Explain the concept of Spark’s checkpointing.

Checkpointing in Spark is a mechanism to persist RDDs (Resilient Distributed Datasets) or DStreams (Discretized Streams) to a stable storage system like Hadoop Distributed File System (HDFS). It is primarily used for fault tolerance by allowing the recovery of lost data or partial computation in case of failures.

Checkpointing involves materializing the RDD/DStream to a reliable storage system, which can be expensive in terms of I/O and storage resources. It is typically used in scenarios where lineage information becomes too long or where iterative computations are performed, as it allows lineage to be truncated and saves the intermediate results.