How to debug and troubleshoot issues during job execution in Spark?

There are several ways to debug and troubleshoot issues during job execution in Spark.

1. Log Viewing: Spark logs can provide detailed information about the job execution process, including job planning, task scheduling, data reading and writing, etc. You can use log viewing to pinpoint the problem.
2. Spark UI is a web interface provided by Spark to display job execution details, including job progress, task execution status, and resource usage. It allows users to monitor the running status of jobs and identify potential issues.
3. Event listeners: You can monitor events during job execution by registering event listeners, such as job start, job end, task start, task end, etc. Listeners allow you to access detailed information during job execution and troubleshoot any issues.
4. Debugging Tools in Spark: Spark offers some debugging tools like spark-shell and spark-submit, which can be used to run jobs and view detailed information during execution to help identify issues.
5. Distributed debugger: To troubleshoot job execution issues in distributed environments, you can use tools like Databricks Debugger.

By following the above methods, it is possible to identify and resolve issues during the execution of Spark jobs, ultimately improving the performance and stability of the jobs.