December 28, 2022

Apache Kafka vs. ActiveMQ: Key Benefits and Use Cases

Apache Kafka
Web Infrastructure

Comparing Apache Kafka vs. ActiveMQ is a valuable exercise for teams who want to deploy an open source messaging system. In this blog, we give an overview of these two open source technologies, including a look at the similarities and differences between enterprise Kafka and ActiveMQ use cases, and other factors teams will need to consider before they deploy.

Table of Contents

  1. Apache Kafka vs. ActiveMQ Overview
  2. Kafka vs. ActiveMQ: Key Differences 
  3. Kafka vs. ActiveMQ Use Cases
  4. Final Thoughts
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Apache Kafka vs. ActiveMQ Overview

If you are looking to move to a microservice architecture, or are looking to improve your existing implementation, finding the right message queue software for your use case is critical. It is the backbone of a distributed system, helping to transport and translate data between applications and services.

Kafka and ActiveMQ are two of the most popular open source messaging systems. They can help you manage and monitor data. While teams use Apache Kafka and ActiveMQ interchangeably, they are actually complementary technologies. Their differences in message management, throughput, and performance can help you determine the right solution (which might be both).

What Is Apache Kafka?

Apache Kafka is an open source software for stream processing that was designed to scale. Written in Scala and Java, Apache Kafka is a distributed, partitioned, and replicated system that can be used as message broker. 

What Is ActiveMQ?

ActiveMQ is open source, message-oriented middleware (MoM). It was written in Java with a full JMS (Java Message Service) client. It comes with easy-to-use Enterprise integration Patterns that can bring diverse systems together. It is known for supporting cross language clients and several protocols.

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Kafka vs. ActiveMQ: Key Differences 

While Kafka and ActiveMQ are similar in some ways, they are ultimately used to solve different problems. The main difference between Kafka vs. ActiveMQ is that ActiveMQ is a message broker while Kafka is an event streaming platform.

1. ActiveMQ Is a Message Broker, Kafka Is an Event Streaming Platform

Both message brokers and event streaming platforms can be used to implement asynchronous, scalable applications. Message brokers, like ActiveMQ, can filter and process individual events. Event streaming platforms, like Kafka, aggregate events over time. This allows teams to discover patterns by looking at the incoming stream of messages. 

What Is a Message Broker?

A message broker is a software application that translates messages between formal messaging protocols and transmits information from the sender to the receiver. Message brokers enable applications and services to effectively exchange data, even if they are written in different languages or are on different platforms. They can provide a standardized flow of data between interdependent services and components. 

What Is Event Stream Processing?

Event stream processing is a programming technique that analyzes continuous data. Being able to process streaming data as it arrives makes it ideal for real-time applications. 

Wait — What Is an Event? What Is an Event Stream?  

An event is any change in state (data point) tracked by a business system. This can be anything from a transaction to user navigation on a website. Event streams are the ordered sequence of these business events.

Event stream processing manages and stores many related events together, not just one event at a time. Unlike message brokers, which often delete data after it is received, event stream data is processed and stored, allowing new consumers to replay events.

2. They Handle Messaging in Different Ways

One of the biggest differences between Kafka and ActiveMQ is how they handle messages. 

Kafka not only transfers but is capable of permanently storing messages/events for multiple applications. While permanent storage is possible, retention time for a given topic can be set to whatever the use case dictates – even down to the millisecond. To avoid unnecessary retention of data, the reigning best practice is to set retention time to as short a time as the use case allows.  

Kafka has the capability to either preserve or ignore order of messaging. This depends on if a partition key is identified and what partitioner method is used. In some cases, the order of message will not be maintained, which can be a preferable configuration depending on the use case.   

With ActiveMQ, messages are managed using the JMS API message selector. It uses a push-type platform where providers push messages to consumers. Unlike Kafka, ActiveMQ can filter the messages so consumers only receive messages they are interested in. It is the responsibility of the producers to ensure the message is delivered. To guarantee that messages are received, ActiveMQ also supports message acknowledgement.  

It is also important to note that ActiveMQ cannot ensure that messages are received in the same order they were sent. In the event of a failure, messages can be duplicated, and always will be received. ActiveMQ is not designed for long-term data storage. Once consumed, the message is temporarily retained using virtual memory but then deleted. Also, ActiveMQ can be used to easily implement one-time message delivery.  

3. Kafka Has Better Throughput and Performance

Kafka is a distributed system, which allows it to process massive amounts of data. It won’t slow down with the addition of new consumers. Due to the replication of partitions, Kafka easily scales, offering higher availability. But speed is not the only thing to consider. In Kafka, producers do not wait for delivery acknowledgement from brokers. Brokers can write messages at a higher rate causing higher throughput, but data can potentially be lost. 

ActiveMQ maintains a delivery state for every single message, resulting in lower throughput. But this level of processing takes time. And when it comes to scale, ActiveMQ scales horizontally via a “network of brokers” approach.

ActiveMQ is known for speed when managing small amounts of data to numerous consumers, and is often picked for systems requiring lower throughput of messages.   

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Kafka vs. ActiveMQ Use Cases

When should you use Kafka vs. ActiveMQ? Like most software decisions, it depends entirely on your use case. Get an overview of ideal Kafka vs. ActiveMQ use cases below.

Kafka Use Cases

Kafka helps teams that need reliable, organized storage at scale. It can handle large amounts of data making it ideal for high volume projects. Teams can easily scale and multiple subscribers can consume the data without impacting performance.

You can use Kafka to:

  • Track website traffic: Monitor user clicks, registrations, likes for a piece of content, time on page, orders, and more. 
  • Process in real-time: Monitor IoT hardware to predict when it will fail and alarm users beforehand. Provide navigation to autonomous vehicles in a physical environment as conditions change. Gather and process payments, track shipments, and even update delivery estimates as new information is received.  
  • Monitor: Aggregate and centralize data. For example, log aggregation, operational metrics, KPIs (key performance indicators). 

ActiveMQ Use Cases

ActiveMQ works well for small amounts of data in a multi-application architecture. It was made for developers. It’s easy and cost effective to get up and running and complex message routing can be set up and more easily maintained. Also, for teams needing exactly-once delivery, ActiveMQ is extremely reliable.

You can use ActiveMQ for:

  • Transactional messaging: Never miss a message and process each one exactly once. This works well for financial information, deposits, withdrawals, and more. 
  • High-performance streaming data: Efficiently route rapidly changing data. For example, stocks, IM conversations, or news.
  • Asynchronous messaging: Deliver messages across modules. 
  • Cross-language technology: Transmit between applications that are in different languages. 

ActiveMQ also can be used to track website streaming data and monitor, highlighting the overlap in uses between Kafka and ActiveMQ use cases.

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Final Thoughts

While Kafka and ActiveMQ can have similar uses, what you choose depends on the final goal. Kafka works well for large datasets. But with Kafka, deployment will be as simple or complex as the configuration needed for the given use case.  

On the other hand, ActiveMQ works well when transporting small amounts of data and routing it through complex flows. It is easy to get up running and doesn’t require the same level of resources, like Kafka. The potential throughput is not as high as Kafka, and performance can be impacted as stored data increases. If you do not need advanced features, like partitioning or replication, ActiveMQ offers a quicker way to get started. 

Lastly, regardless of whether you choose ActiveMQ or Kafka, your team will need to consider the complexity these technologies add to their IT infrastructure and determine if a resource for ongoing maintenance of these technologies is needed.  

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