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.