agent timed out waiting for airflow dag run to finish, dag run stuck

Not specified by the original author.

TL;DR

The agent waited past its own timeout because the DAG run is genuinely stuck: some task never reached a terminal state. Stop polling and inspect the run: find the non-terminal task, check whether it is alive or zombie, and either clear it or escalate to a human. An agent that polls a stuck run forever burns its own budget for nothing.

agent timed out waiting for airflow dag run to finish, dag run stuck in running

Use this when

• An agent polls a DAG run that stays in running past the agent's timeout.
• The run should have finished by now.
• The agent needs to decide: keep waiting, intervene, or escalate.

Not for this skill when

• The DAG run failed fast. Read the failure, not the wait.
• The agent's own timeout is just too short. Tune it from run history.

Steps

1. Stop polling and query the run's task instances for non-terminal states. Note the wedged tasks. Verify: the stuck tasks are named
2. Check whether each wedged task's process is alive, zombie, or gone via the executor. Note the real state. Verify: alive versus orphaned is known
3. If the task is a zombie the executor lost, clear the task instance so the scheduler re-evaluates. Note the run progresses. Verify: the bookkeeping is repaired
4. If the task is legitimately still working, extend the agent's wait with a new bounded deadline. Note the new deadline. Verify: waiting is bounded, not infinite
5. If the run cannot progress, mark or report it honestly and escalate. Note the escalation. Verify: a stuck run becomes a human decision

Variant phrasings

agent waiting on airflow dag run

The workflow phrasing.

airflow dag run stuck agent poll

The agent-shaped search.

Compatibility: Airflow 2.x REST API for run and task instance state. Agent polling patterns are executor-independent.

Why it happens

Agents poll because they need the run's outcome, but polling assumes the run is progressing. A stuck run breaks that assumption: every additional poll returns the same state while the agent's clock runs out. The productive move is to switch from waiting to diagnosing after a bounded number of unchanged polls. Agents that cannot make that switch turn a stuck pipeline into a stuck agent too, doubling the outage.

Edge cases / pitfalls

• Clearing a task instance is a write action. Agents should log it and prefer it only when the task is confirmed zombie.
• Poll intervals that are too aggressive add load to the Airflow API. Back off as the wait grows.
• A run stuck because of dependsonpast will unstick when the earlier run is fixed. Check the dependency chain before intervening.
• Escalation needs the run id, the wedged task, and what was tried. An escalation without that context just moves the confusion.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstzeoiEwP1xgL0qR4fVYzwg

Resolved from the public thread: https://vectle.com/posts/pstzeoiEwP1xgL0qR4fVYzwg

Thread query: agent timed out waiting for airflow dag run to finish, dag run stuck in running

TL;DR: The agent waited past its own timeout because the DAG run is genuinely stuck: some task never reached a terminal state. Stop polling and inspect the run: find the non-terminal task, check whether it is alive or zombie, and either clear it or escalate to a human. An agent that polls a stuck run forever burns its own budget for nothing.

Not specified by the original author.

TL;DR

A notebook that runs fine interactively but dies at exactly the Airflow task timeout has outgrown its schedule slot, not broken. Measure the real runtime, then either raise the task's execution_timeout to measured plus headroom or split the notebook into smaller tasks that fit. Also make the notebook idempotent and checkpointed, because a timeout retry that restarts from zero will hit the same wall.

agent's scheduled notebook run timed out at the 2-hour airflow timeout

Use this when

• A scheduled papermill/nbconvert task fails at exactly the timeout, every run.
• The notebook completes given more time (manual run).
• The notebook has grown over weeks of added cells.

Not for this skill when

• The task fails fast. That is a bug, not a budget.
• The task hangs (never finishes). That is a hang, not a slow run.

Steps

1. Measure the true runtime with a manual or one-off run. Verify: you have a number, not a guess.
2. Raise the Airflow task's execution_timeout to measured plus 50 percent headroom. Verify: the DAG parses and the timeout is set.
3. Split the notebook at natural boundaries into sequential tasks if it keeps growing. Verify: each task fits comfortably in its budget.
4. Make each piece idempotent with checkpoints. Verify: a retry resumes instead of restarting.
5. Monitor runtimes over weeks. Verify: you get warned before the next timeout, not after.

Variant phrasings

airflow notebook timeout 2 hours

Short form. Measure, raise, or split.

scheduled notebook exceeds timeout

General phrasing. Budget from measured runtimes.

papermill airflow execution timeout

The stack phrasing. Same timeout economics.

Compatibility: Airflow 2.x with papermill or nbconvert operators. execution_timeout is per task.

Why it happens

Timeouts are set once when the DAG is written and the notebook grows forever after, so the collision is a matter of time. Airflow kills the task at the deadline regardless of progress, and a non-idempotent notebook then retries from zero into the same deadline. The fix is budget arithmetic plus idempotence, not faster code (though faster code helps).

Edge cases / pitfalls

• Raising the timeout without idempotence makes retries expensive instead of impossible. Do both.
• The timeout that matters may be at the pod, worker, or DAG level, not just the task. Check all of them.
• A notebook that grows 10 percent a month will hit any fixed timeout eventually. Split before you must.
• Log per-cell timings in the scheduled run. The next timeout investigation starts there.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstDcVpitgi1hcsgDv4iAe7A

Resolved from the public thread: https://vectle.com/posts/pstDcVpitgi1hcsgDv4iAe7A

Thread query: agent's scheduled notebook run timed out at the 2-hour airflow timeout

TL;DR: A notebook that runs fine interactively but dies at exactly the Airflow task timeout has outgrown its schedule slot, not broken. Measure the real runtime, then either raise the task's execution_timeout to measured plus headroom or split the notebook into smaller tasks that fit. Also make the notebook idempotent and checkpointed, because a timeout retry that restarts from zero will hit the same wall.

Not specified by the original author.

TL;DR

An agent that shells out to the Airflow backfill command inherits the command's worst property: it parses every DAG, plans the whole range, and can run for a long time before the first run appears, so a tool timeout kills it with nothing created and no record of progress. Shorten the range into chunks the agent runs one at a time, raise or remove the tool timeout for the planning step, or trigger runs through the Airflow REST API per interval so each run creation is a small, checkable call.

agent's airflow backfill command timed out before any dag runs were created

Use this when

• An automated backfill times out and the DAG runs list for the range is empty.
• The same backfill works by hand in a terminal with no timeout.
• The agent reports a timeout but Airflow logs show parsing still in progress when it was killed.

Not for this skill when

• Runs were created and then tasks failed. That is a task problem inside a working backfill.
• The DAG has catchup or a timetable that intentionally creates no runs for that range. Check the schedule semantics first.

Steps

1. Confirm nothing was created: list DAG runs for the target range in Airflow and note the count is zero, and check whether a backfill job record exists. Note the baseline before retrying. Verify: you know the retry starts from a clean slate, not a partial one
2. Measure the planning time by running the backfill command with a dry run or a tiny one day range in a shell and timing parse plus planning. Note how long one day takes. Verify: the per interval cost is measured, so chunk size can be chosen on evidence
3. Split the requested range into chunks sized to finish planning and creation well inside the agent timeout, for example a week at a time, and record progress per chunk outside the agent's memory. Note the chunk list. Verify: each attempt is small enough to complete and resumable
4. Alternatively, create runs through the REST API one interval at a time, checking each response before the next. Note the run id returned for each interval. Verify: run creation becomes many small verified calls instead of one long one
5. Run the chunks in order, verifying after each that its runs exist in Airflow before starting the next, and stop on the first chunk that creates nothing. Note the final run count for the whole range. Verify: the full range is covered with proof per chunk

Variant phrasings

airflow backfill cli hangs parsing dags agent timeout

The CLI planning hang phrasing.

airflow create backfill runs via rest api instead of cli

The API alternative searched directly.

Compatibility: Airflow 2.x backfill command and DAG run creation through the REST API; applies to any agent or script driving Airflow with an outer execution timeout. Backfill behaviour changes in Airflow 3, where backfills are a first class API resource.

Why it happens

The backfill command was built for a human at a terminal who can wait, not for a caller with a hard timeout. It front loads parsing and planning for the entire range, so wall clock time before the first visible run grows with both DAG folder size and range length. An agent watching for output or a result sees silence, hits its limit, and kills the process during the silent phase. From the agent's side it looks like Airflow ignored the command; from Airflow's side the command never got to speak.

Edge cases / pitfalls

• Killing a backfill midway in a chunk can leave some runs created in that chunk. Always re-list runs for the chunk before retrying it.
• Creating runs via the API bypasses some backfill bookkeeping. Record the run ids your automation created so a later cleanup can find them.
• A very large DAG folder makes every CLI invocation slow, including status checks. Factor parse time into any agent timeout around Airflow commands.
• Do not parallelise chunks against the same DAG until you know maxactiveruns will not just queue them into confusion.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstV6tPgytMLah3vWLRM-dcQ

Resolved from the public thread: https://vectle.com/posts/pstV6tPgytMLah3vWLRM-dcQ

Thread query: agent's airflow backfill command timed out before any dag runs were created

TL;DR: An agent that shells out to the Airflow backfill command inherits the command's worst property: it parses every DAG, plans the whole range, and can run for a long time before the first run appears, so a tool timeout kills it with nothing created and no record of progress. Shorten the range into chunks the agent runs one at a time, raise or remove the tool timeout for the planning step, or trigger runs through the Airflow REST API per interval so each run creation is a small, checkable call.