Deciphering the underlying operating principles of Kafka message queue

The implementation mechanism of Kafka message queue

Kafka is a distributed publish-subscribe messaging system that allows producers to publish messages to topics , consumers can subscribe to these topics and receive messages. Kafka uses partitions to store messages, and each partition has a replica set. Each replica in the replica set stores data for that partition and can handle write requests from producers and read requests from consumers.

Kafka uses ZooKeeper to manage the metadata of the cluster, including topics, partitions, and replica sets. ZooKeeper is also used to coordinate producers and consumers. Producers use ZooKeeper to find partitions for a topic, and consumers use ZooKeeper to find partitions for subscribed topics.

Kafka message queue implementation code example

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

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

// 向主题发送消息
producer.send(new ProducerRecord<>("my-topic", "Hello, Kafka!"));

// 关闭生产者
producer.close();

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

// 订阅主题
consumer.subscribe(Arrays.asList("my-topic"));

// 轮询主题中的消息
while (true) {
  ConsumerRecords<String, String> records = consumer.poll(100);

  for (ConsumerRecord<String, String> record : records) {
    System.out.println(record.key() + ": " + record.value());
  }
}

// 关闭消费者
consumer.close();

In-depth analysis of the implementation mechanism of Kafka message queue

Kafka uses partitioning to To store messages, each partition has a replica set. Each replica in the replica set stores data for that partition and can handle write requests from producers and read requests from consumers. Kafka uses ZooKeeper to manage the cluster's metadata, including topics, partitions, and replica sets. ZooKeeper is also used to coordinate producers and consumers. Producers use ZooKeeper to find partitions for a topic, and consumers use ZooKeeper to find partitions for subscribed topics.

Kafka uses a mechanism called "replication factor" to ensure message reliability. Replication factor refers to the number of replicas in the replica set. If one replica fails, the other replicas can continue to provide service. Kafka also uses a mechanism called "consistency levels" to ensure the orderliness of messages. The consistency level can be set to "all" or "one". If the consistency level is set to "all", the message must be successfully replicated by all replicas to be considered committed. If the consistency level is set to "one", a message can be considered committed as long as it has been successfully replicated by one replica.

Kafka uses a mechanism called a "partition key" to ensure even distribution of messages. The partition key is a field of a message that determines in which partition the message is stored. Kafka uses an algorithm called a "hash function" to calculate the hash value of the partition key and then distributes the messages into different partitions based on the hash value.

Kafka uses a mechanism called "offsets" to track where consumers read messages. The offset is a number that indicates how many messages the consumer has read. Consumers use offsets to tell Kafka where to start reading messages.

Kafka uses a mechanism called "commit offset" to ensure that consumers do not read messages repeatedly. When the consumer finishes reading a batch of messages, it submits the offsets to Kafka. Kafka stores committed offsets in ZooKeeper. When the consumer next reads a message, it will start reading from the committed offset.

Advantages of Kafka message queue

• High throughput: Kafka can handle millions of messages per second.
• Low latency: Kafka’s latency is very low, usually only a few milliseconds.
• Reliability: Kafka uses replication factors and consistency levels to ensure message reliability.
• Scalability: Kafka can easily scale to thousands of nodes.
• Persistence: Kafka stores messages on disk, so even if a failure occurs, messages are not lost.

Disadvantages of Kafka message queue

• Complexity: The configuration and management of Kafka is relatively complex.
• Learning curve: Kafka’s learning curve is relatively steep.
• Cost: Kafka is a commercial software that requires payment to use.

Applicable scenarios for Kafka message queue

• Real-time data processing: Kafka is very suitable for processing real-time data, such as log data, sensor data and financial data .
• Stream processing: Kafka is well suited for stream processing, such as machine learning and fraud detection.
• Messaging: Kafka is great for messaging, such as emails, text messages, and social media messages.
• Event-driven architecture: Kafka is very suitable for event-driven architecture, such as microservice architecture and IoT architecture.

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