Merge pull request #264200 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: rmca14

articles/azure-netapp-files/tools-reference.md

@@ -12,7 +12,7 @@ ms.author: anfdocs
 
 # Azure NetApp Files tools 
 
-Azure NetApp Files offers [multiple tools](https://azure.github.io/azure-netapp-files/) to estimate costs, understand features and availability, and monitor your Azure NetApp Files deployment.
+Azure NetApp Files offers [multiple tools](https://aka.ms/anftools) to estimate costs, understand features and availability, and monitor your Azure NetApp Files deployment.
 
 * [**Azure NetApp Files Performance Calculator**](https://aka.ms/anfcalc)
 

articles/mysql/single-server/how-to-restore-server-portal.md

@@ -48,9 +48,9 @@ In the screenshot below it has been increased to 34 days.
 The backup retention period governs how far back in time a point-in-time restore can be retrieved, since it's based on backups available. Point-in-time restore is described further in the following section. 
 
 ## Point-in-time restore
-Azure Database for MySQL allows you to restore the server back to a point-in-time and into to a new copy of the server. You can use this new server to recover your data, or have your client applications point to this new server.
+Azure Database for MySQL allows you to restore the server back to a point-in-time and into a new copy of the server. You can use this new server to recover your data, or have your client applications point to this new server.
 
-For example, if a table was accidentally dropped at noon today, you could restore to the time just before noon and retrieve the missing table and data from that new copy of the server. Point-in-time restore is at the server level, not at the database level.
+For example, if a table was accidentally dropped at noon today, you could restore it to the time just before noon and retrieve the missing table and data from that new copy of the server. Point-in-time restore is at the server level, not at the database level.
 
 The following steps restore the sample server to a point-in-time:
 1. In the Azure portal, select your Azure Database for MySQL server. 
@@ -64,8 +64,8 @@ The following steps restore the sample server to a point-in-time:
    :::image type="content" source="./media/how-to-restore-server-portal/3-restore.png" alt-text="Azure Database for MySQL - Restore information":::
    - **Restore point**: Select the point-in-time you want to restore to.
    - **Target server**: Provide a name for the new server.
-   - **Location**: You cannot select the region. By default it is same as the source server.
-   - **Pricing tier**: You cannot change these parameters when doing a point-in-time restore. It is same as the source server. 
+   - **Location**: You cannot select the region. By default, it is the same as the source server.
+   - **Pricing tier**: You cannot change these parameters when doing a point-in-time restore. It is the same as the source server. 
 
 4. Click **OK** to restore the server to restore to a point-in-time. 
 
@@ -77,7 +77,7 @@ Additionally, after the restore operation finishes, there are two server paramet
 *   time_zone - This value to set to DEFAULT value **SYSTEM**
 *   event_scheduler - The event_scheduler is set to **OFF** on the restored server
 
-You will need to copy over the value from teh primary server and set it on the restored server by reconfiguring the [server parameter](how-to-server-parameters.md)
+You will need to copy over the value from the primary server and set it on the restored server by reconfiguring the [server parameter](how-to-server-parameters.md)
 
 The new server created during a restore does not have the VNet service endpoints that existed on the original server. These rules need to be set up separately for this new server. Firewall rules from the original server are restored.
 
@@ -90,7 +90,7 @@ If you configured your server for geographically redundant backups, a new server
 
 2. Provide the subscription, resource group, and name of the new server. 
 
-3. Select **Backup** as the **Data source**. This action loads a dropdown that provides a list of servers that have geo redundant backups enabled.
+3. Select **Backup** as the **Data source**. This action loads a dropdown that provides a list of servers that have geo-redundant backups enabled.
 
    :::image type="content" source="./media/how-to-restore-server-portal/3-geo-restore.png" alt-text="Select data source.":::
 

articles/synapse-analytics/spark/media/microsoft-spark-utilities/spark-utilities-run-notebook-list.png

@@ -1,12 +1,12 @@
 ---
 title: Introduction to Microsoft Spark utilities
 description: "Tutorial: MSSparkutils in Azure Synapse Analytics notebooks"
-author: ruixinxu
+author: JeneZhang
 ms.service: synapse-analytics
 ms.topic: reference
 ms.subservice: spark
 ms.date: 09/10/2020
-ms.author: ruxu
+ms.author: jingzh
 zone_pivot_groups: programming-languages-spark-all-minus-sql
 ms.custom: subject-rbac-steps, devx-track-python
 ---
@@ -390,6 +390,17 @@ mssparkutils.fs.cp('source file or directory', 'destination file or directory',
 ```
 ::: zone-end
 
+### Performant copy file
+
+This method provides a faster way of copying or moving files, especially large volumes of data.
+
+```python
+mssparkutils.fs.fastcp('source file or directory', 'destination file or directory', True) # Set the third parameter as True to copy all files and directories recursively
+```
+
+> [!NOTE]
+> The method only supports in Spark 3.3 and Spark 3.4.
+
 ### Preview file content
 
 Returns up to the first 'maxBytes' bytes of the given file as a String encoded in UTF-8.
@@ -605,6 +616,75 @@ After the run finished, you will see a snapshot link named '**View notebook run:
 
 ![Screenshot of a snap link python](./media/microsoft-spark-utilities/spark-utilities-run-notebook-snap-link-sample-python.png)
 
+### Reference run multiple notebooks in parallel
+
+The method `mssparkutils.notebook.runMultiple()` allows you to run multiple notebooks in parallel or with a predefined topological structure. The API is using a multi-thread implementation mechanism within a spark session, which means the compute resources are shared by the reference notebook runs.
+
+With `mssparkutils.notebook.runMultiple()`, you can:
+
+- Execute multiple notebooks simultaneously, without waiting for each one to finish.
+
+- Specify the dependencies and order of execution for your notebooks, using a simple JSON format.
+
+- Optimize the use of Spark compute resources and reduce the cost of your Synapse projects.
+
+- View the Snapshots of each notebook run record in the output, and debug/monitor your notebook tasks conveniently.
+
+- Get the exit value of each executive activity and use them in downstream tasks.
+
+You can also try to run the mssparkutils.notebook.help("runMultiple") to find the example and detailed usage.
+
+Here's a simple example of running a list of notebooks in parallel using this method:
+
+```python
+
+mssparkutils.notebook.runMultiple(["NotebookSimple", "NotebookSimple2"])
+
+```
+
+The execution result from the root notebook is as follows:
+
+:::image type="content" source="media\microsoft-spark-utilities\spark-utilities-run-notebook-list.png" alt-text="Screenshot of reference a list of notebooks." lightbox="media\microsoft-spark-utilities\spark-utilities-run-notebook-list.png":::
+
+The following is an example of running notebooks with topological structure using `mssparkutils.notebook.runMultiple()`. Use this method to easily orchestrate notebooks through a code experience.
+
+```python
+# run multiple notebooks with parameters
+DAG = {
+    "activities": [
+        {
+            "name": "NotebookSimple", # activity name, must be unique
+            "path": "NotebookSimple", # notebook path
+            "timeoutPerCellInSeconds": 90, # max timeout for each cell, default to 90 seconds
+            "args": {"p1": "changed value", "p2": 100}, # notebook parameters
+        },
+        {
+            "name": "NotebookSimple2",
+            "path": "NotebookSimple2",
+            "timeoutPerCellInSeconds": 120,
+            "args": {"p1": "changed value 2", "p2": 200}
+        },
+        {
+            "name": "NotebookSimple2.2",
+            "path": "NotebookSimple2",
+            "timeoutPerCellInSeconds": 120,
+            "args": {"p1": "changed value 3", "p2": 300},
+            "retry": 1,
+            "retryIntervalInSeconds": 10,
+            "dependencies": ["NotebookSimple"] # list of activity names that this activity depends on
+        }
+    ]
+}
+mssparkutils.notebook.runMultiple(DAG)
+
+```
+
+> [!NOTE]
+>
+> - The method only supports in Spark 3.3 and Spark 3.4.
+> - The parallelism degree of the multiple notebook run is restricted to the total available compute resource of a Spark session.
+
+
 ### Exit a notebook
 Exits a notebook with a value. You can run nesting function calls in a notebook interactively or in a pipeline.