What is RabbitMQ?
Who uses RabbitMQ?
Why developers like RabbitMQ?
Here are some stack decisions, common use cases and reviews by companies and developers who chose RabbitMQ in their tech stack.
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.
We've been using RabbitMQ as Zulip's queuing system since we needed a queuing system. What I like about it is that it scales really well and has good libraries for a wide range of platforms, including our own Python. So aside from getting it running, we've had to put basically 0 effort into making it scale for our needs.
However, there's several things that could be better about it:
* It's error messages are absolutely terrible; if ever one of our users ends up getting an error with RabbitMQ (even for simple things like a misconfigured hostname), they always end up needing to get help from the Zulip team, because the errors logs are just inscrutable. As an open source project, we've handled this issue by really carefully scripting the installation to be a failure-proof configuration (in this case, setting the RabbitMQ hostname to
127.0.0.1, so that no user-controlled configuration can break it). But it was a real pain to get there and the process of determining we needed to do that caused a significant amount of pain to folks installing Zulip.
pika library for Python takes a lot of time to startup a RabbitMQ connection; this means that Zulip server restarts are more disruptive than would be ideal.
* It's annoying that you need to run the
rabbitmqctl management commands as root.
But overall, I like that it has clean, clear semanstics and high scalability, and haven't been tempted to do the work to migrate to something like Redis (which has its own downsides).
We needed our e-commerce platform (built using WooCommerce) to be able to keep products in sync with our #pim (provided by #akeneo) which is built in Symfony . We hooked into the kernel.event_listener to send RabbitMQ messages to a WordPress API endpoint that triggers the updated product to rebuild with fresh data.
Initially we had just 1 monolithic application with a PostgreSQL database (picked for performance, community & flexibility to work with GIS data), but as we've developed more features, it was clear that some stuff is relatively independent from the rest of the platform - it made sense to split the application into loosely coupled, asynchronously communicated services. As a communication broker we've used RabbitMQ (wrapped in our custom, ProtoBuff-based wrapper). To reduce some excessive inter-process (& inter-dyno) communication, we've applied Redis as a tool to keep short-lived, not-persistent information (but not as a cheap caching workaround for any kind of performance issues ;>).
Around the time of their Series A, Pinterest’s stack included Python and Django, with Tornado and Node.js as web servers. Memcached / Membase and Redis handled caching, with RabbitMQ handling queueing. Nginx, HAproxy and Varnish managed static-delivery and load-balancing, with persistent data storage handled by MySQL.
Beamery runs a #microservices architecture in the backend on top of Google Cloud with Kubernetes There are a 100+ different microservice split between Node.js and Go . Data flows between the microservices over REST and gRPC and passes through Kafka RabbitMQ as a message bus. Beamery stores data in MongoDB with near-realtime replication to Elasticsearch . In addition, Beamery uses Redis for various memory-optimized tasks.
- Robust messaging for applications
- Easy to use
- Runs on all major operating systems
- Supports a huge number of developer platforms
- Open source and commercially supported