The relationship between the number of Tasks and the number of partitions in Spark

1. Contact

In Spark, the number of tasks is directly related to the number of partitions. The following is a detailed description of the relationship between the two:

1. A partition corresponds to a Task :

   • In Spark, each partition will be processed by a separate Task. In other words, a Task is a processing unit of partitioned data. Therefore, the number of partitions of RDD or DataFrame or DataSet determines the number of Tasks Spark starts for the RDD or DataFrame or DataSet.

2. The number of partitions determines the degree of parallelism :

   • The parallelism of a Spark application is determined by the number of Tasks it has. More partitions means more tasks, so the parallelism of the entire application is higher. Therefore, choosing an appropriate number of partitions is an important aspect of optimizing Spark application performance.

3. Affect Compute and Shuffle :

   • Since each Task processes the data of one partition, the number of partitions determines the parallelism of Spark job execution. When performing Shuffle operations (such as groupByKey, reduceByKey, joinetc.), the output partition number is also