Kafka Vs RabbitMQ: Key Differences & Features Explained

What’s the Difference Between a Message Broker and a Publish/Subscribe (Pub/Sub) Messaging System?

Message brokers are software modules that let applications, services, and systems communicate and exchange information. Message brokers do this by translating messages between formal messaging protocols, enabling interdependent services to directly “talk” with one another, even if they are written in different languages or running on other platforms.

Message brokers validate, route, store, and deliver messages to the designated recipients. The brokers operate as intermediaries between other applications, letting senders issue messages without knowing the consumers’ locations, whether they’re active or not, or even how many of them exist.

However, publish/Subscribe is a message distribution pattern that lets producers publish each message they want.

Data engineers and scientists refer to pub/sub as a broadcast-style distribution method, featuring a one-to-many relationship between the publisher and the consumers.

Also Read: How to Become a Data Engineer?

What Is Kafka?

Kafka is an open-source distributed event streaming platform, facilitating raw throughput. Written in Java and Scala, Kafka is a pub/sub message bus geared towards streams and high-ingress data replay. Rather than relying on a message queue, Kafka appends messages to the log and leaves them there, where they remain until the consumer reads it or reaches its retention limit.

Kafka employs a “pull-based” approach, letting users request message batches from specific offsets. Users can leverage message batching for higher throughput and effective message delivery.

Although Kafka only ships with a Java client, it offers an adapter SDK, allowing programmers to build their unique system integration. There is also a growing catalog of community ecosystem projects and open-source clients.

Kafka was released in 2011, so it’s the newcomer. You can find a more detailed intro to Kafka here. You can also learn more about how to use it through this Kafka tutorial and look at the architecture of this pub/sub model here.

What Is RabbitMQ?

RabbitMQ is an open-source distributed message broker that facilitates efficient message delivery in complex routing scenarios. It’s called “distributed” because RabbitMQ typically runs as a cluster of nodes where the queues are distributed across the nodes — replicated for high availability and fault tolerance.

RabbitMQ employs a push model and prevents overwhelming users via the consumer configured prefetch limit. This model is an ideal approach for low-latency messaging. It also functions well with the RabbitMQ queue-based architecture. Think of RabbitMQ as a post office, which receives, stores, and delivers mail, whereas RabbitMQ accepts, stores, and transmits binary data messages.

RabbitMQ natively implements AMQP 0.9.1 and uses plug-ins to offer additional protocols like AMQP 1.0, HTTP, STOMP, and MQTT. RabbitMQ officially supports Elixir, Go, Java, JavaScript, .NET, PHP, Python, Ruby, Objective-C, Spring, and Swift. It also supports various dev tools and clients using community plug-ins.

What Is Kafka Used For?

Kafka is best used for streaming from A to B without resorting to complex routing, but with maximum throughput. It’s also ideal for event sourcing, stream processing, and carrying out modeling changes to a system as a sequence of events. Kafka is also suitable for processing data in multi-stage pipelines.

Bottom line, use Kafka if you need a framework for storing, reading, re-reading, and analyzing streaming data. It’s ideal for routinely audited systems or that store their messages permanently. Breaking it down even further, Kafka shines with real-time processing and analyzing data.

You Have Streaming Data in Kafka. What’s next?

The first step is complete once you have the data in Kafka. However, if you are trying to extract any useful information from it, you will need a much-improved mechanism to store, manage, and analyze your streams. Aside from that, another recommendation is to test SQLake for free (early access). It enables you to construct and execute dependable data pipelines on streaming and batch data using an all-SQL environment. You may try it for free. There is no credit card necessary.

What Is RabbitMQ Used For?

Developers use RabbitMQ to process high-throughput and reliable background jobs, plus integration and intercommunication between and within applications. Programmers also use RabbitMQ to perform complex routing to consumers and integrate multiple applications and services with non-trivial routing logic.

RabbitMQ is perfect for web servers that need rapid request-response. It also shares loads between workers under high load (20K+ messages/second). RabbitMQ can also handle background jobs or long-running tasks like PDF conversion, file scanning, or image scaling.

Summing it up, use RabbitMQ with long-running tasks, reliably running background jobs, and communication/integration between and within applications.

Learn Job Critical Skills To Help You Grow!

Post Graduate Program In Data EngineeringExplore Program

Understanding the Differences Between RabbitMQ vs Kafka

These messaging frameworks approach messaging from entirely different angles, and their capabilities vary wildly. For starters, this chart breaks down some of the most significant differences.

More on the top differences between Kafka vs RabbitMQ:

• Data Flow 

  RabbitMQ uses a distinct, bounded data flow. Messages are created and sent by the producer and received by the consumer. Apache Kafka uses an unbounded data flow, with the key-value pairs continuously streaming to the assigned topic.

• Data Usage

  RabbitMQ is best for transactional data, such as order formation and placement, and user requests. Kafka works best with operational data like process operations, auditing and logging statistics, and system activity.

• Messaging

  RabbitMQ sends messages to users. These messages are removed from the queue once they are processed and acknowledged. Kafka is a log. It uses continuous messages, which stay in the queue until the retention time expires.

• Design Model

  RabbitMQ employs the smart broker/dumb consumer model. The broker consistently delivers messages to consumers and keeps track of their status. Kafka uses the dumb broker/smart consumer model. Kafka doesn’t monitor the messages each user has read. Rather, it retains unread messages only, preserving all messages for a set amount of time. Consumers must monitor their position in each log.

• Topology

  RabbitMQ uses the exchange queue topology — sending messages to an exchange where they are in turn routed to various queue bindings for the consumer’s use. Kafka employs the publish/subscribe topology, sending messages across the stream to the correct topics, and then consumed by users in the different authorized groups.

• Architecture Differences

When choosing between Apache Kafka and RabbitMQ, the internal operations and fundamental design can be important considerations. 

The components of RabbitMQ’s Architecture consist of the following:

• Queue: It is in charge of keeping track of messages that have been received and may have configuration data that specifies what it can do with a message.
• Exchange: An exchange receives messages sent to RabbitMQ and determines where they should be forwarded. Exchanges define the routing strategies that are used for messages, most frequently by examining the data characteristics that are transmitted with the message or included inside its attributes.
• Producer: Produces messages and sends them to a broker server (publishes). A payload and a label are the two components of a message. The user's desired data to convey is the payload. The label specifies who should receive a copy of the message and describes the payload.
• Consumer: It subscribes to a queue and is connected to a broker server. 
• Broker: Applications can exchange information and communicate with one another through a broker.
• Binding: It tells an exchange which queues to distribute messages. Additionally, the binding will instruct the exchange to filter which messages it is permitted to add to a queue for specific exchange types.

Let us now look at Apache Kafka's architecture to compare both of these.

Kafka’s architecture is designed using the following components:

• Mirror Maker: One of the most crucial elements of Kafka is replication, which makes sure that messages are published and consumed even in the event that the broker encounters a problem.
• ZooKeeper: Acts as a liaison between the consumers and the Kafka broker. It maintains coordination data such as configuration, location, and status details.
• Producer: Producers push or publish messages to a Kafka topic created on a Kafka broker. Producers also have the option of sending messages to a broker in a synchronous or asynchronous manner.
• Consumers: Individuals who subscribe to a Kafka topic and pull messages from it. Kafka By default, consumers store messages in ZooKeeper. However, Kafka also allows data to be stored in additional storage platforms used by programs for online transaction processing (OLTP).
• Broker: Acts as a Kafka server, or broker. The number of partitions for each message is defined in accordance with the order in which the messages are stored by the broker.

Let us now look at the Scalability and Redundancy differences between RabbitMQ vs Kafka.

Let us now look at how these two compare to each other in regards to Scalability and Redundancy.

• Scalability and Redundancy

RabbitMQ uses a round-robin queue to repeat messages. To boost throughput and balance the load, the messages are divided among the queues. Additionally, it enables numerous consumers to read messages from various queues at once.

Scalability and redundancy are provided by Kafka partitions. The partition was duplicated across numerous brokers. In the event that one of the brokers fails, the customer can still be served by another broker.

If we store all of the partitions in one broker, our dependence on that broker will grow, which is hazardous and increases the likelihood that it will fail. Additionally, distributing the partitions will vastly improve throughput.

Let us now look at how these two compare to each other in regards to Message Deletion.

• Message Deletion

To be unqueued, RabbitMQ delivers a successful acknowledgment via the consumer.

The messages are returned to the queue on negative ACK and saved to the consumer on positive ACK.

While Kafka uses a retention time, any messages that were retained based on that period are erased once it has passed.

Let us now look at how these two compare to each other when it comes to Message Deletion.

• Message Consumption

A message must be delivered to the customer by one of RabbitMQ's brokers, and these messages are transmitted in batches.

Kafka Consumers read a message from the broker and keep the queue counter offset trackable. As soon as the message is read, the offset is increased.

Let us now look at how they differ in terms of Message Priority.

• Message Priority

Messages can be given priority with the help of a priority queue in RabbitMQ.

In Kafka, all messages have the same priority, which cannot be altered.

What are the Libraries and Language Support provided by Kafka and RabbitMQ? Let us look at them now.

Learn Job Critical Skills To Help You Grow!

Post Graduate Program In Data EngineeringExplore Program

Libraries and Language Support

RabbitMQ supports Python, Ruby, Elixir, PHP, Swift, Go, Java, C, Spring, .Net, and JavaScript.

Kafka supports Node js, Python, Ruby, and Java.

Now, we will be talking about Sequential Ordering when we compare RabbitMQ and Kafka.

Sequential Ordering

The order of the messages in the broker's queue is maintained by RabbitMQ.

Topics are used by Kafka to distinguish between messages, and Zookeeper keeps track of the offset so that it may be used by any consumer who wishes to read a topic.

Next, we will be looking at the Pull vs Push Approaches followed by these two technologies.

Pull vs Push Approach

The push mechanism of RabbitMQ prevents the consumer from being aware of any message retrieval. The Broker makes sure the customer receives the message.

Additionally, it returns an acknowledgment after processing the data to make sure messages reach the customer. When there is a negative response, the message is sent once more by being added to the queue.

Kafka provides a pull mechanism that enables clients to request data in batches from the broker. Smartly, the consumer keeps a tab on the offset of the most recent message encounter. By employing offset, it arranges the data in the partitions' order.

Let us now compare how these two handle messaging.

How Do They Handle Messaging?

The differences between both of these technologies in how each of them handles messaging are summarized in the table below -

Now we will be going through the key features of Apache Kafka vs RabbitMQ.

Features of Kafka

To enable real-time data storage and analysis, Apache Kafka offers the following functions: message communication and stream processing. 

Below are the main key features of Apache Kafka:

• Distributed event streaming platform: Kafka enables per-partition ordering semantics while facilitating message partitioning between Kafka servers and dispersing consumption over a cluster of consumer systems.
• High Throughput: Kafka was built to process millions of messages per second and handle massive amounts of data.
• Real-time solutions: Consumer threads should have immediate access to messages produced by producer threads.
• Persistent Messaging: In order to truly benefit from big data, no kind of information loss may be accepted. O(1) Disc Structures, which are used in the construction of Apache Kafka, provide constant-time performance even with extremely large message storage densities (in the TBs). In event-based systems like Complex Event Processing, this quality is crucial (CEP).

Features of RabbitMQ

Some of the main key features of RabbitMQ consist of the following:

• Reliability: Key characteristics of RabbitMQ that have an immediate influence on performance include persistence, delivery feedback, publisher confirmations, and high availability.
• Built-in Clustering: The clustering in RabbitMQ was created with two objectives in mind. It still enables the consumers and producers to continue operating in the event that one node fails, expanding messaging throughput linearly by adding new nodes.
• Security: It is offered by RabbitMQ at different levels. Secure client connections can be achieved by requiring Client Certificate Checking and SSL-only communication. The virtual host may have user access controls to ensure high-level message isolation.
• Flexible Routing: For routing, RabbitMQ comes with a number of built-in exchange kinds. Messages are often routed through exchanges before they reach queues in conventional routing. Users can also tie exchanges together for complicated routing or even develop their exchange type as a plugin.

Master Gen AI Strategies for Businesses with

Generative AI for Business Transformation ProgramExplore Program

Requirements and Use Cases

In the initial stages, there was considerable difference in design between RabbitMQ and Kafka, and a difference in requirements and use cases. While RabbitMQ’s message broker design was an excellent choice for use cases having specific routing needs and pre message guarantees, Kafka’s append only log meant developers could assess the stream history and more direct stream processing. The Venn diagram of use cases fulfilled by the two technologies was quite tight. There were situations where one was evidently a better choice than the other.

However, this balance will soon be altered. RabbitMQ, besides providing its traditional queue model, will present a new data structure modeling an append-only log, with non-destructive consuming semantics. This new data structure will be an interesting addition for RabbitMQ users looking to enhance their streaming use case.  

• Developer Experience

The developer experience of RabbitMQ and Kafka has been quite similar, with the list of clients and libraries continually rising due to the work of their respective communities. There has been a steady growth in the client library lists of both. As more languages and frameworks are getting popular, it has become easier to find a well-supported and complete library for RabbitMQ and Kafka. 

The client library implementation of Kafka streams have grown substantially, making it easier for developers to process streaming data. The implementation is used for reading data from Kafka, processing it, and writing it to another Kafka queue. Plus, ksqlDB can help developers looking to develop streaming applications leveraging their familiarity with relational databases. 

With RabbitMQ, developers can take help of Spring Cloud Data Flow for powerful streaming and batch processing. 

• Security and Operations

Both RabbitMQ and Kafka provide built in tools for managing security and operations. Plus, both platforms offer third-party tools that enhance monitoring metrics from nodes, clusters, queues, etc. 

The emergence of Kubernetes in recent times has led to allowing infrastructure operators run both Kafka and RabbitMQ on Kubernetes.  

While RabbitMQ comes with a browser based API to manage users and queues, Kafka provides features like Transport Layer Security (TLS) encryption, and JAAS (Java Authentication and Authorization Service). Both Kafka and RabbitMQ support role-based access control (RBAC), and Simple Authentication and Security Layer (SASL) authentication. In Kafka, you can even control security policies through command line interface (CLI). 

• Performance

It can be hard to quantify performance with so many variables involved like how the service is configured, how the code interacts with it, and the hardware. Even things like network, memory and disk speed can significantly impact service performance. Although RabbitMQ and Kafka are optimized for performance, make sure to configure your use case for maximum efficiency. 

For RabbitMQ, refer to how-to guides for maximum performance. Keep in mind things to consider while building clusters, how to benchmark and size your cluster, how to make your code interact with them for optimized performance, how to manage queue size and connections, and taking care about how end user consumes messages. 

Similarly, running Kafka in production guides cover key points on how to configure Kafka cluster, things to keep in mind for running Kafka on JVM, and more.

• Deciding Between Kafka and RabbitMQ

Deciding between Kafka and RabbitMQ can be tricky, especially with both platforms improving every day, and the margins of advantage getting smaller. Your decision will however depend on your specific user case. 

While Kafka is best suited for big data use cases requiring the best throughput, RabbitMQ is perfect for low latency message delivery. 

There are some common use cases for both Kafka and RabbitMQ. Both can be used as component of microservices architecture providing connection between producing and consuming apps. Another commo use case can be as message buffer, providing a temporary location for message storage while consuming apps are unavailable, or fixing spikes in producer-generated messages. 

Both Kafka and RabbitMQ technologies can handle huge amounts of messages - though in different ways – each being suitable for subtly varying use cases. 

When to Use RabitMQ? 

1. A user of your program submits certain files that need to be transcoded into some other formats, such as video or audio. If you don't set up a background process or queue, the user will have to wait for the process to finish before proceeding to the next step. This might be a very long period in the case of video.
2. You may use RabbitMQ to connect consumer apps with legacy apps by using existing plug-ins (or designing your own). JMS client libraries and Java Message Service plug-ins are available for connecting with JMS apps, for example.
3. RabbitMQ is a wonderful choice if you need a flexible and dependable message broker. The RabbitMQ community is active and expanding, and there is a wealth of documentation and assistance available. When you don't require the ability to replay messages on a subject, RabbitMQ might be useful. Although RabbitMQ cannot replay events, transmitted messages are still preserved; hence, you may use the producer to replay the message.
4. When a certain action occurs in your application, you may want to notify the user. You may just deliver the values to the user by detaching your code that deals with sending alerts (by email, SMS, etc.). RabbitMQ (and other sophisticated message queues) may also be used to provide highly complex system rules for managing the flow of messages, such as how many queues, consumers, and bindings are created.
5. When messages must be routed across several consumer apps, RabbitMQ may be the best choice. Its constant hash exchange can be used to spread load processing over a distributed monitoring service.
6. Users can upload files to the Softonic platform, where they will be scanned for viruses, and information about the file gathered before being disseminated to other users. When the upload is finished, the user receives a notice. This is achievable because RabbitMQ's Microservice Architecture feature allows web servers to reply to queries rapidly.

When to Use Apache Kafka?

1. Activity Tracking was Kafka's initial use case. LinkedIn's user activity tracking pipeline has to be rebuilt as a series of real-time publish-subscribe feeds. Because each user page view creates several activity messages (events): user clicks, likes, registrations, orders, time spent on certain pages, environmental changes, and so forth, activity tracking is frequently quite a high volume. These events can be assigned (published) to certain Kafka themes. Each stream can be used (consumed) for a variety of purposes, such as putting into a data lake or warehouse for offline processing and reporting.
2. Kafka is a good substitute for traditional message brokers because it provides higher throughput, built-in partitioning, replication, and fault tolerance, as well as improved scalability capabilities. When it is necessary to replay messages, the consumer can do it directly. Replay ensures that no events are lost if the consumer contains a flaw, is overloaded or is otherwise not ready. Simply correct the problem, bring the customer up to speed, and replay the messages.
3. Kafka is frequently used to store operational monitoring data. This entails gathering statistics from remote apps in order to generate centralized feeds of operational data.
4. It is possible to monitor real-time traffic in multistage pipelines by generating graphs of the data flow in an event stream when the event stream is used to process data from multiple stages of a pipeline.
5. Many businesses utilize Kafka to aggregate logs. Log aggregation often entails gathering physical log files from servers and storing them in a single repository for processing (such as a file server or data lake). Kafka removes file specifics and abstracts the data as a stream of messages. This allows for lower latency processing as well as simpler support for many data sources and dispersed data consumption. When compared to log-centric systems such as Scribe or Flume, Kafka provides comparable speed, higher durability guarantees owing to replication, and significantly reduced end-to-end latency.

Apache Kafka Use Cases

• Tracking High-throughput Activity – you can use Kafka for different high volume, high throughput activity tracking like tracking website activity, ingesting data from IoT sensors, keeping tabs on shipments, monitoring patients in hospitals, etc. 
• Stream Processing – Use Kafka to implement application logic based on streams of events. For example, for an event lasting for several minutes, you can track average value over the duration of the event or keep a running count of the types of events. 
• Event Sourcing – Kafka supports event sourcing, wherein any changes to an app state are stored in the form of sequence of events. For example, while using Kafka for a banking app, if the account balance gets corrupted somehow, you can use the stored history of transactions to recalculate the balance. 
• Log aggregation – Kafka can also be used to collect log files and store them in a centralized location. 

RabbitMQ Use Cases

• Complex Routing – if you want to route messages among many consuming apps like in a microservices architecture, RabbitMQ can be your best choice. RabbitMQ consistent hash exchange can balance load processing across a distributed monitoring service.  You can also use alternate exchanges to route specific portion of events to specific services for A/B testing. 
• Legacy Applications – another use case of RabbitMQ is to deploy it using available plugins (or building your own plugin) for connecting consumer apps to legacy apps. For example, communicate with JMS apps using Java Message Service (JMS) plug-in and JMS client library. 

Which Should You Learn - Kafka vs RabbitMQ?

Although this may sound like a cop-out, the answer is — it depends on what your needs are. Learn and use Apache Kafka if your operation requires any of the following use cases:

• Event sourcing or system modeling changes as a sequence of events
• Streaming and processing data in multiple-stage pipelines
• Applications that need a stream history, delivered in “at least once” partitioned order
• Streams with a throughput of at least 110K/sec events, complex routing, or “at least once” partitioned ordering

And you should learn and use RabbitMQ if any of these use cases apply to your organization:

• Granular control over consistency/set of guarantees on a per-message basis
• Complex routing to users/consumers
• Applications requiring a variety of publish/subscribe, or point-to-point request/reply messaging capabilities
• Applications that must support legacy protocols, like STOMP, MQTT, AMQP, 0-9-1

If you’re uncertain where your career will take you, you may consider learning both. This strategy boosts your skillset, enhances your flexibility in a new job environment, and increases your marketability to future prospective employers. As time and resources permit, consider gaining certification in both RabbitMQ and Kafka, and be ready for anything.

When you’re ready to take the Apache Kafka certification exam, check out these Kafka practice questions first, and then you can check Kafka interview questions for preparing for the job interview.

Looking at Career Opportunities as a Data Engineer?

Data scientist and data engineer skills are among the most in-demand in 2021.  If you’re interested in a data engineering career, Simplilearn offers data engineering courses to give you a boost in the right direction. This Post Graduate Program in Data Engineering focuses on distributed processing using the Hadoop framework, large scale data processing using Spark, data pipelines with Kafka, and processing data on the AWS and Azure cloud infrastructure. Once you complete this data engineering certification course, you will be career-ready for a Data Engineering role.

Once you get the data engineering certification out of the way, consider Simplilearn’s Professional Certificate Program in Data Engineering. This course teaches you how to master architecture, installation, configuration, and Kafka open-source messaging interfaces. You will learn the basics of Apache ZooKeeper as a centralized service and develop the skills to deploy Kafka for real-time messaging.

According to Payscale, data engineers can earn an annual average of USD 92,325, with an upper range of around USD 132,000.

Let Simplilearn help you make your data-oriented career dreams come true. Check out the courses today, and get yourself on a career path that offers generous benefits and stability.