Merge pull request #97792 from orspod/2019-12-scale-in-out

Logic for scale in/scale out

Author: PMEds28

articles/data-explorer/manage-cluster-horizontal-scaling.md

@@ -3,23 +3,19 @@ title: Manage cluster horizontal scaling (scale out) in Azure Data Explorer to a
 description: This article describes steps to scale out and scale in an Azure Data Explorer cluster based on changing demand.
 author: orspod
 ms.author: orspodek
-ms.reviewer: mblythe
+ms.reviewer: gabil
 ms.service: data-explorer
 ms.topic: conceptual
-ms.date: 07/14/2019
+ms.date: 12/09/2019
 ---
 
 # Manage cluster horizontal scaling (scale out) in Azure Data Explorer to accommodate changing demand
 
-Sizing a cluster appropriately is critical to the performance of Azure Data Explorer. A static cluster size can lead to under-utilization or over-utilization, neither of which is ideal.
-
-Because demand on a cluster can’t be predicted with absolute accuracy, it's better to *scale* a cluster, adding and removing capacity and CPU resources with changing demand. 
+Sizing a cluster appropriately is critical to the performance of Azure Data Explorer. A static cluster size can lead to under-utilization or over-utilization, neither of which is ideal. Because demand on a cluster can’t be predicted with absolute accuracy, it's better to *scale* a cluster, adding and removing capacity and CPU resources with changing demand. 
 
 There are two workflows for scaling an Azure Data Explorer cluster: 
-
 * Horizontal scaling, also called scaling in and out.
 * [Vertical scaling](manage-cluster-vertical-scaling.md), also called scaling up and down.
-
 This article explains the horizontal scaling workflow.
 
 ## Configure horizontal scaling
@@ -42,14 +38,41 @@ Optimized autoscale is the recommended autoscale method. This method optimizes c
 
 1. Select **Optimized autoscale**. 
 
-1. Select a minimum instance count and a maximum instance count. The cluster autoscaling ranges between those two numbers, based on load.
+1. Select a minimum instance count and a maximum instance count. The cluster auto-scaling ranges between those two numbers, based on load.
 
 1. Select **Save**.
 
    ![Optimized autoscale method](media/manage-cluster-horizontal-scaling/optimized-autoscale-method.png)
 
 Optimized autoscale starts working. Its actions are now visible in the Azure activity log of the cluster.
 
+#### Logic of optimized autoscale 
+
+**Scale out**
+
+When your cluster approaches a state of over-utilization, scale out to maintain optimal performance. Scale out will occur when:
+* The number of cluster instances is below the maximum number of instances defined by the user.
+* The cache utilization is high for over an hour.
+
+> [!NOTE]
+> The scale out logic doesn't currently consider the ingestion utilization and CPU metrics. If those metrics are important for your use case, use [custom autoscale](#custom-autoscale).
+
+**Scale in**
+
+When your cluster approaches a state of under-utilization, scale in to lower costs but maintain performance. Multiple metrics are used to verify that it's safe to scale in the cluster. The following rules are evaluated daily for 7 days before scale in is performed:
+* The number of instances is above 2 and above the minimum number of instances defined.
+* To ensure that there's no overloading of resources, the following metrics must be verified before scale in is performed: 
+    * Cache utilization isn't high
+    * CPU is below average 
+    * Ingestion utilization is below average 
+    * Streaming ingest utilization (if streaming ingest is used) isn't high
+    * Keep alive events are above a defined minimum, processed properly, and on time.
+    * No query throttling 
+    * Number of failed queries are below a defined minimum.
+
+> [!NOTE]
+> The scale in logic currently requires a 7-day evaluation before implementation of optimized scale in. This evaluation takes place once every 24 hours. If a quick change is needed, use [manual scale](#manual-scale).
+
 ### Custom autoscale
 
 By using custom autoscale, you can scale your cluster dynamically based on metrics that you specify. The following graphic shows the flow and steps to configure custom autoscale. More details follow the graphic.
@@ -103,5 +126,4 @@ You've now configured horizontal scaling for your Azure Data Explorer cluster. A
 ## Next steps
 
 * [Monitor Azure Data Explorer performance, health, and usage with metrics](using-metrics.md)
-
 * [Manage cluster vertical scaling](manage-cluster-vertical-scaling.md) for appropriate sizing of a cluster.