As our reliance on data increases, the need for handling the information flow between various elements increases. Devices, applications, services, and software need to communicate with each other and that is where developers use message brokers to exchange the needed information. Even Microservices – the latest jargon today communicates with each other in an asynchronous mode, through message brokers.
RabbitMQ and Kafka are two popular message brokers that pass messages between the producers and consumers. The message could have any type of information, for e.g., information about an event or plain text message triggering a parallel event. These systems are perfect to connect various components, build microservices, manage real-time data.
Before we compare these two tools, let us quickly understand the concept of a message broker.
A message broker is an intermediary computer program module that translates a message from the sender’s formal messaging protocol to the receiver’s formal messaging protocol.
Asynchronous communication is generally managed via a message broker. Choosing the right message broker depends upon various parameters like persistency of data, the number of messages sent per second, and the competence of the consumer.
A message broker is needed when there is a requirement to control data feeds and put data to several applications without much use of APIs. It helps in dividing the publisher and consumers, store, route, check and organize messages.
Now, let us look at the two popular message broking systems.
RabbitMQ is the most widely deployed open-source message broker. RabbitMQ is used worldwide at small startups and large enterprises. RabbitMQ is lightweight and easy to deploy on-premises and in the cloud.
RabbitMQ is a routinely used message broker that handles high throughput conditions like online payments. It also deals with background jobs and supports different protocols like STOMP etc. It effectively delivers messages in complicated routing situations. It is seamlessly compatible with current technologies and can be enhanced through different plug-ins. In case there is a network failure, brokers can be distributed to be more accurate.
RabbitMQ is a distributed broker since it executes as a cluster of nodes in which the queues are distributed as against the nodes, for fault tolerance and high-end availability. It is based on a push model that is considered best for low latency messaging and queue-based architecture.
Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Kafka is competent to enable applications for processing, persisting, and reprocessing streamed data. It makes use of a routing key for sending messages through a direct routing approach. It is known as a distributed event streaming platform. Here, messages are written at the end of the log and customers can choose from where to read that.
It offers a great degree of availability and can be clustered across multiple servers. In Kafka, the producer is responsible for writing and storing messages in the cluster whereas the consumer is responsible for consuming messages from the cluster. Kafka works on a pull-based approach in which users can ask for message matches from specified offsets. It gives an adapter SDK to let developers create their system integration.
|Point of Origin||Initially, it was developed for implementing AMQP which was an open wire protocol for messaging.||Initially, it was developed at LinkedIn for connecting various internal systems.|
|Licensing||RabbitMQ is open source through Mozilla Public License||Kafka is open source via Apache License 2.0|
|Architecture||Consists of queues and is a pub/sub message broker. Uses different requests and pub-sub communication patterns. Offers constant delivery of messages to consumers. Mature support for Java, .NET, Ruby, etc.||Consists of brokers, partitions, topics, etc. durable, scalable high-volume pub-sub messages. Has a dumb broker and smart consumer model.|
|Client Library Support||Inclusion of mature libraries like Ruby, Java, Go, C, etc.||Inclusion of diverse libraries like NodeJS, Python, Java, etc.|
|Used For||Mainly offering an immediate response to requests. Processes high throughput and trustworthy jobs, integration, and communication. Used best for transaction data like user requests, order formation, and placement.||Mainly used to stream data, metrics, log aggregation, etc. Builds applications to process and reprocess data that is streaming on the disk. Used best for operational data like auditing and logging statistics etc.|
|Programming Language||Written in Erlang||Written in Scala and JVM|
|Distribution||Distribution is done through competitive consumers||Distribution done through topic partitions|
|Performance||Can process multiple messages in a second with more requirement of hardware. It can achieve good throughput with other tools like Apache Cassandra. Approx. 4K-10K messages per second.||Can process multiple messages in a second with lesser requirement of hardware. It can achieve high throughput boosting performance. Approx. 1 million messages per second.|
|Push vs Pull Approach||Uses a push-based approach via a predefined limit. This can be utilized for low latency messaging. It distributes messages quickly for effective processing.||Uses a pull-based approach because of its partitions. Users can enjoy the batching of messages for effective delivery of messages.|
|Support for Message Protocols||Supports standardized queue protocols like STOMP, HTTP, etc.||Supports primitives like int16, int8, etc|
|High Availability||Offers high-end availability through clustering and queues||Supports high-end availability through zookeeper|
|Routing Assistance||Does not support complicated routing scenarios. Does support flexible routing.||Supports complicated routing scenarios. Does support flexible routing.|
|Messages||Cannot be replayed, must be resent from sending side. Sends messages to users which are removed from the queue once processed.||Keeps data and supports message replay. It uses a continual system of maintaining logs of messages until time expires.|
|Support for Transactions||RabbitMQ does not support transactions in a native form||Kafka supports transactions|
|Plug-in Support||There are many plug-ins for supporting developer needs||There are not many plug-ins available|
|Federated Queue Support||RabbitMQ supports federated queues, to balance the load of a single queue across nodes||Kafka does not have any support for federated queues|
|Ordering||Ordering is not assured once there are many consumers||Assures ordering for a partition in a topic|
|Hosting Solution||It can be accessed from CloudAMOP to offer support to RabbitMQ clusters globally||It can be accessed from CloudKarafka for Kafka as a Service plan|
|Data Type||It has a transactional data type||It has an operational data type|
|Mode of Consumer||It is a smart broker and dumb consumer||It is a dumb broker and smart consumer|
|Size of Payload||No restrictions||The default limit of 1 MB|
|Data Flow||It utilizes a bounded data flow in which messages are created by the producer and received by the consumer||It utilizes an unbounded data flow in which the major pairs stream to the assigned topic regularly|
|Topology||Exchange queue topology||Publish/subscribe topology|
|Best Used in||Enterprises, finance, enterprise systems||Customer monitoring, stock management, Operations|
|Message Priority||Supported||Not Supported|
|Monitoring||Monitoring is possible through an inbuilt user interface||Monitoring is done through 3rd party tools|
Since both are message brokers, there are certain key similarities that are distinctly seen:
Use RabbitMQ When-
Use Kafka When-
The above comparison has thrown light on the similarities and differences between the two popular message brokers. Comparing RabbitMQ vs Kafka is like choosing the better out of the best. To each its own! While RabbitMQ and Kafka are often thought of as interchangeable, they have different implementation purviews.
Finally, it depends upon the user requirements. Both are fit to capture millions of messages per second, though they both have their own individual architecture.
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