基于Netty打造RPC服务器设计经验谈

　　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。

　　现在重点整理一下重构思路、经验，记录下来。对应源代码代码，大家可以查看我的开源github：https://github.com/tang-jie/NettyRPC 项目中的NettyRPC 2.0目录。

　　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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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 http://www.cnblogs.com/jietang/
 * @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();
    }
}