What does parallelism in Spark refer to?

In Spark, parallelism refers to the number of tasks running simultaneously in the cluster. It determines the extent of data partitioning and parallel processing, impacting the performance and efficiency of Spark jobs. By adjusting the parallelism, you can control the number of tasks running in parallel, thereby improving the job’s execution speed and resource utilization. Higher parallelism typically speeds up job execution, but also requires more resources to support parallel execution. In Spark, parallelism can be set by adjusting configuration parameters.