Troubleshoot a `pyspark` notebook

Artikel
11/18/2022

Important

The Microsoft SQL Server 2019 Big Data Clusters add-on will be retired. Support for SQL Server 2019 Big Data Clusters will end on February 28, 2025. All existing users of SQL Server 2019 with Software Assurance will be fully supported on the platform and the software will continue to be maintained through SQL Server cumulative updates until that time. For more information, see the announcement blog post and Big data options on the Microsoft SQL Server platform.

This article demonstrates how to troubleshoot a pyspark notebook that fails.

Architecture of a PySpark job under Azure Data Studio

Azure Data Studio communicates with the livy endpoint on SQL Server Big Data Clusters.

The livy endpoint issues spark-submit commands within the big data cluster. Each spark-submit command has a parameter that specifies YARN as the cluster resource manager.

To efficiently troubleshoot your PySpark session you will collect and review logs within each layer: Livy, YARN and Spark.

This troubleshooting steps require that you have:

Azure Data CLI (azdata) installed and with configuration correctly set to your cluster.
Familiarity with running Linux commands and some log troubleshooting skills.

Troubleshooting steps

Review the stack and error messages in the pyspark.

Get the application ID from the first cell in the notebook. Use this application ID to investigate the livy, YARN, and spark logs. SparkContext uses uses this YARN application ID.

Get the logs.

Use azdata bdc debug copy-logs to investigate

The following example connects a big data cluster endpoint to copy the logs. Update the following values in the example before running.

<ip_address>: Big data cluster endpoint
<username>: Your big data cluster username
<namespace>: The Kubernetes namespace for your cluster
<folder_to_copy_logs>: The local folder path where you want your logs copied to

azdata login --auth basic --username <username> --endpoint https://<ip_address>:30080
azdata bdc debug copy-logs -n <namespace> -d <folder_to_copy_logs>

Example output

<user>@<server>:~$ azdata bdc debug copy-logs -n <namespace> -d copy_logs
Collecting the logs for cluster '<namespace>'.
Collecting logs for containers...
Creating an archive from logs-tmp/<namespace>.
Log files are archived in /home/<user>/copy_logs/debuglogs-<namespace>-YYYYMMDD-HHMMSS.tar.gz.
Creating an archive from logs-tmp/dumps.
Log files are archived in /home/<user>/copy_logs/debuglogs-<namespace>-YYYYMMDD-HHMMSS-dumps.tar.gz.
Collecting the logs for cluster 'kube-system'.
Collecting logs for containers...
Creating an archive from logs-tmp/kube-system.
Log files are archived in /home/<user>/copy_logs/debuglogs-kube-system-YYYYMMDD-HHMMSS.tar.gz.
Creating an archive from logs-tmp/dumps.
Log files are archived in /home/<user>/copy_logs/debuglogs-kube-system-YYYYMMDD-HHMMSS-dumps.tar.gz.

Review the Livy logs. The Livy logs are at <namespace>\sparkhead-0\hadoop-livy-sparkhistory\supervisor\log.

Search for the YARN application ID from the pyspark notebook first cell.
Search for ERR status.

Example of Livy log that has a YARN ACCEPTED state. Livy has submitted the yarn application.

HH:MM:SS INFO utils.LineBufferedStream: YYY-MM-DD HH:MM:SS INFO impl.YarnClientImpl: Submitted application application_<application_id>
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream: YYY-MM-DD HH:MM:SS INFO yarn.Client: Application report for application_<application_id> (state: ACCEPTED)
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream: YYY-MM-DD HH:MM:SS INFO yarn.Client: 
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      client token: N/A
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      diagnostics: N/A
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      ApplicationMaster host: N/A
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      ApplicationMaster RPC port: -1
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      queue: default
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      start time: ############
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      final status: UNDEFINED
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      tracking URL: https://sparkhead-1.fnbm.corp:8090/proxy/application_<application_id>/
YY/MM/DD HH:MM:SS INFO utils.LineBufferedStream:      user: <account>

Review the YARN UI

Get the YARN endpoint URL from the Azure Data Studio big data cluster management dashboard or run azdata bdc endpoint list –o table.

For example:

azdata bdc endpoint list -o table

Returns

Description                                             Endpoint                                                          Name                        Protocol
------------------------------------------------------  ----------------------------------------------------------------  --------------------------  ----------
Gateway to access HDFS files, Spark                     https://knox.<namespace-value>.local:30443                               gateway                     https
Spark Jobs Management and Monitoring Dashboard          https://knox.<namespace-value>.local:30443/gateway/default/sparkhistory  spark-history               https
Spark Diagnostics and Monitoring Dashboard              https://knox.<namespace-value>.local:30443/gateway/default/yarn          yarn-ui                     https
Application Proxy                                       https://proxy.<namespace-value>.local:30778                              app-proxy                   https
Management Proxy                                        https://bdcmon.<namespace-value>.local:30777                             mgmtproxy                   https
Log Search Dashboard                                    https://bdcmon.<namespace-value>.local:30777/kibana                      logsui                      https
Metrics Dashboard                                       https://bdcmon.<namespace-value>.local:30777/grafana                     metricsui                   https
Cluster Management Service                              https://bdcctl.<namespace-value>.local:30080                             controller                  https
SQL Server Master Instance Front-End                    sqlmaster.<namespace-value>.local,31433                                  sql-server-master           tds
SQL Server Master Readable Secondary Replicas           sqlsecondary.<namespace-value>.local,31436                               sql-server-master-readonly  tds
HDFS File System Proxy                                  https://knox.<namespace-value>.local:30443/gateway/default/webhdfs/v1    webhdfs                     https
Proxy for running Spark statements, jobs, applications  https://knox.<namespace-value>.local:30443/gateway/default/livy/v1       livy                        https

Check the application ID and individual application_master and container logs.
Review the YARN application logs.

Get application log for the app. Use kubectl to connect to the sparkhead-0 pod, for example:
```
kubectl exec -it sparkhead-0 -- /bin/bash
```
And then run this command within that shell using the right application_id:
```
yarn logs -applicationId application_<application_id>
```

Search for errors or stacks.

An example of permission error against hdfs. In the java stack look for the Caused by:

YYYY-MM-DD HH:MM:SS,MMM ERROR spark.SparkContext: Error initializing SparkContext.
org.apache.hadoop.security.AccessControlException: Permission denied: user=<account>, access=WRITE, inode="/system/spark-events":sph:<bdc-admin>:drwxr-xr-x
     at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.check(FSPermissionChecker.java:399)
     at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:255)
     at org.apache.hadoop.hdfs.server.namenode.FSPermissionChecker.checkPermission(FSPermissionChecker.java:193)
     at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkPermission(FSDirectory.java:1852)
     at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkPermission(FSDirectory.java:1836)
     at org.apache.hadoop.hdfs.server.namenode.FSDirectory.checkAncestorAccess(FSDirectory.java:1795)
     at org.apache.hadoop.hdfs.server.namenode.FSDirWriteFileOp.resolvePathForStartFile(FSDirWriteFileOp.java:324)
     at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.startFileInt(FSNamesystem.java:2504)
     at org.apache.hadoop.hdfs.server.namenode.FSNamesystem.startFileChecked(FSNamesystem.java:2448)

Caused by: org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.AccessControlException): Permission denied: user=<account>, access=WRITE, inode="/system/spark-events":sph:<bdc-admin>:drwxr-xr-x

Review the SPARK UI.

Drill down into the stages tasks looking for errors.

Next steps

Troubleshoot SQL Server Big Data Clusters Active Directory integration

Delen via

Troubleshoot a `pyspark` notebook

Architecture of a PySpark job under Azure Data Studio

Troubleshooting steps

Next steps

Feedback

Aanvullende resources

Delen via

Troubleshoot a pyspark notebook

Architecture of a PySpark job under Azure Data Studio

Troubleshooting steps

Next steps

Feedback

Aanvullende resources

Troubleshoot a `pyspark` notebook