In-depth analysis of the principles and architecture of Kafka: revealing the core of the distributed messaging system

Kafka principle and architecture analysis: in-depth analysis of the core of the distributed messaging system

Introduction

Kafka is a distributed messaging system developed by LinkedIn and originally open sourced in 2011. Kafka is widely used to build real-time data pipelines, stream processing applications, and machine learning platforms.

Basic Principle

The basic principle of Kafka is to store data in a ledger called a topic. A topic can be subscribed to by multiple consumers, each of which reads data from the topic. Kafka uses partitions to shard data so that data can be processed in parallel across multiple servers.

Architecture

A Kafka cluster consists of multiple servers, which are called brokers. Each broker stores a copy of the data for all topics in the cluster. Agents communicate with each other through a distributed coordination service called ZooKeeper.

Data Storage

Kafka stores data in files called log segments. Log segments are immutable, which means that once data is written, it cannot be modified. Log segments are organized into partitions called topics. Each partition consists of multiple log segments.

Data consumption

Consumers read data from the topic. Each consumer has a pointer called an offset that points to the last message the consumer read in the topic. When a consumer reads data from the topic, it updates the offset to ZooKeeper.

Data production

Producers write data to the topic. Producers can write data to any partition. Kafka automatically replicates data to all other brokers in the cluster.

Fault Tolerance

Kafka has strong fault tolerance. If one agent fails, other agents will take over that agent's data. If a partition fails, Kafka automatically copies the data from that partition to another partition.

Scalability

Kafka can easily scale to meet growing data volumes. Just add more agents to the cluster. Kafka automatically rebalances data to all brokers.

High performance

Kafka has high performance. It can handle millions of messages/second. Kafka uses batching and compression techniques to improve performance.

Reliability

Kafka is a reliable messaging system. It ensures that data will not be lost. Kafka uses replication and failover mechanisms to ensure reliability.

Code Example

The following is a simple code example using Kafka:

// 创建一个生产者
Producer<String, String> producer = new KafkaProducer<>(properties);

// 创建一个主题
String topic = "my-topic";
producer.createTopic(topic);

// 向主题发送数据
producer.send(new ProducerRecord<>(topic, "hello, world"));

// 创建一个消费者
Consumer<String, String> consumer = new KafkaConsumer<>(properties);

// 订阅主题
consumer.subscribe(Collections.singletonList(topic));

// 从主题中读取数据
while (true) {
  ConsumerRecords<String, String> records = consumer.poll(100);
  for (ConsumerRecord<String, String> record : records) {
    System.out.println(record.value());
  }
}

Conclusion

Kafka is a powerful distributed messaging system with strong fault tolerance, scalability and high performance. Kafka is widely used to build real-time data pipelines, stream processing applications, and machine learning platforms.

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