It is essential for organizations to efficiently manage several applications that generate a colossal amount of data for building better products and services. Apache Kafka vs RabbitMQ with a common purpose are widely used Open-Source, Message-Handling Solutions that assist in communication in distributed environments.
Today, both solutions help organizations streamline the movement of data in Real-Time for building Streaming Applications. Though, when deciding between Apache Kafka vs RabbitMQ, it all comes down to individual use cases, their architecture, and performance.
In this article, you will learn about the major differences between Apache Kafka vs RabbitMQ.
Table of Contents
What is Apache Kafka?
Kafka was originally developed at LinkedIn to address their need for Monitoring Activity Stream Data and Operational Metrics such as CPU, I/O usage, and request timings. Subsequently, in early 2011, it was Open-Sourced through the Apache Software Foundation.
Apache Kafka is a Distributed Event Streaming Platform written in Java and Scala. It is a Publish-Subscribe (pub-sub) Messaging Solution used to create Real-Time Streaming Data Pipelines and applications that adapt to the Data Streams.
Kafka deals with Real-Time volumes of data and swiftly routes it to various consumers. It provides seamless integration between the information of producers and consumers without obstructing the producers and without revealing the identities of consumers to the producers.
Key Features of Apache Kafka
Apache Kafka provides the following features such as communicating through messaging and stream processing to enable real-time data storage and analysis.
- Persistent messaging: Any type of information loss cannot be tolerated in order to gain real value from big data. Apache Kafka is built with O(1) Disc Structures that deliver constant-time performance even with very high volumes of stored messages (in the TBs).
- High Throughput: Kafka was designed to work with large amounts of data and support Millions of Messages per Second.
- Distributed event streaming platform: Apache Kafka facilitates Message Partitioning across Kafka servers and distributing consumption over a cluster of consumer systems while ensuring per-partition ordering semantics.
- Real-time solutions: Messages created by producer threads should be instantly available to consumer threads. This characteristic is essential in event-based systems like Complex Event Processing (CEP).
What is RabbitMQ?
RabbitMQ was initially developed as RabbitMQ Technologies in a partnership between LShift, LTD, and Cohesive FT. Currently, it is owned by Pivotal Software and released under the Mozilla Public License.
RabbitMQ is a widely-used Open-Source Message Broker and Queueing Platform implemented on Advanced Message Queuing Protocol (AMQP) either written in Erlang or Open Telecom Platform (OTP) language.
It is also known as Message Oriented Middleware (MOM) because it provides advanced routing and message distribution features, even with WAN tolerances, to support reliable, distributed systems that easily interconnect with other systems.
Whether an application is hosted using the Cloud Platform or on users’ Data Centers, RabbitMQ provides tremendous flexibility, high scalability, high availability and can be implemented on various distributed platforms.
RabbitMQ is a Lightweight and Powerful platform for constructing Distributed Software Architectures ranging from the very simple to the highly complex.
Key Features of RabbitMQ
- In-built Clustering: RabbitMQ’s clustering was designed with two purposes in mind. In case one node fails, it still allows the consumers and producers to keep functioning in the event and scaling messaging throughput linearly by adding additional nodes.
- Flexible Routing: RabbitMQ offers several built-in exchange types for routing. For typical routing, messages are routed through exchanges even before arriving at queues. And, for complex routing, users can bind exchanges together or even write their exchange type as a plugin.
- Reliability: Persistence, delivery feedback, publisher confirmations, and high availability are prominent features of RabbitMQ that directly impact performance.
- Security: RabbitMQ provides security on various tiers. Enforcing SSL-only communication and Client Certificate Checking can help secure client connections. User access may be controlled at the virtual host, ensuring high-level isolation of messages.
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What are the differences between Apache Kafka vs RabbitMQ? 12 Critical Aspects
Selecting between Apache Kafka vs RabbitMQ is a daunting task as both of them are widely used Open-Source Message-Handling Solutions. When deciding between Apache Kafka vs RabbitMQ, you can make an informed choice by going through the following aspects:
1) Apache Kafka vs RabbitMQ: Critical Use Cases
Preference over Apache Kafka vs RabbitMQ can vary widely based on your individual use cases. Apache Kafka is an excellent choice for the following scenarios:
- Real-time data processing: In the financial sector, it is critical to stop fraudulent transactions as soon as they occur. IoT devices are often useless without real-time data processing ability. Kafka can be helpful here since it can transmit data from producers to data handlers and then to data storage.
- Website Activity Tracking: At LinkedIn, Apache Kafka is utilized to stream activity data and operational analytics. It enables offline and online data processing in Hadoop systems by offering a parallel load mechanism and the ability to partition in real-time.
This implies that site activities like page views and searches are published to central topics per activity type. These feeds are available for subscription to a wide range of applications, including real-time processing, monitoring, and loading into Hadoop or offline data warehousing systems for processing and reporting.
- Log Aggregation: Kafka is frequently used as a replacement for a log aggregation solution. Log aggregation gathers physical log files from servers and stores them in a central location for processing.
Kafka abstracts away the contents of files and presents log or event data as a stream of messages, which is a cleaner abstraction. Lower latency processing and simpler support for numerous data sources and dispersed data consumption.
For a complete study of differences between Apache Kafka vs RabbitMQ, you can check out the following areas where using RabbitMQ can be advantageous:
- Complex routing: RabbitMQ may be the ideal solution when messages are required to route across multiple consumer applications. Its consistent hash exchange can balance load processing across a distributed monitoring service.
- File Scanning: Users can submit files to the Softonic platform, screened for viruses, and information about the file gathered before being distributed to other users.
A notification is received by the user as soon as the upload is complete. This is possible because of the Microservice Architecture Feature of RabbitMQ that allows the web servers to respond quickly to requests.
- Image scaling: To effectively promote a home and capture a buyer’s interest, real estate brokers require numerous images. When a broker uploads a new image of a property to the platform, RabbitMQ is tasked with scaling it to a user-friendly size.
The image scaling job is retained in the message queue until the consumer receives it, scales it, and publishes it to the website in the new, more efficient size.
2) Apache Kafka vs RabbitMQ: Architecture
The internal working and the basic architecture can be major factors in deciding between Apache Kafka vs RabbitMQ. Apache Kafka’ Architecture consists of the following components:
- Producer: Messages are published/pushed by producers to a Kafka topic that is generated on a Kafka broker. Furthermore, producers can choose between delivering messages to a broker in an asynchronous or synchronous mode.
- Kafka broker: Acts as a Kafka server. The messages are stored in the same sequence as they arrive at the broker, and the number of partitions for each message is accordingly configured.
- ZooKeeper: Serves as a coordinator between the Kafka broker and the consumers. It stores coordination data such as status information, configuration, and location information. To know more, you can check out the Official Apache Kafka Documentation.
- Mirror Maker: Kafka Replication is one of the most important features because it ensures that messages are published and consumed even if the broker fails for any reason.
- Consumers: Subscribes to the Kafka topic to receive/pull the messages. Kafka Consumers store messages in ZooKeeper by default. However, Kafka also permits information to be stored in alternative storage systems that are utilized for Online Transaction Processing (OLTP) applications.
To determine the best architecture between Apache Kafka vs RabbitMQ, you can go through the RabbitMQ’s Architecture that is designed using the following elements:
- Producer: Creates messages and publishes (sends) them to a broker server. A message has two parts: a payload and a label. The payload is the data the user wants to transmit. The label describes the payload and determines who should receive a copy of the message.
- Broker: Enables applications to communicate with one other and exchange information.
- Exchange: Messages delivered to RabbitMQ are received by an exchange, which decides where they should be transmitted.
Exchanges specify the routing behaviors applied to messages, most commonly by looking at data characteristics that are sent along with the message or included inside the message’s properties.
- Binding: It instructs an exchange to distribute messages to specific queues. The binding will also direct the exchange to filter which messages it can send to a queue for particular exchange kinds.
- Queue: It is in charge of storing received messages and may include configuration information that defines what it’s able to do with a message.
- Consumer: It is attached to a broker server and subscribes to a queue. To know more, you can check out the Official RabbitMQ Documentation.
3) Apache Kafka vs RabbitMQ: Performance
Various factors present in the application determine the performance of the application. Thus, it would be only fair to compare the performance of Apache Kafka vs RabbitMQ based on similar factors present in these applications.
While compared to RabbitMQ message brokers, Apache Kafka uses Sequential Disk I/O to improve speed when constructing queues. Kafka can handle millions of messages in a single second. RabbitMQ can handle millions of messages per second, but it will need more resources.
While deciding between Apache Kafka vs RabbitMQ, it is to be noted that unlike Kafka, which can keep a large amount of data with minimum overhead, RabbitMQ queues are only quicker when they’re empty.
4) Apache Kafka vs. RabbitMQ: Language
Kafka was first released in 2011 and written in Scala and Java. It is an open-source technology. On the other hand, RabbitMQ is developed in Erlang in 2007.
5) Apache Kafka vs. RabbitMQ: Push/Pull – Smart/Dumb
Kafka uses a pull mechanism that allows consumers to pull the data from the broker in batches. The consumer smarty keeps an offset tab to the last pulled message encounter. It organizes the data in the order in partitions by using offset.
RabbitMQ comes with a push design in which the consumer is not aware of any message retrieval. The Broker ensures that the message is delivered to the consumer.
Also, it sends back an acknowledgment after processing the data to ensure that messages are delivered to the consumer. On negative feedback, the message is again sent by putting it in the queue.
6) Apache Kafka vs. RabbitMQ: Scalability and Redundancy
Kafka partitions are essential for providing scalability and redundancy. It replicated the partition to many brokers. So, if in any case one of the brokers fails then another broker can serve the consumer.
If you keep all the partitions in one broker then dependency on the single broker will increase which is risky and chances of failure increase. Also, distributing the partitions will increase the throughput manifolds.
RabbitMQ replicates messages using a queue in a round-robin format. The messages are distributed between queues to increase the throughput and distribute the load. Also, it allows multiple consumers to read messages from multiple queues simultaneously.
7) Apache Kafka vs. RabbitMQ: Message Deletion
Kafka follows a retention period and messages stored based on the retention period are deleted once the period is over. On the other hand, RabbitMQ sends a positive acknowledgment via the consumer to get removed from the queue.
The messages are stored on the consumer on positive ACK and put back into the queue on negative ACK.
8) Apache Kafka vs. RabbitMQ: Message Consumption
Kafka Consumers read a message from the broker and maintain offset to keep the track of queue counter. The offset is incremented on reading the message.
The brokers of RabbitMQ are responsible to deliver a message to the consumer and these messages are sent into batches.
9) Apache Kafka vs. RabbitMQ: Message Priority
The priority for all messages is the same in Kafka and it cannot be changed.
RabbitMQ allows assigning priority to the messages using a priority queue.
10) Apache Kafka vs. RabbitMQ: Sequential Ordering
Kafka differentiates between messages using topics and Zookeeper maintains the offset so that whenever any consumer requests to read a topic, it will use the offset.
RabbitMQ maintains orders of the messages in the queue inside the broker.
11) Apache Kafka vs. RabbitMQ: Libraries and Language Support
Kafka supports Ruby, Python, Java, and Node.js.
12) Apache Kafka vs. RabbitMQ: Protocols
Kafka supports Binary Protocol over TCP. While RabbitMQ supports MQTT, AMQP, STOMP, HTTP protocols.
However, when reliability and performance are considered, Apache Kafka outperforms RabbitMQ. Choosing between Apache Kafka vs RabbitMQ has never been easy, and with both technologies improving daily, the margins of advantage have shrunk even further.
For a more comprehensive analysis of your business performance and financial health, it is essential to consolidate from Apache Kafka and all the other applications used across your business.
However, to extract this complex data with everchanging Data Connectors, you would require to invest a section of your Engineering Bandwidth to Integrate, Clean, Transform & Load data to your Data Warehouse or a destination of your choice. On the other hand, a more effortless & economical choice is exploring a Cloud-Based ETL Tool like Hevo Data.
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