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One failed task does not necessarily stop an entire plan. Work on independent branches may continue, while tasks that need the failed task’s result are often blocked. The outcome depends on the workflow engine, task dependencies, retry settings, and rules for running downstream work after a failure.
Why one failure can affect different parts of a plan
A workflow plan is often a dependency graph rather than a single sequence. A task can have downstream tasks that need its output, alongside separate branches that do not depend on it.
- Independent tasks: Work that does not rely on the failed task may remain eligible to run.
- Dependent tasks: Work that requires the failed task’s successful result is commonly blocked under default dependency rules.
This is a common orchestration pattern, not a universal guarantee. The specific engine and its configuration determine what happens.
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In Apache Airflow, downstream tasks normally wait for their upstream tasks to succeed. Airflow trigger rules can change that condition. For example, all_done can let a task run after all its upstream tasks have finished, whether they succeeded or failed. This can be useful for work such as cleanup, but it does not mean the failed task succeeded or that its output is available. See Airflow’s DAG concepts documentation for dependency and trigger-rule behavior.
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Before allowing downstream work to proceed, consider whether it can safely run without the expected result. A task that merely records status may be suitable; one that consumes missing or invalid output may not be.
How retries differ from continuing past failure
Retry policy answers whether the failed operation will be attempted again. Dependency and trigger rules answer whether other tasks are allowed to run. They are separate controls: a task can be retried while dependent work waits, or downstream work can be allowed to run after failure.
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Apache Airflow
Airflow task settings can specify a retry count and delay. Its task documentation also describes exception-specific handling that can choose a retry, an immediate failure, or standard retry behavior. Retries remain bounded by the configured count; they are not the same as continuing downstream after a failure. See Airflow’s task documentation.
Temporal
Temporal distinguishes a Workflow Task failure from a Workflow Execution failure. A Workflow Task failure—often a problem processing work in the worker or execution environment—is automatically retried by the service, and the Workflow Execution generally stays open unless the task completes, the execution is terminated, or its execution timeout is reached. A Workflow Execution failure closes the execution with a failed status. It does not automatically retry as a new execution unless a Workflow Retry Policy is configured. That new run is different from retrying a Workflow Task within the same execution history. See Temporal’s Workflow Task documentation and its retry policy documentation.
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Three checks to understand what will happen
- Trace the dependencies. Identify which remaining tasks actually need the failed task’s result. Unrelated branches may be able to continue.
- Check downstream rules. Find out whether downstream tasks require upstream success or use a rule that permits running after upstream tasks finish.
- Identify the failure type and retry policy. Determine whether the engine will retry the task, close the overall execution, or permit downstream work under a configured rule.
What to compare across workflow systems
- Failure scope: Did a worker task fail, did business logic fail, or did the overall run fail?
- Dependency effect: Which tasks require the failed task to succeed?
- Retry behavior: Is retry automatic or configured, and is it bounded or started as a new run?
- Continue-after-failure behavior: Can a trigger rule or equivalent policy permit downstream work?
- Run status: Does the execution stay open, close as failed, or create another run?
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