What is the purpose of Checkpoint in Spark?

In Spark, Checkpointing is a mechanism used to persist the intermediate results of RDDs. It allows storing the computation results of RDDs in distributed storage systems such as HDFS or S3, enabling the recomputation of RDDs in case of failure without rerunning the entire DAG. Checkpointing enhances the fault tolerance and performance of Spark applications, while also reducing memory usage and avoiding issues with redundant RDD computation. By utilizing Checkpointing, RDD computation results can be persisted on disk, leading to more efficient memory management and improved application performance.