How does DAG scheduling work?

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Directed Acyclic Graph (DAG) scheduling

method used to organize and execute tasks that have dependencies in a workflow. In a DAG, each node represents a task, and edges define the order in which tasks must run, ensuring no cycles exist—so a task never depends on itself. Scheduling starts by identifying tasks with no dependencies (source nodes) and running them first. Once completed, tasks dependent on them become eligible for execution. This continues until all tasks finish. DAG schedulers, such as those in Apache Airflow or Spark, use algorithms like topological sorting to maintain dependency order while optimizing for parallelism—tasks with no mutual dependencies can run concurrently. They may also consider resource constraints, execution time, and priority to improve performance. This approach is widely used in data pipelines, workflow orchestration, and distributed systems to ensure accuracy and efficiency.

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