How jobs are created in spark

In Spark, what are applications, jobs, stages, and tasks?

Task


Spark's smallest execution unit is the task. A spark job performs a set of instructions. Reading data, filtering data, and applying map() to data, for example, may all be integrated into a job. Tasks are carried out inside an executor.


Stage


A stage is made up of numerous tasks, and each job in the stage follows the same set of instructions.


Job


A work is divided into phases. Spark starts a new stage when it meets a function that needs a shuffle. Transformation operations such as reduceByKey(), Join(), and so on will cause a shuffle and result in a new stage. When you read a dataset, Spark will also generate a stage.


Application


An application is made up of multiple jobs. When you perform an action function, such as write, a job is generated ().


Summary


Many jobs may be assigned to a Spark application. A work might have many phases. Many duties may be assigned to a stage. A task is responsible for carrying out a set of instructions.

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