Implementing a highly scalable distributed image processing system: application and practice of go-zero

With the development of modern technology, image processing technology is playing an increasing role in all walks of life. From surveillance systems in smart cities, to diagnosis and treatment of medical imaging, to games and film and television production in the entertainment industry, image processing algorithms are one of the indispensable core technologies. However, with the growth of image data and the number of users, traditional image processing solutions are gradually unable to meet the needs of high concurrency, low latency, and high scalability. Therefore, distributed image processing systems have gradually become a mainstream solution. .

Among many distributed image processing frameworks, go-zero is a back-end development framework worthy of attention. It provides a complete set of distributed microservice solutions, including API gateway, service governance, limited Features such as stream circuit breaker, massive data storage and distributed transactions. When developing and applying image processing systems, go-zero's comprehensive support can greatly improve the reliability and performance of the system. This article will introduce the application and practice of go-zero in distributed image processing from various aspects such as application scenarios, architecture design, technology selection, and code implementation.

1. Application scenarios

The image processing system is a typical data-intensive and computing-intensive application. The main problems it faces include:

1. Large amount of data and high request concurrency: For scenarios that require instant response, such as real-time monitoring systems and live broadcast systems, hundreds of thousands or even millions of image data can be generated per second. It is necessary to be able to quickly process these data and provide high throughput, Low latency service.
2. Complex task calculations: Facing computationally intensive tasks such as complex image algorithms and deep learning models, it is necessary to quickly and accurately complete various image processing operations such as feature extraction, classification, recognition, and synthesis of images.
3. High availability and high scalability: Under the changing business needs, the system needs to be highly available and highly scalable, able to cope with abnormal situations such as sudden traffics and node failures, and achieve continuous and stable services. .

go-zero can be applied to a variety of scenarios that encounter the above problems, such as:

1. Image classification system: Classify images, such as faces, car models , food, etc. for automatic identification.
2. Image synthesis system: Combine multiple images into one image, such as picture splicing, concrete product image synthesis, etc.
3. Monitoring system: Perform real-time processing of images, such as people flow statistics, text recognition, etc., and convert image data into usable statistical data.

2. Architecture design

In order to meet the above needs, we need to design a reliable, scalable and efficient distributed image processing system. With the help of go-zero, we can achieve the following infrastructure design:

1. API Gateway: Provides web API interface gateway services to uniformly manage requests from different clients.
2. RPC Service: Business service, responsible for specific image processing tasks, using a distributed microservice model, divided and deployed according to business.
3. Configuration Service: Configuration service, which uniformly manages and distributes public configurations.
4. Resource Management: Resource management, including monitoring, flow control, circuit breaker degradation, current limiting and other functions, to ensure reasonable utilization of system resources and stable performance.
5. Storage Service: Store the processed data in the cloud distributed shared storage system to facilitate subsequent business access and query.

3. Technology Selection

When designing specific technical solutions, we can first select some traditional technologies and algorithms suitable for image processing, and then use the ones provided by go-zero Microservice framework and some mainstream distributed technologies are used to realize the functions of the entire system.

Specifically, the following technologies can be used to achieve:

1. Image processing algorithm: Using traditional image processing algorithms, such as OpenCV, PIL and other libraries and some deep learning models, it can be achieved Various image processing tasks such as feature extraction, classification, recognition, and synthesis of images.
2. Microservice framework: Using go-zero's microservice framework, you can quickly build a distributed image processing system to achieve tasks division, business logic development, load balancing, fault recovery and other functions.
3. Distributed technology: Distributed coordination technologies such as etcd and zookeeper can be used to realize functions such as service registration and discovery, configuration management, etc., making the system more stable and reliable.
4. Storage layer: You can choose a distributed shared storage system, such as FastDFS, etc., to share and manage the processed data.

4. Code Implementation

When specifically implementing the above functions, we can use the code framework provided by go-zero to complete specific business logic and technical implementation. The following is a sample program that represents the development process of a complete distributed image processing system.

First, introduce the necessary framework and dependency packages in main.go:

package main

import (
    "github.com/tal-tech/go-zero/core/conf"
    "github.com/tal-tech/go-zero/core/logx"
    "github.com/tal-tech/go-zero/rest"
)

func main() {
    logx.Disable()
    var c Config
    conf.MustLoad(&c)

    server := rest.MustNewServer(c.RestConf)
    defer server.Stop()

    InitHandlers(server.Group("/"))

    go func() {
        select {
        case <-server.Done():
            logx.Info("Stopping...")
        }
    }()
    server.Start()
}

Among them, the Config structure stores the system configuration information, which is configured in config.toml; the rest package Provides an encapsulation of HTTP services and implements specific business logic in the InitHandlers function.

func InitHandlers(group *rest.Group) {
    group.POST("/image/:type", func(ctx *rest.Context) {
    // 业务逻辑：根据type参数分发图像任务，调用具体的RPC服务进行处理
    })
}

Next, implements specific business logic in the handlers package.

package handlers

import (
    "context"
    "encoding/base64"
    "github.com/tal-tech/go-zero/core/logx"
    "github.com/tal-tech/go-zero/rest/httpx"
    "github.com/tal-tech/go-zero/zrpc"
    "github.com/yanyiwu/gojieba"
    "go-zero-example/service/image/api/internal/logic"
    "go-zero-example/service/image/api/internal/svc"
    "go-zero-example/service/image/rpc/image"
)

const (
    FACE_DETECT = iota
    FACE_RECOGNITION
    COLOR_DETECT
)

var jieba = gojieba.NewJieba()

type ImageType int32

type ImageHandler struct {
    ctx    context.Context
    svcCtx *svc.ServiceContext
}

func NewImageHandler(ctx context.Context, svcCtx *svc.ServiceContext) *ImageHandler {
    return &ImageHandler{ctx: ctx, svcCtx: svcCtx}
}

func (l *ImageHandler) Handle(reqTypes []ImageType, base64Data string) (*image.Data, error) {
    req := logic.ImageReq{
        ReqTypes:   reqTypes,
        Base64Data: base64Data,
    }

    // 将图像处理请求分发给所有RPC服务
    results := make([]*image.Data, 0, len(reqTypes))
    for _, reqType := range reqTypes {
        data, err := l.svcCtx.ImageRpcClient.DoImage(l.ctx, &image.ImageReq{
            ImageType: int32(reqType),
            ImageData: base64Data,
        })
        if err != nil {
            logx.WithError(err).Warnf("image rpc call failed: %v", data)
            return nil, httpx.Error(500, "服务内部错误")
        }

        results = append(results, data)
    }

    // 直接返回结果
    return logic.MergeResults(results), nil
}

// 字符串转float
func str2float(str string, defVal float64) float64 {
    if len(str) == 0 {
        return defVal
    }
    val, err := strconv.ParseFloat(str, 64)
    if err != nil {
        return defVal
    }
    return val
}

// 字符串转int
func str2int(str string, defVal int64) int64 {
    if len(str) == 0 {
        return defVal
    }
    val, err := strconv.ParseInt(str, 10, 64)
    if err != nil {
        return defVal
    }
    return val
}

// 合并处理结果
func (l *ImageHandler) MergeResults(datas []*image.Data) *image.Data {
    if len(datas) == 1 {
        return datas[0]
    }
    mergeData := &image.Data{
        MetaData: &image.MetaData{
            Status:  0,
            Message: "success",
        },
    }

    for _, data := range datas {
        if data.MetaData.Status != 0 {
            return data // 异常情况
        }
        switch data.DataType {
        case image.DataType_STRING:
            if mergeData.StringData == nil {
                mergeData.StringData = make(map[string]string)
            }
            for k, v := range data.StringData {
                mergeData.StringData[k] = v
            }
        case image.DataType_NUMBER:
            if mergeData.NumberData == nil {
                mergeData.NumberData = make(map[string]float64)
            }
            for k, v := range data.NumberData {
                mergeData.NumberData[k] = v
            }
        case image.DataType_IMAGE:
            if mergeData.ImageData == nil {
                mergeData.ImageData = make([]*image.ImageMeta, 0)
            }
            mergeData.ImageData = append(mergeData.ImageData, data.ImageData...)
        }
    }
    return mergeData
}

Finally, we can define the specific RPC service interface in image.proto, as shown below:

syntax = "proto3";

package image;

service ImageApi {
    rpc DoImage(ImageReq) returns (Data) {}
}

message ImageReq {
    int32 image_type = 1;
    string image_data = 2;
}

message ImageMetaData {
    int32 status = 1;
    string message = 2;
}

message Data {
    ImageMetaData meta_data = 1;
    DataType data_type = 2;
    map<string, string> string_data = 3; 
    map<string, float> number_data = 4;
    repeated ImageMeta image_data = 5;

}

// 可返回的数据类型
enum DataType {
    STRING = 0;
    NUMBER = 1;
    IMAGE = 2;
}

message ImageMeta {
    string url = 1;
    int32 width = 2;
    int32 height = 3;
}

至此，一个完整的分布式图像处理系统就具备了基础的功能和业务逻辑，可以部署到服务器中，供用户使用。

五、总结

本文介绍了go-zero在分布式图像处理中的应用和实践，从应用场景、架构设计、技术选型、代码实现等方面对图像处理系统进行了详细阐述。针对图像处理系统的特点，go-zero提供了一套全面的分布式微服务解决方案，可以快速搭建高可扩展性的系统，提高系统的性能和可靠性，同时也为开发者提供了产品支持和服务保障，适用于多种应用场景。

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