To prevent the indefinite growth of your State Store (even when the watermark is updated), you can improve how efficiently you manage the lifecycle of your state store files in Apache Spark Structured Streaming applications. 

This applies to both Hadoop Distributed File System and RocksDB-based providers.

Handling instructions

In any Stateful Streaming application, the streaming engine creates and manages two types of state files. You can find these in your checkpoint state folder: *.delta and *.snapshot files. Let's examine their lifecycle:

Creation

The streaming engine creates these files as part of the state management process. The .delta files are created for every batch of data processed by the application, while the .snapshot files are generated periodically to provide a consolidated view of the state at a given point in time. This mechanism ensures efficient state recovery in case of application failure.

Deletion

Background maintenance threads running on Spark Executors manage file deletion. These threads are responsible for periodically deleting these files, which helps in managing the lifecycle of the state store files.

Configuration instructions

The spark.sql.streaming.minBatchesToRetain configuration sets the number of delta files retained in the checkpoint location. By default, this is set to 100, but you can adjust this number based on your specific requirements. Reducing this number will result in fewer files being retained in your checkpoint location.

The spark.sql.streaming.stateStore.maintenanceInterval configuration sets the interval between triggering maintenance tasks in the StateStore. These maintenance tasks are executed as background tasks and play a crucial role in managing the lifecycle of the state store files. They can also impact the performance of the Spark Streaming applications. Under normal situations, the default interval is sufficient.

Please note that these configurations should be carefully evaluated and adjusted based on the specific requirements and constraints of your Spark Streaming applications.