I run celery worker with concurrency 20
"-c", "20", "-P", "eventlet", "-Ofair"
And generate packs of 20 task for this worker queue in another task
async_call.apply_async(
(call_id, engine),
expires=60,
countdown=60*random(), # random delay, to prevent spikes
)
In flower viewer i see that there are no more than 20 task.
Question is: who waiting the countdown? is it inside the queue ? or it is inside worker process (some idle time of worker who take the task)?
If it is inside worker, then to use all CPU i need to increase concurrency according to fraction of countdown time (idle time) to work time.
The wait is in a process / thread that each celery worker has that will poll the broker for new tasks if the worker is waiting for tasks.
Related
I am running an airflow cluster on EKS on AWS. I have setup some scaling config for worker setup. If CPU/Mem > 70% then airflow spins up new worker pod. However I am facing an issue when these worker pods are scaling down. When worker pods start scaling down, two things happen:
If no tasks is running on a worker pod, it terminates within 40sec.
If any task is running on a worker pod, it terminates in about 8min, and after one more minute, I find the task failing on UI.
I have setup below two properties in helm chart for worker pod termiantion.
celery:
## if celery worker Pods are gracefully terminated
## - consider defining a `workers.podDisruptionBudget` to prevent there not being
## enough available workers during graceful termination waiting periods
##
## graceful termination process:
## 1. prevent worker accepting new tasks
## 2. wait AT MOST `workers.celery.gracefullTerminationPeriod` for tasks to finish
## 3. send SIGTERM to worker
## 4. wait AT MOST `workers.terminationPeriod` for kill to finish
## 5. send SIGKILL to worker
##
gracefullTermination: true
## how many seconds to wait for tasks to finish before SIGTERM of the celery worker
##
gracefullTerminationPeriod: 180
## how many seconds to wait after SIGTERM before SIGKILL of the celery worker
## - [WARNING] tasks that are still running during SIGKILL will be orphaned, this is important
## to understand with KubernetesPodOperator(), as Pods may continue running
##
terminationPeriod: 120
I can see that worker pod should shutdown after 5 mins or irrespective task running or not. So I am not sure why I see total of 8 min for worker pod termination. And my main issue is there any way I can setup config so that worker pod only terminates when task running on it finishes execution. Since tasks in my dags can run anywhere between few minutes to few hours so I don't want to put a large value for gracefullTerminationPeriod. I Would appreciate any solution around this.
Some more info: Generally the long running task is a python operator which runs either a presto sql query or Databricks job via Prestohook or DatabricksOperator respectively. And I don't want these to recivie SIGTERM before they complete their execution on worker pod scaling down.
This is not possible due to limitations from K8 end. More details are available here. However by using a large value of "gracefulTerminationPeriod" works, although this is not what I intended to do but it works better than I originally thought. When large value of gracefulTerminationPeriod is set, workers doesn't wait around for gracefulTerminationPeriod time to terminate. If a worker pod is marked for termination it terminates as soon as tasks running on it reaches zero.
Until K8 accept proposed changes and new community helm chart is released, I think this is the best solution without incurring costs of keeping worker up.
I am using Airflow (1.10.3) with AWS RDS Postgres as metaStore and celery_backend, SQS as queue service and CeleryExecutor. I have 1 master machine running airflow webserver and scheduler service, and a 1 worker machine.
Airflow worker is always starving for more tasks (queued) with lot of unused resources (CPU and RAM, with usage always below 20%). I've observed worker pick up tasks in batches, for eg: If there are 10 tasks in queue and 2 running tasks, then it will wait for 2 tasks to complete before picking next batch of tasks from the queue.
Parallelism setting in airflow.cfg in Worker instances.
parallelism = 32 .
dag_concurrency = 32.
non_pooled_task_slot_count = 128.
max_active_runs_per_dag = 32.
max_threads = 2 (no issues in scheduler though, as tasks are queued immediately)
One important thing to point out in my implementation - Airflow task is not a single process task, and individual Task further spawns multiple processes (3-5). Though even after considering process counts, my airflow worker never reaches full parallelism.
Any suggestion to -
a). Is there a way to fully utilise parallel execution of tasks on an airflow worker? Or if there's some more info that I am missing while setting up Airflow.
b). Above mentioned parallelism settings are configured at airflow task as atomic-unit, or number of threads/processes that task spawns?
Thanks!
If I schedule the task for every minute and if it is not able to be getting completed in the time(one minute). Would the task wait in queue and it will go on like this? if this happens then after few hours it will be overloaded. Is there any solution for this kind of problems?
I am using beat and worker combination for this. It is working fine for less records to perform tasks. but for large database, I think this could cause problem.
Task is assign to queue (RabbitMQ for example).
Workers are queue consumers, more workers (or worker with high concurrency) - more tasks could be handled in parallel.
Your periodic task produce messages of the same type (I guess) and your celery router route them to the same queue.
Just set your workers to consume messages from that queue and that's all.
celery worker -A celeryapp:app -l info -Q default -c 4 -n default_worker#%h -Ofair
In the example above I used -c 4 for concurrency of four (eqv. to 4 consumers/workers). You can also start move workers and let them consume from the same queue with -Q <queue_name> (in my example it's default queue).
EDIT:
When using celery (the worker code) you are initiate Celery object. In Celery constructor you are setting your broker and backend (celery used them as part of the system)
for more info: http://docs.celeryproject.org/en/latest/getting-started/first-steps-with-celery.html#application
We have a 10 queue setup in our celery, a large setup each queue have a group of 5 to 10 task and each queue running on dedicated machine and some on multiple machines for scaling.
On the other hand, we have a bunch of periodic tasks, running on a separate machine with single instance, and some of the periodic tasks are taking long to execute and I want to run them in 10 queues instead.
Is there a way to scale celery beat or use it purely to trigger the task on a different destination "one of the 10 queues"?
Please advise?
Use celery routing to dispatch the task to where you need:
I am trying to create a bunch of celery tasks asynchronously on the fly. Say there are 1000 tasks I start asynchronously and I have only one celeryd process running to execute tasks. How many threads will be created by celery to handle these tasks?
If there are multiple threads that celery starts automatically to process the task queue, how do I limit celery to execute only 100 threads at a given point of time.
Thanks.
Its starts as many as you specify with the CELERYD_OPTS concurrency parameter.
Which is also discussed here.