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:
go-zero can be applied to a variety of scenarios that encounter the above problems, such as:
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:
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:
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。
The above is the detailed content of Implementing a highly scalable distributed image processing system: application and practice of go-zero. For more information, please follow other related articles on the PHP Chinese website!
What are the image processing software
What is distributed
The difference between distributed and microservices
What is the difference between dubbo and zookeeper
Four major characteristics of blockchain
How to set both ends to be aligned in css
The server cannot be found on the computer solution
Photo display time
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
