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‎data-explorer/hot-windows.md‎

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Now you can expect optimal performance during the use of hot windows.
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## Set hot windows
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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).
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> [!NOTE]
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> It can take up to an hour to fully update the cluster disk cache based on the updated cache policy definition.
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1. Take note of the initial caching policy by using the `.show policy caching` command.
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```kusto
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.show table MyDatabase.MyTable policy caching
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```
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1. Alter the cache policy using the following syntax. Several hot windows may be defined for a single database or table.
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```kusto
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.alter <entity_type> <database_or_table_or_materialized-view_name> policy caching
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hot = <timespan>
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[, hot_window = datetime(*from*) .. datetime(*to*)]
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[, hot_window = datetime(*from*) .. datetime(*to*)]
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...
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```
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Where:
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* `from`: Start time of the hot window (datetime)
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* `to`: End time of the hot window (datetime)
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For example, queries run under the following settings will examine the last 14 days of data, on data that is kept for three years.
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```kusto
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.alter table MyTable policy caching
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hot = 14d,
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hot_window = datetime(2021-01-01) .. datetime(2021-02-01),
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hot_window = datetime(2021-04-01) .. datetime(2021-05-01)
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```
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## Run query
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Run the query or queries you want over the time period specified in the hot windows.
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## Revert settings
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1. Use the original cache settings retrieved above in [Set hot windows](#set-hot-windows).
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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).
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Since you've configured optimized autoscale for that cluster, the cluster will shrink to its original size.
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[!INCLUDE [set-hot-windows](includes/cross-repo/set-hot-windows.md)]
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## Related content
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‎data-explorer/includes/cross-repo/set-hot-windows.md‎

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---
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ms.topic: include
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ms.date: 07/02/2023
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---
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## Set hot windows
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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).
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> [!NOTE]
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> It can take up to an hour to fully update the cluster disk cache based on the updated cache policy definition.
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1. Take note of the initial caching policy by using the `.show policy caching` command.
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```kusto
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.show table MyDatabase.MyTable policy caching
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```
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1. Alter the cache policy using the following syntax. Several hot windows may be defined for a single database or table.
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```kusto
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.alter <entity_type> <database_or_table_or_materialized-view_name> policy caching
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hot = <timespan>
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[, hot_window = datetime(*from*) .. datetime(*to*)]
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[, hot_window = datetime(*from*) .. datetime(*to*)]
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...
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```
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Where:
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* `from`: Start time of the hot window (datetime)
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* `to`: End time of the hot window (datetime)
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For example, queries run under the following settings will examine the last 14 days of data, on data that is kept for three years.
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```kusto
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.alter table MyTable policy caching
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hot = 14d,
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hot_window = datetime(2021-01-01) .. datetime(2021-02-01),
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hot_window = datetime(2021-04-01) .. datetime(2021-05-01)
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```
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## Run query
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Run the query or queries you want over the time period specified in the hot windows.
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## Revert settings
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1. Use the original cache settings retrieved above in [Set hot windows](#set-hot-windows).
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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).
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Since you've configured optimized autoscale for that cluster, the cluster will shrink to its original size.

‎data-explorer/kusto/query/make-graph-operator.md‎

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*Edges* `|` `make-graph` *SourceNodeId* `-->` *TargetNodeId* [ `with_node_id=` *NodeIdPropertyName* ]
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*Edges* `|` `make-graph` *SourceNodeId* `-->` *TargetNodeId* [ `with` *Nodes1* `on` *NodeId1* [`,` *Nodes2* `on` *NodeId2* ]] `partitioned-by` *PartitionColumn* `(` *GraphOperator* `)`
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*Edges* `|` `make-graph` *SourceNodeId* `-->` *TargetNodeId* [ `with_node_id=` *NodeIdPropertyName* ] `partitioned-by` *PartitionColumn* `(` *GraphOperator* `)`
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## Parameters
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| Name | Type | Required | Description |
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| *Nodes1*, *Nodes2* | `string` | | The tabular expressions containing the properties of the nodes in the graph. |
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| *NodesId1*, *NodesId2* | `string` | | The corresponding columns with the node IDs in *Nodes1*, *Nodes2* respectively. |
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| *NodeIdPropertyName* | `string` | | The name of the property for node ID on the nodes of the graph. |
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| *PartitionColumn* | `string` | | The column to partition the graph by. Creates separate graphs for each unique value in this column. |
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| *GraphOperator* | `string` | | The graph operator to apply to each partitioned graph. |
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## Returns
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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.
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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.
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> [!NOTE]
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> 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.
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|---|---|---|
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|Mallory|Bob|Trent|
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### Multitenant partitioned graph
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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.
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:::moniker range="azure-data-explorer"
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> [!div class="nextstepaction"]
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> <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>
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::: moniker-end
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```kusto
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// Nodes table representing users across multiple tenants (organizations)
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let nodes = datatable(userId:string, tenantId:string, name:string, department:string, role:string, location:dynamic)
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[
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// Tenant: CompanyA - San Francisco Bay Area
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"u001", "CompanyA", "Alice Johnson", "Engineering", "Senior Developer", dynamic({"type": "Point", "coordinates": [-122.4194, 37.7749]}),
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"u002", "CompanyA", "Bob Smith", "Engineering", "Team Lead", dynamic({"type": "Point", "coordinates": [-122.4094, 37.7849]}),
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"u003", "CompanyA", "Charlie Brown", "Marketing", "Manager", dynamic({"type": "Point", "coordinates": [-122.4294, 37.7649]}),
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"u004", "CompanyA", "Diana Prince", "HR", "Director", dynamic({"type": "Point", "coordinates": [-122.3994, 37.7949]}),
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"u005", "CompanyA", "Eve Wilson", "Engineering", "Junior Developer", dynamic({"type": "Point", "coordinates": [-122.4394, 37.7549]}),
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// Tenant: CompanyB - New York Area
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"u006", "CompanyB", "Frank Miller", "Sales", "Account Manager", dynamic({"type": "Point", "coordinates": [-74.0060, 40.7128]}),
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"u007", "CompanyB", "Grace Lee", "Engineering", "Senior Developer", dynamic({"type": "Point", "coordinates": [-74.0160, 40.7228]}),
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"u008", "CompanyB", "Henry Davis", "Marketing", "Specialist", dynamic({"type": "Point", "coordinates": [-73.9960, 40.7028]}),
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"u009", "CompanyB", "Ivy Chen", "Engineering", "Team Lead", dynamic({"type": "Point", "coordinates": [-74.0260, 40.7328]}),
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"u010", "CompanyB", "Jack Thompson", "Operations", "Manager", dynamic({"type": "Point", "coordinates": [-73.9860, 40.6928]}),
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// Tenant: CompanyC - Austin Area
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"u011", "CompanyC", "Kate Anderson", "Finance", "Analyst", dynamic({"type": "Point", "coordinates": [-97.7431, 30.2672]}),
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"u012", "CompanyC", "Liam Murphy", "Engineering", "Architect", dynamic({"type": "Point", "coordinates": [-97.7331, 30.2772]}),
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"u013", "CompanyC", "Maya Patel", "Product", "Manager", dynamic({"type": "Point", "coordinates": [-97.7531, 30.2572]}),
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"u014", "CompanyC", "Noah Garcia", "Engineering", "Developer", dynamic({"type": "Point", "coordinates": [-97.7631, 30.2472]}),
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"u015", "CompanyC", "Olivia Rodriguez", "Marketing", "Director", dynamic({"type": "Point", "coordinates": [-97.7231, 30.2872]})
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];
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// Edges table representing relationships/interactions between users
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let edges = datatable(sourceUserId:string, targetUserId:string, tenantId:string, relationshipType:string, strength:int)
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[
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// CompanyA relationships
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"u001", "u002", "CompanyA", "reportsTo", 9,
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"u005", "u002", "CompanyA", "reportsTo", 8,
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"u002", "u003", "CompanyA", "collaborates", 6,
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"u001", "u005", "CompanyA", "mentors", 7,
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"u003", "u004", "CompanyA", "collaborates", 5,
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"u001", "u003", "CompanyA", "communicates", 4,
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// CompanyB relationships
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"u007", "u009", "CompanyB", "reportsTo", 9,
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"u006", "u010", "CompanyB", "reportsTo", 8,
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"u008", "u006", "CompanyB", "collaborates", 6,
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"u009", "u010", "CompanyB", "communicates", 5,
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"u007", "u008", "CompanyB", "mentors", 7,
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"u006", "u007", "CompanyB", "collaborates", 6,
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// CompanyC relationships
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"u014", "u012", "CompanyC", "reportsTo", 9,
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"u012", "u013", "CompanyC", "collaborates", 7,
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"u011", "u013", "CompanyC", "collaborates", 6,
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"u013", "u015", "CompanyC", "reportsTo", 8,
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"u012", "u015", "CompanyC", "communicates", 5,
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"u011", "u014", "CompanyC", "mentors", 6
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];
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edges
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| make-graph sourceUserId --> targetUserId with nodes on userId partitioned-by tenantId (
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graph-match cycles=none (n1)-[e*2..4]->(n2)
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where n1.userId != n2.userId and all(e, relationshipType == "collaborates") and
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geo_distance_2points(todouble(n1.location.coordinates[0]), todouble(n1.location.coordinates[1]),
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todouble(n2.location.coordinates[0]), todouble(n2.location.coordinates[1])) < 10000
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project Start = strcat(n1.name, " (", n1.tenantId, ")"), Tenants = map(e, tenantId), End = strcat(n2.name, " (", n2.tenantId, ")")
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)
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```
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|Start|Tenants|End|
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|---|---|---|
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|Bob Smith (CompanyA)|[<br> "CompanyA",<br> "CompanyA"<br>]|Diana Prince (CompanyA)|
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|Henry Davis (CompanyB)|[<br> "CompanyB",<br> "CompanyB"<br>]|Grace Lee (CompanyB)|
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## Related content
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* [Graph operators](graph-operators.md)

‎data-explorer/kusto/query/toc.yml‎

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href: variancepif-aggregation-function.md
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- name: Graph
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items:
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- name: Graph semantics overview
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href: graph-semantics-overview.md
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- name: Graph sample data
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href: graph-sample-data.md
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- name: Graph exploration basics
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href: graph-exploration-basics.md
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- name: Graph operators
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- name: Start here
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items:
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- name: Graph operators overview
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href: graph-operators.md
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- name: make-graph
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href: make-graph-operator.md
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- name: graph-match
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href: graph-match-operator.md
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- name: graph-shortest-paths
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href: graph-shortest-paths-operator.md
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- name: graph-mark-components
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href: graph-mark-components-operator.md
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- name: graph-to-table
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href: graph-to-table-operator.md
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- name: Graph functions
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- name: Graph semantics overview
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href: graph-semantics-overview.md
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- name: Graph sample data
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href: graph-sample-data.md
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- name: Graph exploration basics
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href: graph-exploration-basics.md
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- name: Reference
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items:
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- name: all()
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displayName: all() graph function
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href: all-graph-function.md
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- name: any()
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displayName: any() graph function
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href: any-graph-function.md
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- name: map()
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displayName: map() graph function
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href: map-graph-function.md
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- name: inner_nodes()
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displayName: inner nodes() graph function, inner nodes
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href: inner-nodes-graph-function.md
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- name: labels()
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displayName: labels() graph function, labels
1317-
href: labels-graph-function.md
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- name: node_degree_in()
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displayName: graph, node, Node degree in node_degree_in, indegree node_degree_in
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href: node-degree-in.md
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- name: node_degree_out()
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displayName: graph, node, Node degree out node_degree_out, outdegree node_degree_out
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href: node-degree-out.md
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- name: Graph scenarios
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- name: Operators
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items:
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- name: Graph operators overview
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href: graph-operators.md
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- name: make-graph
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href: make-graph-operator.md
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- name: graph-match
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href: graph-match-operator.md
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- name: graph-to-table
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href: graph-to-table-operator.md
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- name: graph-shortest-paths
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href: graph-shortest-paths-operator.md
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- name: graph-mark-components
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href: graph-mark-components-operator.md
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- name: Functions
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items:
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- name: all()
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displayName: all() graph function
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href: all-graph-function.md
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- name: any()
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displayName: any() graph function
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href: any-graph-function.md
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- name: map()
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displayName: map() graph function
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href: map-graph-function.md
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- name: inner_nodes()
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displayName: inner nodes() graph function, inner nodes
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href: inner-nodes-graph-function.md
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- name: labels()
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displayName: labels() graph function, labels
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href: labels-graph-function.md
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- name: node_degree_in()
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displayName: graph, node, Node degree in node_degree_in, indegree node_degree_in
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href: node-degree-in.md
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- name: node_degree_out()
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displayName: graph, node, Node degree out node_degree_out, outdegree node_degree_out
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href: node-degree-out.md
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- name: Scenarios
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href: graph-scenarios.md
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- name: Graph best practices
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- name: Best practices
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href: graph-best-practices.md
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- name: Geospatial
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items:

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