What is the DAG scheduler in Spark?

In Spark, the DAG Scheduler (Directed Acyclic Graph Scheduler) is responsible for converting the submitted Spark application into a directed acyclic graph (DAG) and scheduling and executing tasks based on the dependencies between them.

When a user submits a Spark application, the DAG scheduler will convert the RDD transformations (such as map, filter, reduce, etc.) and actions (such as collect, count, etc.) in the application into a directed acyclic graph. This DAG graph depicts the dependencies between different RDDs, as well as how data flows and is transformed.

The DAG scheduler partitions tasks into multiple stages based on dependencies within the DAG graph, where each stage consists of parallelizable tasks. These stages are then submitted to the task scheduler, which assigns tasks to executors for execution.

Using a DAG scheduler, Spark can achieve more efficient task scheduling and execution, improving resource utilization and execution efficiency. Additionally, the DAG scheduler can optimize the order of task execution, reducing the number of data shuffles, thereby enhancing overall computing performance.