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Fix Task SDK dry-run mode crashing on task instance start - #73373

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Eason09053360:fix-task-sdk-dry-run-noop-handler
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Eason09053360 wants to merge 1 commit into
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Eason09053360:fix-task-sdk-dry-run-noop-handler

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@Eason09053360

@Eason09053360 Eason09053360 commented Sep 19, 2026 •

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Why

dry_run=True is the Task SDK's documented way to run or debug a task locally without an API
server. It has been broken for a long time: noop_handler's hand-written dag_run payload has
not changed since the Task SDK became its own distribution, while the generated DagRun model
gained five required fields (data_interval_start, data_interval_end, end_date, state,
partition_key). task_instances.start() raises a ValidationError on the first call, and the
supervisor then kills the child process it has already forked.

Client(base_url=None, dry_run=True, token="").task_instances.start(uuid7(), 1234, datetime.now(tz=timezone.utc))
# pydantic_core._pydantic_core.ValidationError: 5 validation errors for TIRunContext

What

noop_handler serves a TIRunContext built from the generated models instead of a literal dict,
so the next required field added to DagRun fails mypy-task-sdk at the definition rather than
surfacing at runtime under dry_run.

TestClient::test_dry_run is an existing test this PR rewrites, so it is worth saying why: it
compared the payload against a hand-copied duplicate of itself and fetched it with client.get(),
never reaching TIRunContext.model_validate_json(). That is how the drift shipped — the test
passes on main with the bug live. It now goes through task_instances.start() and fails
without the fix.

Also drops a duplicate datetime import in the TYPE_CHECKING block that ruff flags (TC004)
once datetime is used at runtime.

How to test

uv run --project task-sdk pytest task-sdk/tests/task_sdk/api/test_client.py -k dry_run


Was generative AI tooling used to co-author this PR?
  • Yes — Claude Code (Opus 5)

Generated-by: Claude Code (Opus 5) following the guidelines

dry_run=True is the documented way to debug a task locally without an API
server, but it has been unusable since partition_key landed on DagRun: the
hand-written fake response drifted from the generated model, so every
start() call ends in a ValidationError and the supervisor kills the child
process it has already forked.

The drift survived eighteen months because the only test compared the fake
payload against a hand-copied duplicate of itself and never parsed it
through TIRunContext. Building the fixture from the models instead turns
the next missing required field into a mypy error.
This was referenced Sep 25, 2026
@potiuk potiuk added the closed because of open PR limit Closed as a one-time step of introducing the open pull request limit label Sep 25, 2026
@potiuk

potiuk commented Sep 25, 2026

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Hello @Eason09053360 - thank you for your contributions to Apache Airflow!

The Airflow community has introduced a limit of 5 open pull requests at a time for contributors without write access to the repository. You currently have 33 open pull requests, so - as a one-time step of introducing the limit - we closed the ones where maintainers have not engaged yet:

These pull requests stay open because maintainers are already engaged in them - they count towards your limit:

This is not a judgement of you or of your changes. We never told contributors before that opening many pull requests at once was a problem, so there is nothing to feel bad about - and nothing is lost: your branches, commits and the review history stay where they are.

What we ask you to do is to make your first prioritization decision: choose which of the pull requests above matter most to you, and reopen them (up to 5 open at a time, including the ones still open) with the "Reopen pull request" button or gh pr reopen <PR_NUMBER> --repo apache/airflow. Reopen the ones you are ready to follow through - keep them rebased, respond to review comments and fix failing checks.

While your pull requests are waiting for review, the most valuable thing you can do is help in other ways - reviewing other contributors' pull requests, helping with issues, and taking part in the discussions on the devlist and Slack.

Why we introduced the limit, what it means for you and how to reopen or restore a pull request is explained in https://github.com/apache/airflow/blob/main/contributing-docs/32_open_pull_request_limit.rst.


Drafted-by: Claude Code (Opus 5); reviewed by @potiuk before posting

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