Message queues form the fundamental backbone of any distributed system. There are multiple applications that are associated with each other to offer their own set of responsibilities. These applications depend upon transferring data for better communication with other apps and that is where the significance of a message queue comes up.
A message queue software facilitates remote interactions of machines and applications through asynchronous communication. The queue is maintained by them, having the alerts, requests, and answers based on what data has been sent. The best part is that because of the queue, the working of systems keeps going strong despite the system taking time to understand the message. These message brokers also store, transfer, and delete messages from the queue.
ActiveMQ and Kafka are two popular and open-source messaging brokers. Both focus on managing and monitoring the streaming data like app logs and metrics but they have their own ways and means of doing so. There is a heavy comparison about the difference between Kafka and ActiveMQ, often.
Before we seek to compare Kafka vs ActiveMQ, let us first go through the characteristics of both individually.
What Is ActiveMQ?
Apache ActiveMQ is an open-source message broker written in Java together with a full Java Message Service (JMS) client. – Wikipedia
Powered by Apache, ActiveMQ is a powerful, open-source message broker that offers great benefits across a variety of platforms and languages. It is Java-based and provides multiple protocols with integration with AMQP. It is easy to exchange messages between web applications and manage IoT devices. It provides the flexibility and power to support any type of messaging use case.
There are many advanced features like message load balancing and high accessibility to data that make ActiveMQ so well-known. It has simple enterprise integration patterns that integrate well with each other through asynchronous messaging. ActiveMQ leverages them and deploys them to the broker directly. As a standalone process, it offers a good amount of flexibility for allocating resources and hence is a powerful messaging system with smooth message exchange with other apps.
- AMQ message store
- Java-based message broker
- Wide range of platforms and languages
- Message transformation and mirrored queues
- Warning if the user forgets to start a connection
- Implementation of message-oriented middleware
- Scalable, easy to use with broker clustering
- Support for many protocols like STOMP, UDP, etc.
- Provides client APIs for various languages
Organizations Using ActiveMQ:
- Taulia Inc.
- Cox Communications
- All Web Leads
What Is Kafka?
Apache Kafka is a distributed event store and stream-processing platform. It is an open-source system developed by the Apache Software Foundation written in Java and Scala. – Wikipedia
Developed at LinkedIn by the founders of Confluent, Kafka is a community distributed, technology that can manage multiple events in a single day. Confluent offers an effective platform for data created around Kafka that is comprehensive, accessible anytime, and cloud-native. It is a messaging queue and has come up to be a complete event streaming platform.
It is ideally used to create real-time streaming data pipelines and applications that combine stream processing, messaging, and storage of data. It is simple to operate, easy to execute, and hence is popular amidst an increasing number of users. It is publish-subscribe based messaging system that has a large network of users who contribute newer features and support for novices.
- Reliable, durable with effective performance
- Fault tolerance with high volume data
- Great performance parameters
- Zero downtime
- Scalable with event producers, processors, consumers, and connectors
- Easily derives new data streams
- High throughput
- Extensible and can replicate events
Companies That Use Kafka:
- Goldman Sachs
ActiveMQ vs Kafka: A Step By Step Evaluation
|Overview||A distributed system for processing messaging systems, stream processing, website activities, metrics, log aggregation, event sourcing, etc.||A traditional messaging system that handles small volumes of data with high performance, web streaming, clustering, market data distribution|
|Key Characteristics||Processing and reprocessing of streamed data, mainly used for real-time data streaming||Processing of complicated message routing and enterprise patterns, based on JMS specs|
|Transaction Support||Since the release of the 0.11 version, there is the support for transactions||It has transaction support. There are JMS and XA transactions.|
|Throughput||Higher throughput since brokers can write messages at a very high rate||Lower throughput since each delivery message state is maintained|
|Responsible Stakeholders||Responsibility of consumers to consume messages||Responsibility of producers to ensure delivery of messages|
|Order of Messages||Can assure that the messages retain the order in which they are sent||Cannot assure the order of messages to remain the same|
|Filtering||No fundamental filters at the brokers. Hence it must be done by consumers or apps||Work of filtering is done by the JMS API message selector and not by the applications|
|Messaging Type||Pull type messaging platform wherein consumers pull the message from the brokers||Push type messaging platform wherein the providers push the messages to consumers|
|Scalability||Scalable and available because of replication of partitions||No chances of horizontal scalability and replication|
|Performance||Performance remains effective and good even if there are newer consumers being added||Performance slows down as the number of consumers starts increasing|
|Support for Languages||Go, Ruby, PHP, Haskell, Python, Node.js, etc.||Java, C, C++, Python, Ruby, Perl, PHP, etc.|
|Support for synchronous or asynchronous||Supports asynchronous||Supports both|
|Checksums||Inclusion of checksums for detection of corrupt messages, with security measures||No provision of checksums for finding out corrupt messages from the box|
|Compatibility||Kafka has good support for many messaging protocols||ActiveMQ does not have good support for other protocols|
|Source of Development||Originally developed by LinkedIn, as an open-source system, later developed by the Apache Software Foundation as a stream processing platform||Created by founders from LogicBlaze, as an open-source message broker, later donated to Apache Software Foundation, where founders continue to develop the code base|
|Messaging Protocols||Helps in finding messages depending upon their offset||Helps in other messaging protocols but not all|
|Protocols||Binary protocol over TCP||REST, AUTO, OpenWire, etc.|
|Infrastructure||Offers distributed horizontal streaming competencies and hence a distributed platform||Offers integration between services in a Service-oriented architecture|
|Recommendation||Use Kafka for high-performance monitoring and where losing messages is not important||Use ActiveMQ for exactly once delivery and for valuing messages|
|Security||Secures communication with most technologies||Use of JAAS for authentication & encryption of communication|
|Service Competence||Offers point to point or point to many services||Offers request-response and publish-subscribe methods|
|Data Storage||Kafka is not just transport but streams data storage data with the limited addressing capacity||ActiveMQ is not originally designed as a long-term data storage|
|Message Storage||Kafka messages are always forever saved, the developers have no other option||ActiveMQ messages are not stored physically, they work with memory|
Kafka vs ActiveMQ: The Wrap-up
It is interesting to note the difference between Kafka and ActiveMQ, though there are certain similarities too. Any organization can go for Kafka if there is a large amount of data to be processed in real-time.
Any organization can go for ActiveMQ if there is a one-time delivery concept, and the messages are of great value.
Finally, it depends upon the organization, its resources, budgeted cost, project deadlines, skilled expertise that decide the final tool to be used. Whatsoever the users choose, it’s a win-win situation!