Acrolinx

Author: whhender

articles/synapse-analytics/spark/apache-spark-autoscale.md

@@ -11,7 +11,7 @@ ms.date: 03/11/2024
 
 # Automatically scale Azure Synapse Analytics Apache Spark pools
 
-Apache Spark for Azure Synapse Analytics pool's Autoscale feature automatically scales the number of nodes in a cluster instance up and down. During the creation of a new Apache Spark for Azure Synapse Analytics pool, a minimum and maximum number of nodes, up to 200 nodes, can be set when Autoscale is selected. Autoscale then monitors the resource requirements of the load and scales the number of nodes up or down. There's no additional charge for this feature.
+Apache Spark for Azure Synapse Analytics pool's Autoscale feature automatically scales the number of nodes in a cluster instance up and down. During the creation of a new Apache Spark for Azure Synapse Analytics pool, a minimum and maximum number of nodes, up to 200 nodes, can be set when Autoscale is selected. Autoscale then monitors the resource requirements of the load and scales the number of nodes up or down. There's no extra charge for this feature.
 
 ## Metrics monitoring
 
@@ -38,10 +38,10 @@ When the following conditions are detected, Autoscale will issue a scale request
 
 For scale-up, the Azure Synapse Autoscale service calculates how many new nodes are needed to meet the current CPU and memory requirements, and then issues a scale-up request to add the required number of nodes.
 
-For scale-down, based on the number of executors, application masters per node, the current CPU and memory requirements, Autoscale issues a request to remove a certain number of nodes. The service also detects which nodes are candidates for removal based on current job execution. The scale down operation first decommissions the nodes, and then removes them from the cluster.
+For scale-down, based on the number of executors, application masters per node, the current CPU, and memory requirements, Autoscale issues a request to remove some nodes. The service also detects which nodes are candidates for removal based on current job execution. The scale down operation first decommissions the nodes, and then removes them from the cluster.
 
 >[!NOTE]
->A note about updating and force applying autoscale configuration to an existing Spark pool. If **Force new setting** in the Azure portal or `ForceApplySetting` in [PowerShell](/powershell/module/az.synapse/update-azsynapsesparkpool) is enabled, then all existing Spark sessions are terminated and configuration changes are applied immediately. If this option is not selected, then the configuration is applied to the new Spark sessions and existing sessions are not terminated.
+>A note about updating and force applying autoscale configuration to an existing Spark pool. If **Force new setting** in the Azure portal or `ForceApplySetting` in [PowerShell](/powershell/module/az.synapse/update-azsynapsesparkpool) is enabled, then all existing Spark sessions are terminated and configuration changes are applied immediately. If this option isn't selected, then the configuration is applied to the new Spark sessions and existing sessions aren't terminated.
 
 ## Get started
 
@@ -75,7 +75,7 @@ Apache Spark enables configuration of Dynamic Allocation of Executors through co
 ```
 The defaults specified through the code override the values set through the user interface.
 
-In this example, if your job requires only 2 executors, it will use only 2 executors.  When the job requires more, it will scale up to 6 executors (1 driver, 6 executors).  When the job doesn't need the executors, then it will decommission the executors. If it doesn't need the node, it will free up the node.
+In this example, if your job requires only two executors, it uses only two executors. When the job requires more, it will scale up to six executors (one driver, six executors). When the job doesn't need the executors, then it will decommission the executors. If it doesn't need the node, it will free up the node.
 
 >[!NOTE]
 >The maxExecutors will reserve the number of executors configured. Considering the example, even if you use only 2, it will reserve 6.