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gRPC三种Java客户端性能测试实践

2022-07-07    FunTester
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我之前分享了JAVAGo语言版本的gRPC接口的服务端和客户端的开发,使用的基本都是基础的原声API,旧文如下:

经过一段时间的摸索和尝试,我觉得又可以了,今天给大家分享一下三种Java客户端的性能测试实践,其中主要是com.funtester.fungrpc.HelloServiceGrpc#newBlockingStub的性能测试实践。因为在实际的业务测试中这个用的最多,还有阻塞的客户端对于性能测试的指标统计和监控比较友好,对于多接口串联的业务测试来说更贴近HTTP接口的测试,这样能让很多用例思路直接复用。

基于以上,下面开始正题。

PS:本篇文章只做性能测试实践,不会测试各类状况下极限性能,所以硬件配置和软件参数就不单独分享了。

服务端

依旧采用了之前的fun_grpc项目的SDK内容。服务端代码如下:

package com.funtester.grpc;

import com.funtester.frame.execute.ThreadPoolUtil;
import io.grpc.Server;
import io.grpc.ServerBuilder;

import java.io.IOException;
import java.util.concurrent.ThreadPoolExecutor;

public class Service {

    public static void main(String[] args) throws IOException, InterruptedException {
        ThreadPoolExecutor pool = ThreadPoolUtil.createFixedPool(10, "gRPC");
        Server server = ServerBuilder
                .forPort(12345)
                .executor(pool)
                .addService(new HelloServiceImpl())
                .build();

        server.start();
        server.awaitTermination();
    }

}

实际业务处理类:

package com.funtester.grpc;

import com.funtester.frame.SourceCode;
import com.funtester.fungrpc.HelloRequest;
import com.funtester.fungrpc.HelloResponse;
import com.funtester.fungrpc.HelloServiceGrpc;
import com.funtester.utils.Time;
import io.grpc.stub.StreamObserver;
import org.Apache.logging.log4j.LogManager;
import org.apache.logging.log4j.Logger;

public class HelloServiceImpl extends HelloServiceGrpc.HelloServiceImplBase {

    private static final Logger logger = LogManager.getLogger(HelloServiceImpl.class);

    @Override
    public void executeHi(HelloRequest request, StreamObserver<HelloResponse> responseobserver) {
        HelloResponse response = HelloResponse.newBuilder()
                .setMsg("你好 " + request.getName()+ Time.getDate())
                .build();
        SourceCode.sleep(1.0);
        logger.info("用户{}来了",request.getName());
        responseObserver.onNext(response);
        responseObserver.onCompleted();
    }

}

业务上休眠了1s,然后返回响应内容。

客户端

客户端实际使用相对简单,这里就不再分享了,有兴趣的可以读一读文章开头的三篇文章。

静态模型

首先分享一下静态模型的内容,所谓静态内容指的是用例执行之前就设定好了执行的整个过程,用例执行过程除了终止以外没有其他干预措施。

线程模型

下面是基于静态线程模型的性能测试用例:

package com.funtest.grpc

import com.funtester.base.constaint.FixedThread
import com.funtester.frame.SourceCode
import com.funtester.frame.execute.Concurrent
import com.funtester.fungrpc.HelloRequest
import com.funtester.fungrpc.HelloServiceGrpc
import io.grpc.ManagedChannel
import io.grpc.ManagedChannelBuilder

class FixedThreadModel extends SourceCode {

    static int times

    static HelloServiceGrpc.HelloServiceBlockingStub helloServiceBlockingStub

    static HelloRequest requst

    public static void main(String[] args) {
        ManagedChannel managedChannel = ManagedChannelBuilder.forAddress("localhost", 12345)
                .usePlaintext().build()

        helloServiceBlockingStub = HelloServiceGrpc.newBlockingStub(managedChannel).withCompression("gzip")
        requst = HelloRequest.newBuilder()
                .setName("FunTester")
                .build()
        RUNUP_TIME = 0
        times = 2000
        new Concurrent(new FunTester(), 10, "静态线程模型").start()

        managedChannel.shutdown()

    }

    private static class FunTester extends FixedThread {


        FunTester() {
            super(null, times, true)
        }

        @Override
        protected void doing() throws Exception {
            helloServiceBlockingStub.executeHi(requst)
        }

        @Override
        FunTester clone() {
            return new FunTester()
        }
    }

}

QPS模型

下面是基于静态QPS模型的压测用例。

package com.funtest.grpc

import com.funtester.base.event.FunCount
import com.funtester.frame.SourceCode
import com.funtester.frame.execute.FunEventConcurrent
import com.funtester.fungrpc.HelloRequest
import com.funtester.fungrpc.HelloServiceGrpc
import io.grpc.ManagedChannel
import io.grpc.ManagedChannelBuilder

class FixedQpsModel extends SourceCode {

    static HelloServiceGrpc.HelloServiceBlockingStub helloServiceBlockingStub

    static HelloRequest requst

    public static void main(String[] args) {
        ManagedChannel managedChannel = ManagedChannelBuilder.forAddress("localhost", 12345)
                .usePlaintext().build()

        helloServiceBlockingStub = HelloServiceGrpc.newBlockingStub(managedChannel).withCompression("gzip")
        requst = HelloRequest.newBuilder()
                .setName("FunTester")
                .build()
        def count = new FunCount(1, 1, 2, 1000, 10, "静态QPS模型")

        def test= {
            helloServiceBlockingStub.executeHi(requst)
        }
        new FunEventConcurrent(test,count).start()
        managedChannel.shutdown()

    }

}

以上是两个常用的静态模型的演示,还有其他的动态模型这里就不演示了。

动态模型

下面到了喜闻乐见的动态模型的part,动态模型值得是用例执行时都是以固定的最小压力值(通常为1个QPS或者1个线程)启动,然后再用例执行过程中不断调整(调整步长、增减)用例的压力。

动态线程模型

由于动态模型是不限制用例运行时间,所以取消了关闭channel的方法。

package com.funtest.grpc

import com.funtester.base.constaint.FunThread
import com.funtester.frame.SourceCode
import com.funtester.frame.execute.FunConcurrent
import com.funtester.fungrpc.HelloRequest
import com.funtester.fungrpc.HelloServiceGrpc
import io.grpc.ManagedChannel
import io.grpc.ManagedChannelBuilder

import java.util.concurrent.atomic.AtomicInteger

class FunThreadModel extends SourceCode {

    static int times

    static HelloServiceGrpc.HelloServiceBlockingStub helloServiceBlockingStub

    static HelloRequest requst

    static AtomicInteger index = new AtomicInteger(0)

    static def desc = "动态线程模型"

    public static void main(String[] args) {
        ManagedChannel managedChannel = ManagedChannelBuilder.forAddress("localhost", 12345)
                .usePlaintext().build()

        helloServiceBlockingStub = HelloServiceGrpc.newBlockingStub(managedChannel).withCompression("gzip")
        requst = HelloRequest.newBuilder()
                .setName("FunTester")
                .build()
        new FunConcurrent(new FunTester()).start()
    }

    private static class FunTester extends FunThread {


        FunTester() {
            super(null, desc + index.getAndIncrement())
        }

        @Override
        protected void doing() throws Exception {
            helloServiceBlockingStub.executeHi(requst)
        }

        @Override
        FunTester clone() {
            return new FunTester()
        }
    }

}

动态QPS模型

动态QPS模型是我现在最常用的模型,优势多多,除了某些强用户绑定需求外,动态QPS模型都是第一选择。

package com.funtest.grpc


import com.funtester.frame.SourceCode
import com.funtester.frame.execute.FunQpsConcurrent
import com.funtester.fungrpc.HelloRequest
import com.funtester.fungrpc.HelloServiceGrpc
import io.grpc.ManagedChannel
import io.grpc.ManagedChannelBuilder

class FunQpsModel extends SourceCode {

    static HelloServiceGrpc.HelloServiceBlockingStub helloServiceBlockingStub

    static HelloRequest requst

    public static void main(String[] args) {
        ManagedChannel managedChannel = ManagedChannelBuilder.forAddress("localhost", 12345)
                .usePlaintext().build()

        helloServiceBlockingStub = HelloServiceGrpc.newBlockingStub(managedChannel).withCompression("gzip")
        requst = HelloRequest.newBuilder()
                .setName("FunTester")
                .build()
        def test= {
            helloServiceBlockingStub.executeHi(requst)
        }
        new FunQpsConcurrent(test).start()

    }

}

以上就是常用的gRPC阻塞客户端四种模型的性能测试全部内容了,欢迎继续关注FunTester。

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