Merge pull request #209936 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)

Author: huypub

articles/active-directory/enterprise-users/licensing-service-plan-reference.md

@@ -59,23 +59,10 @@ Note the following information before you install the plug-in:
 
 The plug-in supports the following versions of Jira and Confluence:
 
-* Jira Core and Software: 6.0 to 7.12
-* Jira Service Desk: 3.0.0 to 3.5.0
+* Jira Core and Software: 6.0 to 8.22.1
+* Jira Service Desk: 3.0.0 to 4.22.1
 * JIRA also supports 5.2. For more details, click [Microsoft Azure Active Directory single sign-on for JIRA 5.2](./jira52microsoft-tutorial.md)
-* Confluence: 5.0 to 5.10
-* Confluence: 6.0.1
-* Confluence: 6.1.1
-* Confluence: 6.2.1
-* Confluence: 6.3.4
-* Confluence: 6.4.0
-* Confluence: 6.5.0
-* Confluence: 6.6.2
-* Confluence: 6.7.0
-* Confluence: 6.8.1
-* Confluence: 6.9.0
-* Confluence: 6.10.0
-* Confluence: 6.11.0
-* Confluence: 6.12.0
+* Confluence: 5.0 to 7.17.0
 
 ## Installation
 

articles/active-directory/saas-apps/ms-confluence-jira-plugin-adminguide.md

@@ -62,6 +62,9 @@ To connect to TAXII threat intelligence feeds, follow the instructions to [conne
 
 - [Learn about Kaspersky integration with Microsoft Sentinel](https://support.kaspersky.com/15908)
 
+### PickupSTIX
+
+- [Fill out this web form](https://www.celerium.com/pickupstix) to get the API Root, Collection IDs, Username, and Password for the free TAXII 2.1 Feeds on the PickupSTIX TAXII Server.
 
 ### Pulsedive
 

articles/sentinel/threat-intelligence-integration.md

@@ -74,7 +74,12 @@ 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.
 
-On enabling Dynamic allocation, Executors scale up or down based on the utilization of the Executors. This ensure that the Executors are provisioned in accordance with the needs of the job being run.
+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.
+
+>[!NOTE]
+>The maxExecutors will reserve the number of executors configured. Considering the example, even if you use only 2, it will reserve 6. 
+
+Hence, on enabling Dynamic allocation, Executors scale up or down based on the utilization of the Executors. This ensures that the Executors are provisioned in accordance with the needs of the job being run.
 
 ## Best practices
 

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

@@ -23,18 +23,43 @@ You can analyze data with .NET for Apache Spark through Spark batch job definiti
 
 Visit the tutorial to learn how to use Azure Synapse Analytics to [create Apache Spark job definitions for Synapse Spark pools](apache-spark-job-definitions.md). If you haven't packaged your app to submit to Azure Synapse, complete the following steps.
 
-1. Run the following commands to publish your app. Be sure to replace *mySparkApp* with the path to your app.
+1. Configure your `dotnet` application dependencies for compatibility with Synapse Spark.
+The required .NET Spark version will be noted in the Synapse Studio interface under your Apache Spark Pool configuration, under the Manage toolbox.
+
+   :::image type="content" source="./media/apache-spark-job-definitions/net-spark-workspace-compatibility.png" alt-text="Screenshot that shows properties, including the .NET Spark version.":::
+
+   Create your project as a .NET console application that outputs an Ubuntu x86 executable.
+  
+   ```
+   <Project Sdk="Microsoft.NET.Sdk">
+ 
+     <PropertyGroup>
+       <OutputType>Exe</OutputType>
+       <TargetFramework>netcoreapp3.1</TargetFramework>
+     </PropertyGroup>
+ 
+     <ItemGroup>
+       <PackageReference Include="Microsoft.Spark" Version="2.1.0" />
+     </ItemGroup>
+ 
+   </Project>
+   ```
+
+2. Run the following commands to publish your app. Be sure to replace *mySparkApp* with the path to your app.
 
    ```dotnetcli
    cd mySparkApp
    dotnet publish -c Release -f netcoreapp3.1 -r ubuntu.18.04-x64
    ```
 
-2. Zip the contents of the publish folder, `publish.zip` for example, that was created as a result of Step 1. All the assemblies should be in the first layer of the ZIP file and there should be no intermediate folder layer. This means when you unzip `publish.zip`, all assemblies are extracted into your current working directory.
+3. Zip the contents of the publish folder, `publish.zip` for example, that was created as a result of Step 1. All the assemblies should be in the root of the ZIP file and there should be no intermediate folder layer. This means when you unzip `publish.zip`, all assemblies are extracted into your current working directory. 
 
     **On Windows:**
 
-    Use an extraction program, like [7-Zip](https://www.7-zip.org/) or [WinZip](https://www.winzip.com/), to extract the file into the bin directory with all the published binaries.
+    Using Windows PowerShell or PowerShell 7, create a .zip from the contents of your publish directory.
+    ```PowerShell
+    Compress-Archive publish/* publish.zip -Update
+    ```
 
     **On Linux:**
 
@@ -48,7 +73,7 @@ Visit the tutorial to learn how to use Azure Synapse Analytics to [create Apache
 
 Notebooks are a great option for prototyping your .NET for Apache Spark pipelines and scenarios. You can start working with, understanding, filtering, displaying, and visualizing your data quickly and efficiently. 
 
-Data engineers, data scientists, business analysts, and machine learning engineers are all able to collaborate over a shared,  interactive document. You see immediate results from data exploration, and can visualize your data in the same notebook.
+Data engineers, data scientists, business analysts, and machine learning engineers are all able to collaborate over a shared, interactive document. You see immediate results from data exploration, and can visualize your data in the same notebook.
 
 ### How to use .NET for Apache Spark notebooks
 
@@ -79,6 +104,12 @@ The following features are available when you use .NET for Apache Spark in the A
 * Support for defining [.NET user-defined functions that can run within Apache Spark](/dotnet/spark/how-to-guides/udf-guide). We recommend [Write and call UDFs in .NET for Apache Spark Interactive environments](/dotnet/spark/how-to-guides/dotnet-interactive-udf-issue) for learning how to use UDFs in .NET for Apache Spark Interactive experiences.
 * Support for visualizing output from your Spark jobs using different charts (such as line, bar, or histogram) and layouts (such as single, overlaid, and so on) using the `XPlot.Plotly` library.
 * Ability to include NuGet packages into your C# notebook.
+## Troubleshooting
+
+### `DotNetRunner: null` / `Futures timeout` in Synapse Spark Job Definition Run
+Synapse Spark Job Definitions on Spark Pools using Spark 2.4 require `Microsoft.Spark` 1.0.0. Clear your `bin` and `obj` directories, and publish the project using 1.0.0.
+### OutOfMemoryError: java heap space at org.apache.spark...
+Dotnet Spark 1.0.0 uses a different debug architecture than 1.1.1+. You will have to use 1.0.0 for your published version and 1.1.1+ for local debugging.
 
 ## Next steps