties.age as int)) AS AvgAgeLikedAcrossTowns This example shows how GQL lets you express complex multi-pattern queries with cross-variable filtering, inline property matching, string pattern matching, and aggregation—all in one readable statement. +## Temporal data analysis with DURATION functions + +GQL provides comprehensive support for temporal data analysis using duration functions. These functions enable you to perform time-based filtering, calculations, and comparisons on graph data with timestamps. + +### Supported duration units + +The `DURATION()` function supports a wide range of time units with flexible, case-insensitive syntax and returns a `timespan` object: + +| Time Unit | Supported Names | Example | Timespan Output | +|-----------|----------------|---------|-----------------| +| **Days** | `days`, `day` | `DURATION({days: 7})` | `7.00:00:00` | +| **Hours** | `hours`, `hour`, `HOURS` | `DURATION({hours: 24})` | `1.00:00:00` | +| **Minutes** | `minutes`, `minute`, `MINUTES` | `DURATION({minutes: 30})` | `00:30:00` | +| **Seconds** | `seconds`, `second`, `SECONDS`, `secOnd` | `DURATION({seconds: 45})` | `00:00:45` | +| **Milliseconds** | `milliseconds`, `millisecond` | `DURATION({milliseconds: 500})` | `00:00:00.5000000` | +| **Microseconds** | `microseconds`, `microsecond`, `micRosecond` | `DURATION({microseconds: 1000})` | `00:00:00.0010000` | +| **Nanoseconds** | `nanoseconds`, `nanosecond`, `nanoSecond` | `DURATION({nanoseconds: 1000000})` | `00:00:00.0010000` | + +You can combine multiple units in a single duration object: `DURATION({days: 1.8, minutes: 8, seconds: 7})` returns `1.00:08:07`. + +### Complex temporal boundary analysis + +Combine duration functions with timestamp arithmetic for precise temporal filtering: + + +```gql +MATCH (system:System)-[event:generated]->(alert:Alert) +WHERE event.event_timestamp <= zoned_datetime("2012-01-01 08:00:00.0") + DURATION({days: 3}) + AND event.event_timestamp > zoned_datetime("2012-01-01 08:00:00.0") + DURATION({minutes: 1}) +RETURN + system.name, + alert.severity, + event.event_timestamp, + DURATION_BETWEEN(zoned_datetime("2012-01-01 08:00:00.0"), event.event_timestamp) AS time_since_baseline +ORDER BY event.event_timestamp +``` + +This query finds alerts generated within a specific time window (between 1 minute and 3 days after a baseline date) and calculates the duration between the baseline and each event. + +**Output** + +| system.name | alert.severity | event_timestamp | time_since_baseline | +|-------------|----------------|-----------------|---------------------| +| WebServer01 | Warning | 2012-01-01T08:01:30.0Z | 00:01:30 | +| Database02 | Critical | 2012-01-02T20:15:45.0Z | 1.12:15:45 | +| LoadBalancer | Info | 2012-01-04T16:30:00.0Z | 3.08:30:00 | ## Social networks use case: Friend recommendations From f3d49dca45cd9ba7fe1e94e229cc68659175d670 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 18:36:47 +0200 Subject: [PATCH 57/67] Fix formatting for OR operator in GQL functions documentation --- data-explorer/kusto/query/graph-query-language-functions.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 1135020cc9..62fb71df4f 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -70,7 +70,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `LIMIT` | Limit result count | `LIMIT 10` | | **Label Operations** | | `&` (AND) | Label intersection | `MATCH (p:Person & Male)` | -| `|` (OR) | Label union | `MATCH (n:Person | Movie)` | +| `\|` (OR) | Label union | `MATCH (n:Person \| Movie)` | | `!` (NOT) | Label negation | `MATCH (p:!Female)` | ## Best practices From ce380009be0f3a572034d56c53b23ac40d977082 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 18:45:38 +0200 Subject: [PATCH 58/67] Add string trimming and joining functions to GQL documentation with usage examples --- .../kusto/query/graph-query-language-functions.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 62fb71df4f..663039550d 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -54,7 +54,19 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `ENDS WITH` | String ends with pattern | `WHERE person.name ENDS WITH 'Hanks'` | | `CONTAINS` | String contains pattern | `WHERE person.name CONTAINS 'Tom'` | | `\|\|` | String concatenation | `RETURN n.firstName \|\| ' ' \|\| n.lastName` | -| **Type Conversion** | +| `TRIM()` | Remove whitespace from both ends | `RETURN TRIM(' abc ')` | +| `BTRIM()` | Remove whitespace from both ends (alias for TRIM) | `RETURN BTRIM(' abc ')` | +| `LTRIM()` | Remove whitespace from left end | `RETURN LTRIM(' abc ')` | +| `RTRIM()` | Remove whitespace from right end | `RETURN RTRIM(' abc ')` | +| `TRIM(BOTH FROM)` | Remove whitespace from both ends (explicit) | `RETURN TRIM(BOTH FROM ' abc ')` | +| `TRIM(LEADING FROM)` | Remove whitespace from left end (explicit) | `RETURN TRIM(LEADING FROM ' abc')` | +| `TRIM(TRAILING FROM)` | Remove whitespace from right end (explicit) | `RETURN TRIM(TRAILING FROM 'abc ')` | +| `TRIM(chars FROM)` | Remove specified characters from both ends | `RETURN TRIM('_' FROM '_abc_')` | +| `TRIM(BOTH chars FROM)` | Remove specified characters from both ends (explicit) | `RETURN TRIM(BOTH '_' FROM '_abc_')` | +| `TRIM(LEADING chars FROM)` | Remove specified characters from left end | `RETURN TRIM(LEADING 'd' FROM 'dddabc')` | +| `TRIM(TRAILING chars FROM)` | Remove specified characters from right end | `RETURN TRIM(TRAILING 't' FROM 'abcttt')` | +| `STRING_JOIN()` | Join array elements with delimiter | `RETURN STRING_JOIN(["a", "b" \|\| "c"], "-")` | +| **Type Conversion** | | | | `CAST()` | Convert data types | `CAST(person.age AS STRING)` | | **Date/Time Functions** | | `ZONED_DATETIME()` | Create datetime from string | `ZONED_DATETIME('2024-01-01')` | From 4d6dec728bbeea219df9c9b4f92b422d640743e6 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 18:54:13 +0200 Subject: [PATCH 59/67] Clarify optional matches support in GQL documentation --- data-explorer/kusto/query/graph-query-language-functions.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 663039550d..701d67e23b 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -123,7 +123,7 @@ RETURN COUNT(*) > 0 AS HasSuspiciousActivity - **No `INSERT`/`CREATE` support**: Operations to change graph structures are not supported. Instead, use KQL for all graph creation, change, and management tasks. -- **Optional matches**: Supported only for node patterns (not edges) and only on persistent graphs. Optional matching isn't available for transient, in-query graphs. +- **Optional matches**: Supported only for node patterns (not edges). - **Entity equivalence checks not supported**: GQL's`(MATCH (n)-[]-(n2) WHERE n1 <> n2)` is not supported. Use explicit field comparisons instead, for example, `n.id <> n2.id`. From 69f70c2e90ffde3ce433288384099a1f6724ec67 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Mon, 1 Sep 2025 19:09:45 +0200 Subject: [PATCH 60/67] Add FILTER keyword explanation and example to GQL use cases documentation --- .../query/graph-query-language-functions.md | 1 + .../query/graph-query-language-use-cases.md | 24 +++++++++++++++++++ 2 files changed, 25 insertions(+) diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md index 701d67e23b..04945b6b15 100644 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ b/data-explorer/kusto/query/graph-query-language-functions.md @@ -29,6 +29,7 @@ This table lists the core GQL functions and operators, along with their Kusto Qu | `OPTIONAL MATCH` | Find patterns that might not exist | `OPTIONAL MATCH (p)->(c:City)` | | **Filtering** | | `WHERE` | Filter patterns and properties | `WHERE person.age > 25` | +| `FILTER` | Equivalent to WHERE but used without MATCH clauses | `FILTER p.name = 'Carol' OR c.name = 'Seattle'` | | `IS NULL` | Check for null values | `WHERE person.age IS NULL` | | `IS NOT NULL` | Check for non-null values | `WHERE person.age IS NOT NULL` | | **Projection** | diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index 8a68fde2a3..a62bd902c6 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -608,6 +608,30 @@ This query creates a named path variable `full_path` that captures a two-hop pat ## Complex multi-pattern examples +### Cross product filtering with FILTER + +When using multiple separate `MATCH` patterns, GQL creates a cross product of all matching combinations. The `FILTER` keyword allows you to filter this cross product based on conditions involving variables from different patterns. + +This example shows how to filter the cross product of persons and cities: + + +```gql +MATCH (p:Person) +MATCH (c:City) +FILTER p.name = 'Carol' or c.name = 'Seattle' +RETURN p.name as Person, c.name as City +LIMIT 2 +``` + +**Output** + +| Person | City | +|---|---| +| Carol | Portland | +| David | Seattle | + +This query demonstrates how the `FILTER` keyword operates on the cross product of all Person nodes and all City nodes, returning only the combinations where either the person's name is 'Carol' or the city's name is 'Seattle'. + ### Cross-town *likes* with company filter This example combines multiple patterns and filters in one statement: From f623bc157166398303e720df4323a8e95f9d763c Mon Sep 17 00:00:00 2001 From: Yifat Schachter Date: Tue, 2 Sep 2025 08:14:02 +0300 Subject: [PATCH 61/67] fixed link --- data-explorer/kusto/management/update-table-command.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data-explorer/kusto/management/update-table-command.md b/data-explorer/kusto/management/update-table-command.md index 7f34e86e3f..c034fcaf55 100644 --- a/data-explorer/kusto/management/update-table-command.md +++ b/data-explorer/kusto/management/update-table-command.md @@ -68,7 +68,7 @@ You must have at least [Table Admin](../access-control/role-based-access-control | -------- | ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | *whatif* | bool | If `true`, returns the number of records that will be appended / deleted in every shard, without appending / deleting any records. The default is `false`. | | *distributed* | bool | If `true`, the command ingests from all nodes executing the query in parallel. This option is relevant only for update based on ingest from query. Default is `false`. See [performance tips](#performance-tips). | -| *extentTagsToMove* | string | Optional [Extent tags](extent-tags.md) to filter only specific extents, when using update using move extents. The tags are provided as an array, in the same format as in [Ingestion properties](../includes/ingestion-properties.md). See examples in [Update using move extents](#update-using-move-extents).| +| *extentTagsToMove* | string | Optional [Extent tags](extent-tags.md) to filter only specific extents, when using update using move extents. The tags are provided as an array, in the same format as in [Ingestion properties](../ingestion-properties.md). See examples in [Update using move extents](#update-using-move-extents).| > [!IMPORTANT] > We recommend running in `whatif` mode first before executing the update to validate the predicates before deleting or appending data. From 05875597474b751f819c4ea17fa2f3d93e71af70 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Wed, 3 Sep 2025 10:31:57 +0200 Subject: [PATCH 62/67] Add GQL fundamentals documentation with comprehensive coverage of core concepts and examples --- .../graph-query-language-fundamentals.md | 266 ++++++++++++++++++ .../kusto/query/graph-query-language.md | 1 + 2 files changed, 267 insertions(+) create mode 100644 data-explorer/kusto/query/graph-query-language-fundamentals.md diff --git a/data-explorer/kusto/query/graph-query-language-fundamentals.md b/data-explorer/kusto/query/graph-query-language-fundamentals.md new file mode 100644 index 0000000000..8c3a8924f0 --- /dev/null +++ b/data-explorer/kusto/query/graph-query-language-fundamentals.md @@ -0,0 +1,266 @@ +--- +title: Graph Query Language (GQL) Fundamentals +description: Learn the fundamental concepts of Graph Query Language (GQL) including graph patterns, morphism, and core principles for graph data analysis. +ms.reviewer: herauch +ms.topic: reference +ms.date: 09/02/2025 + +#CustomerIntent: As a developer, I want to understand GQL fundamentals so that I can effectively query graph data. +--- +# Graph Query Language (GQL) fundamentals (preview) + +> [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] + +This article covers the fundamental concepts of Graph Query Language (GQL), including graph patterns, graph morphism, and core principles that form the foundation of graph data analysis. + +> [!NOTE] +> GQL support is in preview. Features and syntax can change based on feedback and ongoing development. + +## Overview + +Graph Query Language (GQL) is built on mathematical graph theory concepts that provide a solid foundation for querying graph data. Understanding these fundamentals helps you write more effective queries and better understand how GQL processes your data. + +## Graph patterns + +Graph patterns are the core building blocks of GQL queries. They describe the structure you want to find in your graph data using a declarative syntax that mirrors the visual representation of graphs. + +### Node patterns + +Node patterns specify how to match individual nodes in your graph: + +```gql +(n) -- Any node +(n:Person) -- Node with Person label +(n:Person&City) -- Node with Person AND City label +(:Person) -- Person node, don't bind variable +``` + +**Key concepts:** + +- **Variable binding**: `(n)` creates a variable `n` that you can reference later in the query +- **Anonymous nodes**: `(:Person)` matches nodes without creating a variable +- **Label filtering**: `:Person` restricts matches to nodes with the Person label +- **Label combinations**: Use `&` for AND, `|` for OR operations + +### Edge patterns + +Edge patterns define how nodes connect to each other: + +```gql +-[e]-> -- Directed edge, any type +-[e:works_at]-> -- Directed edge, works_at type +-[e:knows|likes]-> -- knows OR likes edge +<-[e]- -- Incoming edge +-[e]- -- Undirected (any direction) +``` + +**Key concepts:** + +- **Direction**: `->` for outgoing, `<-` for incoming, `-` for any direction +- **Edge types**: Use labels like `:works_at` to filter by relationship type +- **Multiple types**: `knows|likes` matches either relationship type + +### Label expressions + +Labels provide semantic meaning to nodes and edges. GQL supports complex label expressions: + +```gql +:Person&Company -- Both Person AND Company labels +:Person|Company -- Person OR Company labels +:!Company -- NOT Company label +:(Person|!Company)&City -- Complex expressions with parentheses +``` + +**Operators:** + +- `&` (AND): Node must have all specified labels +- `|` (OR): Node must have at least one specified label +- `!` (NOT): Node must not have the specified label +- `()`: Parentheses for grouping complex expressions + +### Path patterns + +Path patterns describe multi-hop relationships in your graph: + +```gql +(a)-[e1]->(b)-[e2]->(c) -- 2-hop path +(a)-[e]->{2,4}(b) -- 2 to 4 hops +(a)-[e]->{1,}(b) -- 1 or more hops +(a)-[:knows|likes]->{1,3}(b) -- 1-3 hops via knows/likes +p=()-[:works_at]->() -- Binding a path variable +``` + +**Variable-length paths:** + +- `{2,4}`: Exactly 2 to 4 hops +- `{1,}`: 1 or more hops (unbounded) +- `{,3}`: Up to 3 hops +- `{5}`: Exactly 5 hops + +**Path variables:** + +- `p=()->()`: Captures the entire path for later analysis +- Access with `NODES(p)`, `RELATIONSHIPS(p)`, `PATH_LENGTH(p)` + +### Multiple patterns + +GQL supports complex, non-linear graph structures: + +```gql +(a)->(b), (a)->(c) -- Multiple edges from same node +(a)->(b)<-(c), (b)->(d) -- Non-linear structures +``` + +**Pattern composition:** + +- Use commas `,` to separate multiple patterns +- All patterns must match simultaneously +- Variables can be shared across patterns + +GQL supports different path matching modes that control how patterns are matched against graph data. These modes affect performance, result completeness, and the types of paths that are returned. + +## Match modes + +Match modes control how graph elements can be reused across pattern variables within a single MATCH clause. + +### DIFFERENT EDGES (default) + +The default mode in Kusto. A matched edge cannot bind to more than one edge variable, but nodes can be reused freely. + +```gql +MATCH (a)-[r1]->(b)-[r2]->(c) +-- r1 and r2 must be different edges +-- a, b, c can be the same or different nodes +``` + +### REPEATABLE ELEMENTS + +Allows both edges and nodes to be reused across pattern variables without restrictions. + +```gql +MATCH REPEATABLE ELEMENTS (a)-[r1]->(b)-[r2]->(c) +-- r1 and r2 can be the same edge +-- a, b, c can be the same or different nodes +``` + +## Path modes + +Path modes control which types of paths are included in results based on repetition constraints. + +### WALK (default) + +The default GQL path mode. Includes all possible paths with no restrictions on node or edge repetition. + +```gql +MATCH WALK (a)-[]->{1,3}(b) +-- Allows paths with repeating nodes and edges +``` + +### TRAIL + +Filters out paths that have repeating edges. Nodes may repeat, but each edge can only appear once per path. + +```gql +MATCH TRAIL (a)-[]->{1,3}(b) +-- No edge can appear twice in the same path +-- Nodes may repeat +``` + +### ACYCLIC + +Filters out paths that have repeating nodes. Each node can only appear once per path. + +```gql +MATCH ACYCLIC (a)-[]->{1,3}(b) +-- No node can appear twice in the same path +-- Prevents cycles entirely +``` + +### SIMPLE + +Same as ACYCLIC but allows the first and last nodes in the path to be the same (forming a simple cycle). + +```gql +MATCH SIMPLE (a)-[]->{1,3}(b) +-- No node repetition except first/last can match +-- Currently not supported in implementation +``` + +## Mode combinations and Kusto cycles parameter + +Different combinations of match modes and path modes map to specific Kusto `cycles` parameter values: + +### DIFFERENT EDGES mode + +| Path Mode | Single Path | Multi-path ("star" pattern) | +|-----------|-------------|----------------------------| +| WALK | `cycles=unique_edges` | `cycles=unique_edges` (only if all paths are WALK/TRAIL) | +| TRAIL | `cycles=unique_edges` | `cycles=unique_edges` (only if all paths are WALK/TRAIL) | +| ACYCLIC | `cycles=none` | Not supported | +| SIMPLE | Not supported | Not supported | + +### REPEATABLE ELEMENTS mode + +| Path Mode | Single Path | Multi-path ("star" pattern) | +|-----------|-------------|----------------------------| +| WALK | `cycles=all` | `cycles=all` (only if all paths are WALK) | +| TRAIL | `cycles=unique_edges` | Not supported | +| ACYCLIC | `cycles=none` | Not supported | +| SIMPLE | Not supported | Not supported | + +## Syntax examples + +### Basic path modes + +```gql +-- Default (WALK with DIFFERENT EDGES) +MATCH (n)-[]->(m) +RETURN n, m + +-- Explicit WALK mode +MATCH WALK (n)-[]->(m) +RETURN n, m + +-- TRAIL mode - no repeating edges +MATCH TRAIL (n)-[]->{1,3}(m) +RETURN n, m + +-- ACYCLIC mode - no repeating nodes +MATCH ACYCLIC (n)-[]->{1,3}(m) +RETURN n, m +``` + +### Match mode combinations + +```gql +-- DIFFERENT EDGES with WALK (default) +MATCH DIFFERENT EDGES WALK (n)-[]->(m) +RETURN n, m + +-- REPEATABLE ELEMENTS with WALK +MATCH REPEATABLE ELEMENTS WALK (n)-[]->(m) +RETURN n, m + +-- REPEATABLE ELEMENTS with TRAIL +MATCH REPEATABLE ELEMENTS TRAIL (n)-[]->(m) +RETURN n, m +``` + +### Multi-pattern queries + +```gql +-- Multiple WALK patterns (star pattern) +MATCH WALK (n)-[]->(a), WALK (n)-[]->(b) +RETURN n, a, b + +-- Mixed path modes (both must be WALK/TRAIL for multi-path) +MATCH WALK (n)-[]->(a), TRAIL (n)-[]->(b) +RETURN n, a, b +``` + +## Related content + +- [Graph Query Language (GQL) overview](graph-query-language.md) +- [GQL query patterns, examples, and use cases](graph-query-language-use-cases.md) +- [Graph Query Language functions](graph-query-language-functions.md) +- [Graph operators overview](graph-operators.md) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index c00ef57834..20d64e05be 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -162,6 +162,7 @@ The following table shows the result of the query. ## Related content +* [Graph Query Language (GQL) fundamentals](graph-query-language-fundamentals.md) * [Graph operators overview](graph-operators.md) * [make-graph operator](make-graph-operator.md) * [graph-match operator](graph-match-operator.md) From b3d851334098e7cb716b976fcfd21bb36e316819 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Wed, 3 Sep 2025 13:56:27 +0200 Subject: [PATCH 63/67] Clarify edge pattern descriptions in GQL fundamentals documentation --- .../kusto/query/graph-query-language-fundamentals.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/data-explorer/kusto/query/graph-query-language-fundamentals.md b/data-explorer/kusto/query/graph-query-language-fundamentals.md index 8c3a8924f0..201e2b7f07 100644 --- a/data-explorer/kusto/query/graph-query-language-fundamentals.md +++ b/data-explorer/kusto/query/graph-query-language-fundamentals.md @@ -47,10 +47,10 @@ Node patterns specify how to match individual nodes in your graph: Edge patterns define how nodes connect to each other: ```gql --[e]-> -- Directed edge, any type --[e:works_at]-> -- Directed edge, works_at type +-[e]-> -- Directed outgoing edge, any label +-[e:works_at]-> -- Directed edge, works_at label -[e:knows|likes]-> -- knows OR likes edge -<-[e]- -- Incoming edge +<-[e]- -- Directed incoming edge -[e]- -- Undirected (any direction) ``` From 09bf0806f0eba5519afa8166cde0e9c7ecbb4e5f Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Wed, 3 Sep 2025 16:19:05 +0200 Subject: [PATCH 64/67] Update GQL documentation references to include the new GQL reference section --- .../query/graph-query-language-reference.md | 434 ++++++++++++++++++ .../query/graph-query-language-use-cases.md | 4 +- .../kusto/query/graph-query-language.md | 15 +- 3 files changed, 444 insertions(+), 9 deletions(-) create mode 100644 data-explorer/kusto/query/graph-query-language-reference.md diff --git a/data-explorer/kusto/query/graph-query-language-reference.md b/data-explorer/kusto/query/graph-query-language-reference.md new file mode 100644 index 0000000000..7091129098 --- /dev/null +++ b/data-explorer/kusto/query/graph-query-language-reference.md @@ -0,0 +1,434 @@ +--- +title: Graph Query Language (GQL) Reference +description: Reference for Graph Query Language (GQL) including fundamentals, functions, operators, and technical concepts +ms.reviewer: herauch +ms.topic: reference +ms.date: 09/02/2025 + +#CustomerIntent: As a developer, I want to understand GQL fundamentals and reference materials so that I can effectively query graph data. +--- +# Graph Query Language (GQL) reference (preview) + +> [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] + +> [!NOTE] +> GQL support is in preview. Features and syntax can change based on feedback and ongoing development. + +## Introduction + +This reference covers the fundamental concepts, functions, and operators of Graph Query Language (GQL). Graph Query Language (GQL) is built on mathematical graph theory concepts that provide a solid foundation for querying graph data. Understanding these fundamentals helps you write more effective queries and better understand how GQL processes your data. GQL also provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. + +## Fundamental concepts + +This section covers the core concepts that form the foundation of graph data analysis with GQL. + +### Graph patterns + +Graph patterns are the core building blocks of GQL queries. They describe the structure you want to find in your graph data using a declarative syntax that mirrors the visual representation of graphs. + +#### Node patterns + +Node patterns specify how to match individual nodes in your graph: + +```gql +(n) -- Any node +(n:Person) -- Node with Person label +(n:Person&City) -- Node with Person AND City label +(:Person) -- Person node, don't bind variable +``` + +**Key concepts:** + +- **Variable binding**: `(n)` creates a variable `n` that you can reference later in the query +- **Anonymous nodes**: `(:Person)` matches nodes without creating a variable +- **Label filtering**: `:Person` restricts matches to nodes with the Person label +- **Label combinations**: Use `&` for AND, `|` for OR operations + +#### Edge patterns + +Edge patterns define how nodes connect to each other: + +```gql +-[e]-> -- Directed outgoing edge, any label +-[e:works_at]-> -- Directed edge, works_at label +-[e:knows|likes]-> -- knows OR likes edge +<-[e]- -- Directed incoming edge +-[e]- -- Undirected (any direction) +``` + +**Key concepts:** + +- **Direction**: `->` for outgoing, `<-` for incoming, `-` for any direction +- **Edge types**: Use labels like `:works_at` to filter by relationship type +- **Multiple types**: `knows|likes` matches either relationship type + +#### Label expressions + +Labels provide semantic meaning to nodes and edges. GQL supports complex label expressions: + +```gql +:Person&Company -- Both Person AND Company labels +:Person|Company -- Person OR Company labels +:!Company -- NOT Company label +:(Person|!Company)&City -- Complex expressions with parentheses +``` + +**Operators:** + +- `&` (AND): Node must have all specified labels +- `|` (OR): Node must have at least one specified label +- `!` (NOT): Node must not have the specified label +- `()`: Parentheses for grouping complex expressions + +#### Path patterns + +Path patterns describe multi-hop relationships in your graph: + +```gql +(a)-[e1]->(b)-[e2]->(c) -- 2-hop path +(a)-[e]->{2,4}(b) -- 2 to 4 hops +(a)-[e]->{1,}(b) -- 1 or more hops +(a)-[:knows|likes]->{1,3}(b) -- 1-3 hops via knows/likes +p=()-[:works_at]->() -- Binding a path variable +``` + +**Variable-length paths:** + +- `{2,4}`: Exactly 2 to 4 hops +- `{1,}`: 1 or more hops (unbounded) +- `{,3}`: Up to 3 hops +- `{5}`: Exactly 5 hops + +**Path variables:** + +- `p=()->()`: Captures the entire path for later analysis +- Access with `NODES(p)`, `RELATIONSHIPS(p)`, `PATH_LENGTH(p)` + +#### Multiple patterns + +GQL supports complex, non-linear graph structures: + +```gql +(a)->(b), (a)->(c) -- Multiple edges from same node +(a)->(b)<-(c), (b)->(d) -- Non-linear structures +``` + +**Pattern composition:** + +- Use commas `,` to separate multiple patterns +- All patterns must match simultaneously +- Variables can be shared across patterns + +### Match modes + +GQL supports different path matching modes that control how patterns are matched against graph data. These modes affect performance, result completeness, and the types of paths that are returned. + +Match modes control how graph elements can be reused across pattern variables within a single MATCH clause. + +#### DIFFERENT EDGES (default) + +The default mode in Kusto. A matched edge cannot bind to more than one edge variable, but nodes can be reused freely. + +```gql +MATCH (a)-[r1]->(b)-[r2]->(c) +-- r1 and r2 must be different edges +-- a, b, c can be the same or different nodes +``` + +#### REPEATABLE ELEMENTS + +Allows both edges and nodes to be reused across pattern variables without restrictions. + +```gql +MATCH REPEATABLE ELEMENTS (a)-[r1]->(b)-[r2]->(c) +-- r1 and r2 can be the same edge +-- a, b, c can be the same or different nodes +``` + +### Path modes + +Path modes control which types of paths are included in results based on repetition constraints. + +#### WALK (default) + +The default GQL path mode. Includes all possible paths with no restrictions on node or edge repetition. + +```gql +MATCH WALK (a)-[]->{1,3}(b) +-- Allows paths with repeating nodes and edges +``` + +#### TRAIL + +Filters out paths that have repeating edges. Nodes may repeat, but each edge can only appear once per path. + +```gql +MATCH TRAIL (a)-[]->{1,3}(b) +-- No edge can appear twice in the same path +-- Nodes may repeat +``` + +#### ACYCLIC + +Filters out paths that have repeating nodes. Each node can only appear once per path. + +```gql +MATCH ACYCLIC (a)-[]->{1,3}(b) +-- No node can appear twice in the same path +-- Prevents cycles entirely +``` + +#### SIMPLE + +Same as ACYCLIC but allows the first and last nodes in the path to be the same (forming a simple cycle). + +```gql +MATCH SIMPLE (a)-[]->{1,3}(b) +-- No node repetition except first/last can match +-- Currently not supported in implementation +``` + +### Mode combinations and Kusto cycles parameter + +Different combinations of match modes and path modes map to specific Kusto `cycles` parameter values: + +#### DIFFERENT EDGES mode + +| Path Mode | Single Path | Multi-path ("star" pattern) | +|-----------|-------------|----------------------------| +| WALK | `cycles=unique_edges` | `cycles=unique_edges` (only if all paths are WALK/TRAIL) | +| TRAIL | `cycles=unique_edges` | `cycles=unique_edges` (only if all paths are WALK/TRAIL) | +| ACYCLIC | `cycles=none` | Not supported | +| SIMPLE | Not supported | Not supported | + +#### REPEATABLE ELEMENTS mode + +| Path Mode | Single Path | Multi-path ("star" pattern) | +|-----------|-------------|----------------------------| +| WALK | `cycles=all` | `cycles=all` (only if all paths are WALK) | +| TRAIL | `cycles=unique_edges` | Not supported | +| ACYCLIC | `cycles=none` | Not supported | +| SIMPLE | Not supported | Not supported | + +#### Syntax examples + +##### Basic path modes + +```gql +-- Default (WALK with DIFFERENT EDGES) +MATCH (n)-[]->(m) +RETURN n, m + +-- Explicit WALK mode +MATCH WALK (n)-[]->(m) +RETURN n, m + +-- TRAIL mode - no repeating edges +MATCH TRAIL (n)-[]->{1,3}(m) +RETURN n, m + +-- ACYCLIC mode - no repeating nodes +MATCH ACYCLIC (n)-[]->{1,3}(m) +RETURN n, m +``` + +##### Match mode combinations + +```gql +-- DIFFERENT EDGES with WALK (default) +MATCH DIFFERENT EDGES WALK (n)-[]->(m) +RETURN n, m + +-- REPEATABLE ELEMENTS with WALK +MATCH REPEATABLE ELEMENTS WALK (n)-[]->(m) +RETURN n, m + +-- REPEATABLE ELEMENTS with TRAIL +MATCH REPEATABLE ELEMENTS TRAIL (n)-[]->(m) +RETURN n, m +``` + +##### Multi-pattern queries + +```gql +-- Multiple WALK patterns (star pattern) +MATCH WALK (n)-[]->(a), WALK (n)-[]->(b) +RETURN n, a, b + +-- Mixed path modes (both must be WALK/TRAIL for multi-path) +MATCH WALK (n)-[]->(a), TRAIL (n)-[]->(b) +RETURN n, a, b +``` + +## Functions and operators reference + +Graph Query Language (GQL) provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. + +### Core GQL functions and operators + +This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. + +| GQL Function/Operator | Description | GQL Example | +|---|---|---| +| `MATCH` | Find graph patterns | `MATCH (a)-[r]->(b)` | +| `OPTIONAL MATCH` | Find patterns that might not exist | `OPTIONAL MATCH (p)->(c:City)` | +| `WHERE` | Filter patterns and properties | `WHERE person.age > 25` | +| `FILTER` | Equivalent to WHERE but used without MATCH clauses | `FILTER p.name = 'Carol' OR c.name = 'Seattle'` | +| `IS NULL` | Check for null values | `WHERE person.age IS NULL` | +| `IS NOT NULL` | Check for non-null values | `WHERE person.age IS NOT NULL` | +| `RETURN` | Project results | `RETURN person.name, person.age` | +| `DISTINCT` | Return unique values | `RETURN DISTINCT person.name` | +| `COUNT(*)` | Count all rows | `RETURN COUNT(*)` | +| `COUNT()` | Count non-null values | `RETURN COUNT(person.name)` | +| `SUM()` | Sum numeric values | `RETURN SUM(person.age)` | +| `MIN()` | Minimum value | `RETURN MIN(person.age)` | +| `MAX()` | Maximum value | `RETURN MAX(person.age)` | +| `AVG()` | Average value | `RETURN AVG(person.age)` | +| `COLLECT_LIST()` | Collect values into array | `RETURN COLLECT_LIST(person.name)` | +| `SIZE()` | Array length | `RETURN SIZE(COLLECT_LIST(n.firstName))` | +| `Labels()` | Show labels for a node or edge | `RETURN labels(entity)` | +| `UPPER()` | Convert to uppercase | `RETURN UPPER(person.name)` | +| `LOWER()` | Convert to lowercase | `RETURN LOWER(person.name)` | +| `LEFT()` | Extract left substring | `WHERE LEFT(person.name, 3) = 'Tom'` | +| `RIGHT()` | Extract right substring | `WHERE RIGHT(person.name, 5) = 'Hanks'` | +| `STARTS WITH` | String starts with pattern | `WHERE person.name STARTS WITH 'Tom'` | +| `ENDS WITH` | String ends with pattern | `WHERE person.name ENDS WITH 'Hanks'` | +| `CONTAINS` | String contains pattern | `WHERE person.name CONTAINS 'Tom'` | +| `\|\|` | String concatenation | `RETURN n.firstName \|\| ' ' \|\| n.lastName` | +| `TRIM()` | Remove whitespace from both ends | `RETURN TRIM(' abc ')` | +| `BTRIM()` | Remove whitespace from both ends (alias for TRIM) | `RETURN BTRIM(' abc ')` | +| `LTRIM()` | Remove whitespace from left end | `RETURN LTRIM(' abc ')` | +| `RTRIM()` | Remove whitespace from right end | `RETURN RTRIM(' abc ')` | +| `TRIM(BOTH FROM)` | Remove whitespace from both ends (explicit) | `RETURN TRIM(BOTH FROM ' abc ')` | +| `TRIM(LEADING FROM)` | Remove whitespace from left end (explicit) | `RETURN TRIM(LEADING FROM ' abc')` | +| `TRIM(TRAILING FROM)` | Remove whitespace from right end (explicit) | `RETURN TRIM(TRAILING FROM 'abc ')` | +| `TRIM(chars FROM)` | Remove specified characters from both ends | `RETURN TRIM('_' FROM '_abc_')` | +| `TRIM(BOTH chars FROM)` | Remove specified characters from both ends (explicit) | `RETURN TRIM(BOTH '_' FROM '_abc_')` | +| `TRIM(LEADING chars FROM)` | Remove specified characters from left end | `RETURN TRIM(LEADING 'd' FROM 'dddabc')` | +| `TRIM(TRAILING chars FROM)` | Remove specified characters from right end | `RETURN TRIM(TRAILING 't' FROM 'abcttt')` | +| `STRING_JOIN()` | Join array elements with delimiter | `RETURN STRING_JOIN(["a", "b" \|\| "c"], "-")` | +| `CAST()` | Convert data types | `CAST(person.age AS STRING)` | +| `ZONED_DATETIME()` | Create datetime from string | `ZONED_DATETIME('2024-01-01')` | +| `CURRENT_TIMESTAMP` | Current timestamp | `WHERE created < CURRENT_TIMESTAMP` | +| `DURATION()` | Create timespan from time units (days to nanoseconds) | `DURATION({days: 3, hours: 2})` | +| `DURATION_BETWEEN()` | Calculate duration between two datetime values | `DURATION_BETWEEN(start_time, end_time)` | +| `NODES()` | Extract nodes from a path | `RETURN NODES(path_variable)` | +| `RELATIONSHIPS()` | Extract edges from a path | `RETURN RELATIONSHIPS(path_variable)` | +| `PATH_LENGTH()` | Get the length of a path | `RETURN PATH_LENGTH(path_variable)` | +| `ORDER BY` | Sort results | `ORDER BY person.age DESC` | +| `LIMIT` | Limit result count | `LIMIT 10` | +| `&` (AND) | Label intersection | `MATCH (p:Person & Male)` | +| `\|` (OR) | Label union | `MATCH (n:Person \| Movie)` | +| `!` (NOT) | Label negation | `MATCH (p:!Female)` | + +### Best practices + +- KQL supports dynamic types, but GQL does not clearly define how these should be handled. To avoid runtime errors, explicitly cast nested fields to their expected type (see `CAST`). + +- Because GQL runs on KQL, some operations inherit KQL semantics. For instance, `COLLECT_LIST` (the GQL equivalent of `make_list`) will flatten arrays if the field is already an array. If results differ from expectations, consult the KQL documentation for the equivalent function. + +### Performance optimization + +Use these strategies to optimize GQL query performance in production environments: + +> [!TIP] +> Start with simple patterns, then increase complexity if needed. Monitor query performance, and adjust path lengths and filters to improve results. + +**Limit path matching scope**: + +- Use specific label filters to reduce the search space: `MATCH (start:SpecificType)` instead of `MATCH (start)` +- Limit variable length paths with reasonable bounds: `MATCH (a)-[]->{1,3}(b)` instead of unbounded paths +- Apply `WHERE` clauses early to filter results before expensive operations. + +**Use COUNT(*) for existence checks**: + +If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results. + +```gql +MATCH (user:User)-[:SUSPICIOUS_ACTIVITY]->(target) +WHERE user.id = 'user123' +RETURN COUNT(*) > 0 AS HasSuspiciousActivity +``` + +### Limitations + +- **Reserved keywords**: Some GQL keywords can't be used as identifiers in queries. Some reserved keywords aren't immediately obvious (for example, `DATE` is a reserved keyword). If your graph data has property names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. + + > [!IMPORTANT] + > When you design your graph schema, some common property names might conflict with GQL reserved keywords. Avoid or rename these property names. + +- **No `INSERT`/`CREATE` support**: Operations to change graph structures are not supported. Instead, use KQL for all graph creation, change, and management tasks. + +- **Optional matches**: Supported only for node patterns (not edges). + +- **Entity equivalence checks not supported**: GQL's`(MATCH (n)-[]-(n2) WHERE n1 <> n2)` is not supported. Use explicit field comparisons instead, for example, `n.id <> n2.id`. + +- **Time and timezone**: The engine operates in UTC. Datetime literals must use zoned datetime; only the UTC zone is supported via `ZONED_DATETIME("2011-12-31 23:59:59.9")`. + +- **Duration granularity**: Durations support up to days and smaller units down to nanoseconds. Larger-than-day units (for example, weeks, months, years) aren't supported. + +- **Traversal modes**: GQL defines configurable traversal modes for matching and paths. For `MATCH`, the modes are `'DIFFERENT EDGES'` and `'REPEATABLE EDGES'`; for `PATH`, the modes are `'WALK'`, `'TRAIL'`, `'ACYCLIC'`, and `'SIMPLE'`. The current implementation defaults to `'DIFFERENT EDGES'` and `'WALK'`, respectively. +Some combinations are not supported. + +### Labels() custom GQL function + +The `labels()` function shows the labels for a node or edge as an array. + +**Syntax:** + +`labels(`*entity*`) + +**Parameters:** + +- `entity`: A node or edge variable from a matched pattern. + +**Returns:** + +Returns an array of strings with all labels for the specified entity. + +**Examples:** + +Show labels for matched nodes: + +```gql +MATCH (entity) +RETURN entity.name, labels(entity) +``` + +#### Output + +This query shows the name and all labels for each node in the graph. + +| entity.name | labels(entity) | +|--|--| +| john.doe | ["User"] | +| admin.user | ["User"] | +| web-server | ["System"] | +| database | ["System"] | +| domain-controller | ["System"] | + +Show labels in projections with aliases: + +```gql +MATCH (n)-[e]->(target) +RETURN n.name, labels(n) AS n_labels, labels(e) AS edge_labels, target.name +``` + +This query shows node names, their labels, and the labels of connecting edges. + +| n.name | n_labels | edge_labels | target.name | +|--|--|--|--| +| john.doe | ["User"] | ["CAN_ACCESS"] | web-server | +| admin.user | ["User"] | ["CAN_ACCESS"] | domain-controller | +| web-server | ["System"] | ["CAN_ACCESS"] | database | +| domain-controller | ["System"] | ["CAN_ACCESS"] | database | + +## Related content + +- [Graph Query Language (GQL) overview](graph-query-language.md) +- [GQL query patterns, examples, and use cases](graph-query-language-use-cases.md) +- [Graph operators overview](graph-operators.md) +- [make-graph operator](make-graph-operator.md) +- [graph-match operator](graph-match-operator.md) +- [Tutorial: Create your first graph](tutorials/your-first-graph.md) +- [Graph functions reference](graph-function.md) diff --git a/data-explorer/kusto/query/graph-query-language-use-cases.md b/data-explorer/kusto/query/graph-query-language-use-cases.md index a62bd902c6..16c18ba172 100644 --- a/data-explorer/kusto/query/graph-query-language-use-cases.md +++ b/data-explorer/kusto/query/graph-query-language-use-cases.md @@ -33,7 +33,7 @@ The following examples show the GQL syntax supported, from simple to complex pat * [Temporal data analysis with DURATION functions](#temporal-data-analysis-with-duration-functions) -For a full list of supported GQL functions and operators, see [Graph Query Language Functions](graph-query-language-functions.md). +For a full list of supported GQL functions and operators, see [Graph Query Language (GQL) reference](graph-query-language-reference.md). ## Basic pattern matching examples @@ -761,4 +761,4 @@ This query suggests friends for Karl who have mutual connections and live in the ## Related topics * [Graph Query Language (GQL)](graph-query-language.md) -* [Graph Query Language (GQL) reference](graph-query-language-functions.md) +* [Graph Query Language (GQL) reference](graph-query-language-reference.md) diff --git a/data-explorer/kusto/query/graph-query-language.md b/data-explorer/kusto/query/graph-query-language.md index 20d64e05be..9bbef1b498 100644 --- a/data-explorer/kusto/query/graph-query-language.md +++ b/data-explorer/kusto/query/graph-query-language.md @@ -124,7 +124,7 @@ After you finish setup, run GQL queries using standard GQL syntax. Use the examp ## Examples -Find basic examples for pattern matching and labels here. For more complex examples, see [GQL query patterns, examples, and common scenarios](graph-query-language-use-cases.md). +Find basic examples for pattern matching and labels here. For more complex examples, see [GQL query patterns, examples, and common scenarios](graph-query-language-use-cases.md). For reference documentation including fundamentals and function details, see [Graph Query Language (GQL) reference](graph-query-language-reference.md). ### Example GQL query for basic pattern matching @@ -162,9 +162,10 @@ The following table shows the result of the query. ## Related content -* [Graph Query Language (GQL) fundamentals](graph-query-language-fundamentals.md) -* [Graph operators overview](graph-operators.md) -* [make-graph operator](make-graph-operator.md) -* [graph-match operator](graph-match-operator.md) -* [Tutorial: Create your first graph](tutorials/your-first-graph.md) -* [Graph functions reference](graph-function.md) +- [Graph Query Language (GQL) reference](graph-query-language-reference.md) +- [GQL query patterns, examples, and use cases](graph-query-language-use-cases.md) +- [Graph operators overview](graph-operators.md) +- [make-graph operator](make-graph-operator.md) +- [graph-match operator](graph-match-operator.md) +- [Tutorial: Create your first graph](tutorials/your-first-graph.md) +- [Graph functions reference](graph-function.md) From c90dcd3f697f58dad742578372ee1541541fe503 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Wed, 3 Sep 2025 17:08:32 +0200 Subject: [PATCH 65/67] Update GQL section in TOC to include reference documentation --- data-explorer/kusto/query/toc.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/data-explorer/kusto/query/toc.yml b/data-explorer/kusto/query/toc.yml index f98fe6e983..7f1be5e2c4 100644 --- a/data-explorer/kusto/query/toc.yml +++ b/data-explorer/kusto/query/toc.yml @@ -1335,8 +1335,8 @@ items: items: - name: Query data using GQL href: graph-query-language.md - - name: GQL functions and operators - href: graph-query-language-functions.md + - name: GQL reference + href: graph-query-language-reference.md - name: GQL examples and use cases href: graph-query-language-use-cases.md - name: Geospatial From bbd0c073b4e4d81e8f7389902ccf7f293d7075d1 Mon Sep 17 00:00:00 2001 From: Sreedhar Pelluru <6722422+spelluru@users.noreply.github.com> Date: Wed, 3 Sep 2025 11:12:27 -0400 Subject: [PATCH 66/67] Fixes, Improved Acrolynx score --- .../kusto/management/update-table-command.md | 57 ++++++++++--------- 1 file changed, 31 insertions(+), 26 deletions(-) diff --git a/data-explorer/kusto/management/update-table-command.md b/data-explorer/kusto/management/update-table-command.md index c034fcaf55..7709ac1a73 100644 --- a/data-explorer/kusto/management/update-table-command.md +++ b/data-explorer/kusto/management/update-table-command.md @@ -1,19 +1,19 @@ --- -title: .update table command -description: Learn how to use the .update table command to perform transactional data updates. +title: The update table command +description: Learn how to use update table command to perform transactional data updates. ms.reviewer: vplauzon ms.topic: reference ms.date: 11/18/2024 --- -# .update table command +# `.update table` command > [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] The `.update table` command performs data updates in a specified table by deleting and appending records atomically. -There are 2 options for appending the records: +There are two options for appending the records: -1. Ingest the records based on a provided query. The query is noted using the *AppendIdentifier*. -1. Move extents containing the records to append from another table to the target table. See [Update using move extents](#update-using-move-extents) for more details. +- Ingest the records based on a provided query. The query is noted using the *AppendIdentifier*. +- Move extents containing the records to append from another table to the target table. For more information, see [Update using move extents](#update-using-move-extents). > [!WARNING] > This command is unrecoverable. @@ -31,16 +31,20 @@ You must have at least [Table Admin](../access-control/role-based-access-control [!INCLUDE [syntax-conventions-note](../includes/syntax-conventions-note.md)] -**Update using append and delete:** +**Update using `append` and `delete`:** -`.update` `[async]` `table` *TableName* `delete` *DeleteIdentifier* `append` *AppendIdentifier* [`with` `(` *propertyName* `=` *propertyValue* `)`] `<|`
-`let` *DeleteIdentifier*`=` *DeletePredicate*`;`
-`let` *AppendIdentifier*`=` *AppendPredicate*`;` +```kusto +.update [async] table TableName delete DeleteIdentifier append AppendIdentifier [with ( propertyName = propertyValue )] <| +let DeleteIdentifier = DeletePredicate; +let AppendIdentifier = AppendPredicate; +``` -**Update using move extents and delete:** +**Update using `move extents` and `delete`:** -`.update` `[async]` `table` *TableName* `delete` *DeleteIdentifier* `move` *SourceTableName* [`with` `(` *propertyName* `=` *propertyValue* `)`] `<|`
-`let` *DeleteIdentifier*`=` *DeletePredicate*`;`
+```kusto +.update [async] table TableName delete DeleteIdentifier move SourceTableName [with ( *propertyName = propertyValue )] <| +let DeleteIdentifier = DeletePredicate; +``` ### Parameters @@ -60,15 +64,15 @@ You must have at least [Table Admin](../access-control/role-based-access-control > * Append and delete queries are expected to produce deterministic results. Nondeterministic queries can lead to unexpected results. A query is deterministic if and only if it would return the same data if executed multiple times. > * For example, use of [`take` operator](../query/take-operator.md), [`sample` operator](../query/sample-operator.md), [`rand` function](../query/rand-function.md), and other such operators isn't recommended because these operators aren't deterministic. > * Queries might be executed more than once within the `update` execution. If the intermediate query results are inconsistent, the update command can produce unexpected results. -> * The delete and append predicates are based on the same snapshot of the table, and therefore they cannot depend on each other. In other words, the append predicate executes on a snapshot of the source table *before* the deletion and vice versa - the delete predicate executes on a snapshot of the source table *before* the append. +> * The delete and append predicates are based on the same snapshot of the table, and therefore they can't depend on each other. In other words, the append predicate executes on a snapshot of the source table *before* the deletion and vice versa - the delete predicate executes on a snapshot of the source table *before* the append. ## Supported properties | Name | Type | Description | | -------- | ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | -| *whatif* | bool | If `true`, returns the number of records that will be appended / deleted in every shard, without appending / deleting any records. The default is `false`. | -| *distributed* | bool | If `true`, the command ingests from all nodes executing the query in parallel. This option is relevant only for update based on ingest from query. Default is `false`. See [performance tips](#performance-tips). | -| *extentTagsToMove* | string | Optional [Extent tags](extent-tags.md) to filter only specific extents, when using update using move extents. The tags are provided as an array, in the same format as in [Ingestion properties](../ingestion-properties.md). See examples in [Update using move extents](#update-using-move-extents).| +| `whatif` | bool | If `true`, returns the number of records that are appended / deleted in every shard, without appending / deleting any records. The default is `false`. | +| `distributed` | bool | If `true`, the command ingests from all nodes executing the query in parallel. This option is relevant only for update based on ingest from query. Default is `false`. See [performance tips](#performance-tips). | +| `extentTagsToMove` | string | Optional [Extent tags](extent-tags.md) to filter only specific extents, when using update using move extents. The tags are provided as an array, in the same format as in [Ingestion properties](../ingestion-properties.md). See examples in [Update using move extents](#update-using-move-extents).| > [!IMPORTANT] > We recommend running in `whatif` mode first before executing the update to validate the predicates before deleting or appending data. @@ -86,12 +90,13 @@ The result of the command is a table where each record represents an [extent](ex ## Update using move extents -When using this option, the records to append to target table are moved from the provided *SourceTableName* using [move extents](move-extents.md). The update moves *all* extents from the table, or only those that match the provided *extentTagsToMove*, if *extentTagsToMove* are specified in the command properties. The source table must be a local table in the current database and must the same schema as the target table. -The move extents option is useful when the records to append are already ingested to a staging table in the database, and should replace existing records in the target table. When this is the case, then using the move extents option is more efficient than ingest from query, as this option doesn't require re-ingesting the records. See examples in [Update rows from a staging table using move extents](#update-rows-from-a-staging-table-using-move-extents). +When you use this option, the records to append to target table are moved from the provided *SourceTableName* using [move extents](move-extents.md). The update moves *all* extents from the table, or only those that match the provided *extentTagsToMove*, if *extentTagsToMove* are specified in the command properties. The source table must be a local table in the current database and must the same schema as the target table. + +The move extents option is useful when the records to append are already ingested to a staging table in the database, and should replace existing records in the target table. In this case, using the move extents option is more efficient than ingest from query, as this option doesn't require reingesting the records. See examples in [Update rows from a staging table using move extents](#update-rows-from-a-staging-table-using-move-extents). ## Choose between `.update table` and materialized views -There are scenarios where you could use either the `.update table` command or a [materialized view](materialized-views/materialized-view-overview.md) to achieve the same goal in a table. For example, a materialized view could be used to keep the latest version of each record or an update could be used to update records when a new version is available. +There are scenarios where you could use either the `.update table` command or a [materialized view](materialized-views/materialized-view-overview.md) to achieve the same goal in a table. For example, a materialized view could be used to keep the latest version of each record or an update could be used to update records when a new version is available. Use the following guidelines to decide which method to use: @@ -101,9 +106,9 @@ Use the following guidelines to decide which method to use: ## Performance tips -* Data ingestion is a resource-intensive operation that might affect concurrent activities on the cluster, including running queries. We recommend that you avoid the following resource-intensive actions: running many `.update` commands at once, and intensive use of the *distributed* property. +* Data ingestion is a resource-intensive operation that might affect concurrent activities on the cluster, including running queries. We recommend that you avoid the following resource-intensive actions: running many `.update` commands at once, and intensive use of the *distributed* property. * Limit the append data to less than 1 GB per operation. If necessary, use multiple update commands. -* Set the `distributed` flag to `true` only if the amount of data being produced by the query is large, exceeds 1 GB and doesn't require serialization: multiple nodes can then produce output in parallel. Don't use this flag when query results are small, since it might needlessly generate many small data shards. +* Set the `distributed` flag to `true` only if the amount of data being produced by the query is large, exceeds 1 GB, and doesn't require serialization: multiple nodes can then produce output in parallel. Don't use this flag when query results are small, since it might needlessly generate many small data shards. ## Examples @@ -248,7 +253,7 @@ The next command updates the main table with the data in the staging table, by m | where true; ``` -Some records in the staging table didn't exist in the main table (that is, had `Id>100`) but were still inserted in the main table (upsert behavior). Note that since the command uses move extents, MyStagingTable will be empty after the update. +Some records in the staging table didn't exist in the main table (that is, had `Id>100`) but were still inserted in the main table (upsert behavior). Since the command uses move extents, MyStagingTable will be empty after the update. #### Update from a staging table using move extents with extent tags @@ -269,7 +274,7 @@ In this example, the extents in the staging table include extent tags, and we're ``` The following command updates the main table with the data from the first ingestion ("drop-by:tag1") in the staging table. -Note that the delete part must filter on the extent tags as well, if you would like to only delete records based on this tag. +The delete part must filter on the extent tags as well, if you would like to only delete records based on this tag. The entire MyStagingTable is considered for the delete query, not only the records in the extents with "drop-by:tag1" so the filter must be explicitly added to the delete query. @@ -278,8 +283,8 @@ must be explicitly added to the delete query. let D = Employees | where Id in (MyStagingTable | where extent_tags() has "drop-by:tag1" | project Id); ``` -At the end of the command, MyStagingTable will include only the records from the 2nd ingestion ("drop-by:tag2"), as the command -moves the extents with "drop-by:tag1". +At the end of the command, MyStagingTable includes only the records from the second ingestion ("drop-by:tag2"), as the command +moves the extents with `drop-by:tag1`. ### Compound key From 29ee778f0fbde81d19795a1caa4b7eab2f915027 Mon Sep 17 00:00:00 2001 From: Henning Rauch Date: Wed, 3 Sep 2025 17:51:13 +0200 Subject: [PATCH 67/67] Remove deprecated GQL functions and fundamentals documentation --- .../query/graph-query-language-functions.md | 196 ------------- .../graph-query-language-fundamentals.md | 266 ------------------ 2 files changed, 462 deletions(-) delete mode 100644 data-explorer/kusto/query/graph-query-language-functions.md delete mode 100644 data-explorer/kusto/query/graph-query-language-fundamentals.md diff --git a/data-explorer/kusto/query/graph-query-language-functions.md b/data-explorer/kusto/query/graph-query-language-functions.md deleted file mode 100644 index 04945b6b15..0000000000 --- a/data-explorer/kusto/query/graph-query-language-functions.md +++ /dev/null @@ -1,196 +0,0 @@ ---- -title: Graph Query Language Functions -description: Learn graph query language (GQL) functions and operators for working with nodes, edges, and properties. Find practical examples and KQL equivalents. -ms.reviewer: hraunch -ms.topic: reference -ms.date: 08/14/2025 - -#CustomerIntent: As a data engineer, I want to learn GQL functions so that I can query graph data efficiently. ---- -# Graph query language (GQL) functions and operators (preview) - -Graph Query Language (GQL) is a powerful language for querying graph data. It provides a rich set of functions and operators to work with graph patterns, nodes, edges, and properties. - -> [!NOTE] -> GQL support is currently in preview. Features and syntax can change based on feedback and ongoing development. - -> [!TIP] -> -> GQL uses standardized syntax for graph operations. Use GQL for standardized graph pattern matching, and combine it with KQL operators for more data processing options. - -## Core GQL functions and operators - -This table lists the core GQL functions and operators, along with their Kusto Query Language (KQL) equivalents and examples. - -| GQL Function/Operator | Description | GQL Example | -|---|---|---| -| **Pattern Matching** | -| `MATCH` | Find graph patterns | `MATCH (a)-[r]->(b)` | -| `OPTIONAL MATCH` | Find patterns that might not exist | `OPTIONAL MATCH (p)->(c:City)` | -| **Filtering** | -| `WHERE` | Filter patterns and properties | `WHERE person.age > 25` | -| `FILTER` | Equivalent to WHERE but used without MATCH clauses | `FILTER p.name = 'Carol' OR c.name = 'Seattle'` | -| `IS NULL` | Check for null values | `WHERE person.age IS NULL` | -| `IS NOT NULL` | Check for non-null values | `WHERE person.age IS NOT NULL` | -| **Projection** | -| `RETURN` | Project results | `RETURN person.name, person.age` | -| `DISTINCT` | Return unique values | `RETURN DISTINCT person.name` | -| **Aggregation Functions** | -| `COUNT(*)` | Count all rows | `RETURN COUNT(*)` | -| `COUNT()` | Count non-null values | `RETURN COUNT(person.name)` | -| `SUM()` | Sum numeric values | `RETURN SUM(person.age)` | -| `MIN()` | Minimum value | `RETURN MIN(person.age)` | -| `MAX()` | Maximum value | `RETURN MAX(person.age)` | -| `AVG()` | Average value | `RETURN AVG(person.age)` | -| `COLLECT_LIST()` | Collect values into array | `RETURN COLLECT_LIST(person.name)` | -| `SIZE()` | Array length | `RETURN SIZE(COLLECT_LIST(n.firstName))` | -| **Graph Functions** | -| `Labels()` | Show labels for a node or edge | `RETURN labels(entity)` | -| **String Functions** | -| `UPPER()` | Convert to uppercase | `RETURN UPPER(person.name)` | -| `LOWER()` | Convert to lowercase | `RETURN LOWER(person.name)` | -| `LEFT()` | Extract left substring | `WHERE LEFT(person.name, 3) = 'Tom'` | -| `RIGHT()` | Extract right substring | `WHERE RIGHT(person.name, 5) = 'Hanks'` | -| `STARTS WITH` | String starts with pattern | `WHERE person.name STARTS WITH 'Tom'` | -| `ENDS WITH` | String ends with pattern | `WHERE person.name ENDS WITH 'Hanks'` | -| `CONTAINS` | String contains pattern | `WHERE person.name CONTAINS 'Tom'` | -| `\|\|` | String concatenation | `RETURN n.firstName \|\| ' ' \|\| n.lastName` | -| `TRIM()` | Remove whitespace from both ends | `RETURN TRIM(' abc ')` | -| `BTRIM()` | Remove whitespace from both ends (alias for TRIM) | `RETURN BTRIM(' abc ')` | -| `LTRIM()` | Remove whitespace from left end | `RETURN LTRIM(' abc ')` | -| `RTRIM()` | Remove whitespace from right end | `RETURN RTRIM(' abc ')` | -| `TRIM(BOTH FROM)` | Remove whitespace from both ends (explicit) | `RETURN TRIM(BOTH FROM ' abc ')` | -| `TRIM(LEADING FROM)` | Remove whitespace from left end (explicit) | `RETURN TRIM(LEADING FROM ' abc')` | -| `TRIM(TRAILING FROM)` | Remove whitespace from right end (explicit) | `RETURN TRIM(TRAILING FROM 'abc ')` | -| `TRIM(chars FROM)` | Remove specified characters from both ends | `RETURN TRIM('_' FROM '_abc_')` | -| `TRIM(BOTH chars FROM)` | Remove specified characters from both ends (explicit) | `RETURN TRIM(BOTH '_' FROM '_abc_')` | -| `TRIM(LEADING chars FROM)` | Remove specified characters from left end | `RETURN TRIM(LEADING 'd' FROM 'dddabc')` | -| `TRIM(TRAILING chars FROM)` | Remove specified characters from right end | `RETURN TRIM(TRAILING 't' FROM 'abcttt')` | -| `STRING_JOIN()` | Join array elements with delimiter | `RETURN STRING_JOIN(["a", "b" \|\| "c"], "-")` | -| **Type Conversion** | | | -| `CAST()` | Convert data types | `CAST(person.age AS STRING)` | -| **Date/Time Functions** | -| `ZONED_DATETIME()` | Create datetime from string | `ZONED_DATETIME('2024-01-01')` | -| `CURRENT_TIMESTAMP` | Current timestamp | `WHERE created < CURRENT_TIMESTAMP` | -| `DURATION()` | Create timespan from time units (days to nanoseconds) | `DURATION({days: 3, hours: 2})` | -| `DURATION_BETWEEN()` | Calculate duration between two datetime values | `DURATION_BETWEEN(start_time, end_time)` | -| **Path Functions** | -| `NODES()` | Extract nodes from a path | `RETURN NODES(path_variable)` | -| `RELATIONSHIPS()` | Extract edges from a path | `RETURN RELATIONSHIPS(path_variable)` | -| `PATH_LENGTH()` | Get the length of a path | `RETURN PATH_LENGTH(path_variable)` | -| **Ordering and Limiting** | -| `ORDER BY` | Sort results | `ORDER BY person.age DESC` | -| `LIMIT` | Limit result count | `LIMIT 10` | -| **Label Operations** | -| `&` (AND) | Label intersection | `MATCH (p:Person & Male)` | -| `\|` (OR) | Label union | `MATCH (n:Person \| Movie)` | -| `!` (NOT) | Label negation | `MATCH (p:!Female)` | - -## Best practices - -- KQL supports dynamic types, but GQL does not clearly define how these should be handled. To avoid runtime errors, explicitly cast nested fields to their expected type (see `CAST`). - -- Because GQL runs on KQL, some operations inherit KQL semantics. For instance, `COLLECT_LIST` (the GQL equivalent of `make_list`) will flatten arrays if the field is already an array. If results differ from expectations, consult the KQL documentation for the equivalent function. - -## Performance optimization - -Use these strategies to optimize GQL query performance in production environments: - -> [!TIP] -> Start with simple patterns, then increase complexity if needed. Monitor query performance, and adjust path lengths and filters to improve results. - -**Limit path matching scope**: - -- Use specific label filters to reduce the search space: `MATCH (start:SpecificType)` instead of `MATCH (start)` -- Limit variable length paths with reasonable bounds: `MATCH (a)-[]->{1,3}(b)` instead of unbounded paths -- Apply `WHERE` clauses early to filter results before expensive operations. - -**Use COUNT(*) for existence checks**: - -If you only need to check if a pattern exists, use `COUNT(*)` instead of returning full results. - -```gql -MATCH (user:User)-[:SUSPICIOUS_ACTIVITY]->(target) -WHERE user.id = 'user123' -RETURN COUNT(*) > 0 AS HasSuspiciousActivity -``` - -## Limitations - -- **Reserved keywords**: Some GQL keywords can't be used as identifiers in queries. Some reserved keywords aren't immediately obvious (for example, `DATE` is a reserved keyword). If your graph data has property names that conflict with GQL reserved keywords, use different property names in your graph schema or rename them to avoid parsing conflicts. - - > [!IMPORTANT] - > When you design your graph schema, some common property names might conflict with GQL reserved keywords. Avoid or rename these property names. - -- **No `INSERT`/`CREATE` support**: Operations to change graph structures are not supported. Instead, use KQL for all graph creation, change, and management tasks. - -- **Optional matches**: Supported only for node patterns (not edges). - -- **Entity equivalence checks not supported**: GQL's`(MATCH (n)-[]-(n2) WHERE n1 <> n2)` is not supported. Use explicit field comparisons instead, for example, `n.id <> n2.id`. - -- **Time and timezone**: The engine operates in UTC. Datetime literals must use zoned datetime; only the UTC zone is supported via `ZONED_DATETIME("2011-12-31 23:59:59.9")`. - -- **Duration granularity**: Durations support up to days and smaller units down to nanoseconds. Larger-than-day units (for example, weeks, months, years) aren't supported. - -- **Traversal modes**: GQL defines configurable traversal modes for matching and paths. For `MATCH`, the modes are `'DIFFERENT EDGES'` and `'REPEATABLE EDGES'`; for `PATH`, the modes are `'WALK'`, `'TRAIL'`, `'ACYCLIC'`, and `'SIMPLE'`. The current implementation defaults to `'DIFFERENT EDGES'` and `'WALK'`, respectively. -Some combinations are not supported. - -## Labels() custom GQL function - -The `labels()` function shows the labels for a node or edge as an array. - -**Syntax:** - -`labels(`*entity*`) - -**Parameters:** - -- `entity`: A node or edge variable from a matched pattern. - -**Returns:** - -Returns an array of strings with all labels for the specified entity. - -**Examples:** - -Show labels for matched nodes: - -```gql -MATCH (entity) -RETURN entity.name, labels(entity) -``` - -**Output** - -This query shows the name and all labels for each node in the graph. - -| entity.name | labels(entity) | -|--|--| -| john.doe | ["User"] | -| admin.user | ["User"] | -| web-server | ["System"] | -| database | ["System"] | -| domain-controller | ["System"] | - -Show labels in projections with aliases: - -```gql -MATCH (n)-[e]->(target) -RETURN n.name, labels(n) AS n_labels, labels(e) AS edge_labels, target.name -``` - -**Output** - -This query shows node names, their labels, and the labels of connecting edges. - -| n.name | n_labels | edge_labels | target.name | -|--|--|--|--| -| john.doe | ["User"] | ["CAN_ACCESS"] | web-server | -| admin.user | ["User"] | ["CAN_ACCESS"] | domain-controller | -| web-server | ["System"] | ["CAN_ACCESS"] | database | -| domain-controller | ["System"] | ["CAN_ACCESS"] | database | - -## Related Context - -* [Graph Query Language (GQL)](graph-query-language.md) -* [Graph Query Language (GQL) use cases](graph-query-language-use-cases.md) \ No newline at end of file diff --git a/data-explorer/kusto/query/graph-query-language-fundamentals.md b/data-explorer/kusto/query/graph-query-language-fundamentals.md deleted file mode 100644 index 201e2b7f07..0000000000 --- a/data-explorer/kusto/query/graph-query-language-fundamentals.md +++ /dev/null @@ -1,266 +0,0 @@ ---- -title: Graph Query Language (GQL) Fundamentals -description: Learn the fundamental concepts of Graph Query Language (GQL) including graph patterns, morphism, and core principles for graph data analysis. -ms.reviewer: herauch -ms.topic: reference -ms.date: 09/02/2025 - -#CustomerIntent: As a developer, I want to understand GQL fundamentals so that I can effectively query graph data. ---- -# Graph Query Language (GQL) fundamentals (preview) - -> [!INCLUDE [applies](../includes/applies-to-version/applies.md)] [!INCLUDE [fabric](../includes/applies-to-version/fabric.md)] [!INCLUDE [azure-data-explorer](../includes/applies-to-version/azure-data-explorer.md)] - -This article covers the fundamental concepts of Graph Query Language (GQL), including graph patterns, graph morphism, and core principles that form the foundation of graph data analysis. - -> [!NOTE] -> GQL support is in preview. Features and syntax can change based on feedback and ongoing development. - -## Overview - -Graph Query Language (GQL) is built on mathematical graph theory concepts that provide a solid foundation for querying graph data. Understanding these fundamentals helps you write more effective queries and better understand how GQL processes your data. - -## Graph patterns - -Graph patterns are the core building blocks of GQL queries. They describe the structure you want to find in your graph data using a declarative syntax that mirrors the visual representation of graphs. - -### Node patterns - -Node patterns specify how to match individual nodes in your graph: - -```gql -(n) -- Any node -(n:Person) -- Node with Person label -(n:Person&City) -- Node with Person AND City label -(:Person) -- Person node, don't bind variable -``` - -**Key concepts:** - -- **Variable binding**: `(n)` creates a variable `n` that you can reference later in the query -- **Anonymous nodes**: `(:Person)` matches nodes without creating a variable -- **Label filtering**: `:Person` restricts matches to nodes with the Person label -- **Label combinations**: Use `&` for AND, `|` for OR operations - -### Edge patterns - -Edge patterns define how nodes connect to each other: - -```gql --[e]-> -- Directed outgoing edge, any label --[e:works_at]-> -- Directed edge, works_at label --[e:knows|likes]-> -- knows OR likes edge -<-[e]- -- Directed incoming edge --[e]- -- Undirected (any direction) -``` - -**Key concepts:** - -- **Direction**: `->` for outgoing, `<-` for incoming, `-` for any direction -- **Edge types**: Use labels like `:works_at` to filter by relationship type -- **Multiple types**: `knows|likes` matches either relationship type - -### Label expressions - -Labels provide semantic meaning to nodes and edges. GQL supports complex label expressions: - -```gql -:Person&Company -- Both Person AND Company labels -:Person|Company -- Person OR Company labels -:!Company -- NOT Company label -:(Person|!Company)&City -- Complex expressions with parentheses -``` - -**Operators:** - -- `&` (AND): Node must have all specified labels -- `|` (OR): Node must have at least one specified label -- `!` (NOT): Node must not have the specified label -- `()`: Parentheses for grouping complex expressions - -### Path patterns - -Path patterns describe multi-hop relationships in your graph: - -```gql -(a)-[e1]->(b)-[e2]->(c) -- 2-hop path -(a)-[e]->{2,4}(b) -- 2 to 4 hops -(a)-[e]->{1,}(b) -- 1 or more hops -(a)-[:knows|likes]->{1,3}(b) -- 1-3 hops via knows/likes -p=()-[:works_at]->() -- Binding a path variable -``` - -**Variable-length paths:** - -- `{2,4}`: Exactly 2 to 4 hops -- `{1,}`: 1 or more hops (unbounded) -- `{,3}`: Up to 3 hops -- `{5}`: Exactly 5 hops - -**Path variables:** - -- `p=()->()`: Captures the entire path for later analysis -- Access with `NODES(p)`, `RELATIONSHIPS(p)`, `PATH_LENGTH(p)` - -### Multiple patterns - -GQL supports complex, non-linear graph structures: - -```gql -(a)->(b), (a)->(c) -- Multiple edges from same node -(a)->(b)<-(c), (b)->(d) -- Non-linear structures -``` - -**Pattern composition:** - -- Use commas `,` to separate multiple patterns -- All patterns must match simultaneously -- Variables can be shared across patterns - -GQL supports different path matching modes that control how patterns are matched against graph data. These modes affect performance, result completeness, and the types of paths that are returned. - -## Match modes - -Match modes control how graph elements can be reused across pattern variables within a single MATCH clause. - -### DIFFERENT EDGES (default) - -The default mode in Kusto. A matched edge cannot bind to more than one edge variable, but nodes can be reused freely. - -```gql -MATCH (a)-[r1]->(b)-[r2]->(c) --- r1 and r2 must be different edges --- a, b, c can be the same or different nodes -``` - -### REPEATABLE ELEMENTS - -Allows both edges and nodes to be reused across pattern variables without restrictions. - -```gql -MATCH REPEATABLE ELEMENTS (a)-[r1]->(b)-[r2]->(c) --- r1 and r2 can be the same edge --- a, b, c can be the same or different nodes -``` - -## Path modes - -Path modes control which types of paths are included in results based on repetition constraints. - -### WALK (default) - -The default GQL path mode. Includes all possible paths with no restrictions on node or edge repetition. - -```gql -MATCH WALK (a)-[]->{1,3}(b) --- Allows paths with repeating nodes and edges -``` - -### TRAIL - -Filters out paths that have repeating edges. Nodes may repeat, but each edge can only appear once per path. - -```gql -MATCH TRAIL (a)-[]->{1,3}(b) --- No edge can appear twice in the same path --- Nodes may repeat -``` - -### ACYCLIC - -Filters out paths that have repeating nodes. Each node can only appear once per path. - -```gql -MATCH ACYCLIC (a)-[]->{1,3}(b) --- No node can appear twice in the same path --- Prevents cycles entirely -``` - -### SIMPLE - -Same as ACYCLIC but allows the first and last nodes in the path to be the same (forming a simple cycle). - -```gql -MATCH SIMPLE (a)-[]->{1,3}(b) --- No node repetition except first/last can match --- Currently not supported in implementation -``` - -## Mode combinations and Kusto cycles parameter - -Different combinations of match modes and path modes map to specific Kusto `cycles` parameter values: - -### DIFFERENT EDGES mode - -| Path Mode | Single Path | Multi-path ("star" pattern) | -|-----------|-------------|----------------------------| -| WALK | `cycles=unique_edges` | `cycles=unique_edges` (only if all paths are WALK/TRAIL) | -| TRAIL | `cycles=unique_edges` | `cycles=unique_edges` (only if all paths are WALK/TRAIL) | -| ACYCLIC | `cycles=none` | Not supported | -| SIMPLE | Not supported | Not supported | - -### REPEATABLE ELEMENTS mode - -| Path Mode | Single Path | Multi-path ("star" pattern) | -|-----------|-------------|----------------------------| -| WALK | `cycles=all` | `cycles=all` (only if all paths are WALK) | -| TRAIL | `cycles=unique_edges` | Not supported | -| ACYCLIC | `cycles=none` | Not supported | -| SIMPLE | Not supported | Not supported | - -## Syntax examples - -### Basic path modes - -```gql --- Default (WALK with DIFFERENT EDGES) -MATCH (n)-[]->(m) -RETURN n, m - --- Explicit WALK mode -MATCH WALK (n)-[]->(m) -RETURN n, m - --- TRAIL mode - no repeating edges -MATCH TRAIL (n)-[]->{1,3}(m) -RETURN n, m - --- ACYCLIC mode - no repeating nodes -MATCH ACYCLIC (n)-[]->{1,3}(m) -RETURN n, m -``` - -### Match mode combinations - -```gql --- DIFFERENT EDGES with WALK (default) -MATCH DIFFERENT EDGES WALK (n)-[]->(m) -RETURN n, m - --- REPEATABLE ELEMENTS with WALK -MATCH REPEATABLE ELEMENTS WALK (n)-[]->(m) -RETURN n, m - --- REPEATABLE ELEMENTS with TRAIL -MATCH REPEATABLE ELEMENTS TRAIL (n)-[]->(m) -RETURN n, m -``` - -### Multi-pattern queries - -```gql --- Multiple WALK patterns (star pattern) -MATCH WALK (n)-[]->(a), WALK (n)-[]->(b) -RETURN n, a, b - --- Mixed path modes (both must be WALK/TRAIL for multi-path) -MATCH WALK (n)-[]->(a), TRAIL (n)-[]->(b) -RETURN n, a, b -``` - -## Related content - -- [Graph Query Language (GQL) overview](graph-query-language.md) -- [GQL query patterns, examples, and use cases](graph-query-language-use-cases.md) -- [Graph Query Language functions](graph-query-language-functions.md) -- [Graph operators overview](graph-operators.md)

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a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd deleted file mode 100644 index c6d9c80f5a..0000000000 --- a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd +++ /dev/null @@ -1,42 +0,0 @@ -%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% -graph TD - %% Connected Financial Network - Real People and Entities - HOUSE[House
Person ID: 467
Female, Born 1995] - BARKER[Barker
Person ID: 6597069767083
Transfer Recipient] - - %% Real Accounts (fully connected) - ACC1[Renato Holness
Account: 4619004367821865972
House's Main Account] - ACC2[Luis Thies
Account: 4687121312185844640
Barker's Account] - ACC3[Daniel Joye
Account: 4786200503987995554
Barker's Second Account] - - %% Real Loan and Mediums - LOAN1[Debt Consolidation Loan
ID: 4843058449283547765
Amount: $63.5M] - MEDIUM1[IPv6 Medium
ID: 4398046511850
Risk: Very High] - MEDIUM2[Phone Medium
ID: 30786325577800
Risk: Severe] - - %% Validated Connected Relationships - HOUSE -->|OWN| ACC1 - BARKER -->|OWN| ACC2 - BARKER -->|OWN| ACC3 - HOUSE -->|APPLY| LOAN1 - - %% Financial Transaction Flow - ACC1 -->|TRANSFER
$4.3M| ACC2 - ACC1 -->|TRANSFER
$9.7M| ACC3 - LOAN1 -->|DEPOSIT
$7.2M| ACC1 - ACC1 -->|REPAY
$7.4M| LOAN1 - - %% Authentication Access - MEDIUM1 -->|SIGN_IN| ACC2 - MEDIUM2 -->|SIGN_IN| ACC3 - - %% Styling - classDef person fill:#e1f5fe,stroke:#0277bd,stroke-width:3px - classDef account fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px - classDef loan fill:#fff3e0,stroke:#f57c00,stroke-width:2px - classDef medium fill:#fce4ec,stroke:#c2185b,stroke-width:2px - - class HOUSE,BARKER person - class ACC1,ACC2,ACC3 account - class LOAN1 loan - class MEDIUM1,MEDIUM2 medium \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.png b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.png deleted file mode 100644 index 84f22a9023ea153fab9fbd30a49f9b731ec9701c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 400513 zcmeF3XH=8x7U$#f*abvE6a-YHHjA$PW}eI32*uT4!#|6)l|3-Dd@bi0A3t*y|1W!^5n_UNwoMkh&1Ebgic5P}}aG%_$S 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a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd new file mode 100644 index 0000000000..c6d9c80f5a --- /dev/null +++ b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.mmd @@ -0,0 +1,42 @@ +%%{ init: { 'flowchart': {'defaultRenderer': 'elk' } } }%% +graph TD + %% Connected Financial Network - Real People and Entities + HOUSE[House
Person ID: 467
Female, Born 1995] + BARKER[Barker
Person ID: 6597069767083
Transfer Recipient] + + %% Real Accounts (fully connected) + ACC1[Renato Holness
Account: 4619004367821865972
House's Main Account] + ACC2[Luis Thies
Account: 4687121312185844640
Barker's Account] + ACC3[Daniel Joye
Account: 4786200503987995554
Barker's Second Account] + + %% Real Loan and Mediums + LOAN1[Debt Consolidation Loan
ID: 4843058449283547765
Amount: $63.5M] + MEDIUM1[IPv6 Medium
ID: 4398046511850
Risk: Very High] + MEDIUM2[Phone Medium
ID: 30786325577800
Risk: Severe] + + %% Validated Connected Relationships + HOUSE -->|OWN| ACC1 + BARKER -->|OWN| ACC2 + BARKER -->|OWN| ACC3 + HOUSE -->|APPLY| LOAN1 + + %% Financial Transaction Flow + ACC1 -->|TRANSFER
$4.3M| ACC2 + ACC1 -->|TRANSFER
$9.7M| ACC3 + LOAN1 -->|DEPOSIT
$7.2M| ACC1 + ACC1 -->|REPAY
$7.4M| LOAN1 + + %% Authentication Access + MEDIUM1 -->|SIGN_IN| ACC2 + MEDIUM2 -->|SIGN_IN| ACC3 + + %% Styling + classDef person fill:#e1f5fe,stroke:#0277bd,stroke-width:3px + classDef account fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px + classDef loan fill:#fff3e0,stroke:#f57c00,stroke-width:2px + classDef medium fill:#fce4ec,stroke:#c2185b,stroke-width:2px + + class HOUSE,BARKER person + class ACC1,ACC2,ACC3 account + class LOAN1 loan + class MEDIUM1,MEDIUM2 medium \ No newline at end of file diff --git a/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.png b/data-explorer/kusto/query/media/graphs/graph-example-ldbc-financial-instance.png new file mode 100644 index 0000000000000000000000000000000000000000..84f22a9023ea153fab9fbd30a49f9b731ec9701c GIT binary patch literal 400513 zcmeF3XH=8x7U$#f*abvE6a-YHHjA$PW}eI32*uT4!#|6)l|3-Dd@bi0A3t*y|1W!^5n_UNwoMkh&1Ebgic5P}}aG%_$S zya7-D^Lmx6WO+EyKd*CwM{NH58I0=Y@5x7gy?j}J?&a}cuOPoYfH42{>g3t~-uQpG 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