JMS vs Kafka

Message brokers are software intermediaries that allow the communication of different apps by transmuting messages into formal messaging protocols. They are crucial for data transmission and ensure that data is efficiently transferred from the sender to the recipient without being directly linked. This allows different systems to connect seamlessly regardless of the architecture they are built on or the technology utilized.

JMS and Kafka are popular message brokers that transmit information between various applications. JMS Java Message, or Java Message Services, communicates between Java-based software and additional software elements. Apache Kafka is an open-source streaming platform distributed to multiple locations that can be used to build real-time pipelines for data and streaming apps.

This blog will outline the differences and similarities between JMS and Apache Kafka to assist with choosing an ideal option. Please continue reading!

What Is a Message Broker?

Message brokers are systems of software or elements that allow the exchange of messages among various components or applications within an integrated system. They function as intermediaries to ensure messages are sent effectively and safely from the sender to the receiver. Message brokers enable synchronous communications that separate receiver and sender systems. Also, ensure that messages can be processed in a flexible and fault-tolerant way.

What Is Kafka?

Apache Kafka is an open-source streaming software system designed by the Apache Software Foundation and written in Scala and Java. It’s intended to be a unidirectional, high-throughput, and low-latency system for managing stream data in real-time. In essence, Kafka functions as a durable distributed publishing-subscribe messaging system with reliability and fault tolerance characteristics. The system allows applications to analyze and process data in real-time. It can be used extensively to create real-time streaming pipelines for data and applications that adapt to the changing data stream.

Importance Of Kafka

Kafka is a critical component in the current data architecture, providing real-time data processing and analysis. The world of data is being generated at unimaginable levels–from social media feeds, IoT devices, system logs, and many more. Kafka assists organizations in making sense of the data they collect through real-time data gathering, integration, and analysis. It is a solid framework to store, read, and analyze data streaming. 

Kafka’s design principles ensure extremely high-throughput, flexible data streaming features. This makes it essential for companies that need a stable platform to handle vast amounts of data in real-time efficiently. Applications vary from real-time analysis and monitoring to data integration and event gathering.

Features Of Kafka

Let’s take a look at Kafka’s main characteristics. Kafka:

High Throughput

Kafka handles billions of events daily, allowing businesses to manage large volumes of data in real-time.


Expanding horizontally to accommodate more significant data without downtime is also possible.

Durability And Reliability

Kafka archives data in streams securely and in a distributed, resilient, and reliable manner.

Low Latency

This system aims to ensure that messages sent by producers to their target consumer audience arrive with minimal latency.


Software development solutions support many uses, such as stream processing, log aggregation, and event gathering.

What Is JMS?

Java Message Service, or JMS, is an API that allows communication between Java-based programs and various software components. JMS has pre-defined messaging protocols that support Java as a programming language. It permits users to write, send, and read messages, as well as read them between computers on the network. Through JMS, developers can make software written in various programming languages communicate with each other.

Importance Of JMS

The value of JMS is its capability to break down the components of a distributed system, which provides greater flexibility and reliability in applications. JMS can be used with point-to-point queuing and publish/subscribe models, allowing an efficient communications system for various applications. This standard for messaging is vital for companies that need constant and reliable information exchange across different infrastructure Applications and Software Components without the need for a direct link between these parts. 

Additionally, JMS provides a standardized method of implementing messaging, making it easier to develop and manage Java applications that communicate asynchronously.

Features Of JMS

Let’s take a closer look at the main characteristics of JMS:

Asynchronous Communication

It lets receivers and senders work independently, enhancing application components’ efficiency and speed of response.


It provides options like long-term subscriptions and message acknowledgment to ensure messages are not lost during transmission.


It helps grow applications by allowing parts to communicate efficiently regardless of location or the number of messages processed.


The platform supports point-to-point and publish/subscribe messages, which can be tailored to various application requirements.

Similarities In Kafka & JMS

The design and structure of JMS Kafka Message platforms have distinct features but have some commonalities. We’ll look at the following:

Messaging Middleware

Kafka and JMS are messaging middleware that facilitates the communication of different components or systems in a distributed system. They allow you to exchange, send, and manage messages asynchronously.

Message Brokers

Kafka and JMS each have usage message brokers. The broker for Kafka is Apache Kafka, and for JMS, there are diverse JMS providers such as Apache ActiveMQ, RabbitMQ, or IBM MQ. The brokers are responsible for overseeing the routing and distribution of messages.

Messaging Patterns

Kafka and JMS support common messaging patterns like publish-subscribe or point-to-point messages. However, while Kafka concentrates on the publish-subscribe pattern, JMS includes support for both types of patterns, making it a versatile tool for various use scenarios.

Message Durability

Both Kafka and JMS can be configured to offer message endurance. Kafka keeps messages for an adjustable period, ensuring data is available after consumption. JMS has the option to store messages to protect data from loss.


Kafka and JMS are compatible with different frameworks and programming languages, making them suitable for many applications. Kafka clients can be found in several languages, and JMS has a standard API for Java applications.


Both Kafka and JMS are scalable to manage increased message volumes. Kafka can be scalable via horizontal scaling, achieved by adding broker nodes, and JMS implementations can provide scalability options based on the service provider.


Both Kafka and JMS provide acknowledgment methods. Once messages are received and processed, producers can receive acknowledgments, which ensures efficient and reliable communication.

Message Transformation

Both Kafka and JMS can convert messages in different formats. Kafka supports various serialization formats, and JMS might offer features for message conversion.

Difference Between JMS And Kafka

We will examine the main differences between Apache Kafka JMS. Also, discover which brokers would be the best option for your business needs.

The Style Of Programming

JMS follows an imperative style of programming. Programmers write specific codes to address particular problems and execute instructions sequentially. JMS processes are often synchronized, and the sender waits for an acknowledgment of message acceptance and processing. This is an excellent choice for applications where exact operation timing control is essential.

Kafka uses a reactive programming method, focusing on synchronous data streams and event-driven processing. Developers process data when it moves across the system. Events trigger actions within the app. Kafka uses reactive programming frameworks and libraries to handle events effectively. This type of programming is suitable for events-driven and real-time data processing designs.

Segregating The Content

JMS divides content into queues and topics. Queues are usually used to facilitate point-to-point messages, which ensures the messages go to just one user. Topics are utilized for publish-subscribe messaging, which allows several subscribers to receive the same email. Kafka segregates content using topics. Topics permit sorting messages into various streams, allowing for effective routing and processing of related data. Both consumers and producers sign up for specific issues of particular importance, facilitating a publish-subscribe method.

Type Of Message Programming

JMS typically handles messages in binary or text format. While custom serialization is available, it will require more setup and implementation efforts than Kafka. Kafka can handle messages in formats like Avro, JSON, or custom serialization and de-serialization. This flexibility allows users to handle information in the best format for their requirements, making it a versatile tool for various scenarios.

Filter Method

JMS offers message selectors to assist in filtering messages. However, the efficiency of the filtering process can vary based on the JMS service provider. JMS selectors work best for simple filtering purposes. Kafka has a robust filtering capability by using Kafka Streams and subscriptions for consumer groups. Kafka Streams provides a powerful stream processing API that allows data transformation to be transformed and filtered. Consumer groups enable multiple customers to sign up for specific topics, and each receives copies of the information that allows for parallel processing and filtering.

Routing System

JMS provides point-to-point and publish-subscribe route routing methods. Queues are utilized for point-to-point communication. They ensure the message gets sent to only one person. Topics can be used to publish-subscribe messages, meaning multiple users get the same information.

Kafka uses a publish-subscribe model based on topic-based routing. Publishers send messages to subjects, and consumers sign up for particular subjects relevant to them. This makes message distribution easier in distributed systems.


JMS typically doesn’t store messages after delivery. The persistence of messages is dependent on the broker’s JMS setting. Sometimes, a further configuration may be needed to ensure message longevity.

Kafka offers durable storage of messages with various retention times that can be set. The messages are kept for a specific time, allowing users to replay old data. This is an excellent feature in applications that need data, analytics, or replayability auditing.


JMS excels in queuing scenarios. It provides point-to-point communication with the assurance of message delivery. Queues guarantee that every message is digested by just one individual, which makes JMS ideal for situations where the strictest requirements for message order and processing are required. Although Kafka may mimic queueing behavior through single-consumer consumer groups, it was intended for use with publish-subscribe patterns. Queuing is possible using a single person per group.

Partitioning Of Topics

JMS topics don’t natively allow partitioning. Scalability in JMS is generally achieved by using multiple instances of a topic, each handling specific messages. Kafka permits topics to be divided, allowing for parallelization and message processing scalability. Each partition is managed through a different consumer, ensuring an efficient work division.

Message Logs

The durability of messages in JMS depends on the broker’s configuration. While JMS brokers provide message persistence, the degree of persistence will differ among JMS suppliers. Kafka is a distributed commit log that makes messages permanent by default. Kafka provides durable guarantees and ensures that messages will not be lost during a broker’s failure.

Scalability And Availability

The availability and scale of JMS implementations are subject to change. Configuring failover and redundancy mechanisms is often necessary to achieve high availability. Scalability can also vary depending on the particular JMS provider and the deployment model. Kafka is specifically designed to support vertical scalability. It allows the addition of additional brokers to manage the increased demand. The platform’s design makes Kafka very scalable and readily available. Kafka’s distributed structure ensures reliability and fault tolerance.

JMS Or Kafka: Which Is The Best Option For You?

The decision between JMS (Java Messenger Service) and Kafka depends on several variables, including elements specific to every use instance of the business. Both JMS as well as Kafka possess strengths as well as strengths, and the choice must be made by weighing the following factors:

Messaging Style And Use Case

Select JMS if the company mainly deals with traditional corporate communication scenarios, demands precise point-to-point messaging, or requires a standard API to run Java Based Applications Software. JMS is ideally suited to scenarios that require precise control over the messages’ ordering and processing, which is essential.

Select Kafka if your business is focused on live data streaming in real-time, event-driven architectures, and log aggregation. It also requires the ability to handle vast quantities of data. Kafka is ideal for situations where you need to manage the data in real-time as it moves throughout the system and requires the ability to scale horizontally.

Scalability And Volume

Select JMS if the company’s messages are moderately large and don’t need extensive scaling. JMS can be expanded; however, it will require more work and customized settings to ensure high capacity.

Choose Kafka if your business is dealing with large volumes of data, requires horizontal scaling, and needs a system capable of effectively handling large data streams. The Kafka architecture was designed to provide the ability to scale and have high throughput.

Message Persistence And Retention

Select JMS if message durability and persistence are significant issues for your business and if it is dependent on JMS services for messaging storage. JMS brokers usually offer customizable messaging persistence options.

Select Kafka if you require both endurance and the capability to store messages for a long time. Kafka keeps messages for an adjustable period, which makes it ideal for situations that require data auditing, analytics, or even data replay.

Programming Paradigm

You should choose JMS if your team’s development is more experienced using an imperative programming style and you’re comfortable writing code sequentially to perform messaging functions.

Pick Kafka if your Software Development Services Company is accustomed to the reactive programming approach and wants to use the power of event-driven processing, synchronous data streams, and other dynamic libraries.

Ecosystem And Integration

Select JMS if you require an integrated messaging system that works effortlessly with Java-based technologies and frameworks. JMS has a long track record of interfacing with its Java ecosystem.

Go for Kafka if you need an expanded ecosystem of connections, stream processing tools such as Kafka Streams, and comprehensive monitoring tools. Kafka provides a wide array of libraries and tools for different Integration scenarios.

Vendor Neutrality

Choose Kafka if your business is looking for an open-source, vendor-neutral solution not tied to any specific vendor. Choose JMS if you’re familiar with vendor-specific JMS implementations and do not require vendor neutrality.


JMS and Kafka are two prevalent alternatives to communication. Although JMS has been used longer, it remains a trendy option in specific scenarios. Before you decide which is the best, it is recommended that you conduct your research and look into the company’s needs and capabilities. Find out what you require from a messaging system before deciding if JSM or Kafka will better meet your needs. Please do not focus on their popularity in the market. Instead, concentrate on the ways they will solve your issues.

Choosing between JMS and Kafka depends on your requirements, Web Application Development Services, and objectives. If you’re searching for a messaging platform that resembles a well-organized recipe, ensuring messages are sent precisely and with control, it’s time to consider JMS. It’s like cooking using an extensive step-by-step recipe and ensuring everything takes place within a specific timeframe.

So, whether you choose to use the precise and meticulously orchestrated JMS or Kafka’s speedy and data-driven nature, both are trusted messengers that facilitate smooth communication between applications. Your choice is based on the kind of conversation you would like your apps to be able to carry out: structured and precise or fluid and highly data-intensive.

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