自从在园子里，发表了两篇如何基于Netty构建RPC服务器的文章：谈谈如何使用Netty开发实现高性能的RPC服务器、Netty实现高性能RPC服务器优化篇之消息序列化 之后，收到了很多同行、园友们热情的反馈和若干个优化建议，于是利用闲暇时间，打算对原来NettyRPC中不合理的模块进行重构，并且增强了一些特性，主要的优化点如下：
- 在原来编码解码器：JDK原生的对象序列化方式、kryo、hessian，新增了：protostuff。
- 优化了NettyRPC服务端的线程池模型，支持LinkedBlockingQueue、ArrayBlockingQueue、SynchronousQueue，并扩展了多个线程池任务处理策略。
- RPC服务启动、注册、卸载支持，通过Spring中自定义的nettyrpc标签进行统一管理。
在最早的NettyRPC消息编解码插件中，我使用的是：JDK原生的对象序列化（ObjectOutputStream/ObjectInputStream）、Kryo、Hessian这三种方式，后续有园友向我提议，可以引入Protostuff序列化方式。经过查阅网络的相关资料，Protostuff基于Google protobuf，但是提供了更多的功能和更简易的用法。原生的protobuff是需要数据结构的预编译过程，需要编写.proto格式的配置文件，再通过protobuf提供的工具翻译成目标语言代码，而Protostuff则省略了这个预编译的过程。以下是Java主流序列化框架的性能测试结果（图片来自网络）：
可以发现，Protostuff序列化确实是一种很高效的序列化框架，相比起其他主流的序列化、反序列化框架，其序列化性能可见一斑。如果用它来进行RPC消息的编码、解码工作，再合适不过了。现在贴出具体的Protostuff序列化编解码器的实现代码。
首先是定义Schema，这个是因为Protostuff-Runtime实现了无需预编译对java bean进行protobuf序列化/反序列化的能力。我们可以把运行时的Schema缓存起来，提高序列化性能。具体实现类SchemaCache代码如下：
package com.newlandframework.rpc.serialize.protostuff;
import com.dyuproject.protostuff.Schema;
import com.dyuproject.protostuff.runtime.RuntimeSchema;
import com.google.common.cache.Cache;
import com.google.common.cache.CacheBuilder;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Callable;
import java.util.concurrent.TimeUnit;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:SchemaCache.java
* @description:SchemaCache功能模块
* @blogs
* @since 2016/10/7
*/
public class SchemaCache {
private static class SchemaCacheHolder {
private static SchemaCache cache = new SchemaCache();
}
public static SchemaCache getInstance() {
return SchemaCacheHolder.cache;
}
private Cache<Class<?>, Schema<?>> cache = CacheBuilder.newBuilder()
.maximumSize(1024).expireAfterWrite(1, TimeUnit.HOURS)
.build();
private Schema<?> get(final Class<?> cls, Cache<Class<?>, Schema<?>> cache) {
try {
return cache.get(cls, new Callable<RuntimeSchema<?>>() {
public RuntimeSchema<?> call() throws Exception {
return RuntimeSchema.createFrom(cls);
}
});
} catch (ExecutionException e) {
return null;
}
}
public Schema<?> get(final Class<?> cls) {
return get(cls, cache);
}
}
然后定义真正的Protostuff序列化、反序列化类，它实现了RpcSerialize接口的方法：
package com.newlandframework.rpc.serialize.protostuff;
import com.dyuproject.protostuff.LinkedBuffer;
import com.dyuproject.protostuff.ProtostuffIOUtil;
import com.dyuproject.protostuff.Schema;
import java.io.InputStream;
import java.io.OutputStream;
import com.newlandframework.rpc.model.MessageRequest;
import com.newlandframework.rpc.model.MessageResponse;
import com.newlandframework.rpc.serialize.RpcSerialize;
import org.objenesis.Objenesis;
import org.objenesis.ObjenesisStd;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ProtostuffSerialize.java
* @description:ProtostuffSerialize功能模块
* @blogs
* @since 2016/10/7
*/
public class ProtostuffSerialize implements RpcSerialize {
private static SchemaCache cachedSchema = SchemaCache.getInstance();
private static Objenesis objenesis = new ObjenesisStd(true);
private boolean rpcDirect = false;
public boolean isRpcDirect() {
return rpcDirect;
}
public void setRpcDirect(boolean rpcDirect) {
this.rpcDirect = rpcDirect;
}
private static <T> Schema<T> getSchema(Class<T> cls) {
return (Schema<T>) cachedSchema.get(cls);
}
public Object deserialize(InputStream input) {
try {
Class cls = isRpcDirect() ? MessageRequest.class : MessageResponse.class;
Object message = (Object) objenesis.newInstance(cls);
Schema<Object> schema = getSchema(cls);
ProtostuffIOUtil.mergeFrom(input, message, schema);
return message;
} catch (Exception e) {
throw new IllegalStateException(e.getMessage(), e);
}
}
public void serialize(OutputStream output, Object object) {
Class cls = (Class) object.getClass();
LinkedBuffer buffer = LinkedBuffer.allocate(LinkedBuffer.DEFAULT_BUFFER_SIZE);
try {
Schema schema = getSchema(cls);
ProtostuffIOUtil.writeTo(output, object, schema, buffer);
} catch (Exception e) {
throw new IllegalStateException(e.getMessage(), e);
} finally {
buffer.clear();
}
}
}
同样为了提高Protostuff序列化/反序列化类的利用效率，我们可以对其进行池化处理，而不要频繁的创建、销毁对象。现在给出Protostuff池化处理类：ProtostuffSerializeFactory、ProtostuffSerializePool的实现代码：
package com.newlandframework.rpc.serialize.protostuff;
import org.apache.commons.pool2.BasePooledObjectFactory;
import org.apache.commons.pool2.PooledObject;
import org.apache.commons.pool2.impl.DefaultPooledObject;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ProtostuffSerializeFactory.java
* @description:ProtostuffSerializeFactory功能模块
* @blogs
* @since 2016/10/7
*/
public class ProtostuffSerializeFactory extends BasePooledObjectFactory<ProtostuffSerialize> {
public ProtostuffSerialize create() throws Exception {
return createProtostuff();
}
public PooledObject<ProtostuffSerialize> wrap(ProtostuffSerialize hessian) {
return new DefaultPooledObject<ProtostuffSerialize>(hessian);
}
private ProtostuffSerialize createProtostuff() {
return new ProtostuffSerialize();
}
}
package com.newlandframework.rpc.serialize.protostuff;
import org.apache.commons.pool2.impl.GenericObjectPool;
import org.apache.commons.pool2.impl.GenericObjectPoolConfig;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ProtostuffSerializePool.java
* @description:ProtostuffSerializePool功能模块
* @blogs
* @since 2016/10/7
*/
public class ProtostuffSerializePool {
private GenericObjectPool<ProtostuffSerialize> ProtostuffPool;
volatile private static ProtostuffSerializePool poolFactory = null;
private ProtostuffSerializePool() {
ProtostuffPool = new GenericObjectPool<ProtostuffSerialize>(new ProtostuffSerializeFactory());
}
public static ProtostuffSerializePool getProtostuffPoolInstance() {
if (poolFactory == null) {
synchronized (ProtostuffSerializePool.class) {
if (poolFactory == null) {
poolFactory = new ProtostuffSerializePool();
}
}
}
return poolFactory;
}
public ProtostuffSerializePool(final int maxTotal, final int minIdle, final long maxWaitMillis, final long minEvictableIdleTimeMillis) {
ProtostuffPool = new GenericObjectPool<ProtostuffSerialize>(new ProtostuffSerializeFactory());
GenericObjectPoolConfig config = new GenericObjectPoolConfig();
config.setMaxTotal(maxTotal);
config.setMinIdle(minIdle);
config.setMaxWaitMillis(maxWaitMillis);
config.setMinEvictableIdleTimeMillis(minEvictableIdleTimeMillis);
ProtostuffPool.setConfig(config);
}
public ProtostuffSerialize borrow() {
try {
return getProtostuffPool().borrowObject();
} catch (final Exception ex) {
ex.printStackTrace();
return null;
}
}
public void restore(final ProtostuffSerialize object) {
getProtostuffPool().returnObject(object);
}
public GenericObjectPool<ProtostuffSerialize> getProtostuffPool() {
return ProtostuffPool;
}
}
现在有了Protostuff池化处理类，我们就通过它来实现NettyRPC的编码、解码接口，达到对RPC消息编码、解码的目的。首先是Protostuff方式实现的RPC解码器代码：
package com.newlandframework.rpc.serialize.protostuff;
import com.newlandframework.rpc.serialize.MessageCodecUtil;
import com.newlandframework.rpc.serialize.MessageDecoder;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ProtostuffDecoder.java
* @description:ProtostuffDecoder功能模块
* @blogs
* @since 2016/10/7
*/
public class ProtostuffDecoder extends MessageDecoder {
public ProtostuffDecoder(MessageCodecUtil util) {
super(util);
}
}
然后是Protostuff方式实现的RPC编码器代码：
package com.newlandframework.rpc.serialize.protostuff;
import com.newlandframework.rpc.serialize.MessageCodecUtil;
import com.newlandframework.rpc.serialize.MessageEncoder;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ProtostuffEncoder.java
* @description:ProtostuffEncoder功能模块
* @blogs
* @since 2016/10/7
*/
public class ProtostuffEncoder extends MessageEncoder {
public ProtostuffEncoder(MessageCodecUtil util) {
super(util);
}
}
最后重构出Protostuff方式的RPC编码、解码器工具类ProtostuffCodecUtil的实现代码：
package com.newlandframework.rpc.serialize.protostuff;
import com.google.common.io.Closer;
import com.newlandframework.rpc.serialize.MessageCodecUtil;
import io.netty.buffer.ByteBuf;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ProtostuffCodecUtil.java
* @description:ProtostuffCodecUtil功能模块
* @blogs
* @since 2016/10/7
*/
public class ProtostuffCodecUtil implements MessageCodecUtil {
private static Closer closer = Closer.create();
private ProtostuffSerializePool pool = ProtostuffSerializePool.getProtostuffPoolInstance();
private boolean rpcDirect = false;
public boolean isRpcDirect() {
return rpcDirect;
}
public void setRpcDirect(boolean rpcDirect) {
this.rpcDirect = rpcDirect;
}
public void encode(final ByteBuf out, final Object message) throws IOException {
try {
ByteArrayOutputStream byteArrayOutputStream = new ByteArrayOutputStream();
closer.register(byteArrayOutputStream);
ProtostuffSerialize protostuffSerialization = pool.borrow();
protostuffSerialization.serialize(byteArrayOutputStream, message);
byte[] body = byteArrayOutputStream.toByteArray();
int dataLength = body.length;
out.writeInt(dataLength);
out.writeBytes(body);
pool.restore(protostuffSerialization);
} finally {
closer.close();
}
}
public Object decode(byte[] body) throws IOException {
try {
ByteArrayInputStream byteArrayInputStream = new ByteArrayInputStream(body);
closer.register(byteArrayInputStream);
ProtostuffSerialize protostuffSerialization = pool.borrow();
protostuffSerialization.setRpcDirect(rpcDirect);
Object obj = protostuffSerialization.deserialize(byteArrayInputStream);
pool.restore(protostuffSerialization);
return obj;
} finally {
closer.close();
}
}
}
这样就使得NettyRPC的消息序列化又多了一种方式，进一步增强了其RPC消息网络传输的能力。
其次是优化了NettyRPC服务端的线程模型，使得RPC消息处理线程池对任务的队列容器的支持更加多样。具体RPC异步处理线程池RpcThreadPool的代码如下：
package com.newlandframework.rpc.parallel;
import com.newlandframework.rpc.core.RpcSystemConfig;
import com.newlandframework.rpc.parallel.policy.AbortPolicy;
import com.newlandframework.rpc.parallel.policy.BlockingPolicy;
import com.newlandframework.rpc.parallel.policy.CallerRunsPolicy;
import com.newlandframework.rpc.parallel.policy.DiscardedPolicy;
import com.newlandframework.rpc.parallel.policy.RejectedPolicy;
import com.newlandframework.rpc.parallel.policy.RejectedPolicyType;
import java.util.concurrent.Executor;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.LinkedBlockingQueue;
import java.util.concurrent.SynchronousQueue;
import java.util.concurrent.ThreadPoolExecutor;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.RejectedExecutionHandler;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:RpcThreadPool.java
* @description:RpcThreadPool功能模块
* @blogs
* @since 2016/10/7
*/
public class RpcThreadPool {
private static RejectedExecutionHandler createPolicy() {
RejectedPolicyType rejectedPolicyType = RejectedPolicyType.fromString(System.getProperty(RpcSystemConfig.SystemPropertyThreadPoolRejectedPolicyAttr, "AbortPolicy"));
switch (rejectedPolicyType) {
case BLOCKING_POLICY:
return new BlockingPolicy();
case CALLER_RUNS_POLICY:
return new CallerRunsPolicy();
case ABORT_POLICY:
return new AbortPolicy();
case REJECTED_POLICY:
return new RejectedPolicy();
case DISCARDED_POLICY:
return new DiscardedPolicy();
}
return null;
}
private static BlockingQueue<Runnable> createBlockingQueue(int queues) {
BlockingQueueType queueType = BlockingQueueType.fromString(System.getProperty(RpcSystemConfig.SystemPropertyThreadPoolQueueNameAttr, "LinkedBlockingQueue"));
switch (queueType) {
case LINKED_BLOCKING_QUEUE:
return new LinkedBlockingQueue<Runnable>();
case ARRAY_BLOCKING_QUEUE:
return new ArrayBlockingQueue<Runnable>(RpcSystemConfig.PARALLEL * queues);
case SYNCHRONOUS_QUEUE:
return new SynchronousQueue<Runnable>();
}
return null;
}
public static Executor getExecutor(int threads, int queues) {
String name = "RpcThreadPool";
return new ThreadPoolExecutor(threads, threads, 0, TimeUnit.MILLISECONDS,
createBlockingQueue(queues),
new NamedThreadFactory(name, true), createPolicy());
}
}
其中创建线程池方法getExecutor是依赖JDK自带的线程ThreadPoolExecutor的实现，参考JDK的帮助文档，可以发现其中的一种ThreadPoolExecutor构造方法重载实现的版本：
参数的具体含义如下：
- corePoolSize是线程池保留大小。
- maximumPoolSize是线程池最大线程大小。
- keepAliveTime是指空闲（idle）线程结束的超时时间。
- unit用来指定keepAliveTime对应的时间单位，诸如：毫秒、秒、分钟、小时、天 等等。
- workQueue用来存放待处理的任务队列。
- handler用来具体指定，当任务队列填满、并且线程池最大线程大小也达到的情形下，线程池的一些应对措施策略。
NettyRPC的线程池支持的任务队列类型主要有以下三种：
- LinkedBlockingQueue：采用链表方式实现的无界任务队列，当然你可以额外指定其容量，使其有界。
- ArrayBlockingQueue：有界的的数组任务队列。
- SynchronousQueue：任务队列的容量固定为1，当客户端提交执行任务过来的时候，有进行阻塞。直到有个处理线程取走这个待执行的任务，否则会一直阻塞下去。
NettyRPC的线程池模型，当遇到线程池也无法处理的情形的时候，具体的应对措施策略主要有：
- AbortPolicy：直接拒绝执行，抛出rejectedExecution异常。
- DiscardedPolicy：从任务队列的头部开始直接丢弃一半的队列元素，为任务队列“减负”。
- CallerRunsPolicy：不抛弃任务，也不抛出异常，而是调用者自己来运行。这个是主要是因为过多的并行请求会加剧系统的负载，线程之间调度操作系统会频繁的进行上下文切换。当遇到线程池满的情况，与其频繁的切换、中断。不如把并行的请求，全部串行化处理，保证尽量少的处理延时，大概是我能想到的Doug Lea的设计初衷吧。
经过详细的介绍了线程池参数的具体内容之后，下面我就详细说一下，NettyRPC的线程池RpcThreadPool的工作流程：
- NettyRPC的线程池收到RPC数据处理请求之后，判断当前活动的线程数小于线程池设置的corePoolSize的大小的时候，会继续生成执行任务。
- 而当达到corePoolSize的大小的时候的时候，这个时候，线程池会把待执行的任务放入任务队列之中。
- 当任务队列也被存满了之后，如果当前活动的线程个数还是小于线程池中maximumPoolSize参数的设置，线程池还会继续分配出任务线程进行救急处理，并且会立马执行。
- 如果达到线程池中maximumPoolSize参数的设置的线程上限，线程池分派出来的救火队也无法处理的时候，线程池就会调用拒绝自保策略RejectedExecutionHandler进行处理。
NettyRPC中默认的线程池设置是把corePoolSize、maximumPoolSize都设置成16，任务队列设置成无界链表构成的阻塞队列。在应用中要根据实际的压力、吞吐量对NettyRPC的线程池参数进行合理的规划。目前NettyRPC暴露了一个JMX接口，JMX是“Java管理扩展的（Java Management Extensions）”的缩写，是一种类似J2EE的规范，这样就可以灵活的扩展系统的监控、管理功能。实时监控RPC服务器线程池任务的执行情况，具体JMX监控度量线程池关键指标代码实现如下：
package com.newlandframework.rpc.parallel.jmx;
import org.springframework.jmx.export.annotation.ManagedOperation;
import org.springframework.jmx.export.annotation.ManagedResource;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ThreadPoolStatus.java
* @description:ThreadPoolStatus功能模块
* @blogs
* @since 2016/10/13
*/
@ManagedResource
public class ThreadPoolStatus {
private int poolSize;
private int activeCount;
private int corePoolSize;
private int maximumPoolSize;
private int largestPoolSize;
private long taskCount;
private long completedTaskCount;
@ManagedOperation
public int getPoolSize() {
return poolSize;
}
@ManagedOperation
public void setPoolSize(int poolSize) {
this.poolSize = poolSize;
}
@ManagedOperation
public int getActiveCount() {
return activeCount;
}
@ManagedOperation
public void setActiveCount(int activeCount) {
this.activeCount = activeCount;
}
@ManagedOperation
public int getCorePoolSize() {
return corePoolSize;
}
@ManagedOperation
public void setCorePoolSize(int corePoolSize) {
this.corePoolSize = corePoolSize;
}
@ManagedOperation
public int getMaximumPoolSize() {
return maximumPoolSize;
}
@ManagedOperation
public void setMaximumPoolSize(int maximumPoolSize) {
this.maximumPoolSize = maximumPoolSize;
}
@ManagedOperation
public int getLargestPoolSize() {
return largestPoolSize;
}
@ManagedOperation
public void setLargestPoolSize(int largestPoolSize) {
this.largestPoolSize = largestPoolSize;
}
@ManagedOperation
public long getTaskCount() {
return taskCount;
}
@ManagedOperation
public void setTaskCount(long taskCount) {
this.taskCount = taskCount;
}
@ManagedOperation
public long getCompletedTaskCount() {
return completedTaskCount;
}
@ManagedOperation
public void setCompletedTaskCount(long completedTaskCount) {
this.completedTaskCount = completedTaskCount;
}
}
线程池状态监控类：ThreadPoolStatus，具体监控的指标如下：
- poolSize：池中的当前线程数
- activeCount：主动执行任务的近似线程数
- corePoolSize：核心线程数
- maximumPoolSize：允许的最大线程数
- largestPoolSize：历史最大的线程数
- taskCount：曾计划执行的近似任务总数
- completedTaskCount：已完成执行的近似任务总数
其中corePoolSize、maximumPoolSize具体含义上文已经详细讲述，这里就不具体展开。
NettyRPC线程池监控JMX接口：ThreadPoolMonitorProvider，JMX通过JNDI-RMI的方式进行远程连接通讯，具体实现方式如下：
package com.newlandframework.rpc.parallel.jmx;
import com.newlandframework.rpc.netty.MessageRecvExecutor;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.ComponentScan;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.DependsOn;
import org.springframework.context.annotation.EnableMBeanExport;
import org.springframework.jmx.support.ConnectorServerFactoryBean;
import org.springframework.jmx.support.MBeanServerConnectionFactoryBean;
import org.springframework.jmx.support.MBeanServerFactoryBean;
import org.springframework.remoting.rmi.RmiRegistryFactoryBean;
import org.apache.commons.lang3.StringUtils;
import javax.management.MBeanServerConnection;
import javax.management.MalformedObjectNameException;
import javax.management.ObjectName;
import javax.management.ReflectionException;
import javax.management.MBeanException;
import javax.management.InstanceNotFoundException;
import java.io.IOException;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:ThreadPoolMonitorProvider.java
* @description:ThreadPoolMonitorProvider功能模块
* @blogs
* @since 2016/10/13
*/
@Configuration
@EnableMBeanExport
@ComponentScan("com.newlandframework.rpc.parallel.jmx")
public class ThreadPoolMonitorProvider {
public final static String DELIMITER = ":";
public static String url;
public static String jmxPoolSizeMethod = "setPoolSize";
public static String jmxActiveCountMethod = "setActiveCount";
public static String jmxCorePoolSizeMethod = "setCorePoolSize";
public static String jmxMaximumPoolSizeMethod = "setMaximumPoolSize";
public static String jmxLargestPoolSizeMethod = "setLargestPoolSize";
public static String jmxTaskCountMethod = "setTaskCount";
public static String jmxCompletedTaskCountMethod = "setCompletedTaskCount";
@Bean
public ThreadPoolStatus threadPoolStatus() {
return new ThreadPoolStatus();
}
@Bean
public MBeanServerFactoryBean mbeanServer() {
return new MBeanServerFactoryBean();
}
@Bean
public RmiRegistryFactoryBean registry() {
return new RmiRegistryFactoryBean();
}
@Bean
@DependsOn("registry")
public ConnectorServerFactoryBean connectorServer() throws MalformedObjectNameException {
MessageRecvExecutor ref = MessageRecvExecutor.getInstance();
String ipAddr = StringUtils.isNotEmpty(ref.getServerAddress()) ? StringUtils.substringBeforeLast(ref.getServerAddress(), DELIMITER) : "localhost";
url = "service:jmx:rmi://" + ipAddr + "/jndi/rmi://" + ipAddr + ":1099/nettyrpcstatus";
System.out.println("NettyRPC JMX MonitorURL : [" + url + "]");
ConnectorServerFactoryBean connectorServerFactoryBean = new ConnectorServerFactoryBean();
connectorServerFactoryBean.setObjectName("connector:name=rmi");
connectorServerFactoryBean.setServiceUrl(url);
return connectorServerFactoryBean;
}
public static void monitor(ThreadPoolStatus status) throws IOException, MalformedObjectNameException, ReflectionException, MBeanException, InstanceNotFoundException {
MBeanServerConnectionFactoryBean mBeanServerConnectionFactoryBean = new MBeanServerConnectionFactoryBean();
mBeanServerConnectionFactoryBean.setServiceUrl(url);
mBeanServerConnectionFactoryBean.afterPropertiesSet();
MBeanServerConnection connection = mBeanServerConnectionFactoryBean.getObject();
ObjectName objectName = new ObjectName("com.newlandframework.rpc.parallel.jmx:name=threadPoolStatus,type=ThreadPoolStatus");
connection.invoke(objectName, jmxPoolSizeMethod, new Object[]{status.getPoolSize()}, new String[]{int.class.getName()});
connection.invoke(objectName, jmxActiveCountMethod, new Object[]{status.getActiveCount()}, new String[]{int.class.getName()});
connection.invoke(objectName, jmxCorePoolSizeMethod, new Object[]{status.getCorePoolSize()}, new String[]{int.class.getName()});
connection.invoke(objectName, jmxMaximumPoolSizeMethod, new Object[]{status.getMaximumPoolSize()}, new String[]{int.class.getName()});
connection.invoke(objectName, jmxLargestPoolSizeMethod, new Object[]{status.getLargestPoolSize()}, new String[]{int.class.getName()});
connection.invoke(objectName, jmxTaskCountMethod, new Object[]{status.getTaskCount()}, new String[]{long.class.getName()});
connection.invoke(objectName, jmxCompletedTaskCountMethod, new Object[]{status.getCompletedTaskCount()}, new String[]{long.class.getName()});
}
}
NettyRPC服务器启动成功之后，就可以通过JMX接口进行监控：可以打开jconsole，然后输入URL：service:jmx:rmi://127.0.0.1/jndi/rmi://127.0.0.1:1099/nettyrpcstatus，用户名、密码默认为空，点击连接按钮。
当有客户端进行RPC请求的时候，通过JMX可以看到如下的监控界面：
这个时候点击NettyRPC线程池各个监控指标的按钮，就可以直观的看到NettyRPC实际运行中，线程池的主要参数指标的实时监控。比如点击：getCompletedTaskCount，想查看一下目前已经完成的线程任务总数指标。具体情况如下图所示：
可以看到，目前已经处理了40280笔RPC请求。这样，我们就可以准实时监控NettyRPC线程池参数设置、容量规划是否合理，以便及时作出调整，合理的最大程度利用软硬件资源。
最后经过重构之后，NettyRPC服务端的Spring配置（NettyRPC/NettyRPC 2.0/main/resources/rpc-invoke-config-server.xml）如下：
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:context="http://www.springframework.org/schema/context"
xmlns:nettyrpc="http://www.newlandframework.com/nettyrpc" xsi:schemaLocation="
http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
http://www.newlandframework.com/nettyrpc http://www.newlandframework.com/nettyrpc/nettyrpc.xsd">
<!--加载rpc服务器的ip地址、端口信息-->
<context:property-placeholder location="classpath:rpc-server.properties"/>
<!--定义rpc服务接口-->
<nettyrpc:service id="demoAddService" interfaceName="com.newlandframework.rpc.services.AddCalculate"
ref="calcAddService"></nettyrpc:service>
<nettyrpc:service id="demoMultiService" interfaceName="com.newlandframework.rpc.services.MultiCalculate"
ref="calcMultiService"></nettyrpc:service>
<!--注册rpc服务器，并通过protocol指定序列化协议-->
<nettyrpc:registry id="rpcRegistry" ipAddr="${rpc.server.addr}" protocol="PROTOSTUFFSERIALIZE"></nettyrpc:registry>
<!--rpc服务实现类声明-->
<bean id="calcAddService" class="com.newlandframework.rpc.services.impl.AddCalculateImpl"></bean>
<bean id="calcMultiService" class="com.newlandframework.rpc.services.impl.MultiCalculateImpl"></bean>
</beans>
通过nettyrpc:service标签定义rpc服务器支持的服务接口，这里的样例声明了当前的rpc服务器提供了加法计算、乘法计算两种服务给客户端进行调用。具体通过Spring自定义标签的实现，大家可以自行参考github：NettyRPC/NettyRPC 2.0/main/java/com/newlandframework/rpc/spring（路径/包）中的实现代码，代码比较多得利用到了Spring框架的特性，希望大家能自行理解和分析。
然后通过bean标签声明了具体加法计算、乘法计算接口对应的实现类，都统一放在com.newlandframework.rpc.services包之中。
最后通过nettyrpc:registry注册了rpc服务器，ipAddr属性定义了该rpc服务器对应的ip/端口信息。protocol用来指定，当前rpc服务器支持的消息序列化协议类型。
目前已经实现的类型有:JDK原生的对象序列化（ObjectOutputStream/ObjectInputStream）、Kryo、Hessian、Protostuff一共四种序列化方式。
配置完成rpc-invoke-config-server.xml之后，就可以启动RPC服务器Main函数入口：com.newlandframework.rpc.boot.RpcServerStarter。通过Maven打包、部署在（Red Hat Enterprise Linux Server release 5.7 (Tikanga) 64位系统，其内核版本号：Kernel 2.6.18-274.el5 on an x86_64），可以启动NettyRPC，如果一切正常的话，在CRT终端上会显示如下输出：
这个时候再进行客户端的Spring配置（NettyRPC/NettyRPC 2.0/test/resources/rpc-invoke-config-client.xml）。
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xmlns:context="http://www.springframework.org/schema/context"
xmlns:nettyrpc="http://www.newlandframework.com/nettyrpc" xsi:schemaLocation="
http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans-3.0.xsd
http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
http://www.newlandframework.com/nettyrpc http://www.newlandframework.com/nettyrpc/nettyrpc.xsd">
<!--加载RPC服务端对应的ip地址、端口信息-->
<context:property-placeholder location="classpath:rpc-server.properties"/>
<!--客户端调用的RPC服务信息（加法计算、乘法计算服务）-->
<nettyrpc:reference id="addCalc" interfaceName="com.newlandframework.rpc.services.AddCalculate"
protocol="PROTOSTUFFSERIALIZE" ipAddr="${rpc.server.addr}"/>
<nettyrpc:reference id="multiCalc" interfaceName="com.newlandframework.rpc.services.MultiCalculate"
protocol="PROTOSTUFFSERIALIZE" ipAddr="${rpc.server.addr}"/>
</beans>
其中加法计算、乘法计算的demo代码如下：
package com.newlandframework.rpc.services;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:Calculate.java
* @description:Calculate功能模块
* @blogs
* @since 2016/10/7
*/
public interface AddCalculate {
//两数相加
int add(int a, int b);
}
package com.newlandframework.rpc.services.impl;
import com.newlandframework.rpc.services.AddCalculate;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:CalculateImpl.java
* @description:CalculateImpl功能模块
* @blogs
* @since 2016/10/7
*/
public class AddCalculateImpl implements AddCalculate {
//两数相加
public int add(int a, int b) {
return a + b;
}
}
package com.newlandframework.rpc.services;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:Calculate.java
* @description:Calculate功能模块
* @blogs
* @since 2016/10/7
*/
public interface MultiCalculate {
//两数相乘
int multi(int a, int b);
}
package com.newlandframework.rpc.services.impl;
import com.newlandframework.rpc.services.MultiCalculate;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:CalculateImpl.java
* @description:CalculateImpl功能模块
* @blogs
* @since 2016/10/7
*/
public class MultiCalculateImpl implements MultiCalculate {
//两数相乘
public int multi(int a, int b) {
return a * b;
}
}
值得注意的是客户端NettyRPC的Spring配置除了指定调用远程RPC的服务服务信息之外，还必须配置远程RPC服务端对应的ip地址、端口信息、协议类型这些要素，而且必须和RPC服务端保持一致，这样才能正常的进行消息的编码、解码工作。
现在我们就模拟1W个瞬时并发的加法、乘法计算请求，一共2W笔请求操作，调用远程RPC服务器上的计算模块，我们默认采用protostuff序列化方式进行RPC消息的编码、解码。注意，测试代码的样例基于1W笔瞬时并发计算请求，不是1W笔循环进行计算请求，这个是衡量RPC服务器吞吐量的一个重要指标，因此这里的测试样例是基于CountDownLatch进行编写的，类java.util.concurrent.CountDownLatch是一个同步辅助类，在完成一组正在其他线程中执行的操作之前，它允许一个或多个线程一直等待。这里是加法计算RPC请求、乘法计算RPC请求，在RPC客户端分别先启动1W个线程，这个时候先挂起，然后等待请求信号，瞬时发起RPC请求。具体代码如下：
首先是加法计算并发请求类AddCalcParallelRequestThread：
package com.newlandframework.test;
import com.newlandframework.rpc.services.AddCalculate;
import java.util.concurrent.CountDownLatch;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:AddCalcParallelRequestThread.java
* @description:AddCalcParallelRequestThread功能模块
* @blogs
* @since 2016/10/7
*/
public class AddCalcParallelRequestThread implements Runnable {
private CountDownLatch signal;
private CountDownLatch finish;
private int taskNumber = 0;
private AddCalculate calc;
public AddCalcParallelRequestThread(AddCalculate calc, CountDownLatch signal, CountDownLatch finish, int taskNumber) {
this.signal = signal;
this.finish = finish;
this.taskNumber = taskNumber;
this.calc = calc;
}
public void run() {
try {
//加法计算线程，先挂起，等待请求信号
signal.await();
//调用远程RPC服务器的加法计算方法模块
int add = calc.add(taskNumber, taskNumber);
System.out.println("calc add result:[" + add + "]");
finish.countDown();
} catch (InterruptedException ex) {
Logger.getLogger(AddCalcParallelRequestThread.class.getName()).log(Level.SEVERE, null, ex);
}
}
}
其次是乘法计算并发请求类MultiCalcParallelRequestThread：
package com.newlandframework.test;
import com.newlandframework.rpc.services.MultiCalculate;
import java.util.concurrent.CountDownLatch;
import java.util.logging.Level;
import java.util.logging.Logger;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:MultiCalcParallelRequestThread.java
* @description:MultiCalcParallelRequestThread功能模块
* @blogs
* @since 2016/10/7
*/
public class MultiCalcParallelRequestThread implements Runnable {
private CountDownLatch signal;
private CountDownLatch finish;
private int taskNumber = 0;
private MultiCalculate calc;
public MultiCalcParallelRequestThread(MultiCalculate calc, CountDownLatch signal, CountDownLatch finish, int taskNumber) {
this.signal = signal;
this.finish = finish;
this.taskNumber = taskNumber;
this.calc = calc;
}
public void run() {
try {
//乘法计算线程，先挂起，等待请求信号
signal.await();
//调用远程RPC服务器的乘法计算方法模块
int multi = calc.multi(taskNumber, taskNumber);
System.out.println("calc multi result:[" + multi + "]");
finish.countDown();
} catch (InterruptedException ex) {
Logger.getLogger(MultiCalcParallelRequestThread.class.getName()).log(Level.SEVERE, null, ex);
}
}
}
现在写出一个调用的测试客户端RpcParallelTest，测试RPC服务器的性能，以及是否正确计算出最终的结果。测试客户端RpcParallelTest的具体代码如下：
package com.newlandframework.test;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.TimeUnit;
import com.newlandframework.rpc.services.AddCalculate;
import com.newlandframework.rpc.services.MultiCalculate;
import org.apache.commons.lang3.time.StopWatch;
import org.springframework.context.support.ClassPathXmlApplicationContext;
/**
* @author tangjie<https://github.com/tang-jie>
* @filename:RpcParallelTest.java
* @description:RpcParallelTest功能模块
* @blogs
* @since 2016/10/7
*/
public class RpcParallelTest {
public static void parallelAddCalcTask(AddCalculate calc, int parallel) throws InterruptedException {
//开始计时
StopWatch sw = new StopWatch();
sw.start();
CountDownLatch signal = new CountDownLatch(1);
CountDownLatch finish = new CountDownLatch(parallel);
for (int index = 0; index < parallel; index++) {
AddCalcParallelRequestThread client = new AddCalcParallelRequestThread(calc, signal, finish, index);
new Thread(client).start();
}
signal.countDown();
finish.await();
sw.stop();
String tip = String.format("加法计算RPC调用总共耗时: [%s] 毫秒", sw.getTime());
System.out.println(tip);
}
public static void parallelMultiCalcTask(MultiCalculate calc, int parallel) throws InterruptedException {
//开始计时
StopWatch sw = new StopWatch();
sw.start();
CountDownLatch signal = new CountDownLatch(1);
CountDownLatch finish = new CountDownLatch(parallel);
for (int index = 0; index < parallel; index++) {
MultiCalcParallelRequestThread client = new MultiCalcParallelRequestThread(calc, signal, finish, index);
new Thread(client).start();
}
signal.countDown();
finish.await();
sw.stop();
String tip = String.format("乘法计算RPC调用总共耗时: [%s] 毫秒", sw.getTime());
System.out.println(tip);
}
public static void addTask(AddCalculate calc, int parallel) throws InterruptedException {
RpcParallelTest.parallelAddCalcTask(calc, parallel);
TimeUnit.MILLISECONDS.sleep(30);
}
public static void multiTask(MultiCalculate calc, int parallel) throws InterruptedException {
RpcParallelTest.parallelMultiCalcTask(calc, parallel);
TimeUnit.MILLISECONDS.sleep(30);
}
public static void main(String[] args) throws Exception {
//并行度10000
int parallel = 10000;
//加载Spring配置信息
ClassPathXmlApplicationContext context = new ClassPathXmlApplicationContext("classpath:rpc-invoke-config-client.xml");
//并发进行RPC加法计算、乘法计算请求
addTask((AddCalculate) context.getBean("addCalc"), parallel);
multiTask((MultiCalculate) context.getBean("multiCalc"), parallel);
System.out.printf("[author tangjie] Netty RPC Server 消息协议序列化并发验证结束!\n\n");
context.destroy();
}
}
Netty RPC客户端运行情况，具体截图如下：下面是开始收到RPC服务器加法计算的结果截图。
好了，加法RPC请求计算完毕，控制台打印出请求耗时。
接着是调用RPC并行乘法计算请求，同样，控制台上也打印出请求耗时。
接着RPC的客户端运行完毕、退出，我们继续看下NettyRPC服务端的运行截图：
可以发现，NettyRPC的服务端确实都收到了来自客户端发起的RPC计算请求，给每个RPC消息标识出了唯一的消息编码，并进行了RPC计算处理之后，成功的把消息应答给了客户端。
经过一系列的模块重构，终于将NettyRPC重新升级了一下，经过这次重构工作，感觉自己对Netty、Spring、Java线程模型的了解更加深入了，不积跬步无以至千里，千里之行始于足下。学习靠的就是这样一点一滴的重复积累，才能将自己的能力提升一个台阶。
原创文章，加上本人才疏学浅，文笔有限，本文中有说得不对的地方，望各位同行不吝赐教。文中有忽略的地方希望读者可以补充，错误的地方还望斧正。
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