Why Does a Job Running Timeout Occur When Processing a Large Amount of Data with a Spark Job?

When running large amounts of data in a Spark job, if a timeout exception error occurs, it is usually due to insufficient resource configuration, data skew, network issues, or too many tasks.

Solution:

• Set concurrency: By setting an appropriate concurrency, you can run multiple tasks concurrently, improving the job processing capacity.

  For example, when accessing large amounts of database data in GaussDB(DWS), set the concurrency and run in a multi-task manner to avoid job timeouts.

  For details about how to set the concurrency, see the partitionColumn and numPartitions fields and Scala Example Code for connecting to GaussDB(DWS).

• Adjust the number of Executors in the Spark job and allocate more resources for the Spark job to run.