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RabbitMQ is open-source traditional message-oriented middleware (MOM) that implements the Advanced Message Queuing Protocol (AMQP) as the standard for low-latency message queuing. Written in Erlang and built on the Open Telecom Platform framework in 2007, it’s an older messaging system often used as a message broker.
Although RabbitMQ enables flexible, highly interoperable messaging for a variety of use cases, adopting the messaging broker over other options comes with drawbacks—particularly when message durability and system scalability are critical requirements. A common alternative to RabbitMQ, Confluent's data streaming platform comes with cloud-native Apache Kafka + Flink, 120+ pre-built connectors, and event-driven communication for modern, scalable, stream processing across all apps, systems, and sources.
RabbitMQ consists of a set of broker processes that host “exchanges” for publishing messages to and queues for consuming messages from. RabbitMQ is often likened to a post office for your applications and messages. RabbitMQ is frequently used to reduce load and delivery time with your messages.
RabbitMQ allows applications that may not have available integrations with other applications to connect and communicate information with each other while remaining independent. RabbitMQ is aware of and tracks the status of each application that connects and manages the queue accordingly. This is often referred to as a smart broker/dumb consumer model where the onus to manage message delivery is on RabbitMQ rather than the consumer.
RabbitMQ is typically run on a cluster of nodes with distributed queues to provide fault tolerance and high availability. RabbitMQ messages are delivered via a push model which enables low-latency messaging since consumers are notified and can query the queue as soon as a new message is available.
There are two main use cases where it makes the most sense to use RabbitMQ: 1) long-running tasks that occur in the background while a user or system continues working and 2) communication between applications, especially when using a microservices architecture.
A typical example of a long-running task would be processing an upload to a web server. Typically a user inputs a document, picture, or other media onto a website, and some processing needs to occur. You can configure your application so the user waits until the processing is complete before allowing the user to continue with the upload or conversion. Or you can process and execute the upload in the background and have the user continue with any additional tasks needed-but without a messaging broker like RabbitMQ, you would have a much harder time using this kind of background processing to improve your website's response times.
The other common use case is as a middleman for application microservices. As microservices interact with each other, there needs to be a way for the messages to trigger events or tasks on other microservices. Using RabbitMQ, each application can avoid bottlenecks while passing messages.
RabbitMQ also has the ability to process a priority queue. An example of this might be with a batch processing job that runs every hour or every day, or manually on-demand. Batch processes are built to store messages in a log until it's time for the next batch to be queried and processed, a task which would occur at preset intervals. However, for on-demand tasks, those messages would enter the priority queue and process when submitted rather than waiting for the next scheduled task.
While there are some obviously beneficial use cases for RabbitMQ, there are some important evaluation criteria to consider first.
RabbitMQ cannot deal with high throughput, as it doesn’t support message batching and is instead optimized to handle one message at a time. Once a message with RabbitMQ has been delivered it is removed from the queue. Whereas in a system like Apache Kafka, the messages are persistent and configurable by the user to a specified retention period, either a certain time or byte size. You can learn more about the differences between RabbitMQ and other messaging systems in the upcoming comparison sections.
Many of RabbitMQ’s disadvantages stem from being written in the Erlang programming language—however, it can also be difficult for a developer to read the source code and understand what’s going on when troubleshooting.
Because RabbitMQ manages the delivery of messages, it can make the overall system difficult to set up, use, and maintain. Message brokers are not as simple as they appear. They have a plethora of configuration and setup options. The size of queues and messages, queue behavior, delivery settings, and message time-to-live (TTL) are just a few of the many options available.
From messaging queues and messaging protocols like AMQP, to pub/sub and streaming, there are numerous messaging systems available. While Kafka and RabbitMQ are most commonly compared, below is the full list of commonly searched alternatives that are worth considering based on your use case:
ActiveMQ is a Java-based message broker that supports both the push and pull modes of message delivery. It is compatible with Java apps and manages dynamic scaling very well. On the other hand, RabbitMQ is built on Erlang, which offers better reliability and fault tolerance suitable for applications that must deliver messages without losing any and must maximize uptime.
Amazon SQS is a highly reliable message queuing service—this managed service hides the infrastructure complexities and simplifies scalability for the user. It has Standard and FIFO queues to accommodate different scenarios to be run. However, RabbitMQ has more functionalities like enhanced routing and message prioritization, which is essential in enterprises that require complicated workflows and system connections.
Red Hat AMQ incorporates both ActiveMQ and Kafka functionalities to give a solution for any messaging challenge. This gives better security and supports a large number of protocols. In contrast, RabbitMQ is more about standard messaging with reliable, fast delivery and it is perfect for simple, high-performance message queuing.
ZeroMQ is a lightweight messaging library for writing fast and scalable applications with real-time communication, such as data processing. It offers simple APIs that resemble sockets for custom patterns. However, RabbitMQ outperforms the simple and lightweight message queuing provided by the Advanced Message Queuing Protocol (AMQP) such as durable queues and complex routing.
While people commonly compare Kafka and RabbitMQ, in reality, they are very different systems. Kafka is a purely distributed log designed for efficient event streaming at a high scale. RabbitMQ is an open-source message broker software that serves as a traditional messaging system, designed to publish large volumes of messages, quickly deliver them to consumers, and delete them.
Depending on how you configure your retention period, using Kafka gives you have the ability to replay messages and discover new insight later on that you didn’t think of when you initially ingested the data.
Additionally, RabbitMQ scales vertically whereas Kafka scales horizontally. Kafka was built to easily add additional machines to process your messages based on demand, whereas RabbitMQ relies on getting more powerful hardware to increase throughput. Kafka was built as a streaming platform where you implement messaging, distributed storage, and processing of data.
RabbitMQ stores messages in memory as long as there is space after which messages will be transferred to disk. This severely impacts the performance of the system. Kafka was built and designed for a wider scale and therefore can handle trillions of messages.
Choosing between them should be a fairly straightforward decision: for lightweight messaging that requires request-response, queuing, and pub-sub, a RabbitMQ server is well suited. For data streaming or event-driven use cases that require high throughput, scalability, and permanent message storage, Kafka is the clear winner. To learn more, check out this blog post on benchmarking Kafka, Pulsar, and RabbitMQ.