I have docker-compose setup for my Dash application. I need suggestion or preferred way to setup my celery image.
I am using celery for following use-cases and these are cancellable/abortable/revoked task:
Upload file
Model training
Create train, test set
Case-1. Create one service as celery,
command: ["celery", "-A", "tasks", "worker", "--loglevel=INFO", "--pool=prefork", "--concurrency=3", "--statedb=/celery/worker.state"]
So, here we are using default queue, single worker (main) and 3 child/worker processes(ie can execute 3 tasks simultaneously)
Now, if I revoke any task, will it kill the main worker or just that child worker processes executing that task?
Case-2. Create three services as celery-{task_name} ie celery-upload etc,
command: ["celery", "-A", "tasks", "worker", "--loglevel=INFO", "--pool=prefork", "--concurrency=1", , "--statedb=/celery/worker.state", "--queues=upload_queue", , "--hostname=celery_worker_upload_queue"]
So, here we are using custom queue, single worker (main) and 1 child/worker processe(ie can execute 1 task) in its container. This way one service for each task.
Now, if I revoke any task, it will only kill the main worker or just the only child worker processes executing that task in respective container and rest celery containers will be alive?
I tried using below signals with command task.revoke(terminate=True)
SIGKILL and SIGTERM
In this, I observed #worker_process_shutdown.connect and #task_revoked.connect both gets fired.
Does this means main worker and concerned child worker process for whom revoke command is issued(or all child processes as main worker is down) are down?
SIGUSR1
In this, I observed only #task_revoked.connect gets fired.
Does this means main worker is still running/alive and only concerned child worker process for whom revoke command is issued is down?
Which case is preferred?
Is it possible to combine both cases? ie having single celery service with individual workers(main) and individual child worker process and individual queues Or
having single celery service with single worker (main), individual/dedicated child worker processes and individual queues for respective tasks?
One more doubt, As I think, using celery is required for above listed tasks, now say I have button for cleaning a dataframe will this too requires celery?
ie wherever I am dealing with dataframes should I need to use celery?
Please suggest.
UPDATE-2
worker processes = child-worker-process
This is how I am using as below
# Start button
result = background_task_job_one.apply_async(args=(n_clicks,), queue="upload_queue")
# Cancel button
result = result_from_tuple(data, app=celery_app)
result.revoke(terminate=True, signal=signal.SIGUSR1)
# Task
#celery_app.task(bind=True, name="job_one", base=AbortableTask)
def background_task_job_one(self, n_clicks):
msg = "Aborted"
status = False
try:
msg = job(n_clicks) # Long running task
status = True
except SoftTimeLimitExceeded as e:
self.update_state(task_id=self.request.id, state=states.REVOKED)
msg = "Aborted"
status = True
raise Ignore()
finally:
print("FINaLLY")
return status, msg
Is this way ok to handle cancellation of running task? Can you elaborate/explain this line [In practice you should not send signals directly to worker processes.]
Just for clarification from line [In prefork concurrency (the default) you will always have at least two processes running - Celery worker (coordinator) and one or more Celery worker-processes (workers)]
This means
celery -A app worker -P prefork -> 1 main worker and 1 child-worker-process. Is it same as below
celery -A app worker -P prefork -c 1 -> 1 main worker and 1 child-worker-process
Earlier, I tried using class AbortableTask and calling abort(), It was successfully updating the state and status as ABORTED but task was still alive/running.
I read to terminate currently executing task, it is must to pass terminate=True.
This is working, the task stops executing and I need to update task state and status manually to REVOKED, otherwise default PENDING. The only hard-decision to make is to use SIGKILL or SIGTERM or SIGUSR1. I found using SIGUSR1 the main worker process is alive and it revoked only the child worker process executing that task.
Also, luckily I found this link I can setup single celery service with multiple dedicated child-worker-process with its dedicated queues.
Case-3: Celery multi
command: ["celery", "multi", "show", "start", "default", "model", "upload", "-c", "1", "-l", "INFO", "-Q:default", "default_queue", "-Q:model", "model_queue", "-Q:upload", "upload_queue", "-A", "tasks", "-P", "prefork", "-p", "/proj/external/celery/%n.pid", "-f", "/proj/external/celery/%n%I.log", "-S", "/proj/external/celery/worker.state"]
But getting error,
celery service exited code 0
command: bash -c "celery multi start default model upload -c 1 -l INFO -Q:default default_queue -Q:model model_queue -Q:upload upload_queue -A tasks -P prefork -p /proj/external/celery/%n.pid -f /proj/external/celery/%n%I.log -S /proj/external/celery/worker.state"
Here also getting error,
celery | Usage: python -m celery worker [OPTIONS]
celery | Try 'python -m celery worker --help' for help.
celery | Error: No such option: -p
celery | * Child terminated with exit code 2
celery | FAILED
Some doubts, what is preferred 1 worker vs multi worker?
If multi worker with dedicated queues, creating docker service for each task increases the docker-file and services too. So I am trying single celery service with multiple dedicated child-worker-process with its dedicated queues which is easy to abort/revoke/cancel a task.
But getting error with case-3 i.e. celery multi.
Please suggest.
If you revoke a task, it may terminate the working process that was executing the task. The Celery worker will continue working as it needs to coordinate other worker processes. If the life of container is tied to the Celery worker, then container will continue running.
In practice you should not send signals directly to worker processes.
In prefork concurrency (the default) you will always have at least two processes running - Celery worker (coordinator) and one or more Celery worker-processes (workers).
To answer the last question we may need more details. It would be easier if you could run Celery task when all dataframes are available. If that is not the case, then perhaps run individual tasks to process dataframes. It is worth having a look at Celery workflows and see if you can build Chunk-ed workflow. Keep it simple, start with assumption that you have all dataframes available at once, and build from there.
I need to calculate the task on my Fargate cluster and after finishing calculating the task should be stopped or terminated to decrease payments.
The consequence of my actions:
One task always running on EC2 Cluster and checking DB to the new data.
If new data appears, Boto3 runs the Fargate container.
After the job is done, the task should be stopped.
Also if in the DB appears the second row of data during proceeding the first task, Fargate should create a second task for the second job. and then stop tasks...
So, I have:
Task written on Python and deployed on ECR
Fargate cluster
Task definition (describe Memory, CPU's and container)
How task know, that it should be closed? Any idea or solution to stop task after job done?
What is the algorithm used to distribute the task load between workers in celery?
I checked the documentation, could not find the info.
This will depend on the broker that is used. For example, for redis, each of worker process uses kombu's redis transport which in turn calls brpop to get the next task available. redis implements brpop using the longest-waiting client algorithm to allocate a certain task to a certain client (waiting celery worker process).
I'm trying to solve integration between Control-M scheduler and batch jobs running within spring-xd.
In our existing environment, Control-M agents run on the host and batch jobs are triggered via bash script from Control-M.
In the spring-xd architecture a batch job is pushed out into the XD container cluster and will run on an available container. This means however I don't know what XD container the job will run on. I could pin it to a single container with a deployment manifest, but that goes against the whole point of the cluster.
One potential solution.
Run a VM outside the XD container cluster with the Control-M agent and trigger jobs through the XD API via a bash script. The script would need to wait for the job to complete, by either polling for the job completion via the XD API or wait for an event to signal the completion.
Thinking further ahead this could be a solution to triggering batch jobs deployed in PCF.
In a previous life, I had the enterprise scheduler use Perl scripts to interact with the old Spring Batch Admin REST API to start jobs and poll for completion.
So, yes, the same technique should work fine with XD.
You can also tap into the job events.
I'm having issues with a celery beat worker not sending out tasks to celery. Celery runs on three servers with a RabbitMQ cluster behind HAProxy as a backend.
Celery beat is used to schedule a task every day at 9AM. When I start the worker, usually the first task succeeds, but after that it seems like the following tasks are never sent to rabbitmq. In the celery beat log file (celery beat is run with the -l debug option), I see messages such as: Scheduler: Sending due task my-task (tasks.myTask), but no sign of the task being received by any celery worker.
I also tried logging messages in rabbitmq via the rabbitmq_tracing plugin, which only confirmed that the task never reached rabbitmq.
Any idea what could be happening? Thanks!