Swap CeleryExecutor over to use TaskSDK for execution. by ashb · Pull Request #46265 · apache/airflow

ashb · 2025-01-29T22:45:29Z

Some points of note about this PR:

Logging is changed in Celery, but only for Airflow 3
Celery does it's own "capture stdout" logging, which conflicts with the ones
we do in the TaskSDK, so we disable that; but to not change anything for
Airflow 3.
Simplify task SDK logging redirection
As part of this discovery that Celery captures stdout/stderr itself (and
before disabling that) I discovered a simpler way to re-open the
stdin/out/err so that the implementation needs fewer/no special casing.
Make JSON task logs more readable by giving them a consistent/useful order
We re-order (by re-creating) the event_dict so that timestamp, level, and
then even are always the first items in the dict
Makes the CeleryExecutor understand the concept of "workloads" instead a
command tuple.
This change isn't done in the best way, but until Kube executor is swapped
over (and possibly the other in-tree executors, such as ECS) we need to
support both styles concurrently.
The change should be done in such a way that the provider still works with
Airflow v2, if it's running on that version.
Upgrade Celery
This turned out to not be 100% necessary but it does fix some deprecation
warnings when running on Python 3.12
Ensure that the forked process in TaskSDK never ever exits
Again, this isn't possible usually, but since the setup step of _fork_main
died, it didn't call os._exit(), and was caught further up, which meant
the process stayed alive as it never closed the sockets properly. We put and
extra safety try/except block in place to catch that

I have not yet included a newsfragment for changing the executor interface as
the old style is currently still supported.

Testing: I ran airflow scheduler, airflow fastapi-api, and airflow celery worker and triggered some dags, viewed logs etc.

And since a picture makes all PRs better

Celery window showing debug logs from the supervisor itself (task output goes to file)

amoghrajesh

Thanks for working on this @ashb!
Have a few comments, nothing serious, general comments / nits, I am OK with the PR mostly

ashb · 2025-01-30T17:18:33Z

RIght I think this is good, the failing tests are fixed in main by Jarek's hard work.

One failure I wasn't happy with, so I've rebased.

o-nikolas

Changes to base look good to me to support both for now. I think it's called out in a comment but we should migrate all in-tree executors (K8s, ECS, Batch, Edge, etc) and add a new min Airflow version for those packages.

ashb · 2025-01-30T18:44:04Z

Changes to base look good to me to support both for now. I think it's called out in a comment but we should migrate all in-tree executors (K8s, ECS, Batch, Edge, etc) and add a new min Airflow version for those packages.

👍 Yup, or we can make them work with 2+3 similar to how I've done for celery. The ecs etc changes right now are very minimal in this or

jscheffl

Wow I would have expected a larger PR from the challenges you faced. Looks pretty solid.

Did a regression with EdgeExecutor and also this is still working. Just one detail - when I executed my "Integration Test" DAG from the Edge Examples, the BranchOperator in EdgeWorker is failing, which seems to be working in Celery. So might be I need a bit of ENV fixing. Exception is:
{"timestamp":"2025-01-30T22:01:35.193579","level":"error","event":"Task failed with exception","logger":"task","error_detail":[{"exc_type":"OperationalError","exc_value":"(sqlite3.OperationalError) no such table: dag_run\n[SQL: SELECT dag_run.state AS dag_run_state, dag_run.id AS dag_run_id, dag_run.dag_id AS dag_run_dag_id, dag_run.queued_at AS dag_run_queued_at, dag_run.logical_date AS dag_run_logical_date, dag_run.start_date AS dag_run_start_date, dag_run.end_date AS dag_run_end_date, dag_run.run_id AS dag_run_run_id, dag_run.creating_job_id AS dag_run_creating_job_id, dag_run.external_trigger AS dag_run_external_trigger, dag_run.run_type AS dag_run_run_type, dag_run.triggered_by AS dag_run_triggered_by, dag_run.conf AS dag_run_conf, dag_run.data_interval_start AS dag_run_data_interval_start, dag_run.data_interval_end AS dag_run_data_interval_end, dag_run.last_scheduling_decision AS dag_run_last_scheduling_decision, dag_run.log_template_id AS dag_run_log_template_id, dag_run.updated_at AS dag_run_updated_at, dag_run.clear_number AS dag_run_clear_number, dag_run.backfill_id AS dag_run_backfill_id, dag_run.dag_version_id AS dag_run_dag_version_id, dag_run.bundle_version AS dag_run_bundle_version \nFROM dag_run \nWHERE dag_run.dag_id = ? AND dag_run.run_id = ?]\n[parameters: ('integration_test', 'manual__2025-01-30T23:01:24+01:00')]\n(Background on this error at: [https://sqlalche.me/e/14/e3q8)","syntax_error":null,"is_cause":false,"frames":%22,%22syntax_error%22:null,%22is_cause%22:false,%22frames%22:[){"filename":"/opt/airflow/task_sdk/src/airflow/sdk/execution_time/task_runner.py","lineno":527,"name":"run"},{"filename":"/opt/airflow/task_sdk/src/airflow/sdk/execution_time/task_runner.py","lineno":626,"name":"_execute_task"},{"filename":"/opt/airflow/airflow/models/baseoperator.py","lineno":173,"name":"wrapper"},{"filename":"/opt/airflow/airflow/decorators/base.py","lineno":252,"name":"execute"},{"filename":"/opt/airflow/airflow/models/baseoperator.py","lineno":173,"name":"wrapper"},{"filename":"/opt/airflow/providers/standard/src/airflow/providers/standard/operators/python.py","lineno":240,"name":"execute"},{"filename":"/opt/airflow/airflow/operators/branch.py","lineno":41,"name":"do_branch"},{"filename":"/opt/airflow/airflow/models/skipmixin.py","lineno":212,"name":"skip_all_except"},{"filename":"/opt/airflow/airflow/utils/session.py","lineno":98,"name":"wrapper"},{"filename":"/opt/airflow/airflow/models/taskinstance.py","lineno":2480,"name":"get_dagrun"},{"filename":"/opt/airflow/airflow/models/taskinstance.py","lineno":2461,"name":"_get_dagrun"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/orm/query.py","lineno":2870,"name":"one"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/orm/query.py","lineno":2916,"name":"_iter"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/orm/session.py","lineno":1717,"name":"execute"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/base.py","lineno":1710,"name":"_execute_20"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/sql/elements.py","lineno":334,"name":"_execute_on_connection"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/base.py","lineno":1577,"name":"_execute_clauseelement"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/base.py","lineno":1953,"name":"_execute_context"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/base.py","lineno":2134,"name":"handle_dbapi_exception"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/util/compat.py","lineno":211,"name":"raise"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/base.py","lineno":1910,"name":"_execute_context"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/default.py","lineno":736,"name":"do_execute"}]},{"exc_type":"OperationalError","exc_value":"no such table: dag_run","syntax_error":null,"is_cause":true,"frames":[{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/base.py","lineno":1910,"name":"_execute_context"},{"filename":"/usr/local/lib/python3.12/site-packages/sqlalchemy/engine/default.py","lineno":736,"name":"do_execute"}]}]}

..but anyway, LGTM!

amoghrajesh

Thanks for addressing the comments, LGTM +1

ashb · 2025-01-31T09:22:02Z

no such table: dag_run

That seems to be the issue @jens, likely this is the task in still trying to use DB as not everything has been ported over yet?

Some points of note about this change: - Logging is changed in Celery, but only for Airflow 3 Celery does it's own "capture stdout" logging, which conflicts with the ones we do in the TaskSDK, so we disable that; but to not change anything for Airflow 3. - Simplify task SDK logging redirection As part of this discovery that Celery captures stdout/stderr itself (and before disabling that) I discovered a simpler way to re-open the stdin/out/err so that the implementation needs fewer/no special casing. - Make JSON task logs more readable by giving them a consistent/useful order We re-order (by re-creating) the event_dict so that timestamp, level, and then even are always the first items in the dict - Makes the CeleryExecutor understand the concept of "workloads" instead a command tuple. This change isn't done in the best way, but until Kube executor is swapped over (and possibly the other in-tree executors, such as ECS) we need to support both styles concurrently. The change should be done in such a way that the provider still works with Airflow v2, if it's running on that version. - Upgrade Celery This turned out to not be 100% necessary but it does fix some deprecation warnings when running on Python 3.12 - Ensure that the forked process in TaskSDK _never ever_ exits Again, this isn't possible usually, but since the setup step of `_fork_main` died, it didn't call `os._exit()`, and was caught further up, which meant the process stayed alive as it never closed the sockets properly. We put and extra safety try/except block in place to catch that I have not yet included a newsfragment for changing the executor interface as the old style is _currently_ still supported.

ashb · 2025-01-31T15:15:43Z

K, finally. One (🤞🏻) unrelated Beam downgrade test failing, but that's happening on other PRs too

Some points of note about this change: - Logging is changed in Celery, but only for Airflow 3 Celery does it's own "capture stdout" logging, which conflicts with the ones we do in the TaskSDK, so we disable that; but to not change anything for Airflow 3. - Simplify task SDK logging redirection As part of this discovery that Celery captures stdout/stderr itself (and before disabling that) I discovered a simpler way to re-open the stdin/out/err so that the implementation needs fewer/no special casing. - Make JSON task logs more readable by giving them a consistent/useful order We re-order (by re-creating) the event_dict so that timestamp, level, and then even are always the first items in the dict - Makes the CeleryExecutor understand the concept of "workloads" instead a command tuple. This change isn't done in the best way, but until Kube executor is swapped over (and possibly the other in-tree executors, such as ECS) we need to support both styles concurrently. The change should be done in such a way that the provider still works with Airflow v2, if it's running on that version. - Upgrade Celery This turned out to not be 100% necessary but it does fix some deprecation warnings when running on Python 3.12 - Ensure that the forked process in TaskSDK _never ever_ exits Again, this isn't possible usually, but since the setup step of `_fork_main` died, it didn't call `os._exit()`, and was caught further up, which meant the process stayed alive as it never closed the sockets properly. We put and extra safety try/except block in place to catch that I have not yet included a newsfragment for changing the executor interface as the old style is _currently_ still supported.

jscheffl · 2025-01-31T20:12:17Z

no such table: dag_run
That seems to be the issue @jens, likely this is the task in still trying to use DB as not everything has been ported over yet?

Yeah, am not sure. In Edge the DB Connection gets dropped, maybe this is the side effect if still defined in ENV for celery. In 2.10 all was clean, maybe some refactoring re-introduced this or the cleanup of AIP-44 leftovers. So no blame to this PR :-D just noticed... LGTM! ...ups was already :-D

Some points of note about this change: - Logging is changed in Celery, but only for Airflow 3 Celery does it's own "capture stdout" logging, which conflicts with the ones we do in the TaskSDK, so we disable that; but to not change anything for Airflow 3. - Simplify task SDK logging redirection As part of this discovery that Celery captures stdout/stderr itself (and before disabling that) I discovered a simpler way to re-open the stdin/out/err so that the implementation needs fewer/no special casing. - Make JSON task logs more readable by giving them a consistent/useful order We re-order (by re-creating) the event_dict so that timestamp, level, and then even are always the first items in the dict - Makes the CeleryExecutor understand the concept of "workloads" instead a command tuple. This change isn't done in the best way, but until Kube executor is swapped over (and possibly the other in-tree executors, such as ECS) we need to support both styles concurrently. The change should be done in such a way that the provider still works with Airflow v2, if it's running on that version. - Upgrade Celery This turned out to not be 100% necessary but it does fix some deprecation warnings when running on Python 3.12 - Ensure that the forked process in TaskSDK _never ever_ exits Again, this isn't possible usually, but since the setup step of `_fork_main` died, it didn't call `os._exit()`, and was caught further up, which meant the process stayed alive as it never closed the sockets properly. We put and extra safety try/except block in place to catch that I have not yet included a newsfragment for changing the executor interface as the old style is _currently_ still supported.

ashb requested review from XD-DENG, hussein-awala, o-nikolas and pierrejeambrun as code owners January 29, 2025 22:45

boring-cyborgBot added area:Executors-core LocalExecutor & SequentialExecutor area:Scheduler including HA (high availability) scheduler area:task-sdk provider:celery labels Jan 29, 2025

ashb requested review from amoghrajesh and jedcunningham January 29, 2025 22:51