Interpreting a Spark DAG

A DAG is just a regular graph with nodes and edges but with no cycles or loops. In order to understand a Spark DAG, we first have to understand where a DAG comes into the picture during the execution of a Spark job.

When a user submits a Spark job, the Spark driver first identifies all the tasks involved in accomplishing the job. It then figures out which of these tasks can be run in parallel and which tasks depend on other tasks. Based on this information, it converts the Spark job into a graph of tasks. The nodes at the same level indicate jobs that can be run in parallel, and the nodes at different levels indicate tasks that need to be run after the previous nodes. This graph is acyclic, as denoted by A in DAG. This DAG is then converted into a physical execution plan. In the physical execution plan, nodes that are at the same level are segregated into stages. Once all the tasks and stages are complete, the Spark job is termed as completed.

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