Why does reading data from Event Hubs using Pyspark batch read hang after a few hours of repeated execution?

Why does reading data from Event Hubs using PySpark in batch mode hang after a few hours of repeated execution, and what could be causing this issue?

There could be several reasons why reading data from Event Hubs using PySpark in batch mode hangs after a few hours of repeated execution. Here are some potential causes and solutions:

Resource Exhaustion: If your system runs out of resources (CPU, memory, or disk space), it can cause the process to hang. Monitor your system resources and ensure there's enough capacity for the task.

Network Issues: Network latency or connectivity problems can cause delays or hangs. Check your network connection and ensure it's stable.

Event Hub Configuration: Ensure that your Event Hub configuration is correct and that you're using the appropriate settings for batch reads. Double-check your connection string and other configurations.

Spark Configuration: Verify that your Spark configuration is optimized for your workload. Adjusting parameters like spark.executor.memory, spark.driver.memory, and spark.sql.shuffle.partitions can help improve performance.

Data Volume: If the volume of data being read is very large, it can take a long time to process. Consider breaking the data into smaller batches or optimizing your query to handle large datasets more efficiently.

Library Versions: Ensure that you're using the latest versions of the Azure Event Hubs Spark library and other dependencies. Outdated libraries can cause compatibility issues and performance problems.

Error Handling: Implement proper error handling and logging to capture any exceptions or issues that might be causing the hang. This can help identify the root cause more quickly.