I have a python/celery setup: I have a queue named "task_queue" and multiple python scripts that feed it data from different sensors. There is a celery worker that reads from that queue and sends an alarm to user if the sensor value changed from high to low. The worker has multiple threads (I have autoscaling parameter enabled) and everything works fine until one sensor decides to send multiple messages at once. That's when I get the race condition and may send multiple alarms to user, since before a thread stores the info that it had already sent an alarm, few other threads also send it.
I have n sensors (n can be more than 10000) and messages from any sensor should be processed sequentially. So in theory I could have n threads, but that would be an overkill. I'm looking for a simplest way to equally distribute the messages across x threads (usually 10 or 20), so I wouldn't have to (re)write routing function and define new queues each time I want to increase x (or decrease).
So is it possible to somehow mark the tasks that originate from same sensor to be executed in serial manner (when calling the delay or apply_async)? Or is there a different queue/worker architecture I should be using to achieve that?
From what I understand, you have some tasks that can run all at the same time and a specific task that can not do this (this task needs to be executed 1 at a time).
There is no way (for now) to set the concurrency of a specific task queue so I think the best approach in your situation would be handling the problem with multiple workers.
Lets say you have the following queues:
queue_1 Here we send tasks that can run all at the same time
queue_2 Here we send tasks that can run 1 at a time.
You could start celery with the following commands (If you want them in the same machine).
celery -A proj worker --loglevel=INFO --concurrency=10 -n worker1#%h -Q queue_1
celery -A proj worker --loglevel=INFO --concurrency=1 -n worker2#%h -Q queue_2
This will make worker1 which has concurrency 10 handle all tasks that can be ran at the same time and worker2 handles only the tasks that need to be 1 at a time.
Here is some documentation reference:
https://docs.celeryproject.org/en/stable/userguide/workers.html
NOTE: Here you will need to specify the task in which queue runs. This can be done when calling with apply_async, directly from the decorator or some other ways.
Related
This question is regarding the use of multiple remote Celery workers on separate machines. The implementation of the App can be conceptualized as:
My App (Producer) will be adding multiple tasks (say 50) to the queue every 5 mins (imagine a python for loop iterating over a list of tasks to be performed asynchronously at every 5 min interval). I want the celery workers (which will be remote machines) to pick these tasks up as soon as they are pushed.
My question is will Celery/RabbitMQ automatically handle task distribution (so no Worker picks up a task that has already been picked up by a worker from the queue - i.e. to ensure work is not duplicated) and distribute the tasks evenly so no worker is left lazying about while other workers are working hard or do these have to be configured/programmed in the settings?*
I would most appreciate it if someone could forward me relevant documentation (I was checking out Celery docs but couldn't find this specific info regarding remote celery workers in this context.)
Automatically but you need to be aware of prefetching feature which is described here: http://docs.celeryproject.org/en/latest/userguide/optimizing.html#prefetch-limits, read until the end of the page.
In short, prefetching works on two levels: worker level and process level, since a worker may have multiple processes. To disable prefetch on worker level you need to specify worker_prefetch_multiplier = 1 in celery settings, to disable on the process level you need to specify -Ofair option in worker's command line.
So after digging around in RabbitMQ docs it seems that the default exchange method is Direct Exchange (ref https://www.rabbitmq.com/tutorials/amqp-concepts.html) which means that tasks will be distributed to workers in a round-robin manner.
I'm creating a celery task in a situation where task producers are more than consumers (workers). Now since my queues are getting filled up and the workers consume in FCFS manner, can I get to execute a specific task(given a task_id) instantly?
for eg:
My tasks are filled in the following fashion. [1,2,3,4,5,6,7,8,9,0]. Now the tasks are fetched from the zeroth index. Now a situation arise where I want to execute task 8 above all. How can I do this?
The worker need not execute that task (because there can be situation where a worker is already occupied). It can be run directly from the application. And when the task is completed (either from the worker or directly from the application), it should get deleted from the queue.
I know how to forcefully revoke a task (given a task_id) but how can I execute a task given an id ?
how can I execute a task given an id ?
the short answer is you can't. Celery workers pull tasks off the broker backend as they become available.
Why not?
Note that's not a limitation of Celery as such, rather it is a characteristic of message queuing systems(MQS) in general. The point of MQS is to desynchronize an application's component so that the producer can go on to do other work while workers execute the tasks asynchronously. In other words, once a task has been sent off it cannot be modified (but it can be removed as long as it has not been started yet).
What options are there?
Celery offers you several options to deal with lower v.s. higher priority or short- and long-running tasks, at task submission time:
Routing - tasks can be routed to different workers. So if your tasks [0 .. 9] are all long-running, except for task 8, you could route task 8 to a worker, or a set of workers, that deal with short-running tasks.
Timed execution - specify a countdown or estimated time of arrival (eta) for each task. That's a good option if you know that some tasks can be delayed for later execution i.e. when the system will be less busy. This leaves workers ready for those tasks that need to be executed immediately.
Task expiry - specify an expire countdown or time with a callback. This way the task will be revoked if it didn't execute within the time alloted to it and the callback can start an alternative course of action.
Check on task results periodically, revoke a task if it didn't start executing within some time. Note this is different from task expiry where the revoking only happens once a worker has fetched the task from the queue - if the queue is full the revoking may happen too late for your use case. Checking results periodically means you have another component in your system that does this and determines an alternate course of action.
Cheers,
I have a celery setup running in a production environment (on Linux) where I need to consume two different task types from two dedicated queues (one for each). The problem that arises is, that all workers are always bound to both queues, even when I specify them to only consume from one of them.
TL;DR
Celery running with 2 queues
Messages are published in correct queue as designed
Workers keep consuming both queues
Leads to deadlock
General Information
Think of my two different task types as a hierarchical setup:
A task is a regular celery task that may take quite some time, because it dynamically dispatches other celery tasks and may be required to chain through their respective results
A node is a dynamically dispatched sub-task, which also is a regular celery task but itself can be considered an atomic unit.
My task thus can be a more complex setup of nodes where the results of one or more nodes serves as input for one or more subsequent nodes, and so on. Since my tasks can take longer and will only finish when all their nodes have been deployed, it is essential that they are handled by dedicated workers to keep a sufficient number of workers free to consume the nodes. Otherwise, this could lead to the system being stuck, when a lot of tasks are dispatched, each consumed by another worker, and their respective nodes are only queued but will never be consumed, because all workers are blocked.
If this is a bad design in general, please make any propositions on how I can improve it. I did not yet manage to build one of these processes using celery's built-in canvas primitives. Help me, if you can?!
Configuration/Setup
I run celery with amqp and have set up the following queues and routes in the celery configuration:
CELERY_QUERUES = (
Queue('prod_nodes', Exchange('prod'), routing_key='prod.node'),
Queue('prod_tasks', Exchange('prod'), routing_key='prod.task')
)
CELERY_ROUTES = (
'deploy_node': {'queue': 'prod_nodes', 'routing_key': 'prod.node'},
'deploy_task': {'queue': 'prod_tasks', 'routing_key': 'prod.task'}
)
When I launch my workers, I issue a call similar to the following:
celery multi start w_task_01 w_node_01 w_node_02 -A my.deployment.system \
-E -l INFO -P gevent -Q:1 prod_tasks -Q:2-3 prod_nodes -c 4 --autoreload \
--logfile=/my/path/to/log/%N.log --pidfile=/my/path/to/pid/%N.pid
The Problem
My queue and routing setup seems to work properly, as I can see messages being correctly queued in the RabbitMQ Management web UI.
However, all workers always consume celery tasks from both queues. I can see this when I start and open up the flower web UI and inspect one of the deployed tasks, where e.g. w_node_01 starts consuming messages from the prod_tasks queue, even though it shouldn't.
The RabbitMQ Management web UI furthermore tells me, that all started workers are set up as consumers for both queues.
Thus, I ask you...
... what did I do wrong?
Where is the issue with my setup or worker start call; How can I circumvent the problem of workers always consuming from both queues; Do I really have to make additional settings during runtime (what I certainly do not want)?
Thanks for your time and answers!
You can create 2 separate workers for each queue and each one's define what queue it should get tasks from using the -Q command line argument.
If you want to keep the number processes the same, by default a process is opened for each core for each worker you can use the --concurrency flag (See Celery docs for more info)
Celery allows configuring a worker with a specific queue.
1) Specify the name of the queue with 'queue' attribute for different types of jobs
celery.send_task('job_type1', args=[], kwargs={}, queue='queue_name_1')
celery.send_task('job_type2', args=[], kwargs={}, queue='queue_name_2')
2) Add the following entry in configuration file
CELERY_CREATE_MISSING_QUEUES = True
3) On starting the worker, pass -Q 'queue_name' as argument, for consuming from that desired queue.
celery -A proj worker -l info -Q queue_name_1 -n worker1
celery -A proj worker -l info -Q queue_name_2 -n worker2
How do you limit the number of instances of a specific Celery task that can be ran simultaneously?
I have a task that processes large files. I'm running into a problem where a user may launch several tasks, causing the server to run out of CPU and memory as it tries to process too many files at once. I want to ensure that only N instances of this one type of task are ran at any given time, and that other tasks will sit queued in the scheduler until the others complete.
I see there's a rate_limit option in the task decorator, but I don't think this does what I want. If I'm understanding the docs correctly, this will just limit how quickly the tasks are launched, but it won't restrict the overall number of tasks running, so this will make my server will crash more slowly...but it will still crash nonetheless.
You have to setup extra queue and set desired concurrency level for it. From Routing Tasks:
# Old config style
CELERY_ROUTES = {
'app.tasks.limited_task': {'queue': 'limited_queue'}
}
or
from kombu import Exchange, Queue
celery.conf.task_queues = (
Queue('default', default_exchange, routing_key='default'),
Queue('limited_queue', default_exchange, routing_key='limited_queue')
)
And start extra worker, serving only limited_queue:
$ celery -A celery_app worker -Q limited_queue --loglevel=info -c 1 -n limited_queue
Then you can check everything running smoothly using Flower or inspect command:
$ celery -A celery_app worker inspect --help
What you can do is to push these tasks to a specific queue and have X number of workers processing them. Having two workers on a queue with 100 items will ensure that there will only be two tasks processed at the same time.
I am not sure you can do that in Celery, what you can do is check how many tasks of that name are currently running when a request arrives and if it exceeds the maximum either return an error or add a mechanism that periodically checks if there are open slots for the tasks and runs it (if you add such a mechanism, you don't need to double check, just at each request add it to it's queue.
In order to check running tasks, you can use the inspect command.
In short:
app = Celery(...)
i = app.control.inspect()
i.active()
I'm just starting out with celery in a Django project, and am kinda stuck at this particular problem: Basically, I need to distribute a long-running task to different workers. The task is actually broken into several steps, each of which takes considerable time to complete. Therefore, if some step fails, I'd like celery to retry this task using the same worker to reuse the results from the completed steps. I understand that celery uses routing to distribute tasks to certain server, but I can't find anything about this particular problem. I use RabbitMQ as my broker.
You could have every celeryd instance consume from a queue named after the hostname of the worker:
celeryd -l info -n worker1.example.com -Q celery,worker1.example.com
sets the hostname to worker1.example.com and will consume from a queue named the same, as well as the default queue (named celery).
Then to direct a task to a specific worker you can use:
task.apply_async(args, kwargs, queue="worker1.example.com")
similary to direct a retry:
task.retry(queue="worker1.example.com")
or to direct the retry to the same worker:
task.retry(queue=task.request.hostname)