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Last updated on February 20th, 2024 at 02:25 pm
Software Comparisons
JMS VS KAFKA: WHICH MESSAGE BROKER SHOULD YOU USE?
By Ritvik Gupta ● September 20, 2023 ● 8 min read
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JMS vs Kafka: which message broker is the best? JMS and Kafka are widely used
message brokers to transmit data between different applications. JMS or Java
Message Services is used to communicate between Java-based applications and
other software components. Apache Kafka is an open-source distributed event
streaming platform used for building real-time data pipelines and streaming
applications.
This blog post will take you through the similarities and differences between
JMS and Apache Kakfa to help you pick the best one. Keep reading.
Table of Contents
Toggle
* What are message brokers?
* What is Apache Kafka?
* What is JMS?
* Similarities between Kafka and JMS
* JMS vs Kafka: Differences
* Apache Kafka vs JMS: Style of programming
* JMS vs Kafka: Segregating the Content
* Type of Message Programming
* JMS vs Kafka: Filter Method
* Routing System
* JMS vs Kafka: Storage
* Apache Kafka vs JMS: Queuing
* Partitioning of Topics
* Message Logs
* Apache Kafka vs JMS: Scalability and Availability
* JMS vs Kafka: What should you choose?
* Messaging Style and Use Case
* Scalability and Volume
* Message Persistence and Retention
* Programming Paradigm
* Ecosystem and Integration
* Vendor Neutrality
* JMS vs Kafka: Conclusion
* FAQs
* * What is the difference between JMS and Kafka?
* What is the purpose of JMS?
* What is the replacement of JMS?
* What is Kafka used for?
* What is JMS used for?
* Which is better JMS or Kafka?
WHAT ARE MESSAGE BROKERS?
Message brokers are software systems or components that facilitate the exchange
of messages between different applications or components within a distributed
system. They serve as intermediaries, ensuring that messages are delivered from
senders to receivers efficiently and reliably. Message brokers play a crucial
role in enabling asynchronous communication, decoupling sender and receiver
systems, and ensuring that messages are processed in a scalable and
fault-tolerant manner.
WHAT IS APACHE KAFKA?
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.
WHAT IS JMS?
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.
SIMILARITIES BETWEEN KAFKA AND JMS
While the architecture and design of these popular message brokers are
different, there are a few similarities between the two. Let’s take a look:
1. Messaging Middleware: Both Kafka and JMS are messaging middleware solutions
used to facilitate communication between different components or systems in
a distributed architecture. They provide a way to send, receive, and process
messages asynchronously.
2. Message Brokers: Kafka and JMS both involve the use of message brokers. In
the case of Kafka, it’s Apache Kafka, and for JMS, it can be various JMS
providers like Apache ActiveMQ, RabbitMQ, or IBM MQ. These brokers are
responsible for managing the routing and delivery of messages.
3. Messaging Patterns: Both Kafka and JMS support common messaging patterns,
such as publish-subscribe and point-to-point messaging. While Kafka
primarily focuses on publish-subscribe, JMS provides support for both
patterns, making it versatile for various use cases.
4. Message Durability: Both Kafka and JMS can be configured to provide message
durability. Kafka stores messages for a configurable retention period,
ensuring data availability even after consumption. JMS offers message
persistence options to prevent data loss.
5. Integration: Kafka and JMS can be integrated with various programming
languages and frameworks, making them suitable for a wide range of
applications. Kafka clients are available in multiple languages, and JMS
provides a standardized API for Java applications.
6. Scaling: Both Kafka and JMS can be scaled to handle increased message
volumes. Kafka achieves scalability through horizontal scaling by adding
more broker nodes, while JMS implementations may offer scalability options
depending on the provider.
7. Acknowledgment: Both Kafka and JMS allow for acknowledgment mechanisms.
Producers can receive acknowledgments when messages are successfully
delivered and processed, ensuring reliable communication.
8. Message Transformation: Both Kafka and JMS offer ways to transform message
formats. Kafka supports various serialization formats, and JMS may provide
message conversion capabilities.
Now that we know what’s similar, let’s head to the differences between JMS vs
Kafka and see which one’s better for your needs.
JMS VS KAFKA: DIFFERENCES
Major Differences between JMS vs Kafka
Let’s see the major difference between JMS and Kafka and get to know which of
the two message brokers will be a better choice for your business requirements.
1. APACHE KAFKA VS JMS: STYLE OF PROGRAMMING
JMS adheres to an imperative programming style. Developers write specific
code to solve particular problems, executing a series of instructions
sequentially. JMS operations often occur synchronously, with the sender
waiting for acknowledgment of message receipt and processing. This style is
well-suited for applications where precise control over the order of
operations is critical.
Kafka follows a reactive programming style, which revolves around
asynchronous data streams and event-driven processing. Developers work with
data as it flows through the system, and events trigger actions across the
application. Kafka leverages reactive programming libraries and frameworks
to handle events efficiently. This style suits real-time data processing
and event-driven architectures.
2. JMS VS KAFKA: SEGREGATING THE CONTENT
JMS segregates content using queues and topics. Queues are typically used
for point-to-point messaging, ensuring that messages are delivered to one
consumer. Topics are used for publish-subscribe messaging, allowing
multiple subscribers to receive the same message.
Kafka segregates content using topics. Topics allow for the categorization
of messages into different streams, enabling efficient routing and
processing of related data. Producers and consumers subscribe to specific
topics of interest, facilitating a publish-subscribe model.
3. TYPE OF MESSAGE PROGRAMMING
JMS traditionally deals with messages in text or binary format. While
custom serialization is possible, it may require additional configuration
and implementation effort compared to Kafka.
Kafka supports messages in various formats, such as Avro, JSON, or custom
serialization and deserialization. This flexibility enables developers to
work with data in the format that best suits their needs, making it
versatile for different use cases.
4. JMS VS KAFKA: FILTER METHOD
JMS provides message selectors for filtering messages. However, the
effectiveness of filtering may vary depending on the JMS provider. JMS
selectors are more suitable for simple filtering criteria.
Kafka offers robust filtering capabilities through Kafka Streams or
consumer group subscriptions. Kafka Streams provides a powerful stream
processing API for data transformation and filtering. Consumer groups allow
multiple consumers to subscribe to topics, each receiving a copy of the
data, enabling parallel processing and filtering.
5. ROUTING SYSTEM
JMS offers both point-to-point and publish-subscribe routing mechanisms.
Queues are used for point-to-point communication, ensuring that a message
is delivered to only one consumer. Topics are used for publish-subscribe
communication, where multiple subscribers can receive the same message.
Kafka employs a publish-subscribe model with topic-based routing. Producers
publish messages to topics, and consumers subscribe to specific topics of
interest. This approach simplifies message distribution in a distributed
system.
6. JMS VS KAFKA: STORAGE
JMS typically does not retain messages beyond delivery. Message persistence
depends on the specific JMS broker configuration. In some cases, additional
setup may be required to achieve message durability.
Kafka provides durable message storage with configurable retention periods.
Messages are stored for a specified duration, allowing consumers to replay
historical data. This feature is valuable for applications that require
data auditing, analytics, or replayability.
7. APACHE KAFKA VS JMS: QUEUING
JMS excels in queuing scenarios. It offers point-to-point messaging with
guaranteed message delivery. Queues ensure that each message is consumed by
only one recipient, making JMS suitable for scenarios where strict message
ordering and processing are essential.
While Kafka can mimic queuing behavior using single-consumer consumer
groups, it is primarily designed for publish-subscribe patterns. Queuing
behavior can be achieved with a single consumer per partition.
8. PARTITIONING OF TOPICS
JMS topics do not natively support partitioning. Scalability in JMS is
typically achieved by deploying multiple instances of a topic, and each
instance is responsible for handling a subset of messages.
Kafka allows topics to be partitioned, enabling parallelism and scalability
in message processing. Each partition can be handled by a separate
consumer, ensuring efficient distribution of work.
9. MESSAGE LOGS
Message durability in JMS depends on the broker’s configuration. While JMS
brokers offer message persistence, the level of persistence may vary
between different JMS providers.
Kafka acts as a distributed commit log, making all messages persistent by
default. It provides strong durability guarantees, ensuring that messages
are not lost even in the case of broker failures.
10. APACHE KAFKA VS JMS: SCALABILITY AND AVAILABILITY
The scalability and availability of JMS implementations can vary. Achieving
high availability often requires configuring redundancy and failover
mechanisms. Scalability may also vary depending on the specific JMS
provider and deployment architecture.
Kafka is designed for horizontal scalability, allowing you to add more
brokers to handle increased load. This design makes Kafka highly scalable
and available. Kafka’s distributed architecture ensures fault tolerance and
high availability.
JMS VS KAFKA: WHAT SHOULD YOU CHOOSE?
The choice between JMS (Java Message Service) and Kafka (Apache Kafka) depends
on various factors, including the specific requirements and use cases of the
company. Both JMS and Kafka have their strengths and weaknesses, so the decision
should be made based on the following considerations:
1. MESSAGING STYLE AND USE CASE
* Choose JMS if: Your company primarily deals with traditional enterprise
messaging scenarios, requires strict point-to-point messaging, or needs a
standardized API for Java-based applications. JMS is well-suited for
scenarios where precise control over message ordering and processing is
critical.
* Choose Kafka if: Your company focuses on real-time data streaming,
event-driven architectures, log aggregation, or needs to handle large
volumes of data efficiently. Kafka excels in scenarios where you want to
process data as it flows through the system and requires horizontal
scalability.
2. SCALABILITY AND VOLUME
* Choose JMS if: Your company’s messaging volume is moderate, and you don’t
require extensive scalability. JMS can be scaled, but it may require more
effort and custom configurations to achieve high scalability.
* Choose Kafka if: Your company deals with high message volumes, requires
horizontal scalability, and needs a system capable of handling massive
data streams efficiently. Kafka’s architecture is designed for scalability
and high throughput.
3. MESSAGE PERSISTENCE AND RETENTION
* Choose JMS if: Message durability and persistence are a primary concern,
and your company relies on the JMS provider’s features for message
storage. JMS brokers often provide configurable message persistence
options.
* Choose Kafka if: You need both durability and the ability to retain
messages for extended periods. Kafka retains messages for a configurable
retention period, making it suitable for use cases that require data
auditing, analytics, or data replay.
4. PROGRAMMING PARADIGM
* Choose JMS if: Your development team is more familiar with an imperative
programming style and you are comfortable with writing sequential code for
messaging operations.
* Choose Kafka if: Your development team is comfortable with a reactive
programming style and is looking to leverage event-driven processing,
asynchronous data streams, and reactive libraries.
5. ECOSYSTEM AND INTEGRATION
* Choose JMS if: You require a messaging solution that integrates seamlessly
with Java-based technologies and frameworks. JMS has a long history of
integration with the Java ecosystem.
* Choose Kafka if: You need a broader ecosystem with connectors, stream
processing tools like Kafka Streams, and extensive monitoring solutions.
Kafka offers a rich set of tools and libraries for various integration
scenarios.
6. VENDOR NEUTRALITY
* Choose Kafka if: Your company prefers an open-source, vendor-neutral
solution that is not tied to a specific vendor.
* Choose JMS if: You are comfortable with vendor-specific JMS
implementations and don’t require vendor neutrality.
JMS VS KAFKA: CONCLUSION
In the end, the decision between JMS and Kafka boils down to your specific needs
and goals. If you’re looking for a messaging system that’s like following a
well-structured recipe, ensuring messages are delivered with precision and
control, then JMS is your choice. It’s like cooking with a detailed cookbook,
step by step, ensuring everything happens in a particular order.
On the other hand, if your applications thrive on real-time data streams,
massive data volumes, and a dynamic, event-driven environment, then Kafka steps
in. Think of Kafka as the express highway for data, where information flows
rapidly and asynchronously, connecting everything seamlessly. Moreover, it’s
open-source and integrates effortlessly with various technologies, making it
incredibly versatile.
So, whether you opt for the meticulous orchestration of JMS or the high-speed,
data-centric nature of Kafka, both serve as reliable messengers, facilitating
seamless communication between your applications. Your choice ultimately depends
on the conversation you want your applications to have – structured and precise
or dynamic and data-intensive.
If you’re a skilled developer looking for remote JMS jobs or Apache Kakfa jobs,
try Turing today. Work with some of the best US companies, earn salaries higher
than the market average, and enjoy a fantastic work-life balance.
--------------------------------------------------------------------------------
FAQS
1. WHAT IS THE DIFFERENCE BETWEEN JMS AND KAFKA?
JMS (Java Message Service) is an API for messaging in Java applications,
while Kafka is an open-source distributed event streaming platform. JMS is
suited for traditional messaging, while Kafka excels in real-time data
streaming and handling large data volumes.
2. WHAT IS THE PURPOSE OF JMS?
JMS facilitates communication between Java-based applications and other
software components, providing standardized messaging protocols.
3. WHAT IS THE REPLACEMENT OF JMS?
JMS doesn’t have a direct replacement, but Kafka and other messaging systems
can offer alternative solutions based on specific use cases.
4. WHAT IS KAFKA USED FOR?
Kafka is used for building real-time data pipelines, streaming applications,
and log aggregation. It enables publishing, subscribing, and processing data
streams.
5. WHAT IS JMS USED FOR?
JMS is used for point-to-point and publish-subscribe messaging between
Java-based applications, ensuring reliable and ordered message delivery.
6. WHICH IS BETTER JMS OR KAFKA?
The choice between JMS and Kafka depends on specific use cases. JMS suits
traditional messaging, while Kafka excels in real-time data streaming,
making one better than the other based on project requirements.
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Summary
Article Name
JMS vs Kafka: Which Message Broker Should You Use?
Description
JMS vs Kafka: JMS is used to communicate between Java-based applications and
other software components. Apache Kafka is an open-source distributed event..
Author
Ritvik Gupta
AUTHOR
* Ritvik Gupta
Ritvik is a copywriter and content writer who has worked with fast-scaling
startups such as GoMechanic and Pitstop. He runs his own automotive blog as
well.
View all posts
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