What is the principle behind receiving data in Kafka?

The principle behind Kafka’s data reception is based on a messaging system using a publish-subscribe pattern. It operates as follows:

1. Kafka organizes and stores data in the form of messages, which are divided into different topics. Each topic can have multiple partitions, and each partition can be replicated on different servers to ensure high availability.
2. Producers publish messages to specific topics, with each message being appended to a partition within the topic. Producers can choose to send messages to specific partitions based on their needs, or use a load balancing mechanism to send messages to multiple partitions.
3. Consumers can subscribe to one or more topics and read data from one or more partitions within the specified topic. Each consumer maintains a consumer group, and each partition can only be consumed by one consumer from a consumer group.
4. Kafka utilizes offsets to track a consumer’s progress in consuming from each partition. Consumers have the flexibility to control where they consume from, either starting from the earliest message or the latest message.
5. Consumers can process messages asynchronously after reading them, such as storing them in a database or performing other business logic operations.
6. Kafka provides high throughput and scalability through persistent storage and batch writes. It utilizes sequential disk writes to improve performance and can dynamically scale storage and processing capacity as data volume grows.

In conclusion, Kafka organizes and stores data in the form of messages, where producers publish messages to partitions within a topic, and consumers read and process messages from the partitions, thus achieving high-performance and reliable data transmission and processing.