Kafka Implementation Principles Explained

The implementation principles of Kafka message queues mainly include the following aspects:

1. Distributed Architecture: Kafka uses a distributed architecture to store data across multiple nodes, enhancing the reliability and scalability of messages. Messages are partitioned and stored across multiple brokers, with each broker able to store multiple partitions.
2. Message storage: Kafka uses logs to store messages, with each partition corresponding to a log file. Messages are appended to the end of the log file, and are stored in the log file in order based on offset to ensure message sequence.
3. Producers and consumers: Producers send messages to specific topics, while consumers subscribe to messages from those topics. Producers and consumers coordinate through Zookeeper, which manages all brokers and partition information in Kafka.
4. High performance and low latency: Kafka leverages sequential I/O and zero-copy technology to enhance performance, while utilizing techniques such as batch sending and compression to reduce the frequency and size of network transfers, thus lowering latency.
5. Message replication and fault tolerance: Kafka ensures message reliability through message replication, where each partition can have multiple replicas that stay in sync through the in-sync replicas (ISR) mechanism. In case one broker goes down, other replicas can take over its work and guarantee the reliable delivery of messages.