gRPC三种Java客户端性能测试实践
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 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，有兴趣的可以文末加群讨论
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，欢迎关注我。
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