Hey Niko,
thanks as well for the response with reasoning. I understand that you do
not prefer a coupling for a +0.
Is there any other option (hint: some where listed "Options we
considered") that you would propose as better solution? Or is the +0
just the "least of worse"?
Jens
On 20.03.26 02:03, Oliveira, Niko wrote:
Hey Jens et al.,
That is three datapoints now so there is indeed some demand. Not sure if
that pushes it over the limit of needing this, but it's more compelling now.
To your 2) in your last reply Jens: My concern wasn't Multi-Team
specifically or anything to do with config. More just that this changes the
(mostly unwritten) contract that the scheduler wholly owns and interacts
with executors. Up until this change one could depend on this being true
and that there is only ever one instance of any executor (per team now,
since two teams can have the same executor between them). But now your
changes are instantiating executors and queuing work with them. So all
future executor/scheduling changes need to keep this in mind. It simply
increases the surface area of execution/scheduling in Airflow and we now
have a multi-writer situation which always brings extra complexity. If
there is enough demand then we should do it. Seems to be a few requesters
now, which I think brings me to a +0 on this one.
Thanks again for the discussion!
________________________________
From: André Ahlert<[email protected]>
Sent: Thursday, March 19, 2026 1:15 PM
To:[email protected] <[email protected]>
Subject: RE: [EXT] [DISCUSS] PR: Option to directly submit to worker
queue from Triggerer
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Hi all,
This discussion resonates with problems we've seen on a fintech client's
setup. Heavy KPO usage in deferred mode, Celery executor, pools with
include_deferred=True. When a batch of tasks finishes on the cluster and
flips back to SCHEDULED, the pool slots are released and tasks have to
recompete through the scheduler for what amounts to seconds of cleanup
work. In our case the cloud cost was not the main concern, but it raised
a
real architectural question: why does a task that already passed all
concurrency checks need to go through the entire scheduling loop again
just
to collect XCom and delete a pod?
On the architectural concerns, I think the mini-scheduler comparison from
Airflow 2 does not hold. That feature made full scheduling decisions
(concurrency, pools, priority). This PR makes none. It re-enqueues a task
that allready satisfied all those checks. The task already had a slot,
already was running. We are just sending it back to finish.
Fact that it is opt-in with a safe fallback to SCHEDULED makes this very
low risk. If nobody configures direct_queueing_executors, behavior is
identical to today. Marking it experimental for a release or two would
also
be fine.
On the use case being narrow: any deployment using deferred mode with
Celery at scale will eventually hit this. Issue #57210 shows independent
reports of the same pattern. It is inherent to the DEFERRED -> SCHEDULED
->
QUEUED state machine under pool contention, not specific to one setup.
+1 (non-binding) on getting this into main.
Thanks Jens for bringing this one.
André Ahlert
Em qui., 19 de mar. de 2026 às 14:24, Jens Scheffler<[email protected]
escreveu:
Hi Niko,
thanks for the feedback.
(1) I think the use case is not really narrow, @tirkarthi also pointed
to issuehttps://github.com/apache/airflow/issues/57210 - So this would
be closed as well
(2) I aimed to include support for Multi-Team (that was even adding some
complexity compared to the local patch in 3.1.7. Yes so the config
property is atm global such that if you set it enabled for
CeleryExecutor it would be for all Executors in all teams. But if you
wish and see a reason we can also model the config being team specific
(e.g. only team_a uses CeleryExecutor and team_b does not optimize -
though not sure if there is a need to separate). Routing and queueing
for sure is respectinv the correct Executor/queue instance to route to
in the PR.
See
https://github.com/apache/airflow/pull/63489/changes#diff-c30603fe0a3527e23af541ba115c91c85c9c213e6d105af6a48c88a7018a5799R333
and
https://github.com/apache/airflow/pull/63489/changes#diff-c30603fe0a3527e23af541ba115c91c85c9c213e6d105af6a48c88a7018a5799R347
Jens
On 18.03.26 23:34, Oliveira, Niko wrote:
Thanks for the write-up Jens, it helps to have the full context of your
thought process.
The code changes themselves are small and fairly elegant. But this
breaks the invariant that there is only ever one instance of each
executor
(per team) and that they live in the scheduler process and the
scheduler is
the only thing that interacts with them in a scheduling capacity. To me
it's quite a large logical change in Airflow behaviour/operation. When
reasoning about execution and scheduling there is now another source
that
will always need to be considered, ensuring it works and is tested when
executor related changes are made, etc. This has been fraught for us in
the
past in ways that were hard to predict beforehand.
The usecase seems quite narrow and focused on how you folks use
Airflow.
Are you hearing from any other users who are asking for something like
this? I'm just not sure I see enough evidence that it truly belongs in
apache/airflow main.
Cheers,
Niko
________________________________
From: Jens Scheffler<[email protected]>
Sent: Wednesday, March 18, 2026 2:56 PM
To:[email protected] <[email protected]>
Subject: [EXT] [DISCUSS] PR: Option to directly submit to worker queue
from Triggerer
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Dear Airflow Devs!
TLDR: Because of operational problems in processing workload we propose
an extension allowing to directly re-queue tasks from triggerer. The PR
raised demand to discuss to ensure awareness for the change is
available.
Pull Request: Allow direct queueing from triggerer
<https://github.com/apache/airflow/pull/63489#top> #63489
https://github.com/apache/airflow/pull/63489
The Use Case/Problem Statement:
We use Airflow for many workflows of scaled long and large Dags in
running 80% KPO workload. To ensure KPO can run scaled and long w/o
operation interruptions (worker restart due to re-deployment, Pods with
workload sometimes running 4-10h) and to be able to scale to thousands
of running KPO Pods we need to use and leverage deferred mode
excessively.
In KPO with deferred a task is first scheduled to a (Celery in our
case)
worker which prepares the Pod manifest and starts the Pod. From there
it
hands-over to triggerer which monitors the Pod running and tails the
log
so that a user can watch progress. Once the Pod is completed it returns
back to a (Celery) worker that finishes-up work, extracts XCom, makes
error handling and cleans-up the Pod from K8s. This also means that the
Pod is only finished when the XCom is pulled from side-car, the "base"
container might be completed and the Pod is only done and deleted when
the XCom is collected. Until KPO collects XCom the Pod keeps running.
The current method of scheduling in Airflow is that the Scheduler
checks
all rules of concurrency (max_active_tasks, max_tis_per_dagrun,
pools...) in state scheduled before a task is queued to be started. On
the worker when started it is directly set to "deferred" and then a
triggerer picks-up (no re-scheduling or active distribution to a
triggerer). On the way back today triggerer marks the task "scheduled"
which means the scheduler logic needs to pick-up the task again for
competition. With all other workload. And re-schedule with all
concurrency and priority checks like initially to get to queued to be
re-assigned to a (Celery) worker. This implicitly means leaving the
state of "deferred" to "scheduled" the task loses the allocated pool
slot and also need to re-allocate this.
It most regular situations this is okay. In our scenario it is a
problem: We have many Dags competing for the K8s cluster resources and
the concurrency features of Airflow joined with priority controls
should
ensure that important workload runs first. Once there is residual
capacity less important batches can consume cluster resources. And with
"consume resources" also refers to Pods sitting on the cluster. They
free up the cluster space only at point of XCom collected and Pod
removed. Before they still consume CPU and ephemeral storage
allocations. We limit the amount of workload being able to be sent to
K8s by Airflow pools which are the ultimate limit for concurrency on
different node pools (e.g. nodes with GPU and nodes w/o GPU). Other
workload often runs on Edge workers or directly as Python in Celery.
With multiple Dags and different priorities we had these two effects:
(1) A lower priority batch is running ~N*100 Pods in deferred. A higher
priority large batch is started. Pods finishing from the lower priority
tasks are assumed to drain the cluster, when they end the task
instances
are set to "scheduled" and... then stay there until all tasks of the
higher priority tasks are worked off (assuming the higher priority
tasks
are not limited leaving room for the lower priority tasks). So base
container of the Pods are completed, the XCom side car waits long - we
have seen even 24h - to be XCom collected to be cleaned.
(a) Additional side effect if pending long the AutoScaler might
pick such a node as scaling victim because really idle and after grace
period kills the Pod - Later when the workload returns to worker the
Pod
is showing a HTTP 404 as being gone, XCom is lost... in most cases need
to run a retry, else it is anyway a delay and additional hours of
re-execution. If no retry just raising failures to users.
(b) We had the side effect that newer high priority workload was
not scheduled by K8s to the (almost idle) Nodes because the previous
pending Pods allocated still ephemeral storage and not sufficient space
was on K8s nodes for new workload... so the old Pods blocked the new
and
the higher priority task instances blocked the cleanup of the lower
prio
instances. A lot of tasks were in a kind of dead-lock.
(c) As the re-set to state "scheduled" from triggerer also sets
the
"scheduled" date of the task instance also the from the same "low
priority" Dag other pending scheduled task instances are often started
earlier. So workers pick-up new tasks to start new Pods but a lot of
old
Pods are sitting there idle waiting to collect XCom to clean-up
(2) Also sometimes because of operational urgency we use the
"enable/disable" scheduling flag on Dags in the UI to administratively
turn-off Dag scheduling to leave space for other Dags... or to drain
the
cluster for some operational procedures e.g. to have a safe ground
before maintenance. But as the Dag needs to be actively scheduled to
process the return from triggerer. If you turn off scheduling the
workload in flight is never finishing and is getting stuck like
described before. Pods are stale on the cluster, nobody picks-up the
XCom. And the problematic scenario is also there is no way to "clean
up"
such tasks to finish these Dags w/o turning on scheduling... but then
also new tasks are queued and you are just not able to drain the
cluster. I know we discussed multiple times that we might need a
"drain"
mode to let existing Dags finish but not scheduling new Dag runs... but
such feature is also missing. To say: Scheduling new tasks is tightly
coupled with the scheduling of cleanup. Not possible to separate.
Getting to the problems as 1 (a-c) as well in the scenario (2).
We thought a while about which options we would have to contribute to
improve in general. Assumption and condition is that the initial start
on the (Celery) Worker is fast, most time is spent (once) on the
triggerer and the return to worker is actually only made for a few
seconds to clean-up. And of course we want to minimize latency to (I)
free the allocated resources and (II) not to have any additional
artificial delay for the user. Which a bit contradicts with the efforts
to flip from worker to triggerer and back again.
Options we considered:
* Proposing a new "state" for a task instance, e.g. "re-schedule"
that
is handled with priority by scheduler.
But the scheduler is already a big beast of complexity, adding
another loop to handle re-scheduled with all existing complexity
might be a large complexity to be added and adding another state
in
the state model also adds a lot of overhead from documentation to
UI...
* Finish-up the work on triggerer w/o return to Worker. It is only
about cleaning the KPO and...
Unfortunately more than just monitoring is very complex to
implement
and especially XCom DB access is not a desired concept and
triggerer
does not have support. We also have some specific triaging and
error
handling automation extended on top of KPO which all in async
with
the limited capabilities of triggerer would be hard to implement.
Main blocker in this view is XCom access.
* Dynamically increase priority of a task returning from triggerer.
We considered "patching" the priority_weight value of the task
instance on the triggerer before return to ensure that tasks
returning are just elevated in priority. First we made this from
the
side via SQL (UPDATE task_instance SET priority_weight=1000 WHERE
state='scheduled' AND next_method='trigger_reentry') but
actually if
the task failed and restarted then it is hard to find and reset
the
priority back... still a retry would need to be reset down... all
feels like a workaround.
* Implementing a special mode in Scheduler to select tasks with
"next_method" being set as signal they are returning from
triggerer
in a special way... assuming they have a Pool slot and exclude
some
of the concurrency checks (As in "scheduled" state the pool slot
is
actually "lost")
But this hard to really propose... as this might be even harder
than
the first option as well as the today complex code would get even
harder in scheduler to consider exceptions in concurrency... with
the risk that such special cases exceed the planned concurrency
limits if otherwise the pools are exhausted before already.
* Adding a REST API that the triggerer can call on scheduler to
cross-post workload.
That would need to add a new connection and component bundling, a
REST API endpoint would need to be added for schedulers to
receive
these push calls. Probably an alternative but also adds
architectural dependability.
* The PR we propose to discuss here: If the task skips "scheduled"
state and moves to queue directly the pool slot keeps allocated
(assuming that Deferred in actively counting into pool and
concurrency limits). Code looked not too complex as just the
enqueuing logic from Scheduler could be integrated.
In this it is considering that such direct queuing is only
possible
if the executor supports queues (not working for
LocalExecutor!). So
the proposed PR made it explicitly opt-in.
Reasons (and pro-arguments) why we propose to have the PR on main:
* As of a lot of operational problems recently we tested this and
patched this locally into our 3.1.7 triggerer. Works smoothly on
production since ~1 week
* If something goes wrong or Executor is not supported then the
existing path setting to "scheduled" is always used as safe
fallback
* It is selective and is an opt-in feature
* We dramatically reduced latency from Pod completion to cleanup
some
sometimes 6-24h to a few seconds
* We assume the cleanup as return from Worker is a small effort
only
so no harm even if temporarily over-loading some limits
* ...But frankly speaking the concurrency limits and Pools were
checked initially at time of start. Limiting cleanup later on
concurrency limits is not adding any benefits but just delays and
problems. We just want to finish-up work.
* But finally actually over-loading is not possible as still the
Redis
queue is in between - so any free Celery Worker will pick the
task.
Even in over-load it will just sit in Celery queue for a moment.
* It is a relatively small change
* Off-loads scheduler by 50% for all deferred tasks (need to pass
scheduler only once)
* Due to reduced latency on cleanup more "net workload"
schedulable on
the cluster, higher cloud utilization / less idle time.
Hearing the feedback on PR reflecting with the devils advocate I could
understand the counter arguments:
* In Airflow 2 there was a mini-scheduler, there was a hard fight
in
Airflow 3 to get rid of this!
Understand. But we do not want to add a "mini scheduler" we just
want to use parts of the Executor code to push the task instance
to
queue and skip scheduling. It is NOT the target to make any more
and
schedule anything else.
* This would skip all concurrency checks and potentially over-load
the
workers!
No. Concurrency rules are checked when the workload is initially
started. I know there are parallel bugs we are fighting with to
ensure deferred status is counted on all levels into concurrency
to
correctly keep limits. Assuming that you enable counting deferred
into pools, a direct re-queue to worker is just keeping the
level of
concurrency not adding more workload... just transferring back.
And
Celery for example has a queue so not really over-loading. It is
mainly intended to clean-up workload which is a low effort task.
* We plan to cut-off components and untangle package dependencies.
After worker the Dag parser and triggerer are next. Linking to
Executor defeats these plans!
Yes, understood. But also today the setting of the task_instance
is
using direct DB access... and would in such surgery need to be
cut
to the level that the DB access would need to be moved to
execution
API back-end. So the cut for re-queueing would move to execution
API
in future, not triggerer. I think it would be valid to think
about
the options if such distribution is made how that might evolve in
future.
* This option is risky and we have concerns people have more
errors.
Feature is opt-in, need to be configured. Per default as
proposed in
the PR it is not active. Would be also acceptable to mark this
experimental for a while.
Sorry, a bit longer text. Happy to get feedback.
Jens
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