Spark Parallelism Explained

In Spark, parallelism refers to the number of tasks that are executed simultaneously in a distributed computing environment, or the number of tasks being executed concurrently. More specifically, in Spark, parallelism usually refers to the number of partitions in an RDD (Resilient Distributed Dataset) or the number of tasks in a job.

1. The number of partitions in RDD determines the amount of tasks that can be executed in parallel, impacting the performance and resource utilization of the job.
2. Number of tasks in a job: When you submit a Spark job, you can control the way the job is executed by setting the parallelism. Higher parallelism can speed up job execution but also increases resource consumption.

Adjusting parallelism can optimize the performance of a job, selecting the appropriate parallelism based on factors such as data volume and cluster resources can make the job execute more efficiently. In Spark, you can adjust parallelism by setting different parameters (such as spark.default.parallelism) to meet specific needs.