This document describes Celery 3.0. For development docs, go here.
The default configuration makes a lot of compromises. It’s not optimal for any single case, but works well enough for most situations.
There are optimizations that can be applied based on specific use cases.
Optimizations can apply to different properties of the running environment, be it the time tasks take to execute, the amount of memory used, or responsiveness at times of high load.
In the book Programming Pearls, Jon Bentley presents the concept of back-of-the-envelope calculations by asking the question;
❝ How much water flows out of the Mississippi River in a day? ❞
The point of this exercise [*] is to show that there is a limit to how much data a system can process in a timely manner. Back of the envelope calculations can be used as a means to plan for this ahead of time.
In Celery; If a task takes 10 minutes to complete, and there are 10 new tasks coming in every minute, the queue will never be empty. This is why it’s very important that you monitor queue lengths!
A way to do this is by using Munin. You should set up alerts, that will notify you as soon as any queue has reached an unacceptable size. This way you can take appropriate action like adding new worker nodes, or revoking unnecessary tasks.
|[*]||The chapter is available to read for free here: The back of the envelope. The book is a classic text. Highly recommended.|
If you’re using RabbitMQ (AMQP) as the broker then you can install the librabbitmq module to use an optimized client written in C:
$ pip install librabbitmq
The ‘amqp’ transport will automatically use the librabbitmq module if it’s installed, or you can also specify the transport you want directly by using the pyamqp:// or librabbitmq:// prefixes.
Prefetch is a term inherited from AMQP that is often misunderstood by users.
The prefetch limit is a limit for the number of tasks (messages) a worker can reserve for itself. If it is zero, the worker will keep consuming messages, not respecting that there may be other available worker nodes that may be able to process them sooner [†], or that the messages may not even fit in memory.
If you have many tasks with a long duration you want the multiplier value to be 1, which means it will only reserve one task per worker process at a time.
However – If you have many short-running tasks, and throughput/round trip latency is important to you, this number should be large. The worker is able to process more tasks per second if the messages have already been prefetched, and is available in memory. You may have to experiment to find the best value that works for you. Values like 50 or 150 might make sense in these circumstances. Say 64, or 128.
If you have a combination of long- and short-running tasks, the best option is to use two worker nodes that are configured separately, and route the tasks according to the run-time. (see Routing Tasks).
|[†]||RabbitMQ and other brokers deliver messages round-robin, so this doesn’t apply to an active system. If there is no prefetch limit and you restart the cluster, there will be timing delays between nodes starting. If there are 3 offline nodes and one active node, all messages will be delivered to the active node.|
|[‡]||This is the concurrency setting; CELERYD_CONCURRENCY or the -c option to celeryd.|
When using early acknowledgement (default), a prefetch multiplier of 1 means the worker will reserve at most one extra task for every active worker process.
When users ask if it’s possible to disable “prefetching of tasks”, often what they really want is to have a worker only reserve as many tasks as there are child processes.
But this is not possible without enabling late acknowledgements acknowledgements; A task that has been started, will be retried if the worker crashes mid execution so the task must be idempotent (see also notes at Should I use retry or acks_late?).
You can enable this behavior by using the following configuration options:
CELERY_ACKS_LATE = True CELERYD_PREFETCH_MULTIPLIER = 1
The system responsible for enforcing rate limits introduces some overhead, so if you’re not using rate limits it may be a good idea to disable them completely. This will disable one thread, and it won’t spend as many CPU cycles when the queue is inactive.
Set the CELERY_DISABLE_RATE_LIMITS setting to disable the rate limit subsystem:
CELERY_DISABLE_RATE_LIMITS = True