Requested access to Google doc to read more details. Am interested and
also as Daniel what the difference is/would be.
Especially also as progress tracking might be important. Yes, Task
Mapping is very expensive if you want to download 17k XML files, but
also when running Async and you are at 5000 files, if you resume would
you know what was complete or would it start from scratch all times?
I think such micro-batching is cool but some state tracking is important
- which might if it is in the DB also overload the DB or add very many
transactions.
Trioggerer though I think still is cool for long running tasks where you
just wait for response, e.g. you triggered another job remote or you
started a Pod that runs for an hour. We have Pods runnign for 10h
sometimes and then it is important to be able to roll new SW to workers
and with triggerers we cann de-couple this.
So maybe - without missing details - I would judge such micro-batching
as a third execution option but most probably would not replace the others.
Also knowin from own experience, writing async code is more complex and
error prone, so if you would request all normal code being async you
might scare users away. Proper review needed to ensure all IO is async
(also DB calls!)
On 12/4/25 18:08, Daniel Standish via dev wrote:
Here's what I'm hearing from this
1. not using task mapping, but just looping instead, can be much more
efficient.
Yes, of course it can.
2. there are ways in which triggerer / deferrable operators are not fully
complete, or do not fully have feature parity with regular operators (such
as the custom xcom backend example)
I believe it. But this could certainly be worked on.
Question for you:
How is your proposal different / better than say, just calling
`asyncio.run(...)` in a python task?
On Thu, Dec 4, 2025 at 8:38 AM Blain David <[email protected]> wrote:
As I already discussed with Jarek in the past but also with Hussein during
the Airflow Summit, we at a certain moment encountered performance issues
when using a lot of deferred operators.
Allowing PythonOperators (and thus also @task decorated methods) to
natively execute async Python code in Airflow solved our performance issues.
And yes, you could argue if that’s really necessary and also what’s the
added value? And at first you would indeed think it doesn’t make sense at
all do so, right?
But please bear with me first and hear me out first why we did it that way
and how it solved our performance issues and it will become crystal clear 😉
So below is the article I wrote, which is also publicly available here<
https://docs.google.com/document/d/1pNdQUB0gH-r2X1N_g774IOUEurowwQZ5OJ7yiY89qok>
on Google Docs which makes it easier to read than through the devlist.
Here is my article:
Rethinking deferrable operators, async hooks and performance in Airflow 3
At our company, we strive to avoid custom code in Airflow as much as
possible to improve maintainability.
For years this meant favouring dedicated Airflow operators over Python
operators.
However, in Airflow 3, as the number of deferred operators in our DAGs
continued to grow, we began facing severe performance issues with
deferrable operators, which forced us to re-evaluate that approach.
Initially we expected deferrable operators to improve performance for
I/O-related tasks—such as REST API calls—because triggerers follow an async
producer/consumer pattern. But in practice we discovered the opposite.
Why Deferrable Operators Became the Bottleneck?
Deferrable operators and sensors delegate async work to triggerers.
This is perfectly fine for lightweight tasks such as polling or waiting
for messages on a queue.
But in our case:
* MSGraphAsyncOperator performs long-running async operations.
* HttpOperator in deferrable mode can perform long-running HTTP
interactions, especially if pagination is involved.
* There is no native deferrable SFTPOperator, so if we want to use the
SFTPHookAsync, we must use the PythonOperator which natively doesn’t
support async code (not that big of challenge).
* Both can return large payloads.
* Triggerers must store yielded events directly into the Airflow
metadata database.
Triggerers are not designed for sustained high-load async execution or
large data transfers. Unlike Celery workers, triggerers scale poorly and
quickly become the bottleneck.
Yielded events from triggers are stored directly in the Airflow metadata
database because, unlike workers, triggers cannot leverage a custom XCom
backend to offload large payloads, which can lead to increased database
load and potential performance bottlenecks.
Dynamic task mapping with deferrable operators amplifies the problem even
further which AIP‑88 partially solves.
Triggerers also cannot be run on the Edge Executor as triggerers are still
tightly coupled with the Airflow metadata database (possibly addressed in
AIP‑92).
Rethinking the approach: Async hooks + Python tasks
These limitations led us to reconsider calling async hooks directly from
Python @task decorated functions or PythonOperators, thus avoiding
deferrable operators and thus triggerers entirely.
Operators are wrappers around hooks. Well‑written operators should contain
little logic and delegate all the work to the hooks which do the real
work,so why not call them directly?
This idea is also a bit in line with what Bolke already presented<
https://airflowsummit.org/slides/2023/ab1-1400-Operators.pdf> in 2023.
Advantages of this approach include:
* No dynamic task mapping needed when iterating—just loop in Python,
unless you really need to track each individual step but that comes with a
cost.
* Massive reduction in scheduler load.
* No triggerers involved.
* Async code can run on Edge Workers.
* Celery workers scale far much better than triggerers, so by moving
from deferred operators and thus triggerers to async operators on celery
workers, our performance issues on the triggerer were gone and run times
were much shorter probably because the trigger mechanism also puts more
load on the scheduler.
* Sync or async doesn’t make any difference in performance, unless you
have to execute the same async function multiple times, that’s when async
shines compared to sync especially with I/O related operations.
Concrete Example: Async SFTP Downloads
Below is an example comparing the download of ~17,000 XML-files and
storing into our Datawarehouse.
A single Celery worker can orchestrate many concurrent downloads using
asyncio.
A semaphore (here used internally by the AsyncSFTPConnectionPool)
protects the SFTP server from being overloaded.
Benchmark results:
Approach
Environment Time
Mapped SFTPOperator
production 3h 25m 55s
PythonOperator + SFTPHook
local laptop 1h 21m 09s
Async Python task + SFTPHookAsync (without pool) local laptop
8m 29s
Async Python task + AsyncSFTPConnectionPool production
3m32s
As you all can conclude, DagRun time went down from more than 3 hours to
only 3 minutes and a half, which is huge!
In the google docs there are 2 different code snippets on how it’s done
sync and async which I will not put here.
Conclusion
Using async hooks inside async Python tasks provides better performance,
scalability, and flexibility, and avoids reliance on triggerers entirely.
This hybrid approach—'async where it matters, operators where they make
sense'—may represent the future of high‑performance Airflow data processing
workloads.
What did I change in Airflow?
Not that much, I only:
* Introduced an async PythonOperator so you don’t have to handle the
event loop yourself, not that special, but also natively supported on async
@task decorated python methods, which is nice to read.
* Did some improvements on the SFTPHookAsync to fully take advantage
of the async.
* Introduced a SFTPHookPool so multiple asyncio tasks can re-use
connection instance to gain even more performance, in this case it meant a
reduction of 5 minutes in processing time, so we went from 8 to 3 minutes.
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