Merge pull request #276436 from MicrosoftDocs/main

5/27/2024 PM Publish

Author: Taojunshen

articles/sap/automation/configure-devops.md

@@ -44,7 +44,7 @@ Open PowerShell ISE and copy the following script and update the parameters to m
       az login --output none --only-show-errors --scope https://graph.microsoft.com//.default
     }
     else {
-      az login --output none --tenant $ARM_TENANT_ID --only-show-errors --scope https://graph.microsoft.com//.default
+      az login --output none --tenant $Env:ARM_TENANT_ID --only-show-errors --scope https://graph.microsoft.com//.default
     }
 
     az config set extension.use_dynamic_install=yes_without_prompt --only-show-errors
@@ -55,7 +55,7 @@ Open PowerShell ISE and copy the following script and update the parameters to m
     if ($differentTenant -eq 'y') {
         $env:AZURE_DEVOPS_EXT_PAT = Read-Host "Please enter your Personal Access Token (PAT) with permissions to add new projects, manage agent pools to the Azure DevOps organization $Env:ADO_Organization"
         try {
-            az devops login --organization $Env:ADO_Organization
+            az devops project list
         }
         catch {
             $_
@@ -171,14 +171,16 @@ Open PowerShell ISE and copy the following script and update the parameters to m
             New-Item -Path $sdaf_path -Type Directory
         }
     }
+
+    $branchName = "main"
     
     Set-Location -Path $sdaf_path
     
     if ( Test-Path "New-SDAFDevopsWorkloadZone.ps1") {
         remove-item .\New-SDAFDevopsWorkloadZone.ps1
     }
     
-    Invoke-WebRequest -Uri https://raw.githubusercontent.com/Azure/sap-automation/main/deploy/scripts/New-SDAFDevopsWorkloadZone.ps1 -OutFile .\New-SDAFDevopsWorkloadZone.ps1 ; .\New-SDAFDevopsWorkloadZone.ps1
+    Invoke-WebRequest -Uri https://raw.githubusercontent.com/Azure/sap-automation/$branchName/deploy/scripts/New-SDAFDevopsWorkloadZone.ps1 -OutFile .\New-SDAFDevopsWorkloadZone.ps1 ; .\New-SDAFDevopsWorkloadZone.ps1
     
 ```
 

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

@@ -57,7 +57,13 @@ Spark provides its own native caching mechanisms, which can be used through diff
 Spark operates by placing data in memory, so managing memory resources is a key aspect of optimizing the execution of Spark jobs.  There are several techniques you can apply to use your cluster's memory efficiently.
 
 * Prefer smaller data partitions and account for data size, types, and distribution in your partitioning strategy.
-* Consider the newer, more efficient [Kryo data serialization](https://github.com/EsotericSoftware/kryo), rather than the default Java serialization.
+* In Synapse Spark (Runtime 3.1 or higher), Kryo data serialization is enabled by default Kryo data serialization.
+* You can customize the kryoserializer buffer size using Spark configuration based on your workload requirements:
+
+  ```scala
+  // Set the desired property
+  spark.conf.set("spark.kryoserializer.buffer.max", "256m")
+
 * Monitor and tune Spark configuration settings.
 
 For your reference, the Spark memory structure and some key executor memory parameters are shown in the next image.
@@ -172,6 +178,7 @@ MAX(AMOUNT) -> MAX(cast(AMOUNT as DOUBLE))
 
 ## Next steps
 
+- [Learn about Azure Synapse runtimes for Apache Spark](./apache-spark-version-support.md)
 - [Tuning Apache Spark](https://spark.apache.org/docs/2.4.5/tuning.html)
 - [How to Actually Tune Your Apache Spark Jobs So They Work](https://www.slideshare.net/ilganeli/how-to-actually-tune-your-spark-jobs-so-they-work)
 - [Kryo Serialization](https://github.com/EsotericSoftware/kryo)