Differences Between Kafka and JMS

Explaining the function of Java Message Service (JMS) entails discussing its role as a Java-oriented communication system between two or more applications or clients. It’s crucial to recognise that the key difference between using a topic and a queue in JMS lies in the fact that a topic transmits a message to multiple subscribers, whereas a queue sends a message to a single consumer. It’s important to note that Apache Kafka is a distributed streaming platform and not a database. Lastly, JMS is included in Java since it’s a fundamental component of the Java Enterprise Edition (EE) platform.

To find the answers to your questions, please read this article. Let’s get started, shall we?

To facilitate communication and interaction among different software programs, services, and infrastructures, the development of message brokers has been crucial. These message brokers primarily consist of producers/publishers, consumers/subscribers, queues, and topics, which together form the backbone of their data architecture. Java Message Service (JMS) and Apache Kafka are two of the most extensively used message brokers.

For businesses to communicate effectively among different departments as well as with their stakeholders and customers, messaging platforms like Java Message Service (JMS) and Apache Kafka have been instrumental. These platforms provide developers with the capability to exchange data effectively between programs, ensuring a smooth and reliable exchange of information.

To understand the major differences between these two message brokering platforms, kindly read on.

What is Apache Kafka?

Apache Kafka is a distributed streaming system that enables the transmission of messages from one location to another. With a cluster of computers, it provides a dependable logging mechanism for distributed applications by maintaining a continual stream of records. Kafka also aids in the publishing and subscribing of records, as well as the real-time processing and storage of record streams, allowing developers to create pipelines and applications that can process data in real-time.

What is JMS?

The Java Message Service (JMS) is an application programming interface (API) that enables Java programs to share data with one another and external programs effortlessly by using pre-defined messaging protocols. By adhering to this messaging standard, users can compose, send, receive, and read messages across computers connected to a network. Additionally, JMS allows programmers to bridge the communication gap between programs built with different programming languages.

Outlined below are the key differences between Kafka and JMS.

  1. Programming Methods for Kafka and JMS

    Reacting to data streams asynchronously is the primary feature of Apache Kafka’s programming paradigm. A library that assures the smooth flow of data and the uniform propagation of modifications is enabled. When a single data flow is modified, this reactive programming approach guarantees that these modifications are automatically applied to other data components of the system.

    On the other hand, while other programming approaches exist, JMS has chosen to employ the imperative programming paradigm, which is the traditional coding technique for solving problems. This methodology necessitates that programmers adhere to a set of predetermined instructions in order to achieve their desired outcome. This is useful because it ensures that issues are handled in a predictable, consistent manner.
  2. Message types in Kafka and JMS

    A broker is responsible for transferring messages from a sender to a receiver in the Java Messaging Service (JMS) platform. This is accomplished by using the ‘push’ technique, which relies on a datastore continuously pushing data to the user as changes occur. Once the data is processed by the system, JMS then ‘pushes’ it to other programs that may require it.

    Kafka, on the other hand, is a pull-type messaging system, where consumers issue requests to brokers for messages. In this model, the consumer periodically requests information from the data store to obtain the necessary data. Moreover, Kafka is also a streaming platform that receives data requests from applications.
  3. Dissecting Kafka vs. JMS

    Apache Kafka utilises topic logs, which offer distinct categorisation based on topics, for collecting data. In a Kafka cluster, a single log can be partitioned into multiple logs across various nodes. The handling of messages in Apache Kafka is divided into three components: receiving, storing, and processing.

    Users of the Java Messaging Service (JMS) must group messages into specific categories before distributing and delivering them to subscribers. Until the messages have accumulated in the JMS queue, the user interface remains inactive, at which point they are pushed to the consumer.
  4. Distinguishing Between Kafka and JMS for Filtering

    Message selectors are provided by JMS (Java Messaging Service), which allows users to specify the messages they wish to receive. During the broadcast phase, recipients can apply property and header filters to the messages they receive, ensuring that only relevant messages are sent. This functionality allows users to personalise their messaging experience, resulting in messages that are tailored to their specific needs.

    Despite the fact that topics are segmented, Kafka lacks even the most basic filtering capabilities. In Kafka, users must navigate through multiple topics to locate the data they require in order to filter data.
  5. Comparing Kafka vs JMS for Message Routing

    Through the use of a ‘Topic name extractor,’ Kafka stream nodes can dynamically determine the topics’ names used for transmitting records. Once the data has been gathered from various sources, it can be directed as events through newly created Kafka topics.

    The implementation of JMS routing can be a multifaceted process since users have the option of choosing between header routing and message routing. While message routing necessitates XML work, header routing eliminates the need for data parsing. Thus, developers must carefully consider which routing option to employ when creating software.


Prior to choosing a messaging framework, developers must evaluate the benefits and drawbacks of both Kafka and Java Message Service (JMS). Kafka is better equipped for handling large amounts of data, whereas JMS is better suited for multi-node cluster systems. As each framework provides specific advantages and disadvantages, it is crucial to assess their distinct characteristics to make an informed decision.

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  1. What is the structure of JMS, and how does it operate?

    JMS consists of servers, modules, queues, topics, file stores, destination keys, connection factories, and JDBC stores.
  2. Can you identify Kafka’s basic principles?

    Kafka is built on various components, the most vital of which are consumer groups, topics, partitions, clusters, replicas, leaders, and followers. These components are crucial to Kafka’s essential functions, and comprehending their individual roles is crucial to the successful utilisation of the platform.
  3. What sets apart a JMS topic from a JMS queue?

    A topic can have multiple subscribers, while a queue can only have one.

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