What is Kafka?
Apache Kafka is an open-source, fast, scalable, fault-tolerant publish-subscribe messaging system that enables connections between producers and consumers using reliable and conversational message-based leads. It designs on a high-end new generation contributed platform and distributed applications.
Apache Kafka permits many permanent or ad-hoc consumers, and Kafka is highly available and resilient to node failures and supports automatic recovery. These characteristics make Kafka ideal for communication and integration between components of large-scale data systems in real-world data systems. In this article, we might see the Kafka v/s RabbitMQ.
How is Kafka helpful:
- Kafka is used heavily in big data as a reliable way to ingest and move large amounts of data quickly.
- Kafka shows up well as a replacement for a more traditional message broker. Message brokers decoupled data producers’ processing, buffer unprocessed messages, and many more.
- The original use and adjacent case for Kafka are to rebuild a user activity in such a case of tracking pipeline of real-time publish-subscribe feeds.
- Kafka is generally used for operational and monitoring data. This contains aggregating statistics from distributed applications to produce centralized operational data feeds.
- Many people have started using Kafka as a replacement for a log aggregation solution. Log aggregation typically collects physical log files off servers and puts them in a central place.
What is RabbitMQ? When to use it?
A couple of basic examples.
- File transcoding. A user of your application uploads some that need to be transcoded into another format, such as video or audio. If you don’t create some background process or queue, the user will have to wait for the process to complete before moving to the next step. In the case of video, this could be a very long time.
- Delivering notifications. When a specific action happens in your application, you may wish to provide a message to a user. By decoupling your code, that deals with sending notifications (via email, SMS, or however). You can pass the values to the consumer.
- Accounting, billing, and other delayed calculations. If you need to make some complex calculations and another process that the user doesn’t need to view immediately, these can be passed through a message queue for consumption.
RabbitMQ (and other advanced message queues) can also be used to create particular system rules between how many lines, consumers, and bindings you start to manage the flow of messages.
This will help scale your application regarding the more processing-intensive tasks and manage communication between many different services or applications.
The actual difference between Kafka and RabbitMQ
RabbitMQ is famous for its documentation management, and its program codes are easily adapted. The installation was also easy, and topics were easy to create by the web interface. Although good performance, the behavior is not compatible with my system requirements.
Kafka was designed originally by LinkedIn and is written in Java. Kafka has a unique architecture, where you can store messages in flat files. The server is straightforward, which makes it very fast to operate, and old notes can be retained regularly. However, one negative point is that Kafka is not good at synchronizing and launching large data sets, so you may need an additional tool to help you. Overall, Kafka is suitable for low resource usage.
- Kafka is distributed. Data is shared and replicated with guaranteed durability and availability.
- High-performance rate to the tune of 100,000 messages/second.
- Kafka also comes with consumer frameworks that allow reliable log-distributed processing. There is stream processing semantics built into the Kafka Streams.
- RabbitMQ provides relatively less support for these features.
- The performance rate is around 20,000 messages/second.
- The consumer in RabbitMQ is just FIFO based, reading from the HEAD and processing one by one.
Performance and architecture
Let go another way; Kafka states that producers generate a massive stream of events on their schedule – there’s no stayover for throttling producers because consumers are slow since the data is too huge.
The whole experience of Kafka is to circulate the “shock absorber” between the ranges of events and those who want to absorb them in their ways online, and soon others shifting to offline only to consider batch consuming on an hourly or even daily basis.
To look according to performance-wise, both are excellent performers but have significant architectural differences.
RabbitMQ has demonstrated setups of over a million messages/sec, and Kafka has demonstrated several million messages/sec. The primary architectural difference is that RabbitMQ handles its messages largely in memory and thus uses a large cluster in these benchmarks (30+ nodes). In contrast, Kafka proudly leverages the powers of sequential disk I/O and requires less hardware (this benchmark uses 3x six-core / 32 GB RAM nodes).
RabbitMQ starts the innovative broker consumer model. The broker, through it delivers messages to consumers and keeps the status of the data.
Kafka uses the innovative consumer model. It doesn’t monitor the messages the users have read. Instead, it unread messages only, containing all letters for a short period. Every consumer needs to monitor the position in each log.
Kafka uses the subscribe topology, sending messages across the stream to the suitable topics and the consumer’s uses in different authorized groups.
Whereas, RabbitMQ employs the exchange line topology, which means sending messages to exchange with U-turn routes in various buildings for the consumer’s use.
Scalability and redundancy
Kafka groups serve scalability and redundancy, and the partition they provided was duplicated across numerous brokers. For example, if the brokers fail, there are chances to be operated by another broker.
Whereas, RabbitMQ uses the round-robin line to repeat messages. The messages are divided among the queues to engage or boost the balance of the load.
Over to you
Kafka and RabbitMQ are both great career opportunities to take up near future. With time and resource availability, please consider taking both courses from Simplilearn online learning, which opens up a great opportunity, boosts your skillset, and enhances your flexibility in not only one sector but also gives you exposure to many others.