Differences Between Kafka and JMS

When asked to explain the function of Java Message Service (JMS), it is important to note that JMS is a Java-based system for communication between two or more clients or applications. One of the key differences between using a topic and using a queue in JMS is that topics deliver a message to multiple subscribers, while queues deliver a message to only one consumer. Additionally, Apache Kafka cannot be classified as a database, as it is a distributed streaming platform. Finally, JMS is included in Java as it is a part of the Java Enterprise Edition (EE) platform.

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In order to enable communication and interaction between various computer programs, services, and infrastructures, message brokers have been developed. Message brokers are composed of producers/publishers, consumers/subscribers, queues, and topics, which form the core components of their data structure. Two of the most widely used message brokers are Java Message Service (JMS) and Apache Kafka.

Java Message Service (JMS) and Apache Kafka are two messaging platforms that enable businesses to effectively communicate between their various departments, as well as with their customers and external stakeholders. JMS and Apache Kafka provide developers with the ability to send and receive data between programs, allowing for efficient and reliable exchange of information.

Continue reading to learn the main distinctions between these two message brokering platforms.

Apache Kafka: what is it?

Apache Kafka is a distributed streaming system that enables the transmission of messages from one location to another. It provides a reliable logging technique for distributed applications by maintaining a continuous stream of records within a cluster of computers. Not only does Kafka facilitate the publishing and subscribing of records, but it also enables the real-time processing and storage of record streams, making it possible for programmers to create pipelines and applications that process data in real time.

JMS: what is it?

The Java Message Service (JMS) is an application programming interface (API) that allows Java programs to conveniently share data with one another and with external programs, with the help of pre-defined messaging protocols. By utilising this messaging standard, users are able to compose, transmit, receive, and read messages across computers connected to a network. Furthermore, JMS provides a way for programmers to bridge the communication gap between programs that have been built with different programming languages.

The main differences between Kafka and JMS are outlined here.

  1. Programming Methods for Kafka and JMS

    Apache Kafka‘s reactive programming paradigm is based on an asynchronous data stream programming technique. It utilises a library that facilitates the flow of data and the propagation of modifications; when one data flow is modified, reactive programming is used to ensure that the changes are automatically applied to the other data components of the system.

    Despite the fact that other programming styles exist, JMS has opted to use an imperative programming style, which is the conventional approach to designing code for solving problems. This method requires developers to follow a set of predetermined instructions in order to achieve a desired outcome. This is beneficial as it ensures that issues are addressed in a consistent, predictable manner.
  2. Message types in Kafka and JMS

    On the Java Messaging Service (JMS) platform, a broker facilitates the transfer of messages from a sender to a receiver. This is achieved through the ‘push’ method, which relies on a datastore constantly pushing data to the user according to any changes that have occurred. Once the information has been processed by the system, JMS then ‘pushes’ it to other programs that may require it.

    Kafka is a pull-style messaging network, whereby consumers make requests to brokers for messages. In this type of system, the consumer periodically sends requests to the data storage in order to acquire the desired information. Additionally, Kafka is a streaming system that receives data requests from applications.
  3. Dissecting Kafka vs. JMS

    Apache Kafka makes use of topic logs, which offer a unique segmentation based on topics, to collect data. In a Kafka cluster, a single log can be divided into multiple logs across various nodes. The task of handling messages in Apache Kafka is divided into three components: receiving, storing and processing.

    Before distributing and delivering content to subscribers, users of the Java Messaging Service (JMS) must first group the messages into distinct categories. The user interface will remain idle until the messages have built up in the JMS queue, at which point they will be pushed to the consumer.
  4. Distinguishing Between Kafka and JMS for Filtering

    JMS (Java Messaging Service) provides users with message selectors that allow them to specify the messages they wish to receive. This enables recipients to apply both property and header philtres to the messages they receive during the broadcast phase, ensuring that only messages that are relevant to their interests are sent. This feature allows users to customise their messaging experience, allowing them to receive messages that are tailored to their individual needs.

    Despite the fact that topics are divided, Kafka does not possess even the most basic filtering capabilities. In order to philtre data in Kafka, users must go through multiple topics in order to identify the data they are looking for.
  5. Comparing Kafka vs JMS for Message Routing

    Nodes within the Kafka stream are able to dynamically determine the name of topics used for transmitting records, with the help of a ‘Topic name extractor.’ Once the data has been harvested from various sources, it can be directed as events through newly created Kafka topics.

    The implementation of the JMS routing process can be a complex endeavour as users have the option of selecting either header routing or message routing. While message routing requires users to work with XML, header routing eliminates the need for data parsing. Consequently, when developing software, developers must carefully consider which routing option to utilise.

Summary

It is important for developers to consider the advantages and disadvantages of both Kafka and Java Message Service (JMS) before deciding which messaging framework to use. While Kafka is more suitable for dealing with large volumes of data, JMS is more appropriate for working with multi-node cluster systems. Each framework offers specific advantages and drawbacks, so it is important to analyse their unique qualities before making a decision.

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FAQs

  1. How does JMS work, and what does it consist of?

    Server, modules, queues, topics, file stores, destination keys, connection factories, and JDBC stores are all parts of JMS.
  2. What do you think Kafka’s core ideas are?

    Kafka is composed of several distinct components, the most essential of which are topics, consumer groups, clusters, partitions, replicas, followers, and leaders. These components are integral to the fundamental operations of Kafka, and understanding their respective roles is paramount to effective utilisation of the platform.
  3. Can you explain the difference between a JMS queue and a topic?

    There may be more than one subscriber to a topic, but only one to a queue.

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