Kafka vs RabbitMQ: What are the Core Differences?

1. Introduction

Microservices are a specific type of distributed system that has been a hot topic for some time. The architecture can use a message broker to facilitate message exchanges between services. Both Kafka and RabbitMQ are two of the most popular message brokers. There are several fundamental differences between the two, and in this post, we will analyse Apache Kafka and RabbitMQ.

1.2. Kafka vs RabbitMQ – Overview

	Apache Kafka	RabbitMQ
Released	2011	2007
License	Apache License 2.0 (Open Source)	Mozilla Public License(Open Source)
Language Support	Majority of Languages	Majority of Languages
Message Storage	Yes, controlled by the retention period	Yes but removed upon acknowledgement
Message Routing	Flexible and complex routing using pattern matching	No, need to send messages to a different topic
Message Processing	Consumer pulls data from the broker. Concurrency via partitioning data.	The broker pushes data to the consumer. Concurrency via additional consumer applications.
Message Priority	Not supported	Supported with priority queues
Monitoring	Build-in UI plugin	Available through third-party tools

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2. What is Apache Kafka?

Apache Kafka is a durable message broker that Apache Software Foundation developed. It can ingest and process input from distinct data sources while making the processing data available to target systems in real-time. The records are processed simultaneously using stateless and stateful operations.

High throughput capabilities allow Kafka to process massive amounts of big data using a partitioned log model. This model stores records in an ordered sequence across multiple topic partitions, and each partition can have a distinct subscriber for concurrent processing and increased scalability. An excellent intro to Apache Kafka architecture is available in this post.

2.1. Kafka vs RabbitMQ – (Kafka – High-Level Features)

• Kafka as a distributed event streaming data platform allows you to ensure messages remain in the order they were sent and simultaneously balance out consumption across multiple consumer clusters. Apache Kafka simplifies partitioning between servers so your data can be quickly streamed over a vast network.
  
• Apache Kafka is a powerhouse designed to manage an immense throughput of up to millions of messages per second; big data.
  
• Consumer threads should be granted immediate access to producer thread messages in real-time to ensure maximum efficiency.
  
• No information should be overlooked to derive the most out of big data. Disc Structures from Apache Kafka—these are robust structures that can handle massive amounts of message storage (up to terabytes!) with constant-time performance for unsurpassed efficiency and reliability.
  
• Kafka log aggregation collects and consolidates log data from multiple sources into a centralized location. This process allows organizations to gain valuable insights into their systems, networks, and applications and identify potential issues or trends. Log aggregation offers real-time visibility into system activity, allowing users to detect abnormalities and respond quickly.

2.2. Apache Kafka Ecosystem

Besides being a broker, Apache Kafka is also distributed event streaming platform. Numerous tools can be easily integrated with the main distribution of Kafka outside its ecosystem. This includes components such as Apache’s Kafka Core and Streams and REST Proxy, Connect and Schema Registry – all essential elements to get the most out of your data storage solution!

Kafka Connect is a powerful data integration and transfer platform that allows you to quickly move large volumes of non-Kafka sources, such as databases or cloud storage, into Apache Kafka. Horizontally scalable and fault-tolerant, this platform can be assigned multiple workers to connect the Kafka cluster with several external sources through reliable connectors.

The stream processor API acts as a consumer that enables developers to easily process streaming data from a topic using data transformation operations that can be stateless or stateful. The stateful operations use state stores that remember received input so it can be mutated as needed.

Kafka REST Proxy allows interaction with Apache Kafka clusters over the REST API, making it easier to use the Kafka Streams API in a distributed environment. Proxy users can manage topics, create and delete, and scale-out applications.

Confluent Schema Registry allows Avro schemas to be managed within an Apache Kafka cluster. It enables developers to register Avro schemas and store them in a central location, allowing for easier management of topics and data streams.

3. What is RabbitMQ?

RabbitMQ is a lightweight general-purpose messaging system that supports multiple messaging protocols. The AMQP(Advanced Message Queuing Protocol) is available out of the box, while the other protocols can be added with the support of additional plugins. RabbitMQ can deliver messages in an asynchronous, non-blocking manner and with the help of exchanges, route messages using a flexible and complex routing mechanism. In case you are new to RabbitMQ, an excellent overview of RabbitMQ architecture is here.

3.1. Kafka vs RabbitMQ – (RabbitMQ – High-Level Features)

• With RabbitMQ, you have ultimate control over your routing system. It offers a variety of built-in exchange types that can be used for message routing. You can use it to send messages through exchanges before they are sent to queues or link together different exchanges for more complex messaging requirements.
  
• RabbitMQ as a general purpose message broker offers extensive security measures, including the ability to require Client Certificate Checking and SSL communication for secure client connections. Furthermore, user access controls may be employed at the virtual host level to guarantee a high degree of message isolation.
  
• With RabbitMQ’s built-in clustering, producers and consumers can continue working even if one of the nodes fails. As you add more nodes to your system, this not only allows you to maximize messaging throughput but also offers unbeatable stability for all of your business operations!
  
• RabbitMQ’s advanced continuous delivery feedback, publisher confirmations, and high availability feature guarantee superb performance and reliability. With these outstanding offerings, RabbitMQ is the perfect choice for ensuring remarkable results consistently!

4. Apache Kafka vs RabbitMQ – Charakteristics

Kafka vs RabbitMQ is an interesting discussion of both popular open-source message brokers. While they have a lot of similarities, there are some critical differences between them.

RabbitMQ uses a queue push model that can be controlled with a pre-fetch limit while Apache Kafka depends on a publish-subscribe message bus model that is log based. With RabbitMQ, messages are stored on the server until it is consumed. However, Kafka stores the messages for a certain period. If a message is not consumed in the specified time frame, it will be discarded.

Also, the has performance capabilities between the two. RabbitMQ offers lower latency and high throughput, while Apache Kafka is designed to process data in large volumes. Let’s take a look at some of the broker characteristics and how they differ.

4.1. Message Deletion and Storage

A message key is a unique identifier associated with each message in a Kafka topic partition. By using the key when sending a message to a topic, the producer can ensure that all messages with a given key will end up in the same partition. This allows producers and consumers to coordinate their reading and message consumption across partitions, allowing one consumer to read only records with keys for which he is responsible and interested.

Message keys may also be used to determine the order of messages within a topic partition, as the messages are stored in key-sorted order. This ensures that producers and consumers can both rely on the same ordering of messages when reading from or writing to a topic partition.

The Apache Kafka cluster retains all the published messages on a disk according to the retention policy, regardless of whether they have been consumed. This policy can be configured individually for each topic or Kafka server. After the retention period expiry, the messages are discarded to free up the space on the disk.

In RabbitMQ, the messages are stored in a queue in a FIFO manner until the acknowledgement has been received. The consumer can acknowledge as soon as the message retrieval and processing has taken place. After which, the message is completely removed from a queue.

The concept of a lazy message queue in RabbitMQ enables automatic message storage on disk. It can be beneficial during short bursts of data load where consumers may not be able to cope with the publishing power of publishers. Messages stored on disk will reduce RAM usage.

4.2. Message Routing

In general, Apache Kafka does not support message routing directly. We can use Kafka stream processing to create flexible routing on the fly by using stream operations to segregate and route records to different Kafka topics. Using a key identifier on the message during persistence can control messages with the identical key to be stored on the same partition. As a result, they can be consumed in order by a single consumer from a specific consumer group.

On the other hand, RabbitMQ is relatively good when it comes to message routing. It uses the concept of exchanges to route messages where different exchange types provide different complex routing scenarios. The routing key is used to route messages to the specific message queue that is bound to the exchange with a matching key. Depending on the exchange, the routing key can be a wildcard or direct match.

Take a look at another article for a detailed explanation of different exchange types.

4.3. Message Processing

Kafka vs RabbitMQ – both brokers have support for producer confirmations to confirm that a record has reached the broker. Acknowledgement is a signal between the sender and receiver to ensure the successful receipt of a record.

Apache Kafka accomplishes concurrent processing using partitioning. Each topic can have multiple partitions, and each partition can only have one consumer per consumer group. As a result, this preserves the message order within that partition. Consumers pull messages from the topic partition, and Kafka uses consumer offset to track consumer position. It can be committed automatically or manually, called delivery semantics.

RabbitMQ message broker pushes messages to each consumer. Individual queues support parallel processing by multiple consumer subscriptions. However, the order is not guaranteed since each consumer may compete for the messages to process. Messages pushed by the broker can be acknowledged automatically during sending, or the consumer can do it manually upon successful processing. The acknowledgement can also be negative if a consumer has failed to process the message. This will enqueue the message back to the message queue to be processed again.

4.4. Message Priority

Apache Kafka doesn’t allow users to assign priorities or deliver arrival orders to messages. All communications are saved and sent in the same sequence acquired, regardless of how congested consumers may be.

RabbitMQ offers an incredibly useful feature for developers: priority queue. You can set each message to a certain level of importance, and RabbitMQ will handle the rest, placing it in its designated priority queue at lightning speed! This makes managing your messages so much easier as you no longer have to sort them based on their value manually.

4.5. Scaling

In Apache Kafka, only one consumer from the same consumer group can process messages from the topic partition, which is how message order is preserved. Therefore increasing the number of partitions on the topic is linked directly with several consumers that can process records in parallel.

RabbitMQ holds messages in a queue while multiple consumers compete to process them. It’s possible to overload the broker by causing out-of-memory errors when consumers cannot cope with the publishing load. We can add more processing power by adding extra consumers to the broker to distribute the messages evenly between more instances.

4.6. Topology

RabbitMQ utilizes an exchange queue structure, which sends messages directly to the consumer’s designated queues after passing through an exchange. Different exchange types support different complex routing mechanisms and patterns.

On the other hand, Kafka relies on a publish/subscribe configuration and transmits messages across its stream before they are sorted into respective topics so that individuals can consume messages in approved groups.

4.7. Broker & Consumer Model

Both Kafka and RabbitMQ message brokers have quite different models for brokers and consumers.

RabbitMQ message broker leverages the savvy broker/stupid consumer model, where messages are consistently provided to applications that read messages called consumers, and their status is closely monitored.

On the other hand, the Kafka approach favours a dumb broker/intelligent consumer model – it merely stores unread messages for a specified duration; consumers need to keep track of their position within each log file.

4.8. Monitoring

Kafka vs RabbitMQ both has quite different directions to monitoring.

Apache Kafka provides numerous open-source and commercial monitoring tools for efficient administration and operational capabilities.

RabbitMQ’s user-friendly interface makes managing your RabbitMQ server a breeze. The management UI allows you to quickly and easily manipulate broker resources. You can efficiently generate, eliminate, or view queues, connections, channels and exchanges. Additionally, manage user permissions; track message speeds; and send/receive messages with instantaneous response times.

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5. Apache Kafka vs RabbitMQ – When to Use

In Kafka vs RabbitMQ, we have come across many conflicting data about what particular systems can and cannot provide. Thus, let’s discuss two primary use cases detailing how customers like me ponder this dilemma when selecting the appropriate system for their needs. In addition, we’ve encountered scenarios where people erroneously select one technology while they really should go with another instead.

5.1. RabbitMQ Use Cases

• Suppose you are looking for a simple, straightforward pub-sub message broker service. In that case, RabbitMQ will probably exceed your expectations when requirements revolve around system communication via channels/queues and don’t require retention or streaming capabilities.
• Optimal selection for complex routings, such as sending data among many applications in a microservices architecture. Furthermore, you can use exchanges to direct specific events to certain services.
• RabbitMQ queues have become the go-to event buses for web servers as they can rapidly respond to requests and eliminate any taxing computational tasks that used to be done immediately.
• Suppose you must support legacy protocols like STOMP, MQTT, AMQP or JMS(Java Message Service). In that case, RabbitMQ broker probably has a plugin available, and due to its popularity, it will be supported by most programming languages.

5.2. Apache Kafka Use Cases

• Kafka is a powerful, high-speed distributed system that doesn’t break under the weight of massive data streams. Source services easily push their voluminous information into target services which quickly pull messages and process them in real-time. This technology can modernize high-performance data pipelines, such as monitoring and tracking website activity.
• Make the most of Kafka to build application logic based on event streams. For instance, you can use it to monitor and calculate the average value over an extended period or track the number of different events taking place simultaneously e.g. monitoring fluctuating market prices.
• The concept of event sourcing allows any changes to an app state to be documented in a chronological sequence of events. Take Kafka and its use as a banking application. If the account balance gets disrupted, you can always analyze data, access stored transaction history, and recalculate it accordingly. This dependability makes event sourcing perfect for mission-critical applications like banking software! 

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6. Summary

In this post, we have analysed Apache Kafka vs RabbitMQ. We have looked at both brokers scaling capabilities and how they handle messages in terms of handling, routing and storage.

Apache Kafka vs RabbitMQ – it can be tough to choose between Apache Kafka and RabbitMQ. While both have pros, the deciding factor depends on your specific needs. For most use cases, we’d recommend sticking with RabbitMQ as it’s reliable in day-to-day communications within an event-driven microservice architecture. But for any real-time processing and analyzing streaming data where long-term storage is needed, there’s no doubt you should go with Kafka – due to its powerful retention policy!