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JMS vs Kafka: What’s the Difference?

by Pranisha Rai
Kafka Vs JMS
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JMS and Kafka are popular messaging brokers that are widely used by enterprises and companies. Although these are both messaging systems, JMS and Kafka function differently. JMS is a Java-based API that enables business applications to create and interchange business data. Kafka, on the other hand, is the ultimate solution for any kind of software that deals with huge amounts of real-time data processing. In this article, we’ll explore and find out the difference between them. 

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Kafka: An Open-Source Data Streaming Platform

Apache Kafka Characteristics

Kafka helps businesses overcome the challenges they face in generating real-time data. By providing real-time pub/sub solutions. It also helps businesses establish a reliable system that can route to multiple servers quickly. To keep up with the modern-day software solution, Kafka uses Yet Another Resource Negotiator (YARN). And it works with several other technologies, like Apache HBase, Apache Sparke, and Apache Storm. As a result, it can smoothly support parallel loading in the Hadoop system. Since Kafka is distributed by nature, it consists of multiple message brokers to balance the load of a topic. These topics are further broken down into multiple partitions that are again stored in each broker. The following are the characteristics of Kafka: 

  • High Throughput: Kafka can handle massive amounts of data quickly in a short duration, which makes it a trustworthy choice for working with big data. 
  • Multiple Clients Support: It can integrate easily with different platforms such as Python, .NET, Ruby, Java, and PHP. 
  • Fault Tolerant: While dealing with extensive data, Kafka ensures that none of the important data gets lost in between. It is designed using the disk structure O1, which can provide exceptional continuous performance.  
  • Real-Time Messaging: Whenever the producer thread generates and sends the message it is soon visible to the consumer thread. This promptness is crucial for Complex Event Processing (CEP) systems to react immediately.  
  • Distributed System: Kafka supports partition messages explicitly across its server and distributes the workloads over the cluster of consumer machines. But at the same time, maintains the order of messages within each section.
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Use Cases of Kafka

Some of the popular companies use Kafka for their respective uses: 

  • Twitter: It relies on Kafka for stream processing infrastructure. 
  • DataSift: It uses Kafka to monitor events and track the user’s consumption of data in real-time. 
  • Square Company: It uses Kafka to transfer various business and financial services across its different data centers. Further, it helps with real-time altering.  
  • LinkedIn: It uses Kafka to operate data on metrics and stream activity. 
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JMS: Java API for Enterprise Messaging

Characteristics of JMS

JMS is an API component of Java Enterprise Edition (EE) that allows applications to exchange information seamlessly. It provides flexibility and eliminates the complexities of communication. JMS provides a perfect platform to transfer critical information without any network congestion. By leveraging other enabling technologies like XML, it can increase the capabilities of JMS with robust performance. The following are some of the characteristics of JMS.

  • Asynchronous and Synchronous Communication: JMS supports both asynchronous and synchronous messaging; providing flexibility to clients. The server in asynchronous messaging acts as the intermediary between the client and another component. Whereas synchronous messaging lets the use of a “reply-to” feature to send the response. But it does not guarantee a prompt response.
  • Supports Two Messaging Models: JMS supports both point-to-point and pub/sub messaging models. Where point-to-point is for sending one client to another client and pub/sub is for multicasting messages from one sender to multiple receivers.
  • Expressive Message: JMS supports various types of messaging including TextMessage, ByteMessage, StreamMessage, and MapMessage. From these, TextMessage and ByteMessage are basic, whereas StreamMessage, and MapMessage are functional message types.
  • Messaging Security: Enterprises need to secure their messages at any cost and delivery of the right message at the exact place is crucial. For this, JMS provides simple security measures. It asks for basic credentials such as a username and password but it hands over the responsibility of message encryption to JMS products providers. 
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Use cases of JMS

  • In e-commerce, JMS is used for seamless operation between order processing, shipment, and inventory. 
  • It allows seamless connection and data exchange between enterprise applications 
  • For the supply chain, it provides effective logistics coordination and on-time delivery.
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JMS vs Kafka: What are the key differences?

Filter MethodUses JMS selectorsUses Kafka Stream
Programming StyleImperative programming that follows both asynchronous and synchronous operationReactive programming that follows asynchronous, data-driven architecture
Format SupportedText and binary format. For custom serialization, additional configuration is required.JSON, Avro, custom serialization, and deserialization
Routing SystemQuite complexSimple and Easy
Topic PartitioningUses queues and topics for content segregationSegregates the content through topics
Messaging ModelSupports both point-to-point and pub/sub-message modelSupports real-time publish-subscribe
StorageMessages are stored in the queue systemMessages are stored for a specified duration
Message LogRelies on the broker’s configuration and follows the first-in and first-out approachHas distributed committed logs
Suitable forTraditional enterprise messagingReal-time data streaming and event-driven architecture
IntegrationIntegrates well with Java framework and technologiesIntegrates well with a broader ecosystem with stream processing tools and connectors
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JMS vs . Kafka: Which One Should I Choose in 2024?

Choosing between these two comes down to the specific use case and architectural needs. You can choose JMS for an enterprise system that needs reliable messaging and seamless integration. It provides moderate support for scaling data, but it requires additional effort for extensive scalability. Besides, JMS’ persistence characteristics make the message durable. All in all, JMS offers a neutral solution for enterprises that need precise control over message ordering without any ties to a specific vendor.

Kafka is the preferred choice for a modern real-time data streaming platform that requires horizontal scaling of large data. Its fault-tolerant nature and high throughput can help organizations and businesses deal with massive data streams efficiently. It provides excellent use cases for data that require data analytics and data auditing. You can go for Kafka if the organization doesn’t need to maintain neutrality.  


JMS is a traditional enterprise messaging solution that works well with Java-based products and services. Whereas Kafka is used for real-time processing well-suited for high throughput and fault-tolerant for data streaming platforms.

Its ecosystem consists of producers, consumers, brokers, partitions, topics, and Zookeeper.

JMS is Java-centric, and due to this reason, integrating with different languages is challenging. It requires all the systems to agree on a common message format, this causes limitations.

It is used to track the website activity, stream processing for multiple stages, and operational monitoring data.

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