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learn-build-service-prod merged 8 commits into from
Aug 22, 2025
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Repo sync for protected branch #2714

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c58e251
Set hot windows in an include file
spelluru Aug 22, 2025
eda16c1
Enhance graph documentation: add partitioning options to make-graph o…
cosh Aug 22, 2025
8aadc9e
moved include to cross repo
spelluru Aug 22, 2025
4013010
Term fixes
v-regandowner Aug 22, 2025
1de7562
Merge pull request #7055 from spelluru/adxhotwindows
Stacyrch140 Aug 22, 2025
53bc6b4
Merge pull request #7056 from cosh/tocAndPartitionedGraphs
v-regandowner Aug 22, 2025
2c8ecd8
Merge pull request #7057 from MicrosoftDocs/main
learn-build-service-prod[bot] Aug 22, 2025
a27db69
Merging changes synced from https://github.com/MicrosoftDocs/dataexpl…
Aug 22, 2025
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47 changes: 1 addition & 46 deletions data-explorer/hot-windows.md
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Expand Up @@ -39,52 +39,7 @@ After changing the cache policy, the cluster automatically caches the relevant d

Now you can expect optimal performance during the use of hot windows.

## Set hot windows

Hot windows are part of the [cache policy commands syntax](/kusto/management/show-table-cache-policy-command?view=azure-data-explorer&preserve-view=true) and are set with the [`.alter policy caching` command](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true).

> [!NOTE]
> It can take up to an hour to fully update the cluster disk cache based on the updated cache policy definition.

1. Take note of the initial caching policy by using the `.show policy caching` command.

```kusto
.show table MyDatabase.MyTable policy caching
```

1. Alter the cache policy using the following syntax. Several hot windows may be defined for a single database or table.

```kusto
.alter <entity_type> <database_or_table_or_materialized-view_name> policy caching
hot = <timespan>
[, hot_window = datetime(*from*) .. datetime(*to*)]
[, hot_window = datetime(*from*) .. datetime(*to*)]
...
```

Where:
* `from`: Start time of the hot window (datetime)
* `to`: End time of the hot window (datetime)

For example, queries run under the following settings will examine the last 14 days of data, on data that is kept for three years.

```kusto
.alter table MyTable policy caching
hot = 14d,
hot_window = datetime(2021-01-01) .. datetime(2021-02-01),
hot_window = datetime(2021-04-01) .. datetime(2021-05-01)
```

## Run query

Run the query or queries you want over the time period specified in the hot windows.

## Revert settings

1. Use the original cache settings retrieved above in [Set hot windows](#set-hot-windows).
1. Revert the cache policy to the original settings with the [`.alter policy caching` command](/kusto/management/show-table-cache-policy-command?view=azure-data-explorer&preserve-view=true).

Since you've configured optimized autoscale for that cluster, the cluster will shrink to its original size.
[!INCLUDE [set-hot-windows](includes/cross-repo/set-hot-windows.md)]

## Related content

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51 changes: 51 additions & 0 deletions data-explorer/includes/cross-repo/set-hot-windows.md
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@@ -0,0 +1,51 @@
---
ms.topic: include
ms.date: 07/02/2023
---

## Set hot windows

Hot windows are part of the [cache policy commands syntax](/kusto/management/show-table-cache-policy-command?view=azure-data-explorer&preserve-view=true) and are set with the [`.alter policy caching` command](/kusto/management/cache-policy?view=azure-data-explorer&preserve-view=true).

> [!NOTE]
> It can take up to an hour to fully update the cluster disk cache based on the updated cache policy definition.

1. Take note of the initial caching policy by using the `.show policy caching` command.

```kusto
.show table MyDatabase.MyTable policy caching
```

1. Alter the cache policy using the following syntax. Several hot windows may be defined for a single database or table.

```kusto
.alter <entity_type> <database_or_table_or_materialized-view_name> policy caching
hot = <timespan>
[, hot_window = datetime(*from*) .. datetime(*to*)]
[, hot_window = datetime(*from*) .. datetime(*to*)]
...
```

Where:
* `from`: Start time of the hot window (datetime)
* `to`: End time of the hot window (datetime)

For example, queries run under the following settings will examine the last 14 days of data, on data that is kept for three years.

```kusto
.alter table MyTable policy caching
hot = 14d,
hot_window = datetime(2021-01-01) .. datetime(2021-02-01),
hot_window = datetime(2021-04-01) .. datetime(2021-05-01)
```

## Run query

Run the query or queries you want over the time period specified in the hot windows.

## Revert settings

1. Use the original cache settings retrieved above in [Set hot windows](#set-hot-windows).
1. Revert the cache policy to the original settings with the [`.alter policy caching` command](/kusto/management/show-table-cache-policy-command?view=azure-data-explorer&preserve-view=true).

Since you've configured optimized autoscale for that cluster, the cluster will shrink to its original size.
80 changes: 80 additions & 0 deletions data-explorer/kusto/query/make-graph-operator.md
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Expand Up @@ -17,6 +17,10 @@ The `make-graph` operator builds a graph structure from tabular inputs of edges

*Edges* `|` `make-graph` *SourceNodeId* `-->` *TargetNodeId* [ `with_node_id=` *NodeIdPropertyName* ]

*Edges* `|` `make-graph` *SourceNodeId* `-->` *TargetNodeId* [ `with` *Nodes1* `on` *NodeId1* [`,` *Nodes2* `on` *NodeId2* ]] `partitioned-by` *PartitionColumn* `(` *GraphOperator* `)`

*Edges* `|` `make-graph` *SourceNodeId* `-->` *TargetNodeId* [ `with_node_id=` *NodeIdPropertyName* ] `partitioned-by` *PartitionColumn* `(` *GraphOperator* `)`

## Parameters

| Name | Type | Required | Description |
Expand All @@ -27,11 +31,15 @@ The `make-graph` operator builds a graph structure from tabular inputs of edges
| *Nodes1*, *Nodes2* | `string` | | The tabular expressions containing the properties of the nodes in the graph. |
| *NodesId1*, *NodesId2* | `string` | | The corresponding columns with the node IDs in *Nodes1*, *Nodes2* respectively. |
| *NodeIdPropertyName* | `string` | | The name of the property for node ID on the nodes of the graph. |
| *PartitionColumn* | `string` | | The column to partition the graph by. Creates separate graphs for each unique value in this column. |
| *GraphOperator* | `string` | | The graph operator to apply to each partitioned graph. |

## Returns

The `make-graph` operator returns a graph expression and must be followed by a [graph operator](graph-operators.md#supported-graph-operators). Each row in the source *Edges* expression becomes an edge in the graph with properties that are the column values of the row. Each row in the *Nodes* tabular expression becomes a node in the graph with properties that are the column values of the row. Nodes that appear in the *Edges* table but don't have a corresponding row in the *Nodes* table are created as nodes with the corresponding node ID and empty properties.

When using the `partitioned-by` clause, separate graphs are created for each unique value in the specified *PartitionColumn*. The specified *GraphOperator* is then applied to each partitioned graph independently, and the results are combined into a single output. This is particularly useful for multitenant scenarios where you want to analyze each tenant's data separately while maintaining the same graph structure and analysis logic.

> [!NOTE]
> Each node has a unique identifier. If the same node ID appears in both the *Nodes1* and *Nodes2* tables, a single node is created by merging their properties. If there are conflicting property values for the same node, one of the values is arbitrarily chosen.

Expand Down Expand Up @@ -115,6 +123,78 @@ edges
|---|---|---|
|Mallory|Bob|Trent|

### Multitenant partitioned graph

This example demonstrates using the `partitioned-by` clause to analyze a multitenant social network. The `partitioned-by` clause creates separate graphs for each unique value in the partition column (in this case, `tenantId`), applies the graph operator to each partition independently, and combines the results.

:::moniker range="azure-data-explorer"
> [!div class="nextstepaction"]
> <a href="https://dataexplorer.azure.com/clusters/help/databases/Samples?query=H4sIAAAAAAAAA62XbW%2FiOBDH3%2FMp5ngFJ6AkhKfesRLQ7kOv7VYLq9WpqlZuMiK%2BBjtyTCt27777jUPCgxO0W%2B4iVY2NPf%2Bfx%2BPx5OwMbmWACWj2GCEojBUmKDQXC1glqBJgvpJJAstVpHlMQzQKJnQCNakWTPBvTHMpknolQg0iNTWCgGmWGqwZGx%2BC80QrstjIJu91CLbEbSPAmCm9JPltl5LR7vdI%2BqnaebCmedyvQ%2BW%2BAvScncE8tXwOU7mMmViPoQkzJuCtYsLniS9hwtYwVsjSCdVVu%2B1UG1DNh5v3ccR9hCsZikQK03EpFlwgGnHTnKHgUsEFPmMkY1TUl4HUvlf1OsbqOVTvJBfajPalVAEXTGNC%2FfdNx3VbnjP0GtDpt%2Fp9b%2FjwT72xhXFtmIl8hNmS67AEZI5sCdfIghMI2jnBwCLo2ATTkKmII0yUfEndccPUE%2BqM4YYJtjjJB25O0LMIPJvggpMI3NGyfTTt9582vQp9LU%2BQ7gxz6aEl3bWlL58RvvCoPBCuVv85EDo5SXePpBjGEwrjW3yBP6V6SqMXYMvc22OemHcT609ww6MoRarOWESSJrB9X66EhpP2rO%2B1SKvdAK%2Fd6jvu4MBvfZvhnWJ0hq4R%2F%2FfzYzicnMO1OAY2x3sUag0X7JknhdCdxehzFvFEv5Kg0xoOc4K2RTC0CT48r2EaYln8nHh%2BjQfcXL9zqO%2B0bf0r5j%2FBPKSeLIg%2Fkss3yfrk82s8MMgIesM9gmLkTilyx6uEfH6QdZ39rDs173%2BQdRiLgO6aDedbEswO%2FFiwaP3abRrSqfI6Dh2vdsvt9d0DN7m2%2FDWnvbhZqThcl%2BzUWPkh15RuXo%2FQyRH6FkLHRrhha8pyNDkyrTslg1UqeNoeGe1urt21tD1b%2B1ayEN4xRQeiZPknHlWD0MsRPAuhayN8jPgzZ%2FBJBoovVvitcF5PS%2FgGws0hBilE5eG3CkXqZbAoL3gURpsDEvI4OSO7dGD8tAMeUb8gik1NlNY6mFrZr3USuVI%2BfrYqHqYWqO1OuwzaV57TyrY%2F0H8UCx2eE019V%2B1sq5wD5MPSpqyqoOVKpZO5pMbQuv5%2BNH5gVStlNYMvo4g9SpXuQgN6jQJS4aY15Z5UZnTfKkbKSgJLoFsUKGFaLunC9rMpXsNy4uSIE%2FuZwUJmP%2BLE3mZ8MRMfceIgs1%2B4x487cXhMwlpjt1FYReGGLPV7tobivV7OtHPi9NCJuzJlk3LKMm%2B5G50stopp0kLYUTvOT07pWUm4LBuVb9aWqjD%2BqOe3VIWcu%2FN8z2SkNJFU%2FoYle8LmQrE4hP1MAs3mm4MsAi%2F0VZB9aslNRqJO8%2BXEjf8xaD6utxkGailPara5ZNoPwV%2F7VBeOBA2FmnDqzXv81W21vIfmm5pw6%2Bl487yEqBCE08oUfhmBcPMGE%2FQXRTUs5i4YjSzP183wrd2UB%2BXXgAowc9F%2FdWOTw5OaloFcmURKmvmnXmsvrd%2B3H%2BqUO380ynnIbpujz86E%2B1NCR0aRUB1%2BB6dNz1YwVvIvuq1gRlum6XqgBE5TDaz51KXdhxptPDXzHaKuepXk5tl39YgCITZ%2BzQfQb5fk7Z0p99CUa5mq1P8FZTuk0NUPAAA%3D" target="_blank">Run the query</a>
::: moniker-end

```kusto
// Nodes table representing users across multiple tenants (organizations)
let nodes = datatable(userId:string, tenantId:string, name:string, department:string, role:string, location:dynamic)
[
// Tenant: CompanyA - San Francisco Bay Area
"u001", "CompanyA", "Alice Johnson", "Engineering", "Senior Developer", dynamic({"type": "Point", "coordinates": [-122.4194, 37.7749]}),
"u002", "CompanyA", "Bob Smith", "Engineering", "Team Lead", dynamic({"type": "Point", "coordinates": [-122.4094, 37.7849]}),
"u003", "CompanyA", "Charlie Brown", "Marketing", "Manager", dynamic({"type": "Point", "coordinates": [-122.4294, 37.7649]}),
"u004", "CompanyA", "Diana Prince", "HR", "Director", dynamic({"type": "Point", "coordinates": [-122.3994, 37.7949]}),
"u005", "CompanyA", "Eve Wilson", "Engineering", "Junior Developer", dynamic({"type": "Point", "coordinates": [-122.4394, 37.7549]}),
// Tenant: CompanyB - New York Area
"u006", "CompanyB", "Frank Miller", "Sales", "Account Manager", dynamic({"type": "Point", "coordinates": [-74.0060, 40.7128]}),
"u007", "CompanyB", "Grace Lee", "Engineering", "Senior Developer", dynamic({"type": "Point", "coordinates": [-74.0160, 40.7228]}),
"u008", "CompanyB", "Henry Davis", "Marketing", "Specialist", dynamic({"type": "Point", "coordinates": [-73.9960, 40.7028]}),
"u009", "CompanyB", "Ivy Chen", "Engineering", "Team Lead", dynamic({"type": "Point", "coordinates": [-74.0260, 40.7328]}),
"u010", "CompanyB", "Jack Thompson", "Operations", "Manager", dynamic({"type": "Point", "coordinates": [-73.9860, 40.6928]}),
// Tenant: CompanyC - Austin Area
"u011", "CompanyC", "Kate Anderson", "Finance", "Analyst", dynamic({"type": "Point", "coordinates": [-97.7431, 30.2672]}),
"u012", "CompanyC", "Liam Murphy", "Engineering", "Architect", dynamic({"type": "Point", "coordinates": [-97.7331, 30.2772]}),
"u013", "CompanyC", "Maya Patel", "Product", "Manager", dynamic({"type": "Point", "coordinates": [-97.7531, 30.2572]}),
"u014", "CompanyC", "Noah Garcia", "Engineering", "Developer", dynamic({"type": "Point", "coordinates": [-97.7631, 30.2472]}),
"u015", "CompanyC", "Olivia Rodriguez", "Marketing", "Director", dynamic({"type": "Point", "coordinates": [-97.7231, 30.2872]})
];
// Edges table representing relationships/interactions between users
let edges = datatable(sourceUserId:string, targetUserId:string, tenantId:string, relationshipType:string, strength:int)
[
// CompanyA relationships
"u001", "u002", "CompanyA", "reportsTo", 9,
"u005", "u002", "CompanyA", "reportsTo", 8,
"u002", "u003", "CompanyA", "collaborates", 6,
"u001", "u005", "CompanyA", "mentors", 7,
"u003", "u004", "CompanyA", "collaborates", 5,
"u001", "u003", "CompanyA", "communicates", 4,
// CompanyB relationships
"u007", "u009", "CompanyB", "reportsTo", 9,
"u006", "u010", "CompanyB", "reportsTo", 8,
"u008", "u006", "CompanyB", "collaborates", 6,
"u009", "u010", "CompanyB", "communicates", 5,
"u007", "u008", "CompanyB", "mentors", 7,
"u006", "u007", "CompanyB", "collaborates", 6,
// CompanyC relationships
"u014", "u012", "CompanyC", "reportsTo", 9,
"u012", "u013", "CompanyC", "collaborates", 7,
"u011", "u013", "CompanyC", "collaborates", 6,
"u013", "u015", "CompanyC", "reportsTo", 8,
"u012", "u015", "CompanyC", "communicates", 5,
"u011", "u014", "CompanyC", "mentors", 6
];
edges
| make-graph sourceUserId --> targetUserId with nodes on userId partitioned-by tenantId (
graph-match cycles=none (n1)-[e*2..4]->(n2)
where n1.userId != n2.userId and all(e, relationshipType == "collaborates") and
geo_distance_2points(todouble(n1.location.coordinates[0]), todouble(n1.location.coordinates[1]),
todouble(n2.location.coordinates[0]), todouble(n2.location.coordinates[1])) < 10000
project Start = strcat(n1.name, " (", n1.tenantId, ")"), Tenants = map(e, tenantId), End = strcat(n2.name, " (", n2.tenantId, ")")
)
```

|Start|Tenants|End|
|---|---|---|
|Bob Smith (CompanyA)|[<br> "CompanyA",<br> "CompanyA"<br>]|Diana Prince (CompanyA)|
|Henry Davis (CompanyB)|[<br> "CompanyB",<br> "CompanyB"<br>]|Grace Lee (CompanyB)|

## Related content

* [Graph operators](graph-operators.md)
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Expand Up @@ -1278,52 +1278,56 @@ items:
href: variancepif-aggregation-function.md
- name: Graph
items:
- name: Graph semantics overview
href: graph-semantics-overview.md
- name: Graph sample data
href: graph-sample-data.md
- name: Graph exploration basics
href: graph-exploration-basics.md
- name: Graph operators
- name: Start here
items:
- name: Graph operators overview
href: graph-operators.md
- name: make-graph
href: make-graph-operator.md
- name: graph-match
href: graph-match-operator.md
- name: graph-shortest-paths
href: graph-shortest-paths-operator.md
- name: graph-mark-components
href: graph-mark-components-operator.md
- name: graph-to-table
href: graph-to-table-operator.md
- name: Graph functions
- name: Graph semantics overview
href: graph-semantics-overview.md
- name: Graph sample data
href: graph-sample-data.md
- name: Graph exploration basics
href: graph-exploration-basics.md
- name: Reference
items:
- name: all()
displayName: all() graph function
href: all-graph-function.md
- name: any()
displayName: any() graph function
href: any-graph-function.md
- name: map()
displayName: map() graph function
href: map-graph-function.md
- name: inner_nodes()
displayName: inner nodes() graph function, inner nodes
href: inner-nodes-graph-function.md
- name: labels()
displayName: labels() graph function, labels
href: labels-graph-function.md
- name: node_degree_in()
displayName: graph, node, Node degree in node_degree_in, indegree node_degree_in
href: node-degree-in.md
- name: node_degree_out()
displayName: graph, node, Node degree out node_degree_out, outdegree node_degree_out
href: node-degree-out.md
- name: Graph scenarios
- name: Operators
items:
- name: Graph operators overview
href: graph-operators.md
- name: make-graph
href: make-graph-operator.md
- name: graph-match
href: graph-match-operator.md
- name: graph-to-table
href: graph-to-table-operator.md
- name: graph-shortest-paths
href: graph-shortest-paths-operator.md
- name: graph-mark-components
href: graph-mark-components-operator.md
- name: Functions
items:
- name: all()
displayName: all() graph function
href: all-graph-function.md
- name: any()
displayName: any() graph function
href: any-graph-function.md
- name: map()
displayName: map() graph function
href: map-graph-function.md
- name: inner_nodes()
displayName: inner nodes() graph function, inner nodes
href: inner-nodes-graph-function.md
- name: labels()
displayName: labels() graph function, labels
href: labels-graph-function.md
- name: node_degree_in()
displayName: graph, node, Node degree in node_degree_in, indegree node_degree_in
href: node-degree-in.md
- name: node_degree_out()
displayName: graph, node, Node degree out node_degree_out, outdegree node_degree_out
href: node-degree-out.md
- name: Scenarios
href: graph-scenarios.md
- name: Graph best practices
- name: Best practices
href: graph-best-practices.md
- name: Geospatial
items:
Expand Down