Distributed task queue practice based on go-zero

With the development of Internet technology, distributed technology is becoming more and more mature, and its application scenarios are becoming more and more extensive. In distributed systems, task queues are common components that can process tasks asynchronously, reduce system pressure, and improve system performance. This article will introduce the practice of distributed task queue based on go-zero.

1. Introduction to go-zero

go-zero is a microservice framework that integrates a variety of components, including RPC framework, web framework, cache components, current limiting, circuit breaker, etc. Common components. Simple to use and powerful in performance, it is the best choice for developing microservice applications.

2. Introduction to task queue

Task queue is a common distributed system component, which is mainly used for asynchronous processing of tasks. Task queues can be used to cut peaks and fill valleys, reduce system load, and improve system performance. A task queue usually consists of two parts: a producer and a consumer. The producer is responsible for generating tasks and putting them into the task queue, while the consumer is responsible for retrieving tasks from the task queue and executing them.

3. Implementation of task queue in go-zero

The task queue in go-zero is implemented using the list structure of redis. In go-zero, you can easily create a task queue. The specific operations are as follows:

1. Create a task structure

The task structure contains information such as task type, business data, etc., as follows Design according to actual needs.

type Task struct {

Type int //任务类型
Data interface{} //业务数据

}

2. Create a task queue

Use the redis list structure to implement the task queue, and use the redis lpush command Put the task into the queue and get the task from the queue through the rpop command. In go-zero, you can connect to the redis service through the goredis package and execute related commands.

func pushTask(task Task) {

data, _ := json.Marshal(task)
conn := redis.RedisClient().Get()
defer conn.Close()
conn.Do("lpush", "task_queue", data)

}

func popTask() Task {

conn := redis.RedisClient().Get()
defer conn.Close()
taskStr, _ := redis.String(conn.Do("rpop", "task_queue"))
var task Task
json.Unmarshal([]byte(taskStr), &task)
return task

}

In the actual project , the task queue can be expanded according to needs, such as increasing task timeout, task retry mechanism, etc.

4. Distributed processing tasks

In actual distributed systems, task queues are usually deployed on independent servers, and different service nodes are connected to the same task queue for task processing. In order to achieve load balancing and high availability, distributed deployment of task queues can be achieved by introducing middleware. Commonly used middlewares include kafka, rabbitmq, etc.

In go-zero, we can achieve seamless integration of task queues and middleware through library storage.

1. Create a task queue

To create a task queue in go-zero, you need to create a storage first, through which you can connect to different middleware.

// Create storage
c := &redis.CacheConf{

CacheConf: cache.CacheConf{
    Mode: cache.CacheRedis,
    Redis: redis.RedisConf{
        Type:     redis.NodeType,
        Node:     redisConfig.Redis.Node,
        Name:     redisConfig.Redis.Name,
        Password: redisConfig.Redis.Password,
    },
},

}

// Create a task queue through storage
taskQueue := queue.New ("task_queue", c)

2. Create producers and consumers

Producers and consumers are connected through the task queue. The producer is responsible for sending tasks to the task queue, and the consumer Responsible for obtaining tasks from the task queue and executing them.

// Create a producer
producrer := taskQueue.Producer()

// Create a consumer group and subscribe to the task queue
consumer := taskQueue.NewConsumerGroup(

"task_group",
[]string{"task_queue"},
handleTask,
queue.WithConsumerGroupConcurrency(concurrency),

)

3. Write the task processing function

The task processing function is used to implement specific task processing logic and can be customized according to actual project needs.

func handleTask(ctx context.Context, msgs []*primitive.Message) error {

for _, msg := range msgs {
    fmt.Printf("Received message: %s

", msg.Body)

    // TODO: 处理具体业务逻辑
}
return nil

}

Through the above steps, we can easily seamlessly integrate the task queue and middleware to achieve distributed task processing.

5. Summary

Through the above practices, we have learned about go- How to implement task queue in zero, and how to seamlessly integrate task queue with middleware to achieve distributed task processing. As a high-performance microservice framework, go-zero has rich components that can help developers quickly build High-performance distributed system. Let us experience the charm of go-zero together!

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