RabbitMQ vs Kafka for Microservices 2022 : Aalpha
This is an open-sourced message intermediary or broker that handles background jobs or acts as a message intermediary between microservices. Due to its high throughput, it is used in functions such as processing online payments. It is older than Kafka as it was released in 2007.
Features of RabbitMQ
Highly reliable and its performance as a message broker cannot be put into question.
Complex routing is possible because before messages arrive at queues they are routed through exchanges. It also has several exchange built-in types for the typical routing logic.
Multiple RabbitMQ servers can be clustered together to form a single broker.
For servers needing more loosely connection than clustering allows, it offers a federation model for them.
In the event of hardware failure and to increase reliability, queues can be mirrored in a cluster across several machines.
It supports messaging over several messaging protocols.
This is by virtue of the many languages you may use.
You can easily control and manage every aspect of the message broker thanks to its easy-to-use management UI.
Offers a variety of plugins that are ready to use and provides an option to write custom plugins.
Its architecture consists of:
Creates and then publishes messages to a broker server. The message usually has two parts, a payload which is the data that a user wants to transmit, and a label which essentially determines who the copy of the message should go to and also describes the payload.
Enable applications to communicate and exchange information.
Receives messages delivered and decides where they should go.
Instructs an exchange on the messages to be distributed to specific queues. It also directs the exchange on which messages to filter when sending to a queue for exchange kinds that are particular.
It stores messages received and may include configured information defining what it can do with a message.
This one is attached to a broker server and is subscribed to a queue.
When to use RabbitMQ
- When you don’t have a clear end-to-end picture of the architecture. This is because it’s flexible and can cope with any changes in the structure.
- Since it does not require you to change the publisher you can use it when you need to dynamically add consumers.
- When you need to create microservices in different languages since it’s language agnostic.
- When point-to-point interactions are complex between publish/subscribe microservices.
What is Kafka?
Initially developed at LinkedIn and later open sourced, Kafka is a publish-subscribe messaging solution that deals with real-time data that it routes to consumers. It provides for the integration of information seamlessly between producers and consumers. This is without the producers being obstructed or the identities of consumers being revealed. It is written in java and Scala. It is newer as compared to RabbitMQ as it was released in 2011.
Features of Kafka
Also referred to as Apache Kafka, below are some of its features:
They are built with O(1) Disk structures that provide for constant-time performance even when stored messages are in high volumes. There is also no information loss
Since its inception, it was designed to work with large data amounts.
It facilitates message partitioning across servers and consumption is distributed across a consumer systems cluster.
Messages created are instantly available to the consumer.
Scalability is easy without downtime by it being handled in all four dimensions.
Its fault-tolerant, partitioned and replicated nature makes it reliable
They publish messages to a topic that is generated on a broker. They can also choose to deliver the message to a broker in either an asynchronous or synchronous mode.
When messages arrive at the broker they are stored in the same sequence and the number of partitions is configured accordingly for each message.
This is the coordinator between the consumers and the broker. Coordination data such as configuration, location, and status information are stored by it.
This one ensures that messages that are published are consumed even if the broker fails.
These are subscribers to the topic to receive messages.
When to use Kafka
- When messages need to be replayed.
- When the messages need to be consumed very quickly.
- When you have an application that has high throughput.
- When you need to process data in multi-stage pipelines.
Differences between RabbitMQ and Kafka
RabbitMQ can process 4k-10K per second while Kafka can process 1 million messages per second.
For RabbitMQ it is transactional while the data type for Kafka is operational.
The consumer mode for RabbitMQ is Smart broker/dumb consumer while that for Kafka is Dumb broker/smart consumer.
RabbitMQ has no constraints on the payload while Kafka has a default of 1Mb limit.
RabbitMQ is effective for simple use cases while Kafka supports massive data or high throughput cases.
In RabbitMQ the data flow is distinct and bounded while Kafka uses an unbounded data flow.
In RabbitMQ when messages are sent to users after they are processed and acknowledged they are removed from the queue while in Kafka they stay in the queue as long as the retention time has not expired.
RabbitMQ uses the queue topology while Kafka employs the publish/subscribe topology.
In RabbitMQ the data unit is a message while in Kafka it’s a continuous stream.
Complex routing is possible in RabbitMQ while it’s not possible in Kafka
In RabbitMQ events are stored in a queue while Kafka on logs.
RabbitMQ has decoupled consumer queues while Kafka has coupled consumer partition/groups.
When the architecture of an application is not known one can use RabbitMQ due to its flexibility and ease to use in such a situation. But as the application matures and there are requirements for large throughput, reliability, scaling and even replayability of messages, then one will have to switch to Kafka.
Finally, to know more about differences between RabbitMQ and Kafka, connect with microservices development company!
Originally published at https://www.aalpha.net on July 21, 2022.