#30 Task, job and stage in spark

In Apache Spark, jobs, tasks, and stages are fundamental concepts that play a crucial role in the distributed execution of computations.

Here's an overview of each:

Job:

• A job in Spark refers to a complete computation triggered by an action, such as collect(), saveAsTextFile(), or count().
• When an action is called on an RDD, Spark prepares to execute one or more jobs to fulfill that action.
• Each job consists of one or more stages.

Stage:

• A stage is a logical division of a job's computation, corresponding to a sequence of transformations that can be executed without shuffling data across the network.
• Stages are determined by the presence of shuffle operations (e.g., reduceByKey, groupByKey, sortByKey) or data partitioning operations (e.g., repartition, partitionBy).
• Stages are further divided into tasks for actual execution.

Task:

• A task is the smallest unit of work in Spark and represents the actual execution of a computation on a single partition of data.
• Each task corresponds to a single partition of an RDD and performs the transformations defined in the stage it belongs to.
• Tasks are executed in parallel across the worker nodes in the Spark cluster.
• Tasks are created for each partition of data in the RDD being operated on within a stage.

When you submit a Spark application, it is divided into multiple stages, and each stage is further divided into tasks. These tasks are then scheduled and executed across the available resources in the Spark cluster. The division into stages allows Spark to optimize the execution plan by minimizing data shuffling and maximizing parallelism.

Understanding these concepts is crucial for optimizing Spark applications, as inefficiencies in job, stage, or task execution can lead to longer processing times or resource wastage.