Use go-zero to implement distributed cross-language RPC calls
With the growth of business scale, the existence of single applications can no longer meet the needs of the system, and distributed architecture has gradually become the mainstream. In distributed systems, RPC has become an indispensable part. It provides a convenient, efficient, and reliable way to remotely call services, enabling fast and stable data interaction and calls between various services.
For cross-language RPC calls, both the communication protocol and the serialization protocol need to be compatible with multiple programming languages, so it is relatively difficult to implement. This article will introduce how to use the go-zero framework to implement cross-language distributed RPC calls, aiming to provide readers with a practical solution.
- Introduction to go-zero framework
go-zero is a lightweight Web framework that uses the native net/http module of the go language and provides a set of A simple, easy-to-use, high-performance API development method that can easily combine HTTP services with microservices. go-zero can help us quickly build distributed, high-concurrency server applications, and the code and documentation can be obtained for free on GitHub.
- Realize cross-language RPC calls
2.1 Define services
When we define services in go-zero, we need to first write a proto file and define Communication interface between server and client. Suppose we define a service named Example, which contains two methods:
syntax = "proto3"; package rpc; service Example { rpc SayHello (Request) returns (Response); rpc GetUser (UserRequest) returns (UserResponse); } message Request { string name = 1; } message Response { string message = 1; } message UserRequest { string id = 1; } message UserResponse { string name = 1; string email = 2; }
After defining the proto file, we need to use the protobuf compiler to compile it into a go language source file and execute the following command:
protoc --go_out=. --go-grpc_out=. rpc.proto
This will generate two files, rpc.pb.go and rpc_grpc.pb.go.
2.2 Implementing the server
In the go-zero framework, we can use the go-grpc module to implement the grpc service. When implementing the server, you need to implement the interface defined in the proto file, use server.NewServer provided by go-zero and call the AddService method to add the service, and then start the grpc service in the Init method.
package server import ( "context" "rpc" "github.com/tal-tech/go-zero/core/logx" "github.com/tal-tech/go-zero/core/stores/sqlx" "github.com/tal-tech/go-zero/core/syncx" "github.com/tal-tech/go-zero/zrpc" "google.golang.org/grpc" ) type ExampleContext struct { Logx logx.Logger SqlConn sqlx.SqlConn CacheConn syncx.SharedCalls } type ExampleServer struct { Example rpc.ExampleServer } func NewExampleServer(ctx ExampleContext) *ExampleServer { return &ExampleServer{ Example: &exampleService{ ctx: ctx, }, } } func (s *ExampleServer) Init() { server := zrpc.MustNewServer(zrpc.RpcServerConf{ BindAddress: "localhost:7777", }) rpc.RegisterExampleServer(server, s.Example) server.Start() } type exampleService struct { ctx ExampleContext } func (s *exampleService) SayHello(ctx context.Context, req *rpc.Request) (*rpc.Response, error) { return &rpc.Response{ Message: "Hello, " + req.Name, }, nil } func (s *exampleService) GetUser(ctx context.Context, req *rpc.UserRequest) (*rpc.UserResponse, error) { // 查询数据库 return &rpc.UserResponse{ Name: "name", Email: "email", }, nil }
On the server, we can use the Init method to start the RPC server and use MustNewServer to create the RPC server. We must pass in an RpcServerConf structure containing the address we want to bind.
2.3 Implementing the client
In the go-zero framework, we can use the zrpc module to implement the grpc client. Use zrpc.Dial to create a connection and instantiate the rpc client.
package client import ( "context" "rpc" "google.golang.org/grpc" ) type ExampleClient struct { client rpc.ExampleClient } func NewExampleClient(conn *grpc.ClientConn) *ExampleClient { return &ExampleClient{ client: rpc.NewExampleClient(conn), } } func (c *ExampleClient) SayHello(name string) (string, error) { resp, err := c.client.SayHello(context.Background(), &rpc.Request{ Name: name, }) if err != nil { return "", err } return resp.Message, nil } func (c *ExampleClient) GetUser(id string) (*rpc.UserResponse, error) { return c.client.GetUser(context.Background(), &rpc.UserRequest{ Id: id, }) }
On the client, we only need to use the NewExampleClient function to create an RPC client. The function of the SayHello method is to obtain a response from the server and return it. The GetUser method obtains the user information response from the server and returns it in the form of UserResponse.
2.4 Test
Now that we have implemented the server and client code, we can test it through the following code:
package main import ( "fmt" "log" "rpc_example/client" "rpc_example/server" "google.golang.org/grpc" ) func main() { ctx := server.ExampleContext{} conn, err := grpc.Dial("localhost:7777", grpc.WithInsecure()) if err != nil { log.Fatalf("grpc.Dial err :%v", err) } defer conn.Close() client := client.NewExampleClient(conn) resp, err := client.SayHello("Alice") if err != nil { log.Fatalf("client.SayHello err : %v", err) } fmt.Println(resp) user, err := client.GetUser("123") if err != nil { log.Fatalf("client.GetUser err : %v", err) } fmt.Println(user) }
In the above code, we create Open a grpc connection and call the SayHello and GetUser methods to test our RPC service. We can successfully respond with the correct data and confirm that the RPC service is working normally.
- Summary
In this article, we introduced how to use the go-zero framework to implement distributed cross-language RPC calls, which involves go-zero's Def module , grpc, protobuf and zrpc and other technologies. When implementing RPC services, we first define the RPC interface, and then write server and client code based on the interface. Finally add the Init method to start the RPC service. If you are looking for a lightweight, easy-to-use distributed system framework, go-zero is definitely a good choice.
The above is the detailed content of Use go-zero to implement distributed cross-language RPC calls. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











How to use Redis to achieve distributed data synchronization With the development of Internet technology and the increasingly complex application scenarios, the concept of distributed systems is increasingly widely adopted. In distributed systems, data synchronization is an important issue. As a high-performance in-memory database, Redis can not only be used to store data, but can also be used to achieve distributed data synchronization. For distributed data synchronization, there are generally two common modes: publish/subscribe (Publish/Subscribe) mode and master-slave replication (Master-slave).

How Redis implements distributed session management requires specific code examples. Distributed session management is one of the hot topics on the Internet today. In the face of high concurrency and large data volumes, traditional session management methods are gradually becoming inadequate. As a high-performance key-value database, Redis provides a distributed session management solution. This article will introduce how to use Redis to implement distributed session management and give specific code examples. 1. Introduction to Redis as a distributed session storage. The traditional session management method is to store session information.

MongoDB is an open source NoSQL database with high performance, scalability and flexibility. In distributed systems, task scheduling and execution are a key issue. By utilizing the characteristics of MongoDB, distributed task scheduling and execution solutions can be realized. 1. Requirements Analysis for Distributed Task Scheduling In a distributed system, task scheduling is the process of allocating tasks to different nodes for execution. Common task scheduling requirements include: 1. Task request distribution: Send task requests to available execution nodes.

How to use Swoole to implement distributed scheduled task scheduling Introduction: In traditional PHP development, we often use cron to implement scheduled task scheduling, but cron can only execute tasks on a single server and cannot cope with high concurrency scenarios. Swoole is a high-performance asynchronous concurrency framework based on PHP. It provides complete network communication capabilities and multi-process support, allowing us to easily implement distributed scheduled task scheduling. This article will introduce how to use Swoole to implement distributed scheduled task scheduling

Using Redis to achieve distributed cache consistency In modern distributed systems, cache plays a very important role. It can greatly reduce the frequency of system access to the database and improve system performance and throughput. In a distributed system, in order to ensure cache consistency, we need to solve the problem of data synchronization between multiple nodes. In this article, we will introduce how to use Redis to achieve distributed cache consistency and give specific code examples. Redis is a high-performance key-value database that supports persistence, replication, and collection

Using Redis to implement distributed task scheduling With the expansion of business and the development of the system, many businesses need to implement distributed task scheduling to ensure that tasks can be executed on multiple nodes at the same time, thereby improving the stability and availability of the system. As a high-performance memory data storage product, Redis has the characteristics of distribution, high availability, and high performance, and is very suitable for implementing distributed task scheduling. This article will introduce how to use Redis to implement distributed task scheduling and provide corresponding code examples. 1. Redis base

Details, techniques, and best practices for implementing distributed log collection and analysis with Golang and RabbitMQ. In recent years, with the popularity of microservice architecture and the complexity of large-scale systems, log collection and analysis have become more and more important. In a distributed system, the logs of each microservice are often scattered in different places. How to efficiently collect and analyze these logs becomes a challenge. This article will introduce the details, techniques, and best practices on how to use Golang and RabbitMQ to implement distributed log collection and analysis. Ra

Sharing practical experience in Java development: Building a distributed log collection function Introduction: With the rapid development of the Internet and the emergence of large-scale data, the application of distributed systems is becoming more and more widespread. In distributed systems, log collection and analysis are very important. This article will share the experience of building distributed log collection function in Java development, hoping to be helpful to readers. 1. Background introduction In a distributed system, each node generates a large amount of log information. These log information are useful for system performance monitoring, troubleshooting and data analysis.
