Merge pull request #89099 from dagiro/cats154

cats154

Author: ktoliver

articles/hdinsight/spark/apache-azure-spark-history-server.md

@@ -1,10 +1,10 @@
 ---
 title: Extended Spark History Server to debug Spark applications - Azure HDInsight
 description: Use extended Spark History Server to debug and diagnose Spark applications - Azure HDInsight.
-ms.service: hdinsight
 author: hrasheed-msft
 ms.author: hrasheed
 ms.reviewer: jasonh
+ms.service: hdinsight
 ms.custom: hdinsightactive,hdiseo17may2017
 ms.topic: conceptual
 ms.date: 09/04/2019
@@ -16,16 +16,17 @@ This article provides guidance on how to use extended Apache Spark History Serve
 
 ## Get access to Apache Spark History Server
 
-Apache Spark History Server is the web UI for completed and running Spark applications. 
+Apache Spark History Server is the web UI for completed and running Spark applications.
 
 ### Open the Apache Spark History Server Web UI from Azure portal
 
 1. From the [Azure portal](https://portal.azure.com/), open the Spark cluster. For more information, see [List and show clusters](../hdinsight-administer-use-portal-linux.md#showClusters).
-2. From **Quick Links**, click **Cluster Dashboard**, and then click **Spark History Server**. When prompted, enter the admin credentials for the Spark cluster. 
+2. From **Quick Links**, click **Cluster Dashboard**, and then click **Spark History Server**. When prompted, enter the admin credentials for the Spark cluster.
 
-    ![Spark History Server](./media/apache-azure-spark-history-server/launch-history-server.png "Spark History Server")
+    ![portal launch Spark History Server](./media/apache-azure-spark-history-server/launch-history-server.png "Spark History Server")
 
 ### Open the Spark History Server Web UI by URL
+
 Open the Spark History Server by browsing to the following URL, replace `<ClusterName>` with Spark cluster name of customer.
 
    ```
@@ -36,67 +37,67 @@ The Spark History Server web UI looks like:
 
 ![HDInsight Spark History Server](./media/apache-azure-spark-history-server/hdinsight-spark-history-server.png)
 
-
 ## Data tab in Spark History Server
+
 Select job ID then click **Data** on the tool menu to get the data view.
 
 + Check the **Inputs**, **Outputs**, and **Table Operations** by selecting the tabs separately.
 
-    ![Data tabs](./media/apache-azure-spark-history-server/apache-spark-data-tabs.png)
+    ![Data for Spark application tabs](./media/apache-azure-spark-history-server/apache-spark-data-tabs.png)
 
 + Copy all rows by clicking button **Copy**.
 
-    ![Data copy](./media/apache-azure-spark-history-server/apache-spark-data-copy.png)
+    ![Data for Spark application copy](./media/apache-azure-spark-history-server/apache-spark-data-copy.png)
 
 + Save all data as CSV file by clicking button **csv**.
 
-    ![Data save](./media/apache-azure-spark-history-server/apache-spark-data-save.png)
+    ![Data for Spark application save](./media/apache-azure-spark-history-server/apache-spark-data-save.png)
 
 + Search by entering keywords in field **Search**, the search result will display immediately.
 
-    ![Data search](./media/apache-azure-spark-history-server/apache-spark-data-search.png)
+    ![Data for Spark application search](./media/apache-azure-spark-history-server/apache-spark-data-search.png)
 
 + Click the column header to sort table, click the plus sign to expand a row to show more details, or click the minus sign to collapse a row.
 
-    ![Data table](./media/apache-azure-spark-history-server/apache-spark-data-table.png)
+    ![Data for Spark application table](./media/apache-azure-spark-history-server/apache-spark-data-table.png)
 
 + Download single file by clicking button **Partial Download** that place at the right, then the selected file will be downloaded to local, if the file does not exist any more, it will open a new tab to show the error messages.
 
-    ![Data download row](./media/apache-azure-spark-history-server/sparkui-data-download-row.png)
+    ![Data for Spark application download row](./media/apache-azure-spark-history-server/sparkui-data-download-row.png)
 
 + Copy full path or relative path by selecting the **Copy Full Path**, **Copy Relative Path** that expands from download menu. For azure data lake storage files, **Open in Azure Storage Explorer** will launch Azure Storage Explorer, and locate to the folder when sign-in.
 
-    ![Data copy path](./media/apache-azure-spark-history-server/sparkui-data-copy-path.png)
+    ![Data for Spark application copy path](./media/apache-azure-spark-history-server/sparkui-data-copy-path.png)
 
-+ Click the number below the table to navigate pages when too many rows to display in one page. 
++ Click the number below the table to navigate pages when too many rows to display in one page.
 
-    ![Data page](./media/apache-azure-spark-history-server/apache-spark-data-page.png)
+    ![Data for Spark application page](./media/apache-azure-spark-history-server/apache-spark-data-page.png)
 
 + Hover on the question mark beside Data to show the tooltip, or click the question mark to get more information.
 
-    ![Data more info](./media/apache-azure-spark-history-server/sparkui-data-more-info.png)
+    ![Data for Spark application more info](./media/apache-azure-spark-history-server/sparkui-data-more-info.png)
 
 + Send feedback with issues by clicking **Provide us feedback**.
 
-    ![graph feedback](./media/apache-azure-spark-history-server/sparkui-graph-feedback.png)
-
+    ![Spark graph provide us feedback again](./media/apache-azure-spark-history-server/sparkui-graph-feedback.png)
 
 ## Graph tab in Apache Spark History Server
+
 Select job ID then click **Graph** on the tool menu to get the job graph view.
 
-+ Check overview of your job by the generated job graph. 
++ Check overview of your job by the generated job graph.
 
 + By default, it will show all jobs, and it could be filtered by **Job ID**.
 
-    ![graph job ID](./media/apache-azure-spark-history-server/apache-spark-graph-jobid.png)
+    ![Spark application and job graph job ID](./media/apache-azure-spark-history-server/apache-spark-graph-jobid.png)
 
 + By default, **Progress** is selected, user could check the data flow by selecting **Read/Written** in the dropdown list of **Display**.
 
-    ![graph display](./media/apache-azure-spark-history-server/sparkui-graph-display.png)
+    ![Spark application and job graph display](./media/apache-azure-spark-history-server/sparkui-graph-display.png)
 
     The graph node display in color that shows the heatmap.
 
-    ![graph heatmap](./media/apache-azure-spark-history-server/sparkui-graph-heatmap.png)
+    ![Spark application and job graph heatmap](./media/apache-azure-spark-history-server/sparkui-graph-heatmap.png)
 
 + Play back the job by clicking the **Playback** button and stop anytime by clicking the stop button. The task display in color to show different status when playback:
 
@@ -106,30 +107,29 @@ Select job ID then click **Graph** on the tool menu to get the job graph view.
   + White for waiting or skipped: The task is waiting to run, or the stage has skipped.
   + Red for failed: The task has failed.
 
-    ![graph color sample, running](./media/apache-azure-spark-history-server/sparkui-graph-color-running.png)
- 
+    ![Spark application and job graph color sample, running](./media/apache-azure-spark-history-server/sparkui-graph-color-running.png)
+
     The skipped stage display in white.
-    ![graph color sample, skip](./media/apache-azure-spark-history-server/sparkui-graph-color-skip.png)
+    ![Spark application and job graph color sample, skip](./media/apache-azure-spark-history-server/sparkui-graph-color-skip.png)
+
+    ![Spark application and job graph color sample, failed](./media/apache-azure-spark-history-server/sparkui-graph-color-failed.png)
 
-    ![graph color sample, failed](./media/apache-azure-spark-history-server/sparkui-graph-color-failed.png)
- 
     > [!NOTE]  
     > Playback for each job is allowed. For incomplete job, playback is not supported.
 
-
 + Mouse scrolls to zoom in/out the job graph, or click **Zoom to fit** to make it fit to screen.
- 
-    ![graph zoom to fit](./media/apache-azure-spark-history-server/sparkui-graph-zoom2fit.png)
+
+    ![Spark application and job graph zoom to fit](./media/apache-azure-spark-history-server/sparkui-graph-zoom2fit.png)
 
 + Hover on graph node to see the tooltip when there are failed tasks, and click on stage to open stage page.
 
-    ![graph tooltip](./media/apache-azure-spark-history-server/sparkui-graph-tooltip.png)
+    ![Spark application and job graph tooltip](./media/apache-azure-spark-history-server/sparkui-graph-tooltip.png)
 
 + In job graph tab, stages will have tooltip and small icon displayed if they have tasks meet the below conditions:
   + Data skew: data read size > average data read size of all tasks inside this stage * 2 and data read size > 10 MB.
   + Time skew: execution time > average execution time of all tasks inside this stage * 2 and execution time > 2 mins.
 
-    ![graph skew icon](./media/apache-azure-spark-history-server/sparkui-graph-skew-icon.png)
+    ![Spark application and job graph skew icon](./media/apache-azure-spark-history-server/sparkui-graph-skew-icon.png)
 
 + The job graph node will display the following information of each stage:
   + ID.
@@ -149,49 +149,51 @@ Select job ID then click **Graph** on the tool menu to get the job graph view.
 
 + Send feedback with issues by clicking **Provide us feedback**.
 
-    ![graph feedback](./media/apache-azure-spark-history-server/sparkui-graph-feedback.png)
-
+    ![Spark application and job graph feedback](./media/apache-azure-spark-history-server/sparkui-graph-feedback.png)
 
 ## Diagnosis tab in Apache Spark History Server
+
 Select job ID then click **Diagnosis** on the tool menu to get the job Diagnosis view. The diagnosis tab includes **Data Skew**, **Time Skew**, and **Executor Usage Analysis**.
-    
+
 + Check the **Data Skew**, **Time Skew**, and **Executor Usage Analysis** by selecting the tabs respectively.
 
-    ![Diagnosis tabs](./media/apache-azure-spark-history-server/sparkui-diagnosis-tabs.png)
+    ![SparkUI diagnosis data skew tab again](./media/apache-azure-spark-history-server/sparkui-diagnosis-tabs.png)
 
 ### Data Skew
-Click **Data Skew** tab, the corresponding skewed tasks are displayed based on the specified parameters. 
+
+Click **Data Skew** tab, the corresponding skewed tasks are displayed based on the specified parameters.
 
 + **Specify Parameters** - The first section displays the parameters which are used to detect Data Skew. The built-in rule is: Task Data Read is greater than 3 times of the average task data read, and the task data read is more than 10MB. If you want to define your own rule for skewed tasks, you can choose your parameters, the **Skewed Stage**, and **Skew Char** section will be refreshed accordingly.
 
 + **Skewed Stage** - The second section displays stages which have skewed tasks meeting the criteria specified above. If there are more than one skewed task in a stage, the skewed stage table only displays the most skewed task (e.g. the largest data for data skew).
 
-    ![Data skew section2](./media/apache-azure-spark-history-server/sparkui-diagnosis-dataskew-section2.png)
+    ![sparkui diagnosis data skew tab](./media/apache-azure-spark-history-server/sparkui-diagnosis-dataskew-section2.png)
 
 + **Skew Chart** – When a row in the skew stage table is selected, the skew chart displays more task distributions details based on data read and execution time. The skewed tasks are marked in red and the normal tasks are marked in blue. For performance consideration, the chart only displays up to 100 sample tasks. The task details are displayed in right bottom panel.
 
-    ![Data skew section3](./media/apache-azure-spark-history-server/sparkui-diagnosis-dataskew-section3.png)
+    ![sparkui skew chart for stage 10](./media/apache-azure-spark-history-server/sparkui-diagnosis-dataskew-section3.png)
 
 ### Time Skew
-The **Time Skew** tab displays skewed tasks based on task execution time. 
+
+The **Time Skew** tab displays skewed tasks based on task execution time.
 
 + **Specify Parameters** - The first section displays the parameters which are used to detect Time Skew. The default criteria to detect time skew is: task execution time is greater than 3 times of average execution time and task execution time is greater than 30 seconds. You can change the parameters based on your needs. The **Skewed Stage** and **Skew Chart** display the corresponding stages and tasks information just like the **Data Skew** tab above.
 
 + Click **Time Skew**, then filtered result is displayed in **Skewed Stage** section according to the parameters set in section **Specify Parameters**. Click one item in **Skewed Stage** section, then the corresponding chart is drafted in section3, and the task details are displayed in right bottom panel.
 
-    ![Time skew section2](./media/apache-azure-spark-history-server/sparkui-diagnosis-timeskew-section2.png)
+    ![sparkui diagnosis time skew section](./media/apache-azure-spark-history-server/sparkui-diagnosis-timeskew-section2.png)
 
 ### Executor Usage Analysis
+
 The Executor Usage Graph visualizes the Spark job actual executor allocation and running status.  
 
 + Click **Executor Usage Analysis**, then four types curves about executor usage are drafted, including **Allocated Executors**, **Running Executors**,**idle Executors**, and **Max Executor Instances**. Regarding allocated executors, each "Executor added" or "Executor removed" event will increase or decrease the allocated executors, you can check "Event Timeline" in the “Jobs" tab for more comparison.
 
-    ![Executors tab](./media/apache-azure-spark-history-server/sparkui-diagnosis-executors.png)
+    ![sparkui diagnosis executors tab](./media/apache-azure-spark-history-server/sparkui-diagnosis-executors.png)
 
 + Click the color icon to select or unselect the corresponding content in all drafts.
 
-    ![Select chart](./media/apache-azure-spark-history-server/sparkui-diagnosis-select-chart.png)
-
+    ![sparkui diagnosis select chart](./media/apache-azure-spark-history-server/sparkui-diagnosis-select-chart.png)
 
 ## FAQ
 
@@ -206,33 +208,32 @@ To revert to community version, do the following steps:
 5. The property sets to **false** now.
 6. Click **Save** to save the configuration.
 
-    ![feature turns off](./media/apache-azure-spark-history-server/apache-spark-turn-off.png)
+    ![Apache Ambari feature turns off](./media/apache-azure-spark-history-server/apache-spark-turn-off.png)
 
 7. Click **Spark2** in left panel, under **Summary** tab, click **Spark2 History Server**.
 
-    ![restart server1](./media/apache-azure-spark-history-server/apache-spark-restart1.png) 
+    ![Apache Ambari Spark2 Summary view](./media/apache-azure-spark-history-server/apache-spark-restart1.png)
 
 8. Restart history server by clicking **Restart** of **Spark2 History Server**.
 
-    ![restart server2](./media/apache-azure-spark-history-server/apache-spark-restart2.png)  
-
+    ![Apache Ambari Spark2 History restart](./media/apache-azure-spark-history-server/apache-spark-restart2.png)  
 9. Refresh the Spark history server web UI, it will be reverted to community version.
 
 ### 2. Upload history server event
 
 If you run into history server error, follow the steps to provide the event:
+
 1. Download event by clicking **Download** in history server web UI.
 
-    ![download event](./media/apache-azure-spark-history-server/sparkui-download-event.png)
+    ![Spark2 History Server download](./media/apache-azure-spark-history-server/sparkui-download-event.png)
 
 2. Click **Provide us feedback** from data/graph tab.
 
-    ![graph feedback](./media/apache-azure-spark-history-server/sparkui-graph-feedback.png)
+    ![Spark graph provide us feedback](./media/apache-azure-spark-history-server/sparkui-graph-feedback.png)
 
 3. Provide the title and description of error, drag the zip file to the edit field, then click **Submit new issue**.
 
-    ![file issue](./media/apache-azure-spark-history-server/apache-spark-file-issue.png)
-
+    ![apache spark file issue example](./media/apache-azure-spark-history-server/apache-spark-file-issue.png)
 
 ### 3. Upgrade jar file for hotfix scenario
 
@@ -285,45 +286,43 @@ If you want to upgrade with hotfix, use the script below which will upgrade spar
     fi
    ```
 
-**Usage**: 
+**Usage**:
 
 `upgrade_spark_enhancement.sh https://${jar_path}`
 
 **Example**:
 
-`upgrade_spark_enhancement.sh https://${account_name}.blob.core.windows.net/packages/jars/spark-enhancement-${version}.jar` 
+`upgrade_spark_enhancement.sh https://${account_name}.blob.core.windows.net/packages/jars/spark-enhancement-${version}.jar`
 
 **To use the bash file from Azure portal**
 
 1. Launch [Azure portal](https://ms.portal.azure.com), and select your cluster.
 2. Click **Script actions**, then **Submit new**. Complete the **Submit script action** form, then click **Create** button.
-    
+
     + **Script type**: select **Custom**.
     + **Name**: specify a script name.
     + **Bash script URI**: upload the bash file to private cluster then copy URL here. Alternatively, use the URI provided.
-    
+
    ```upgrade_spark_enhancement
     https://hdinsighttoolingstorage.blob.core.windows.net/shsscriptactions/upgrade_spark_enhancement.sh
    ```
 
    + Check on **Head** and **Worker**.
    + **Parameters**: set the parameters follow the bash usage.
 
-     ![upload log or upgrade hotfix](./media/apache-azure-spark-history-server/apache-spark-upload1.png)
-
+     ![Azure portal submit script action](./media/apache-azure-spark-history-server/apache-spark-upload1.png)
 
 ## Known issues
 
-1.	Currently, it only works for Spark 2.3 and 2.4 cluster.
+1. Currently, it only works for Spark 2.3 and 2.4 cluster.
 
-2.	Input/output data using RDD will not show in data tab.
+2. Input/output data using RDD will not show in data tab.
 
 ## Next steps
 
 * [Manage resources for an Apache Spark cluster on HDInsight](apache-spark-resource-manager.md)
 * [Configure Apache Spark settings](apache-spark-settings.md)
 
-
 ## Contact us
 
 If you have any feedback, or if you encounter any other problems when using this tool, send an email at ([[email protected]](mailto:[email protected])).