What is the role of checkpoints in Spark Structured Streaming?

Checkpoints in Spark Structured Streaming play a crucial role in ensuring fault tolerance and enabling recovery of applications. They do this by storing the state of an application at specific intervals. This means that if an application encounters a failure, it can recover from the last checkpoint, minimizing data loss and allowing the application to continue processing from that point instead of restarting from the beginning.

When a streaming application processes data in real-time, it often maintains state information that can evolve over time. Checkpointing allows the application to save this state, including any intermediate results or progress on currently processed batches of data. Therefore, in the event of a failure, Spark can use these checkpoints to restore the application to its last known good state, thus supporting reliable streaming operations.

The other choices represent different concepts that don't align with the primary purpose of checkpoints in Spark Structured Streaming. For example, while ensuring a backup of data is generally important in data management, checkpoints specifically focus on application state recovery rather than data backup. Additionally, optimizing storage costs or increasing the speed of data ingestion are not direct functions of checkpoints; those tasks are addressed through other methodologies within Spark.