Apache Spark Checkpoints: Ensuring Reliability and Fault Tolerance

Does Apache Spark provide checkpoints?

a. Yes, Spark provides checkpoints for saving application state.

b. No, Spark does not support checkpoints.

c. Checkpoints are only available in Spark Streaming.

d. Checkpoints are provided by Apache Hadoop, not Spark.

Answer:

Yes, Spark provides checkpoints for saving application state.

Explanation: Apache Spark does indeed support checkpoints, enabling the saving of application state. These checkpoints are crucial for fault tolerance and ensuring that computations can be recovered in case of failures. They allow Spark to persist intermediary job data to reliable storage systems like Hadoop Distributed File System (HDFS) or other fault-tolerant storage systems.

Checkpoints are not limited to Spark Streaming; they are applicable across various Spark applications, including batch processing and structured streaming. By using checkpoints, Spark applications can restart from a known state in case of failures, enhancing reliability and fault tolerance. This feature is particularly valuable in long and complex data processing tasks where interruptions or system failures can occur.

Furthermore, while Apache Hadoop provides its own checkpointing mechanism called HDFS checkpoints, Spark offers its own internal checkpointing functionality, distinct from Hadoop. Spark's checkpointing operates within its framework, allowing users to store the application state efficiently based on specific intervals or criteria defined within the Spark application itself.

In conclusion, Spark's provision of checkpoints significantly contributes to the reliability and fault tolerance of its applications, enabling the recovery of stateful computations and enhancing the overall robustness of data processing tasks.