It is essential for organizations to efficiently manage several applications that generate a colossal amount of data for building better products and services. Apache Kafka vs RabbitMQ with a common purpose are widely used Open-Source, Message-Handling Solutions that assist in communication in distributed environments. Today, both solutions help organizations streamline the movement of data in Real-Time for building Streaming Applications. Though, when deciding between Apache Kafka vs RabbitMQ, it all comes down to individual use cases, their architecture, and performance. In this article, you will learn about the major differences between Apache Kafka vs RabbitMQ.
Kafka was originally developed at LinkedIn to address their need for Monitoring Activity Stream Data and Operational Metrics such as CPU, I/O usage, and request timings. Subsequently, in early 2011, it was Open-Sourced through the Apache Software Foundation.
Apache Kafka is a Distributed Event Streaming Platform written in Java and Scala. It is a Publish-Subscribe (pub-sub) Messaging Solution used to create Real-Time Streaming Data Pipelines and applications that adapt to the Data Streams.
Kafka deals with Real-Time volumes of data and swiftly routes it to various consumers. It provides seamless integration between the information of producers and consumers without obstructing the producers and without revealing the identities of consumers to the producers.
Key Features of Apache Kafka :
RabbitMQ was initially developed as RabbitMQ Technologies in a partnership between LShift, LTD, and Cohesive FT. Currently, it is owned by Pivotal Software and released under the Mozilla Public License.
RabbitMQ is a widely-used Open-Source Message Broker and Queueing Platform implemented on Advanced Message Queuing Protocol (AMQP) either written in Erlang or Open Telecom Platform (OTP) language.
It is also known as Message Oriented Middleware (MOM) because it provides advanced routing and message distribution features, even with WAN tolerances, to support reliable, distributed systems that easily interconnect with other systems.
Whether an application is hosted using the Cloud Platform or on users’ Data Centers, RabbitMQ provides tremendous flexibility, high scalability, high availability and can be implemented on various distributed platforms.
RabbitMQ is a Lightweight and Powerful platform for constructing Distributed Software Architectures ranging from the very simple to the highly complex.
Key Features of RabbitMQ:
Preference over Apache Kafka vs RabbitMQ can vary widely based on your individual use cases. Apache Kafka is an excellent choice for the following scenarios:
For a complete study of differences between Apache Kafka vs RabbitMQ, you can check out the following areas where using RabbitMQ can be advantageous:
The internal working and the basic architecture can be major factors in deciding between Apache Kafka vs RabbitMQ. Apache Kafka’ Architecture consists of the following components:
To determine the best architecture between Apache Kafka vs RabbitMQ, you can go through the RabbitMQ’s Architecture that is designed using the following elements:
Various factors present in the application determine the performance of the application. Thus, it would be only fair to compare the performance of Apache Kafka vs RabbitMQ based on similar factors present in these applications. While compared to RabbitMQ message brokers, Apache Kafka uses Sequential Disk I/O to improve speed when constructing queues. Kafka can handle millions of messages in a single second. RabbitMQ can handle millions of messages per second, but it will need more resources. While deciding between Apache Kafka vs RabbitMQ, it is to be noted that unlike Kafka, which can keep a large amount of data with minimum overhead, RabbitMQ queues are only quicker when they’re empty.
Kafka was first released in 2011 and written in Scala and Java. It is an open-source technology. On the other hand, RabbitMQ is developed in Erlang in 2007.
Kafka uses a pull mechanism that allows consumers to pull the data from the broker in batches. The consumer smarty keeps an offset tab to the last pulled message encounter. It organizes the data in the order in partitions by using offset. RabbitMQ comes with a push design in which the consumer is not aware of any message retrieval. The Broker ensures that the message is delivered to the consumer. Also, it sends back an acknowledgment after processing the data to ensure that messages are delivered to the consumer. On negative feedback, the message is again sent by putting it in the queue.
Kafka partitions are essential for providing scalability and redundancy. It replicated the partition to many brokers. So, if in any case one of the brokers fails then another broker can serve the consumer. If you keep all the partitions in one broker then dependency on the single broker will increase which is risky and chances of failure increase. Also, distributing the partitions will increase the throughput manifolds. RabbitMQ replicates messages using a queue in a round-robin format. The messages are distributed between queues to increase the throughput and distribute the load. Also, it allows multiple consu
Kafka follows a retention period and messages stored based on the retention period are deleted once the period is over. On the other hand, RabbitMQ sends a positive acknowledgment via the consumer to get removed from the queue. The messages are stored on the consumer on positive ACK and put back into the queue on negative ACK.
Kafka Consumers read a message from the broker and maintain offset to keep the track of queue counter. The offset is incremented on reading the message. The brokers of RabbitMQ are responsible to deliver a message to the consumer and these messages are sent into batches.
The priority for all messages is the same in Kafka and it cannot be changed. RabbitMQ allows assigning priority to the messages using a priority queue.
Kafka differentiates between messages using topics and Zookeeper maintains the offset so that whenever any consumer requests to read a topic, it will use the offset. RabbitMQ maintains orders of the messages in the queue inside the broker.
Kafka supports Ruby, Python, Java, and Node.js. RabbitMQ supports Elixir, Go, Java, JavaScript, Ruby, C, Swift, Spring, .Net, Python, and PHP.
Kafka supports Binary Protocol over TCP. While RabbitMQ supports MQTT, AMQP, STOMP, HTTP protocols.
In this article, you have learned about the differences between Apache Kafka vs RabbitMQ in terms of use cases, architecture, and performance. When flexibility is considered, RabbitMQ is more flexible and easier to use than Apache Kafka. However, when reliability and performance are considered, Apache Kafka outperforms RabbitMQ. Choosing between Apache Kafka vs RabbitMQ has never been easy, and with both technologies improving daily, the margins of advantage have shrunk even further. For a more comprehensive analysis of your business performance and financial health, it is essential to consolidate from Apache Kafka and all the other applications used across your business.