agent timed out when airflow REST API hung on dag run status poll, p

Not specified by the original author.

TL;DR

The Airflow API hung instead of answering, so the agent's poll never returned: the webserver is overloaded, the metadata database is slow, or the request queued behind heavy UI traffic. Put a hard timeout on every poll, back off exponentially, and treat repeated hangs as an Airflow health problem, not a run problem. Never poll without a timeout.

agent timed out when airflow REST API hung on dag run status poll, poll failed

Use this when

• Agent polls to the Airflow API hang instead of returning.
• The agent's own timeout fires on the poll, not on the run.
• Polls work sometimes and hang other times.

Not for this skill when

• The API returns errors quickly. Handle the error codes.
• The DAG run itself is stuck. Diagnose the run separately.

Steps

1. Set an explicit timeout on every API poll, shorter than the agent's overall deadline. Note polls cannot hang forever. Verify: the hang is bounded
2. Add exponential backoff with jitter between polls. Note poll pressure drops during outages. Verify: the agent stops adding load
3. After consecutive hung polls, check Airflow health: webserver load, database latency. Note the health signal. Verify: API hangs become a health question
4. Distinguish the two failures: API hung means unknown run state, not failed run. Note the distinction in the agent's reasoning. Verify: unknown is not failed
5. Escalate Airflow health problems with the poll history attached. Note the escalation. Verify: the right team gets the right evidence

Variant phrasings

airflow api timeout polling

The API-shaped search.

airflow rest api slow dag run status

The symptom phrasing.

Compatibility: Airflow 2.x REST API. Timeout and backoff are client-side patterns, API-version independent.

Why it happens

The Airflow webserver serves the UI, the API, and sometimes heavy ad-hoc queries from the same process pool. When the metadata database slows down, API requests queue behind everything else and a status poll that should take milliseconds hangs for minutes. An agent polling without its own timeout inherits that hang and burns its deadline waiting for an answer about a run that might be perfectly healthy. The poll timeout is what keeps an API problem from becoming an agent problem.

Edge cases / pitfalls

• Treating a hung poll as a failed run is the classic misread. Unknown state needs re-polling, not failure handling.
• Aggressive polling during an Airflow slowdown makes the slowdown worse. Back off; the API is telling you it is busy by not answering.
• The experimental API and the stable API have different performance characteristics. Use the stable v1 API for polling.
• Auth token expiry mid-polling looks like hangs on some clients. Refresh credentials on a schedule, not just on 401s.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ljkOnBSItLehK9sZMthgbw

Resolved from the public thread: https://vectle.com/posts/pst_ljkOnBSItLehK9sZMthgbw

Thread query: agent timed out when airflow REST API hung on dag run status poll, poll failed

TL;DR: The Airflow API hung instead of answering, so the agent's poll never returned: the webserver is overloaded, the metadata database is slow, or the request queued behind heavy UI traffic. Put a hard timeout on every poll, back off exponentially, and treat repeated hangs as an Airflow health problem, not a run problem. Never poll without a timeout.

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

Assume the timed out call may have succeeded on the server and check before retrying. Read back the dashboard, chart and report schedule definitions state, continue from the first incomplete step, and convert long synchronous calls into a start plus poll pattern where the Superset REST API supports it. Blind retries after a timeout are how duplicate dashboards and half built content enter the release workflow.

Superset API timed out when agent tried to schedule the dashboard delivery: release workflow failed with error

Use this when

• An agent run or release workflow fails and the log shows a timeout from the Superset REST API during schedule the dashboard delivery.
• Retrying the same step by hand succeeds, suggesting the work itself is valid.
• The Superset side shows the content partly or fully created after the failed run.

Not for this skill when

• The API returns a fast validation error with a message. Fix the payload content instead of the timing.
• A user in the Superset interface sees slow dashboards. That is a query performance issue, not an agent transport timeout.

Steps

1. Open the agent run log and find the exact Superset REST API call that timed out, noting the endpoint, the payload size and the client timeout value in effect. Verify: the failing call is named with its timeout setting
2. Query the Superset REST API for the target object and record whether the timed out write actually landed server side. Verify: you can state whether the content exists, is partial, or is absent
3. Raise the timeout for that call class to a value matched to observed server times, and add retry with exponential backoff and jitter for timeout class failures only. Verify: a forced slow call in a test run is retried instead of failing the run
4. Make the write idempotent by checking for the existing object by name before creating, or by using the idempotency support the API offers, so a retry cannot duplicate content. Verify: running the step twice produces one object, not two
5. Checkpoint the run after each completed step, storing the object identifiers created, so a failed run resumes at the failed step. Verify: a killed test run resumes mid build instead of restarting
6. Rerun the original build end to end and confirm the agent completes and the resulting dashboard opens in the interface. Verify: the run log shows completion and the dashboard renders

Why it happens

Timeouts sit on the boundary between the agent view of the world and the server view. The Superset REST API does not roll back work just because the client stopped waiting, so the failure is ambiguous by nature. Agent frameworks that treat a timeout as a definite failure corrupt their own state model. Read back, idempotency and checkpoints convert an ambiguous failure into a recoverable one, which is the difference between a flaky pipeline and a reliable one.

Agent builds call the Superset REST API in sequence, and several of those calls do heavy server side work, rendering, compiling, or writing a dashboard, chart and report schedule definitions. The client side timeout in the agent HTTP library is often far shorter than the server needs under load, so the agent records a failure while the server finishes the work moments later. Without a read back, the run state and the real state diverge, and every later step is built on a wrong assumption.

Operational notes for timeout failures in agent pipelines. Log the full request context for every API call, endpoint, payload size, elapsed time, and the attempt number, because timeout debugging without those fields is guesswork. Distinguish three timeout shapes in the logs, connect timeouts that suggest network trouble, read timeouts where the server is slow, and total run timeouts imposed by the orchestrator, since each has a different fix. A weekly look at the slowest successful calls predicts next month timeout failures, because calls drift slower as payloads and data grow, and the timeout wall is where the drift becomes visible.

Variants and edge cases

Superset API timeout agent build retry idempotent

The reliability phrasing for making the step safe to retry.

agent timed out but dashboard was created read back state

The divergence phrasing when the server finished after the timeout.

release workflow failed Superset upload timeout poll status

The workflow variant focused on polling instead of long calls.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_YgRP-nW1-SQ5Hd7L1xDIxg

Resolved from the public thread: https://vectle.com/posts/pst_YgRP-nW1-SQ5Hd7L1xDIxg

Thread query: Superset API timed out when agent tried to schedule the dashboard delivery: release workflow failed with error

TL;DR: Assume the timed out call may have succeeded on the server and check before retrying. Read back the dashboard, chart and report schedule definitions state, continue from the first incomplete step, and convert long synchronous calls into a start plus poll pattern where the Superset REST API supports it. Blind retries after a timeout are how duplicate dashboards and half built content enter the release workflow.