In today's world, it is increasingly common for scalable and high-performing services to rely on messaging systems, such as message brokers or publish/subscribe mechanisms.
The main reason for using either is to facilitate asynchronous communication among services.
What is RabbitMQ?
RabbitMQ is an "open-source message-broker software". A message broker is also known as a queue manager or message-queuing manager. In straightforward terms, it is software where queues are defined & to which services can connect to transfer messages.
The architecture of RabbitMQ is shown below. The producers produce messages to the Queues and Consumers consume from Queues.
What is Kafka?
Kafka is a pub/sub message bus and is described as an "open-source distributed event streaming platform". It is based on the concept of "distributed append-only log" where the messages are written to the end of a log which is persisted to a disk & services can choose where to begin from the log.
Application level Differences:
| Feature | Kafka | RabbitMQ |
| Re-read of messages | Supports re-read of consumed messages | Doesn't support |
| Message ordering | Supports ordering of messages in partition | Supports it with some constraint such as one exchange routing to the queue,one queue, one consumer to queue |
| Message Publishing Performance | Kafka is faster in publishing data into partition compared to RabbitMQ | Slower |
| Capabilities | Message Broker is just one subset of Kafka, but Kafka can also act as Message storage and stream processing | Just acts as Message Broker |
| Robustness | Kafka is more robustness than RabbitMQ as it supports Replication (for availability), Partitioning (for Scalability), Replay of messages (if needed to reprocess) | Not as Robust as Kafka, but upto some extent. |
| Scaling | Kafka is designed to scale horizontally by adding more machines. | RabbitMQ is designed to scale vertically by adding more power. |
Let's Understand the Differences Between RabbitMQ and Kafka
Kafka is a Log, RabbitMQ is a Queue
Kafka is a log, i.e it retains messages by default and the writes are appended to the tail, but consumers can read from wherever they want.
RabbitMQ is a queue i.e messages are deleted from queue as soon as they are consumed and RabbitMQ provides acknowledgment to the producer.
Kafka Uses Pull-based Approach, RabbitMQ Uses Push-based Approach
Kafka Pull-based approach:
Kafka uses a pull-based approach, which means consumers request batches of messages from a specific offset. This makes consumers are responsible for fetching messages, instead of brokers being responsible for pushing messages to connected consumers.
There are different advantages with this approach.
- Because consumers pull data from the topic, different consumers can consume the messages at different pace.
- Kafka also supports different consumption models, which means, one consumer can process messages at real time & another consumer consumes messages in batch mode.
- Kafka is designed to support different consumers which have diverse needs and capabilities.
RabbitMQ Push-based approach:
RabbitMQ uses push based approach to send messages to consumers. Here RabbitMQ takes care of delivering message to consumer. After consumer processed the received message, it sends back acknowledgement to RabbitMQ to ensure message is consumed correctly & RabbitMQ removes message from Queue.
If a negative acknowledgment is returned from consumer, then the message delivery is re-attempted by putting it back in the queue.
Performance Comparison:
Kafka offers higher performance than RabbitMQ. It uses sequential disk I/O to get higher throughput than RabbitMQ.
Kafka can process millions of messages per second with limited resources. Even RabbitMQ also offers higher throughput with millions of messages per second but requires higher resources than Kafka.
Suitable Use Cases:
RabbitMQ:
A common use case for RabbitMQ is to handle background jobs or long-running tasks such as file scanning, image scaling, PDF conversion etc.
RabbitMQ is also used between microservices, where it serves as a means of communicating between applications, avoiding bottlenecks in message passing.
Kafka:
Kafka is designed to support high-throughput ingestion data streams and replay. With this in mind, use Kafka when you have the need to move large amount of data, process data in real-time, analyze the data over a time period. i.e if the use case is to collect, store and handle data.
One example is if you want to track user activity on a e-commerce website and suggest items to buy.
Another example is data analysis for tracking, ingestion, logging, security etc.
Durable message broker is another feature supported by Kafka where application can process & reprocess messages if required.
Use kafka if you need to support batch consumers that can go offline or consumers which want to consume messages at low latency.
In general, there are two main situations where RabbitMQ is preferrable, for long-running tasks, background jobs, for communication & integration within & between applications i.e as middleman between microservices, where a system is simply needs to notify another part of the system to start work on the task like order handling in e-commerce websites like order placed, update order status, order payment etc.
On the other hand, use Kafka if you want a middleware for storing, reading, re-reading & analyze stream data. i.e the two main use cases of analyzing data & real-time processing. Kafka is most popular choice for powering data pipelines.