Analyze the key implementation principles of Kafka message queue

Analysis of the core implementation principles of Kafka message queue

1. Topics and partitions

Kafka The data in is stored in topics, and each topic can have multiple partitions. A partition is the physical storage unit of data in Kafka. Each partition is an independent, ordered, and immutable log file. Partitioning is key to Kafka's high throughput and high availability because data can be written to and read from different partitions in parallel.

2. Message producer

The message producer (producer) is the client that sends data to the Kafka topic. A producer can be any application as long as it implements Kafka's producer API. The producer API allows producers to send data to specific topics and partitions. If the producer does not specify a partition, Kafka will automatically choose one.

3. Message Consumer

The message consumer (consumer) is the client that reads data from the Kafka topic. A consumer can be any application as long as it implements Kafka's consumer API. The consumer API allows consumers to subscribe to specific topics and partitions. When a consumer subscribes to a topic, it starts reading data from the beginning of the topic. Consumers can read data in parallel because each consumer can read data from a different partition.

4. Message storage

Kafka stores data on disk. Each partition is an independent log file, and the log file is composed of multiple segments. The size of each segment is 1GB. When a segment is full, Kafka creates a new segment. Kafka periodically compresses old segments to save storage space.

5. Message replication

Kafka ensures data reliability through replication. The data of each partition will be copied to multiple replicas. Replicas can be on different servers. When one replica fails, other replicas can continue to provide services.

6. Message submission

After the consumer reads data from Kafka, it needs to submit (commit) its consumption progress to Kafka. The commit operation stores the consumer's consumption progress into Kafka's metadata. Metadata is stored in ZooKeeper. The commit operation ensures that consumers will not consume data repeatedly.

7. Message offset

Each message has an offset. An offset is a unique identifier that identifies the location of a message within a partition. The offset can be used to track the consumer's consumption progress.

8. Consumer group

Consumer group is a logical grouping of consumers. Consumers in a consumer group can consume data from the same topic in parallel. When consumers in one consumer group consume data, consumers in other consumer groups do not consume that data.

9. Load balancing

Kafka uses load balancing to ensure that data is evenly distributed across different partitions. The load balancer is responsible for distributing data to different partitions. Load balancers can distribute data based on different strategies, such as round-robin, random, or consistent hashing.

10. Code Example

The following is a simple Java code example that demonstrates how to use the Kafka producer and consumer API:

// 创建生产者
Properties producerProps = new Properties();
producerProps.put("bootstrap.servers", "localhost:9092");
producerProps.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
producerProps.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer<String, String> producer = new KafkaProducer<>(producerProps);

// 创建消费者
Properties consumerProps = new Properties();
consumerProps.put("bootstrap.servers", "localhost:9092");
consumerProps.put("group.id", "my-group");
consumerProps.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
consumerProps.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
KafkaConsumer<String, String> consumer = new KafkaConsumer<>(consumerProps);

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

// 发送消息
producer.send(new ProducerRecord<String, String>("my-topic", "hello, world"));

// 接收消息
while (true) {
    ConsumerRecords<String, String> records = consumer.poll(100);
    for (ConsumerRecord<String, String> record : records) {
        System.out.println(record.key() + ": " + record.value());
    }
}

Summary

Kafka is a distributed, scalable message queue system. It can be used to build a variety of applications, such as log collection, data analysis, real-time stream processing, etc. The core implementation principles of Kafka include topics, partitions, message producers, message consumers, message storage, message replication, message submission, message offsets, consumer groups and load balancing, etc.

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