What is Celery?
What is RabbitMQ?
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As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.
Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.
The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.
I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.
For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).
I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.
So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.
I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.
Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.
We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.
I developed one of the largest queue based medical results delivery systems in the world, 18,000+ queues and still growing over a decade later all using MQSeries, later called Websphere MQ. When I left that company I started using RabbitMQ after doing some research on free offerings.. it works brilliantly and is incredibly flexible from small scale single instance use to large scale multi-server - multi-site architectures.
If you can think in queues then RabbitMQ should be a viable solution for integrating disparate systems.
All of our background jobs (e.g., image resizing, file uploading, email and SMS sending) are done through Celery (using Redis as its broker). Celery's scheduling and retrying features are especially useful for error-prone tasks, such as email and SMS sending.
For orchestrating the creation of the correct number of instances, managing errors and retries, and finally managing the deallocation of resources we use RabbitMQ in conjunction with the Celery Project framework, along with a self-developed workflow engine.
We maintain a fork of Celery 3 that adds HTTPS support for Redis brokers. The Winning Model currently uses Celery 3 because Celery 4 dropped support for Windows.
We plan on migrating to Celery 4 once Azure ASE supports Linux apps
The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings.
Rabbit acts as our coordinator for all actions that happen during game time. All worker containers connect to rabbit in order to receive game events and emit their own events when applicable.
Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.
RabbitMQ is the enterprise message bus for our platform, providing infrastructure for managing our ETL queues, real-time event notifications for applications, and audit logging.
RabbitMQ is an all purpose queuing service for our stack. We use it for user facing jobs as well as keeping track of behind the scenes jobs.
We used celery, in combination with RabbitMQ and celery-beat, to run periodic tasks, as well as some user-initiated long-running tasks on the server.