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Hadoop RPC通信Client客户端的流程分析

来源:程序员人生   发布时间:2015-01-10 08:58:38 阅读次数:3844次

            Hadoop的RPC的通讯与其他系统的RPC通讯不太1样,作者针对Hadoop的使用特点,专门的设计了1套RPC框架,这套框架个人感觉还是有点小复杂的。所以我打算分成Client客户端和Server服务端2个模块做分析。如果你对RPC的整套流程已非常了解的条件下,对Hadoop的RPC,你也1定可以非常迅速的了解的。OK,下面切入正题。

            Hadoop的RPC的相干代码都在org.apache.hadoop.ipc的包下,首先RPC的通讯必须遵照许多的协议,其中最最基本的协议即便以下;

/** * Superclass of all protocols that use Hadoop RPC. * Subclasses of this interface are also supposed to have * a static final long versionID field. * Hadoop RPC所有协议的基类,返回协议版本号 */ public interface VersionedProtocol { /** * Return protocol version corresponding to protocol interface. * @param protocol The classname of the protocol interface * @param clientVersion The version of the protocol that the client speaks * @return the version that the server will speak */ public long getProtocolVersion(String protocol, long clientVersion) throws IOException; }

他是所有协议的基类,他的下面还有1堆的子类,分别对应于不同情况之间的通讯,下面是1张父子类图:

          

 顾名思义,只有客户端和服务端遵守相同的版本号,才能进行通讯。

           RPC客户真个所有相干操作都被封装在了1个叫Client.java的文件中:

/** A client for an IPC service. IPC calls take a single {@link Writable} as a * parameter, and return a {@link Writable} as their value. A service runs on * a port and is defined by a parameter class and a value class. * RPC客户端类 * @see Server */ public class Client { public static final Log LOG = LogFactory.getLog(Client.class); //客户端到服务真个连接 private Hashtable<ConnectionId, Connection> connections = new Hashtable<ConnectionId, Connection>(); //回调值类 private Class<? extends Writable> valueClass; // class of call values //call回调id的计数器 private int counter; // counter for call ids //原子变量判断客户端是不是还在运行 private AtomicBoolean running = new AtomicBoolean(true); // if client runs final private Configuration conf; //socket工厂,用来创建socket private SocketFactory socketFactory; // how to create sockets private int refCount = 1; ......
从代码中明显的看到,这里存在着1个类似于connections连接池的东西,其实这暗示着连接是可以被复用的,在hashtable中,与每一个Connecttion连接的对应的是1个ConnectionId,明显这里不是1个Long类似的数值:

/** * This class holds the address and the user ticket. The client connections * to servers are uniquely identified by <remoteAddress, protocol, ticket> * 连接的唯1标识,主要通过<远程地址,协议类型,用户组信息> */ static class ConnectionId { //远程的socket地址 InetSocketAddress address; //用户组信息 UserGroupInformation ticket; //协议类型 Class<?> protocol; private static final int PRIME = 16777619; private int rpcTimeout; private String serverPrincipal; private int maxIdleTime; //connections will be culled if it was idle for //maxIdleTime msecs private int maxRetries; //the max. no. of retries for socket connections private boolean tcpNoDelay; // if T then disable Nagle's Algorithm private int pingInterval; // how often sends ping to the server in msecs ....
这里用了3个属性组成唯1的标识属性,为了保证可以进行ID的复用,所以作者对ConnectionId的equal比较方法和hashCode 进行了重写:

/** * 作者重写了equal比较方法,只要成员变量都想等也就想到了 */ @Override public boolean equals(Object obj) { if (obj == this) { return true; } if (obj instanceof ConnectionId) { ConnectionId that = (ConnectionId) obj; return isEqual(this.address, that.address) && this.maxIdleTime == that.maxIdleTime && this.maxRetries == that.maxRetries && this.pingInterval == that.pingInterval && isEqual(this.protocol, that.protocol) && this.rpcTimeout == that.rpcTimeout && isEqual(this.serverPrincipal, that.serverPrincipal) && this.tcpNoDelay == that.tcpNoDelay && isEqual(this.ticket, that.ticket); } return false; } /** * 重写了hashCode的生成规则,保证不同的对象产生不同的hashCode值 */ @Override public int hashCode() { int result = 1; result = PRIME * result + ((address == null) ? 0 : address.hashCode()); result = PRIME * result + maxIdleTime; result = PRIME * result + maxRetries; result = PRIME * result + pingInterval; result = PRIME * result + ((protocol == null) ? 0 : protocol.hashCode()); result = PRIME * rpcTimeout; result = PRIME * result + ((serverPrincipal == null) ? 0 : serverPrincipal.hashCode()); result = PRIME * result + (tcpNoDelay ? 1231 : 1237); result = PRIME * result + ((ticket == null) ? 0 : ticket.hashCode()); return result; }
这样就可以保证对应同类型的连接就可以够完全复用了,而不是仅仅凭仗援用的关系判断对象是不是相等,这里就是1个不错的设计了

            与连接Id对应的就是Connection了,它里面保护是1下的1些变量;

/** Thread that reads responses and notifies callers. Each connection owns a * socket connected to a remote address. Calls are multiplexed through this * socket: responses may be delivered out of order. */ private class Connection extends Thread { //所连接的服务器地址 private InetSocketAddress server; // server ip:port //服务真个krb5的名字,与安全方面相干 private String serverPrincipal; // server's krb5 principal name //连接头部,内部包括了,所用的协议,客户端用户组信息和验证的而方法 private ConnectionHeader header; // connection header //远程连接ID private final ConnectionId remoteId; // connection id //连接验证方法 private AuthMethod authMethod; // authentication method //下面3个变量都是安全方面的 private boolean useSasl; private Token<? extends TokenIdentifier> token; private SaslRpcClient saslRpcClient; //下面是1组socket通讯方面的变量 private Socket socket = null; // connected socket private DataInputStream in; private DataOutputStream out; private int rpcTimeout; private int maxIdleTime; //connections will be culled if it was idle for //maxIdleTime msecs private int maxRetries; //the max. no. of retries for socket connections //tcpNoDelay可设置是不是阻塞模式 private boolean tcpNoDelay; // if T then disable Nagle's Algorithm private int pingInterval; // how often sends ping to the server in msecs // currently active calls 当前活跃的回调,1个连接 可能会有很多个call回调 private Hashtable<Integer, Call> calls = new Hashtable<Integer, Call>(); //最后1次IO活动通讯的时间 private AtomicLong lastActivity = new AtomicLong();// last I/O activity time //连接关闭标记 private AtomicBoolean shouldCloseConnection = new AtomicBoolean(); // indicate if the connection is closed private IOException closeException; // close reason .....
里面保护了大量的和连接通讯相干的变量,在这里有1个很成心思的东西connectionHeader,连接头部,里面的数据时为了在通讯最开始的时候被使用:

class ConnectionHeader implements Writable { public static final Log LOG = LogFactory.getLog(ConnectionHeader.class); //客户端和服务端通讯的协议名称 private String protocol; //客户真个用户组信息 private UserGroupInformation ugi = null; //验证的方式,关系到写入数据的时的格式 private AuthMethod authMethod; .....
起到标识验证的作用。1个Client类的基本结构我们基本可以描绘出来了,下面是完全的类关系图:


在上面这幅图中,你肯定会发现我少了1个很关键的类了,就是Call回调类。Call回调在很多异步通讯中是常常出现的。由于在通讯进程中,当1个对象通过网络发送要求给另外1个对象的时候,如果采取同步的方式,会1直阻塞在那里,会带来非常不好的效力和体验的,所以很多时候,我们采取的是1种叫回调接口的方式。在这期间,用户可以继续做自己的事情。所以一样的Call这个概念固然也是适用在Hadoop RPC中。在Hadoop的RPC的核心调用原理, 简单的说,就是我把parame参数序列化到1个对象中,通过参数的情势把对象传入,进行RPC通讯,最后服务端把处理好的结果值放入call对象,在返回给客户端,也就是说客户端和服务端都是通过Call对象进行操作,Call里面存着,要求的参数,和处理后的结构值2个变量。通过Call对象的封装,客户单实现了完善的不必知道细节的调用。下面是Call类的类按时

/** A call waiting for a value. */ //客户真个1个回调 private class Call { //回调ID int id; // call id //被序列化的参数 Writable param; // parameter //返回值 Writable value; // value, null if error //出错时返回的异常 IOException error; // exception, null if value //回调是不是已被完成 boolean done; // true when call is done ....
看到这个Call回调类,或许你渐渐的会明白Hadoop RPC的1个基本原型了,这些Call固然是存在于某个连接中的,1个连接可能会产生多个回调,所以在Connection中保护了calls列表:
private class Connection extends Thread { .... // currently active calls 当前活跃的回调,1个连接 可能会有很多个call回调 private Hashtable<Integer, Call> calls = new Hashtable<Integer, Call>();
作者在设计Call类的时候,比较聪明的斟酌1种并发情况下的Call调用,所以为此设计了下面这个Call的子类,就是专门用于短时间内的瞬间Call调用:

/** Call implementation used for parallel calls. */ /** 继承自Call回调类,可以并行的使用,通过加了index下标做Call的辨别 */ private class ParallelCall extends Call { //每一个ParallelCall并行的回调就会有对应的结果类 private ParallelResults results; //index作为Call的辨别 private int index; ....
如果要查找值,就通过里面的ParallelCall查找,原理是根据index索引:

/** Result collector for parallel calls. */ private static class ParallelResults { //并行结果类中具有1组返回值,需要ParallelCall的index索引匹配 private Writable[] values; //结果值的数量 private int size; //values中已知的值的个数 private int count; ..... /** Collect a result. */ public synchronized void callComplete(ParallelCall call) { //将call中的值赋给result中 values[call.index] = call.value; // store the value count++; // count it //如果计数的值等到终究大小,通知caller if (count == size) // if all values are in notify(); // then notify waiting caller } }
由于Call结构集是这些并发Call共有的,所以用的是static变量,都存在在了values数组中了,只有所有的并发Call都把值取出来了,才算回调成功,这个是个非常细小的辅助设计,这个在有些书籍上并没有多少提及。下面我们看看1般Call回调的流程,正如刚刚说的,终究客户端看到的情势就是,传入参数,取得结果,疏忽内部1切逻辑,这是怎样做到的呢,答案在下面:

在履行之前,你会先得到ConnectionId:

public Writable call(Writable param, InetSocketAddress addr, Class<?> protocol, UserGroupInformation ticket, int rpcTimeout) throws InterruptedException, IOException { ConnectionId remoteId = ConnectionId.getConnectionId(addr, protocol, ticket, rpcTimeout, conf); return call(param, remoteId); }
接着才是主流程:

public Writable call(Writable param, ConnectionId remoteId) throws InterruptedException, IOException { //根据参数构造1个Call回调 Call call = new Call(param); //根据远程ID获得连接 Connection connection = getConnection(remoteId, call); //发送参数 connection.sendParam(call); // send the parameter boolean interrupted = false; synchronized (call) { //如果call.done为false,就是Call还没完成 while (!call.done) { try { //等待远端程序的履行终了 call.wait(); // wait for the result } catch (InterruptedException ie) { // save the fact that we were interrupted interrupted = true; } } //如果是异常中断,则终止当前线程 if (interrupted) { // set the interrupt flag now that we are done waiting Thread.currentThread().interrupt(); } //如果call回到出错,则返回call出错信息 if (call.error != null) { if (call.error instanceof RemoteException) { call.error.fillInStackTrace(); throw call.error; } else { // local exception // use the connection because it will reflect an ip change, unlike // the remoteId throw wrapException(connection.getRemoteAddress(), call.error); } } else { //如果是正常情况下,返回回调解理后的值 return call.value; } } }
在这上面的操作步骤中,重点关注2个函数,获得连接操作,看看人家是如何保证连接的复用性的:

private Connection getConnection(ConnectionId remoteId, Call call) throws IOException, InterruptedException { ..... /* we could avoid this allocation for each RPC by having a * connectionsId object and with set() method. We need to manage the * refs for keys in HashMap properly. For now its ok. */ do { synchronized (connections) { //从connection连接池中获得连接,可以保证相同的连接ID可以复用 connection = connections.get(remoteId); if (connection == null) { connection = new Connection(remoteId); connections.put(remoteId, connection); } } } while (!connection.addCall(call));
有点单例模式的味道哦,还有1个方法叫sendParam发送参数方法:

public void sendParam(Call call) { if (shouldCloseConnection.get()) { return; } DataOutputBuffer d=null; try { synchronized (this.out) { if (LOG.isDebugEnabled()) LOG.debug(getName() + " sending #" + call.id); //for serializing the //data to be written //将call回调中的参数写入到输出流中,传向服务端 d = new DataOutputBuffer(); d.writeInt(call.id); call.param.write(d); byte[] data = d.getData(); int dataLength = d.getLength(); out.writeInt(dataLength); //first put the data length out.write(data, 0, dataLength);//write the data out.flush(); } ....
代码只发送了Call的id,和要求参数,并没有把所有的Call的内容都扔出去了,1定是为了减少数据量的传输,这里还把数据的长度写入了,这是为了方燕服务端准确的读取到不定长的数据。这服务端中间的处理操作不是今天讨论的重点。Call的履行进程就是这样。那末Call是如何被调用的呢,这又要重新回到了Client客户端上去了,Client有1个run()函数,所有的操作都是始于此的;

public void run() { if (LOG.isDebugEnabled()) LOG.debug(getName() + ": starting, having connections " + connections.size()); //等待工作,等待要求调用 while (waitForWork()) {//wait here for work - read or close connection //调用完要求,则立即获得回复 receiveResponse(); } close(); if (LOG.isDebugEnabled()) LOG.debug(getName() + ": stopped, remaining connections " + connections.size()); }
操作很简单,程序1直跑着,有要求,处理要求,获得要求,没有要求,就死等

private synchronized boolean waitForWork() { if (calls.isEmpty() && !shouldCloseConnection.get() && running.get()) { long timeout = maxIdleTime- (System.currentTimeMillis()-lastActivity.get()); if (timeout>0) { try { wait(timeout); } catch (InterruptedException e) {} } } ....
获得回复的操作以下:

/* Receive a response. * Because only one receiver, so no synchronization on in. * 获得回复值 */ private void receiveResponse() { if (shouldCloseConnection.get()) { return; } //更新最近1次的call活动时间 touch(); try { int id = in.readInt(); // try to read an id if (LOG.isDebugEnabled()) LOG.debug(getName() + " got value #" + id); //从获得call中获得相应的call Call call = calls.get(id); //判断该结果状态 int state = in.readInt(); // read call status if (state == Status.SUCCESS.state) { Writable value = ReflectionUtils.newInstance(valueClass, conf); value.readFields(in); // read value call.setValue(value); calls.remove(id); } else if (state == Status.ERROR.state) { call.setException(new RemoteException(WritableUtils.readString(in), WritableUtils.readString(in))); calls.remove(id); } else if (state == Status.FATAL.state) { // Close the connection markClosed(new RemoteException(WritableUtils.readString(in), WritableUtils.readString(in))); } ..... } catch (IOException e) { markClosed(e); } }
从之前保护的Call列表中取出,做判断。Client本身的履行流程比较的简单:




Hadoop RPC客户真个通讯模块的部份大致就是我上面的这个流程,中间其实还疏忽了很多的细节,大家学习的时候,针对源码会有助于更好的理解,Hadoop RPC的服务真个实现更加复杂,所以建议采取分模块的学习也许会更好1点。

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