What is Spark’s Checkpoint?

Checkpoint in Spark is a mechanism used to save the data of RDD (Resilient Distributed Dataset) to a reliable storage system so that the data can be quickly recovered later. This operation triggers a job to compute the RDD and write its result to persistent storage, eliminating the need for repeated calculations each time the RDD is used.

When a checkpoint operation is performed on an RDD, Spark will recompute all its dependencies and save the results to a specified persistent storage system, such as HDFS or S3. This can effectively reduce computational costs in case of task failures and improve job fault tolerance and performance.

It is important to note that Checkpointing introduces additional IO overhead and storage consumption, so its usage should be carefully considered. It is typically recommended for long-running jobs or scenarios where the same RDD needs to be reused multiple times.