dag-executor

You can orchestrate tasks with dependencies by organizing them in a Directed Acyclic Graph (DAG). This approach validates the graph structure to prevent cycles and ensures ordered execution for complex workflows like multi-stage software build processes.

Running independent workflow tasks in parallel is achieved by defining them in a DAG. The DAG executor runs independent operations concurrently to maximize throughput while applying configurable parallelization limits to manage concurrency.

DAG validation prevents cycles by checking the Directed Acyclic Graph structure before execution. This ensures that task dependencies form a valid acyclic order, guaranteeing that no circular dependencies exist to disrupt the workflow.

Yes, you can configure parallelization limits for task execution in distributed computing workflows. The executor allows you to set these limits to manage concurrency and control how many independent tasks run simultaneously.

Handling task failure in a workflow with complex dependencies is managed through built-in failure handling mechanisms. When a task fails, the executor applies its configured failure handling rules to determine the subsequent behavior of the DAG.

You need a DAG for workflow orchestration when task order and concurrency are critical, such as in distributed computing or project management. It is required when independent tasks must run in parallel while dependent tasks wait for completion.