Apache Kafka vs Message Queue


Apache Kafka and message queues are used for different purposes. Although they both provide a mechanism for exchanging messages between applications. They can be seen as similar. However, they both have different purposes. This post compares Kafka vs message queues to help you understand their key differences and alternatives to consider achieving application decoupling while exchanging data.

What is a message queue?

A message queue is an asynchronous communication architecture that allows inter-microservices applications and systems to send tasks via a queue as messages are pending to be processed.

The producer creates messages; a message queue accepts stores and makes these messages available so that the respective consumers can process them. It follows a very straightforward pattern. A message created by a producer contains a payload of the actual data being sent or received. A queue stores and manages the flow of these messages. Any consumer the producer intends to communicate with can pick these messages from the queue and process them accordingly.

A good example of a message queue communication can be identified between a web app and a backend server. In this case, the web app sends a message containing a user request to the queue. The server then processes the request and sends a response back to the app via the message queue. Messages are produced by the producer, received by the consumer via a queue, and then removed from the queue once the communication exchange is processed.

What is Apache Kafka?

Apache Kafka is an open-source, distributed streaming platform. Just like a message queue, it provides you with the capability of sending, transferring, and receiving data from one application to another application, as well as storage and processing capabilities.

However, Kafka provides a distributed, partitioned, replicated commit log service architecture. It provides the functionality of a messaging queue but with a unique design.

Apache Kafka allows you to write streaming events by ensuring you are able to process large volumes of data from multiple sources in real time.

Is Kafka a message queue?

You can think of Kafka as a message queuing system with a few tweaks. Kafka is able to provide a high availability and fault tolerance, low-latency message processing approach just like a traditional message queue. However, it brings additional possibilities that a typical message queuing system can fail to provide.

Message queues provide a basic messaging model for background task processing and simple application integration. They provide primary message storage and processing capabilities. However, they don't have the advanced features and scalability of Kafka. Message queues are limited based on the fact that messages are removed from the queue after a single consumer processes them. This technique is incompatible when creating highly scalable applications.

Kafka addresses the weaknesses of traditional message queues strategies providing fault-tolerant, high-throughput stream processing. This way, Kafka cannot fully be categorized as the conventional message queue. Kafka is a distributed messaging platform that includes components of both message queue and publish-subscribe ("pub-sub") systems. It provides publish-subscribe patterns that have the ability to scale horizontally across multiple servers while retaining the ability to replay messages.

This makes it a very good choice for patterns that require real-time processing and high scalability, such as streaming and big data platforms.

Comparing Kafka vs message queues vs Memphis

Let'sLet's dive in and discuss the battle between message queueing and data streaming. To understand their core difference, we will compare Kafka vs message queues vs Memphis and explore the differences, trade-offs, and architectures.

Architecture difference

A message queue uses a straightforward architecture. It's made up of three major components. The producer, queue, and consumer are as follows:

Kafka is a pub-sub based model. You have multiple data producers, and the same data is consumed by multiple applications. Message queues may fail to handle such data pipelines to match the throughput and scalability of enterprise messaging systems.

Like a message queue, Kafka has two parties: a producer (sends data) and a consumer (reads the data). Producer sends data to Kafka. Kafka will store data on its server; whenever consumers want to consume it, they can request it from Kafka.

This is where Kafka starts to get different from queues; whenever producer API creates a new message, it is split into Partitions and stored on disk in an ordered, immutable log called a topic which can persist forever. Kafka then distributed and replicated these topics in a cluster. A single cluster can contain different servers (Brokers) to give Kafka the characteristics of being fault-tolerant and highly scalable to any workload.

The following is the above Kafka ecosystem representation in a nutshell:

Based on its pub/sub model, consumers can subscribe to data sent by the producers to read the most recent message in the entire topic log. This way, consumers can listen to updates in real time.

While Kafka is a great broker, you can leverage the same producer-consumer paradigm using open-source Memphis as an alternative. Like Kafka, it provides a real-time data processing platform with an embedded distributed messaging queue.

Kafka uses topics, and Memphis uses stations to achieve a robust data processing model. Below is how a topic like the Kafka model is represented using a station:

Key Features

A message queue enables subscribers to retrieve messages from the queue for processing. A subscriber can retrieve a single message or a batch of messages all at once. Queues often check if the message's requested task was completed successfully. If so, the message is permanently removed from the queue. It has the following features:

  • It uses a push or pull delivery model. Pulling means constantly querying the queue for new messages, while push ensures the consumer is notified when a communication is available.
  • Queues process messages in the FIFO approach, a First-In-First-Out model.
  • Each message (without duplicate tolerance) is delivered exactly once.
  • Provide Dead-letter queues for retry and process messages that cannot be processed successfully.

At its core, Kafka provides the following features and twists to the traditional messaging queue:

  • Its Scalability ensures that you have a few data producers generating a large dataset and distributing it to a large number of data consumers or that your application can scale out when needed.
  • Performant and low latency with high-throughput capabilities for real-time data processing.
  • Its replication and partitioning features provide robust fault tolerance and ensure your application and data work even in server failures.
  • Ability to handle high-volume based data streams.
  • Zero downtime ensures you upgrade and maintain your services without affecting the system's availability.
  • Kafka is open-source and is free to use.

Memphis, a simple, robust, and durable cloud-native next-generation message broker, has the following features:

  • Native cloud-native deployment architecture packed as a container.
  • Provide natively managed GUI.
  • Has support for Dead-letter Queue, unlike Kafka.
  • Message delivery is guaranteed at least once with idempotency support.
  • Provide distributed cluster mirroring for highly availability and scalability.
  • Automatic based on policy for storage balancing.
  • A guaranteed ordering when working with a single consumer group.
  • Has Datadog integration for efficient application observability for real-time cluster resources monitoring
  • Memphis is open-source and is free to use.

Check this guide and learn more about how Memphis compares to Kafka.

Performance and scalability

Based on this discussion, the traditional message queues cannot handle the scalability for highly scalable events that require extensive data volume processing. You may require more resources to maintain performance.

This gives the option to run Kafka of Memphis as your ideal performant and scalable broker to handle large volumes of data for big data and streaming events applications for handling real-time data feeds.

Use cases

While message queues aren't the right choice for real-time communication, queues can be used:

  • Acts as a buffer to facilitate messaging queuing for bulk processing.
  • Decoupling processing.
  • A good choice for a single consumer microservice architecture.

Kafka its use cases such as:

  • Stream processing using Kafka Streams APIs to process large volumes of data efficiently and scalable.
  • Commit log for log records that capture all your application changes.
  • Advanced messaging as a replacement for the traditional messaging queues.
  • Metrics and log aggregation - You can collect metric monitoring data and logs for application observability.
  • Website activity tracking for user activity tracking pipelines.

Memphis use cases include:

  • A replacement for traditional message queuing for sync task management and communication.
  • Provide Real-time streaming pipelines.
  • A great use case for streaming video frames.
  • Notification centre for multiple alerting channels.
  • Centralised monitoring with Datadog.

Choosing between Kafka and message queues

The choice of which broker best fits your architecture will always depend on your objectives and the specific requirements of your application. A traditional message queue best fits smaller applications that don't require intensive data processing.

If your ideal application requires real-time data processing, horizontal scalability, and streams events, choosing Apache Kafka will be the optimal solution.


This post has helped compare and contrast Kafka and message queues. You have learned their key differences and Memphis as an alternative to consider achieving application decoupling while exchanging data. I hope you found the blog helpful.

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