Monitor Apache Spark applications with Azure Log Analytics

In this tutorial, you learn how to enable the Synapse Studio connector that's built in to Log Analytics. You can then collect and send Apache Spark application metrics and logs to your Log Analytics workspace. Finally, you can use an Azure Monitor workbook to visualize the metrics and logs.

Configure workspace information

Follow these steps to configure the necessary information in Synapse Studio.

Step 1: Create a Log Analytics workspace

Consult one of the following resources to create this workspace:

• Create a workspace in the Azure portal.
• Create a workspace with Azure CLI.
• Create and configure a workspace in Azure Monitor by using PowerShell.

Step 2: Prepare an Apache Spark configuration file

Use any of the following options to prepare the file.

Option 1: Configure with Log Analytics workspace ID and key

Copy the following Apache Spark configuration, save it as spark_loganalytics_conf.txt, and fill in the following parameters:

• <LOG_ANALYTICS_WORKSPACE_ID>: Log Analytics workspace ID.
• <LOG_ANALYTICS_WORKSPACE_KEY>: Log Analytics key. To find this, in the Azure portal, go to Azure Log Analytics workspace > Agents > Primary key.

spark.synapse.logAnalytics.enabled true
spark.synapse.logAnalytics.workspaceId <LOG_ANALYTICS_WORKSPACE_ID>
spark.synapse.logAnalytics.secret <LOG_ANALYTICS_WORKSPACE_KEY>
spark.synapse.logAnalytics.uriSuffix ods.opinsights.azure.cn

Option 2: Configure with Azure Key Vault

To configure Azure Key Vault to store the workspace key, follow these steps:

1. Create and go to your key vault in the Azure portal.

2. On the settings page for the key vault, select Secrets.

3. Select Generate/Import.

4. On the Create a secret screen, choose the following values:

   • Name: Enter a name for the secret. For the default, enter SparkLogAnalyticsSecret.
   • Value: Enter the <LOG_ANALYTICS_WORKSPACE_KEY> for the secret.
   • Leave the other values to their defaults. Then select Create.

5. Copy the following Apache Spark configuration, save it as spark_loganalytics_conf.txt, and fill in the following parameters:

   • <LOG_ANALYTICS_WORKSPACE_ID>: The Log Analytics workspace ID.
   • <AZURE_KEY_VAULT_NAME>: The key vault name that you configured.
   • <AZURE_KEY_VAULT_SECRET_KEY_NAME> (optional): The secret name in the key vault for the workspace key. The default is SparkLogAnalyticsSecret.

spark.synapse.logAnalytics.enabled true
spark.synapse.logAnalytics.workspaceId <LOG_ANALYTICS_WORKSPACE_ID>
spark.synapse.logAnalytics.keyVault.name <AZURE_KEY_VAULT_NAME>
spark.synapse.logAnalytics.keyVault.key.secret <AZURE_KEY_VAULT_SECRET_KEY_NAME>
spark.synapse.logAnalytics.uriSuffix ods.opinsights.azure.cn

Option 3. Configure with a linked service

To configure a Key Vault linked service in Synapse Studio to store the workspace key, follow these steps:

1. Follow all the steps in the preceding section, "Option 2."

2. Create a Key Vault linked service in Synapse Studio:

   a. Go to Synapse Studio > Manage > Linked services, and then select New.

   b. In the search box, search for Azure Key Vault.

   c. Enter a name for the linked service.

   d. Choose your key vault, and select Create.

3. Add a spark.synapse.logAnalytics.keyVault.linkedServiceName item to the Apache Spark configuration.

spark.synapse.logAnalytics.enabled true
spark.synapse.logAnalytics.workspaceId <LOG_ANALYTICS_WORKSPACE_ID>
spark.synapse.logAnalytics.keyVault.name <AZURE_KEY_VAULT_NAME>
spark.synapse.logAnalytics.keyVault.key.secret <AZURE_KEY_VAULT_SECRET_KEY_NAME>
spark.synapse.logAnalytics.keyVault.linkedServiceName <LINKED_SERVICE_NAME>
spark.synapse.logAnalytics.uriSuffix ods.opinsights.azure.cn

For a list of Apache Spark configurations, see Available Apache Spark configurations

Step 3: Upload your Apache Spark configuration to an Apache Spark pool

You can upload the configuration file to your Azure Synapse Analytics Apache Spark pool. In Synapse Studio:

1. Select Manage > Apache Spark pools.

2. Next to your Apache Spark pool, select the ... button.

3. Select Apache Spark configuration.

4. Select Upload, and choose the spark_loganalytics_conf.txt file.

5. Select Upload, and then select Apply.

   

Step 4: Create an Apache Spark Configuration

You can create an Apache Spark Configuration to your workspace, and when you create Notebook or Apache spark job definition can select the Apache Spark configuration that you want to use with your Apache Spark pool. When you select it, the details of the configuration are displayed.

1. Select Manage > Apache Spark configurations.

2. Click on New button to create a new Apache Spark configuration, or click on Import a local .json file to your workspace.

3. New Apache Spark configuration page will be opened after you click on New button.

4. For Name, you can enter your preferred and valid name.

5. For Description, you can input some description in it.

6. For Annotations, you can add annotations by clicking the New button, and also you can delete existing annotations by selecting and clicking Delete button.

7. For Configuration properties, customize the configuration by clicking Add button to add properties. If you do not add a property, Azure Synapse will use the default value when applicable.

   

Submit an Apache Spark application and view the logs and metrics

Here's how:

1. Submit an Apache Spark application to the Apache Spark pool configured in the previous step. You can use any of the following ways to do so:

   • Run a notebook in Synapse Studio.
   • In Synapse Studio, submit an Apache Spark batch job through an Apache Spark job definition.
   • Run a pipeline that contains Apache Spark activity.

2. Go to the specified Log Analytics workspace, and then view the application metrics and logs when the Apache Spark application starts to run.

Write custom application logs

You can use the Apache Log4j library to write custom logs.

Example for Scala:

%%spark
val logger = org.apache.log4j.LogManager.getLogger("com.contoso.LoggerExample")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")
//log exception
try {
      1/0
 } catch {
      case e:Exception =>logger.warn("Exception", e)
}
// run job for task level metrics
val data = sc.parallelize(Seq(1,2,3,4)).toDF().count()

Example for PySpark:

%%pyspark
logger = sc._jvm.org.apache.log4j.LogManager.getLogger("com.contoso.PythonLoggerExample")
logger.info("info message")
logger.warn("warn message")
logger.error("error message")

Use the sample workbook to visualize the metrics and logs

1. Download the workbook.

2. Open and copy the workbook file content.

3. In the Azure portal, select Log Analytics workspace > Workbooks.

4. Open the Empty workbook. Use the Advanced Editor mode by selecting the </> icon.

5. Paste over any JSON code that exists.

6. Select Apply, and then select Done Editing.

   

   

Then, submit your Apache Spark application to the configured Apache Spark pool. After the application goes to a running state, choose the running application in the workbook dropdown list.

You can customize the workbook. For example, you can use Kusto queries and configure alerts.

Query data with Kusto

The following is an example of querying Apache Spark events:

SparkListenerEvent_CL
| where workspaceName_s == "{SynapseWorkspace}" and clusterName_s == "{SparkPool}" and livyId_s == "{LivyId}"
| order by TimeGenerated desc
| limit 100 

Here's an example of querying the Apache Spark application driver and executors logs:

SparkLoggingEvent_CL
| where workspaceName_s == "{SynapseWorkspace}" and clusterName_s == "{SparkPool}" and livyId_s == "{LivyId}"
| order by TimeGenerated desc
| limit 100

And here's an example of querying Apache Spark metrics:

SparkMetrics_CL
| where workspaceName_s == "{SynapseWorkspace}" and clusterName_s == "{SparkPool}" and livyId_s == "{LivyId}"
| where name_s endswith "jvm.total.used"
| summarize max(value_d) by bin(TimeGenerated, 30s), executorId_s
| order by TimeGenerated asc

Create and manage alerts

Users can query to evaluate metrics and logs at a set frequency, and fire an alert based on the results. For more information, see Create, view, and manage log alerts by using Azure Monitor.

Synapse workspace with data exfiltration protection enabled

After the Synapse workspace is created with data exfiltration protection enabled.

When you want to enable this feature, you need to create managed private endpoint connection requests to Azure Monitor private link scopes (A M P L S) in the workspace's approved Microsoft Entra tenants.

You can follow below steps to create a managed private endpoint connection to Azure Monitor private link scopes (A M P L S):

1. If there is no existing A M P L S, you can follow Azure Monitor Private Link connection setup to create one.
2. Navigate to your A M P L S in Azure portal, on the Azure Monitor Resources page, click Add to add connection to your Azure Log Analytics workspace.
3. Navigate to Synapse Studio > Manage > Managed private endpoints, click New button, select Azure Monitor Private Link Scopes, and continue.

   

4. Choose your Azure Monitor Private Link Scope you created, and click Create button.

   

5. Wait a few minutes for private endpoint provisioning.
6. Navigate to your A M P L S in Azure portal again, on the Private Endpoint connections page, select the connection provisioned and Approve.

Next steps

• Use serverless Apache Spark pool in Synapse Studio.
• Run a Spark application in notebook.
• Create Apache Spark job definition in Azure Studio.
• Collect Apache Spark applications logs and metrics with Azure Storage account.
• Collect Apache Spark applications logs and metrics with Azure Event Hubs.