Critical/Important Spark Listener for performance tuning of spark code

[subha2]

Please suggest some key/important  Listener which is most helpful in performance tuning of spark code.

Kindly suggest how to use the listener in pyspark code to access the metrics as well for reference.

[Kaniz_Fatma]

Hi @subha2, 

1. SparkListener: This is the base class for all listeners. It allows you to listen to various events in Spark, such as job start, job end, stage completion, and task completion. By overriding methods in this class, you can gather metrics and insights about job execution.
2. SparkListenerJobEnd: This listener is triggered at the end of a job. It provides insights into job duration, the number of stages executed, and any failures that occurred during execution.
3. SparkListenerStageCompleted: This listener is useful for monitoring the completion of stages within a job. It can help identify which stages are taking longer than expected and may require optimization.
4. SparkListenerTaskEnd: This listener provides information about the completion of individual tasks, including task duration and any errors that may have occurred. This can be particularly useful for diagnosing issues in specific tasks.
5. SparkListenerExecutorAdded: This listener is triggered when an executor is added to the Spark application. It can help track resource allocation and utilization.