One failed task does not necessarily stop an entire plan. Independent work may continue, while tasks that depend on the failed task are often blocked. The result depends on the workflow engine, the plan’s dependency rules, the kind of failure, and its retry settings.
Why one failure can affect tasks differently
A workflow plan is often a dependency graph rather than a single sequence. If Task C needs Task A’s output, a failure in A can prevent C from running. A separate branch that does not rely on A may still be eligible to proceed.
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In Apache Airflow, tasks normally wait for their upstream tasks to succeed. Airflow also supports configurable trigger rules, so a task’s eligibility can depend on conditions other than universal upstream success. See Airflow’s DAG concepts documentation.
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Independent tasks
Tasks with no dependency on the failed task may continue, subject to the workflow’s scheduling and failure policies. This is common behavior, not a guarantee for every planner or application.
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Downstream tasks
Tasks that need the failed task to succeed or produce an output are usually blocked under default dependency behavior. A configured rule such as Airflow’s all_done can allow a downstream task to run once its upstream tasks have finished, even if one failed. Running after failure does not mean the failed task’s result exists or that the downstream task can safely complete its purpose.
Retries and continuing are different decisions
A retry policy determines whether a failed operation will be attempted again. A dependency or trigger rule determines whether later work is eligible to run. One setting does not answer the other.
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Apache Airflow
Airflow lets workflow authors configure retry counts and delays. Its task documentation also describes exception-specific handling that can select a retry, immediate failure, or standard retry behavior. The configured retry count bounds the attempts; it does not by itself make downstream tasks run after a final failure. See Airflow’s task documentation.
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Temporal
Temporal distinguishes a Workflow Task failure from a Workflow Execution failure. A Workflow Task failure—often a problem processing work, such as an infrastructure, code, or execution-environment issue—is automatically retried while the Workflow Execution remains open, unless the task completes, the execution is terminated, or its execution timeout is reached.
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A Workflow Execution failure closes that execution as failed. It does not automatically retry as the same kind of task-level retry; a configured Workflow Retry Policy can start a new run. That is different from retrying a Workflow Task within the existing execution history. See Temporal’s Workflow Execution documentation and its Retry Policies documentation.
How to work out what will happen
- Identify the failure scope. Determine whether an individual operation failed, the workflow’s business logic concluded it could not complete, or the overall run failed. In Temporal, distinguishing Workflow Task failure from Workflow Execution failure is particularly important.
- Trace the dependencies. Follow the failed task’s outgoing dependency edges. Tasks that require its successful result are the ones most likely to be blocked; unrelated branches may remain eligible.
- Check the retry policy. Find out whether the failed operation will be retried, how many attempts are allowed, and whether delay or exception-specific rules apply.
- Check downstream execution rules. Look for trigger rules or equivalent settings that determine whether later tasks require upstream success or can run after upstream tasks finish.
- Inspect the run’s outcome. Establish whether it remains open, closes as failed, or starts a new run. These states affect what recovery action is appropriate.
What to check before changing the plan
- Outputs: Does the downstream task need a valid result from the failed task, or can it perform a safe cleanup or notification without one?
- Side effects: Could a retry repeat an action that already partially completed? Confirm whether the operation is safe to repeat before increasing retries.
- Failure handling: Does the workflow distinguish a recoverable error from one that should stop execution?
- Visibility: Can you tell from the run status whether work is waiting on a dependency, retrying, or finished?
Allowing downstream work to run after an upstream failure is useful for tasks such as cleanup or reporting, but it can also pass incomplete or invalid data into later steps. Configure that behavior only when the downstream task can handle the failure state explicitly.
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