JMS vs Kafka
For Developers

Kafka vs JMS: 5 Key Differences

By August 2, 2022 4 min read

What is Apache Kafka? What is JMS used for? Is there a difference between JMS topic and queue? Is Kafka a database? Is JMS part of Java?

This post will help you answer all these questions. Let’s get started.

Message brokers are computer programs that enable communication between different applications, services, and systems. Message brokers consist of data structures that involve various producers/publishers, consumers/subscribers, queues, and topics for sending and receiving messages. Two popular message brokers are JMS and Apache Kafka. 

JMS and Apache Kafka provide messaging services to organizations to facilitate communication between internal departments, consumers, and third parties. Developers use Apache Kafka and JMS to transmit data between different applications.

Read on to find the key differences between these message brokering systems.

What is Apache Kafka?

Apache Kafka is a distributed streaming system that enables you to pass messages from one point to another. Kafka maintains a stream of records within a cluster of servers, offering a robust logging mechanism for distributed systems. Kafka helps users publish and subscribe to streams of records, process records in real-time, and store streams of records. With Apache Kafka, developers can create streaming data applications and pipelines.

Kafka

Apache Kafka data streaming platform.

What is JMS?

Java Message Service or JMS is an API that facilitates communication between Java-based applications and other software components. JMS comes with pre-defined messaging protocols that support the Java programming language. The messaging standard allows users to create, send, receive and read messages between computers in a network. With JMS, developers can make software applications written in different programming languages talk to each other.

JMS- Java Message Service

JMS message broker.

Here are the key differences between Kafka and JMS.

  1. Kafka vs JMS: Programming style 

    Apache Kafka has a reactive programming style which is the practice of programming with asynchronous data streams. Kafka involves a reactive programming library, data flows, and propagation of change. Reactive programming revolves around data in such a way that when a single data flow changes, the change is propagated to all related data components.

    JMS, on the other hand, has an imperative programming style which is the traditional style of writing a specific code to solve a particular problem. Developers execute a series of instructions sequentially to solve different problems and produce desired results.

  2. Kafka vs JMS: Message type

    JMS is a push-type messaging platform wherein the broker pushes a message to the consumer. In the push approach, a datastore pushes data to the consumer continuously as per the updates. JMS pushes data to different software applications after processing it through the system.

    Kafka is a pull-type messaging platform in which the consumers request the broker for messages. In the pull approach, the consumer makes requests from time to time to get specific data from the data store. Software applications request Kafka for data that the streaming system processes.

  3. Kafka vs JMS: Partitioning

    Kafka collects data in the form of topic logs that offer unique topic-based partitioning. Each log can break into multiple logs on separate nodes in the Kafka cluster. By doing this, the workload of storing, writing, and processing messages is split in Apache Kafka.

    In JMS, users must first compartmentalize the messages for distribution and delivery to the subscribers. The messages wait in the JMS queue before the interface pushes the messages to the consumer.

    Related Post: Kotlin vs. Java: What Should You Choose for Your Next Project?

  4. Kafka vs JMS: Filtering

    JMS uses message selectors that offer message specification features to consumers for filtering. By doing this action, during message broadcast consumers receive only interest-specific messages. Consumers can filter messages using properties and header filters.

    On the other hand, Kafka does not provide basic filtering features due to the partitioning of topics. To filter data in Kafka, users need to go through several topics and choose the relevant data.

  5. Kafka vs JMS: Routing

    Kafka offers dynamic routing with the help of a ‘Topic name extractor’ that allows nodes in the Kafka stream to determine the name of topics for sending records. After extracting data from different streams, event routing takes place in new Kafka topics.

    Routing in JMS is relatively complex as users must choose between two routing types, namely header and message routing. While using header routing, users do not need to parse data, whereas in message routing, using XML is necessary to parse data. Developers must choose the type of routing for different software applications.

Related Post: Python for Data Scraping and Data Wrangling

Differences between Apache Kafka and JMS

5 key differences between JMS and Kafka

Comparison between Kafka and JMS

Summary 

JMS is ideal for communication with systems that utilize multi-node clusters whereas Kafka is more suitable for handling large volumes of data. Software developers must choose between Kafka and JMS as per the project requirement and size. Also, they must check out the unique features of both message brokering systems.

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FAQs

What are the components of JMS?

The components of JMS include server, module, queue, topic, file store, destination key, connection factory, and JDBC store. 

What are the main components of Kafka?

The main components of Kafka are topics, consumer groups, clusters, partitions, replicas, followers, and leaders. 

What is the difference between queue and topic in JMS?

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

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Summary
Kafka vs JMS: 5 Key Differences
Article Name
Kafka vs JMS: 5 Key Differences
Description
JMS can develop messaging applications to enable communication between two or more clients. Kafka is a streaming platform used for real-time data processing …..
Author
Ashwin Dua
Ashwin is a content writer, editor, and wordsmith who has experience in writing website content, blogs, articles, opinion pieces, press releases, and social media content. He has worked with clients like IRCTC, Hero Cycles, Fortis Healthcare, among others.
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