Establish real-time caching technology based on Kafka message queue in Golang.

With the continuous development of Internet technology and the continuous expansion of application scenarios, real-time caching technology has increasingly become an essential skill for Internet companies. As a method of real-time caching technology, message queue is increasingly favored by developers in practical applications. This article mainly introduces how to establish real-time caching technology based on Kafka message queue in Golang.

What is Kafka message queue?

Kafka is a distributed messaging system developed by LinkedIn that can handle tens of millions of messages. It has the characteristics of high throughput, low latency, durability, and high reliability. Kafka has three main components: producers, consumers and topics. Among them, producers and consumers are the core parts of Kafka.

The producer sends messages to the specified topic, and can also specify the partition and key (Key). Consumers receive corresponding messages from the topic. In Kafka, producers and consumers are independent and have no dependencies on each other. They only interact with each other by sharing the same topic. This architecture implements distributed message delivery and effectively solves message queue requirements in various business scenarios.

The combination of Golang and Kafka

Golang is an efficient programming language that has become popular in recent years. With its high concurrency, high performance and other characteristics, it is increasingly widely used. It has the inherent advantage of combining with message queues, because in Golang, the number of goroutines has a one-to-one relationship with the number of kernel threads, which means that Golang can handle large-scale concurrent tasks efficiently and smoothly, while Kafka can Distribute various messages to different broker nodes according to customizable partition rules to achieve horizontal expansion.

By using the third-party Kafka library sarama in Golang, we can easily implement interaction with Kafka. The specific implementation steps are as follows:

1. Introduce the sarama library into the Golang project:

import "github.com/Shopify/sarama"

2. Create a message sender (Producer) instance:

config := sarama.NewConfig()
config.Producer.Return.Successes = true
producer, err := sarama.NewAsyncProducer([]string{"localhost:9092"}, config)

Among them, NewConfig() is used to create a new configuration file instance. Return.Successes indicates that success information will be returned when each message is sent successfully. NewAsyncProducer() is used to create a producer instance. The string array in the parameter represents the Broker in the Kafka cluster. The IP address and port number of the node.

3. Send a message:

msg := &sarama.ProducerMessage{
  Topic: "test-topic",
  Value: sarama.StringEncoder("hello world"),
}
producer.Input() <- msg

Among them, ProducerMessage represents the message structure, Topic represents the topic to which the message belongs, and Value represents the message content.

4. Create a message consumer (Consumer) instance:

config := sarama.NewConfig()
config.Consumer.Return.Errors = true
consumer, err := sarama.NewConsumer([]string{"localhost:9092"}, config)

Among them, NewConfig() is used to create a new configuration file instance, and Return.Errors means that each time a message is consumed, Returns an error message of consumption failure. NewConsumer() is used to create a consumer instance.

5. Consume messages:

partitionConsumer, err := consumer.ConsumePartition("test-topic", 0, sarama.OffsetNewest)
for msg := range partitionConsumer.Messages() {
  fmt.Printf("Consumed message: %s
", string(msg.Value))
  partitionConsumer.MarkOffset(msg, "") // 确认消息已被消费
}

Among them, ConsumePartition() is used to specify the topic, partition and consumption location (latest message or oldest message) of consumption, and Messages() is used to obtain from Messages consumed in the topic. After consuming a message, we need to use the MarkOffset() method to confirm that the message has been consumed.

Kafka real-time cache implementation

In Golang, it is very convenient to establish a real-time cache through the Kafka message queue. We can create a cache management module in the project, convert the cache content into the corresponding message structure according to actual needs, send the message to the specified topic in the Kafka cluster through the producer, and wait for the consumer to consume the message from the topic and proceed. deal with.

The following are the specific implementation steps:

1. Define a cache structure and a cache variable in the project:

type Cache struct {
  Key   string
  Value interface{}
}

var cache []Cache

Among them, Key represents the cache key (Key) , Value represents the cached value (Value).

2. Convert the cache into the corresponding message structure:

type Message struct {
  Operation string // 操作类型（Add/Delete/Update）
  Cache     Cache  // 缓存内容
}

func generateMessage(operation string, cache Cache) Message {
  return Message{
    Operation: operation,
    Cache:     cache,
  }
}

Among them, Message represents the message structure, Operation represents the cache operation type, and generateMessage() is used to return a Message instance.

3. Write a producer and send the cached content as a message to the specified topic:

func producer(messages chan *sarama.ProducerMessage) {
  config := sarama.NewConfig()
  config.Producer.Return.Successes = true
  producer, err := sarama.NewAsyncProducer([]string{"localhost:9092"}, config)
  if err != nil {
    panic(err)
  }

  for {
    select {
    case msg := <-messages:
      producer.Input() <- msg
    }
  }
}

func pushMessage(operation string, cache Cache, messages chan *sarama.ProducerMessage) {
  msg := sarama.ProducerMessage{
    Topic: "cache-topic",
    Value: sarama.StringEncoder(generateMessage(operation, cache)),
  }
  messages <- &msg
}

Among them, producer() is used to create a producer instance and wait for the message incoming from the pipeline to be sent. , pushMessage() is used to convert the cached content into a Message instance and send it to the specified topic using the producer.

4. Write a consumer, listen to the specified topic and perform corresponding operations when the message arrives:

func consumer() {
  config := sarama.NewConfig()
  config.Consumer.Return.Errors = true
  consumer, err := sarama.NewConsumer([]string{"localhost:9092"}, config)
  if err != nil {
    panic(err)
  }

  partitionConsumer, err := consumer.ConsumePartition("cache-topic", 0, sarama.OffsetNewest)
  if err != nil {
    panic(err)
  }

  for msg := range partitionConsumer.Messages() {
    var message Message
    err := json.Unmarshal(msg.Value, &message)
    if err != nil {
      fmt.Println("Failed to unmarshal message: ", err.Error())
      continue
    }

    switch message.Operation {
    case "Add":
      cache = append(cache, message.Cache)
    case "Delete":
      for i, c := range cache {
        if c.Key == message.Cache.Key {
          cache = append(cache[:i], cache[i+1:]...)
          break
        }
      }
    case "Update":
      for i, c := range cache {
        if c.Key == message.Cache.Key {
          cache[i] = message.Cache
          break
        }
      }
    }
    partitionConsumer.MarkOffset(msg, "") // 确认消息已被消费
  }
}

Among them, consumer() is used to create a consumer instance and listen to the specified topic, use The json.Unmarshal() function parses the Value field of the message into a Message structure, and then performs corresponding caching operations based on the Operation field. After consuming a message, we need to use the MarkOffset() method to confirm that the message has been consumed.

Through the above steps, we have successfully used the Kafka library sarama in Golang to establish real-time caching technology based on Kafka message queue. In practical applications, we can choose different Kafka cluster configurations and partition rules according to actual needs to flexibly cope with various business scenarios.

The above is the detailed content of Establish real-time caching technology based on Kafka message queue in Golang.. For more information, please follow other related articles on the PHP Chinese website!