What is the purpose of Apache Spark job, task and stage?

I am learning about Apache Spark and want to know the meaning of each Task created on the Jobs recorded on Spark history.

For example, the application I write creates 17 jobs, in which job 0 runs for 10 minutes, there are 2384 small tasks and I want to learn about the meaning of these 2384, is it possible?

I found a picture of DAG in the Jobs and want to know the relationship between DAG and Task, is it possible (Specifically from the attached file DAG and 2384 tasks below)?

https://i.stack.imgur.com/Azva4.png

First of all, we need to understand a couple of concepts in Spark, an application is a combination of several jobs. Each job is a sequence of stages that depend on each other, and these stages are formed by tasks. Tasks are the smallest unit of work in Spark, and they are responsible for performing the actual computation on data partitions.

To understand the meaning of each task, you need to understand the operations you have performed in your Spark application. Here's a breakdown of the relationship between your application, jobs, stages, and tasks:

1. Application: The top-level Spark program you have written.

2. Jobs: Your application is divided into multiple jobs based on actions like count(), save(), or collect(). Each job corresponds to an action.

3. Stages: A job is divided into stages based on operations that can be executed in parallel. Stages are created due to transformations such as map(), filter(), or reduce().

4. Tasks: Each stage is further divided into tasks that can be executed in parallel on different data partitions.

To understand the meaning of the 2384 tasks in Job 0 on the application you are referring, you should analyze the transformations and actions applied in that specific job. This will help you figure out the operations each task is performing.

Now, the Directed Acyclic Graph (DAG) is a graph representation of the sequence of operations (transformations and actions) in your Spark application. In other words, it's a representation of the entire computation process. DAGs are used to plan the execution of tasks by dividing them into stages, which are then assigned to different worker nodes in the cluster. Each node in the DAG represents an RDD operation, and the edges between the nodes represent the dependencies between these operations.

The relationship between DAG and Task is as follows:

1. The DAG represents the overall flow of operations in your Spark application, from the input data to the final result.

2. Tasks are the individual units of work that correspond to a specific operation on a partition of data.

3. The DAG scheduler in Spark divides the entire computation into stages based on the dependencies between tasks.

4. These stages are further divided into tasks that are executed in parallel on different data partitions.

In summary, tasks are the smallest units of work in your Spark application, and the DAG represents the sequence of operations and dependencies between these tasks. Understanding the operations in your application and the corresponding tasks will help you grasp the meaning of each task in your jobs.

You can check the following book, is a great resource to understand Spark concepts in depth.: Learning Spark, 2nd Edition by Jules S. Damji, Brooke Wenig
https://www.oreilly.com/library/view/learning-spark-2nd/9781492050032/