Traditional message brokers like Java Messaging Service (JMS), Apache ActiveMQ, RabbitMQ, and others have been effective at relaying messages. However, they are not designed to handle high volumes of messages and provide fault tolerance.
Apache Kafka is designed to handle large volumes of messages and provide fault tolerance. It can be used as the central nervous system of a distributed architecture that delivers data to multiple systems.
This article will explain why Apache Kafka is the future of messaging and how you can use Kafka to modernize traditional messaging brokers like the Java Messaging Service (JMS).
Apache Kafka is a distributed, partitioned, replicated commit log service. It helps you process a large amount of data through the use of various Kafka consumers.
Kafka is available as a standalone server that you can install on your local machine and various other applications can consume it. The primary reason behind developing Apache Kafka was to serve companies with fast and efficient data pipelines.
Apache embraced this role and has helped enhance the performance of organizations in event stream processing. In particular, its high throughput makes its use essential in several event streaming platforms. In this article, we will assess the features of Kafka that are enhancing its dominance in messaging.
How Kafka differs from traditional message brokers
Companies are using Apache Kafka to replace traditional message brokers due to various reasons.
Below are some of the major factors:
Support for an event-driven architecture
Unlike many traditional message brokers, Kafka integrates event-driven architecture (EDA) in its processes. An EDA makes messaging flexible and responsive.
Upon generation of an event, the system records what happens and waits to provide feedback. The app that received the notification usually replies immediately but, in a few cases, may delay until the status changes.
Apache Kafka decouples the microservices and relies on its data pipelines to connect consumers with the producers. So, the system can directly assess events and respond to them instead of checking for new data.
Besides, event-driven architecture is such that the events published in Apache Kafka remain for some time before their removal. Thus, various consumers can read the data and respond to it before its eventual deletion.
Real-time data processing
One of the strengths of Kafka is its ability to process large volumes of data in real-time. This feature enables the analysis of data and deduction of meaning from large data inflows within the shortest time possible. Platforms relying on Kafka do not have to maintain an extensive database of unprocessed records.
Enterprises can process big streams of incoming data without experiencing time lags. The enhanced speed is a primary reason why Kafka is very popular among businesses. Corporate and non-corporate firms want a quick analysis of their data, which many traditional message brokers cannot offer.
Another advantage is that Kafka can handle data as it arrives at high speeds with minimal resources. If an organization were to rely on traditional messaging systems, the costs would be significantly higher than when using Kafka.
Kafka’s architecture favors efficient utilization of resources in many ways, including:
Relying on Change Data Capture (CDC) methods like logs and queries to track any recent data alterations. In this way, Kafka only transforms a limited section of the data rather than a whole load in case of any change.
Performing incremental data changes to the analytic business system and facilitating on-the-go data analysis. This method works well with real-time data and saves on the costs of piling up unprocessed data in a database.
Retaining the originality of data at the source. Kafka records are persistent. This enables businesses to handle rapid and continuous changes occurring at a high rate from several users.
Kafka supports high-volume data delivery because it subdivides the events into small batches to reduce the network calls. It compresses data and sends it to a server that writes them in a compressed form.
The decompression of data happens at the subscriber’s level. That way, Kafka transmits large amounts of data, occupying a smaller volume than they would in their usable forms.
Kafka implements low latency storage and caching system. It uses the file system to avoid the costly use of RAM (which would provide low latency, too). But how does a file system provide low latency considering it’s high seek time?
Kafka implements Sequential Input/Output operation. Sequential I/O operation consistently outperforms RAM I/O in data transmission speeds, enhancing high throughput.
Also, Kafka’s horizontal scaling enhances low latency and high throughput. Kafka can have several partitions from a single topic distributed across thousands of machines. The scaling facilitates quick per unit time data processing and relaying.
Kafka can be used on multiple platforms. The event streaming system works with apps such as Twitter, LinkedIn, and Netflix to make their streaming features efficient.
Apache Storm and Kafka form an essential aspect of Twitter’s energy processing infrastructure. For LinkedIn, Kafka helps with streaming products like LinkedIn Newsfeed. It also enhances messaging analysis on the platform. For Netflix, Kafka facilitates real-time event monitoring.
Support for multiple message formats
Another essential feature of Kafka that makes it different from traditional messaging brokers is its support for multiple message formats. Messages can take any form, most commonly the JSON and Avro formats. Kafka messages may have other formats, including Strings, ByteBuffers, Long, ByteArrays, ProtoBuf, and SerDes.
Using Apache Kafka to replace traditional message brokers
Java Messaging Service (JMS) is the earliest enterprise-level messaging Application Programming interface. Kafka’s rise and increasing popularity could replace traditional messaging brokers like JMS, due to the following factors:
Kafka temporarily stores received messages allowing consumers to keep accessing them long after reading and responding. However, JMS deletes records of these messages immediately after a consumer reads them.
Apache Kafka facilitates increased functionality by partitioning the topics into small chunks of data that a distributed system can handle. In JMS, segregation does not follow a sequential format, resulting in lower throughput.
In the Kafka system, brokers can choose the message to read first. But JMS limits this freedom by implementing the first-out approach. One must read the messages in the order of their arrival. Thus, Kafka wins over JMS in that regard.
Kafka provides higher scalability and availability than JMS. Its higher auto-reproduction of messages guarantees high availability without compromising its simplicity.
Kafka provides consumers with higher freedom than JMS. Apache links consumers to producers through data pipelines. Users can choose what data to consume, and the producers’ only role is to ensure that their messages reach the final consumer. At that point, the consumer can decide to subscribe to the data or ignore it.
Kafka for data pipelines
Data pipelines facilitate the transmission of information from one system to another for processing and storage. Apache Kafka has a complex data pipeline that relays data in real-time.
A good instance of Apache Kafka using data pipelines is the Extraction, Transformation, and Loading (ETL) pipelines. Integration of data pipelines in Apache Kafka facilitates the development and deployment of stream-processing apps that send messages between Kafka topics.
Data pipelines are a crucial element of Kafka because the event streaming platform relies on them to ingest data from several sources. It then streams the data to several other targets. For instance, data pipelines allow several users on platforms like LinkedIn to send messages to other people. These data pipelines link two or more users facilitating content streaming.
Apache Kafka is a messaging and event-streaming system. It is popular in the modern business world due to the reasons discussed above. It fulfills corporates’ needs for quick data analysis and transmission. The system is simple, scalable, and fast, making it superior to traditional messaging system brokers.
Kafka is replacing traditional messaging systems, such as JMS. It preserves data records longer than JMS and provides higher availability. It embraces different messaging formats and handles big data more efficiently, and several platforms are already implementing it.
As Kafka keeps advancing, many firms could abandon traditional messaging systems for this modern event-streaming service.
- Do We Need a Database When We Have Apache Kafka?
- Breaking Down Kafka vs. Pulsar
- Introduction to Kafka
- What is Kafka? How to Build and Dockerize a Kafka Cluster
Peer Review Contributions by: Wanja Mike