In Apache Spark, executors accept and execute tasks from the driver. Here’s a breakdown of the relationship:
- Driver Program: The driver is the central coordinator of the Spark application. It is responsible for the following:
- Transforming the logical plan of a Spark job into a physical execution plan.
- Breaking the job into smaller tasks based on the partitions of the data.
- Distributing these tasks to the executors on the cluster.
- Executors: The executors are worker processes running on the cluster nodes. They are responsible for:
- Receiving tasks from the driver.
- Executing these tasks (such as transformations and actions) on data partitions.
- Storing data in memory or on disk (for caching purposes).
- Sending the results of tasks back to the driver once the tasks are completed.
Summary:
- The driver sends tasks to the executors.
- Executors do not accept jobs; they accept tasks, which are the smaller units of work derived from the jobs.
Each job is divided into stages, and each stage consists of tasks that are executed in parallel by the executors across different data partitions.