Celery vs Gearman: What are the differences?
Celery: Distributed task queue. Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well; Gearman: A generic application framework to farm out work to other machines or processes. Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.
Celery and Gearman can be categorized as "Message Queue" tools.
"Task queue" is the primary reason why developers consider Celery over the competitors, whereas "Free" was stated as the key factor in picking Gearman.
Celery is an open source tool with 12.9K GitHub stars and 3.33K GitHub forks. Here's a link to Celery's open source repository on GitHub.
Udemy, Robinhood, and Sentry are some of the popular companies that use Celery, whereas Gearman is used by Instagram, Hootsuite, and Grooveshark. Celery has a broader approval, being mentioned in 272 company stacks & 77 developers stacks; compared to Gearman, which is listed in 19 company stacks and 5 developer stacks.
What is Celery?
What is Gearman?
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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.
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.
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
Internal, distributed message queue. Main communication happens via port 4730 and consists of simple json messages. Completely independent of the main website back-end.
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.