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Spark技术内幕:Master基于ZooKeeper的High Availability(HA)源代码实现

     假设Spark的部署方式选择Standalone,一个採用Master/Slaves的典型架构,那么Master是有SPOF(单点故障,Single Point of Failure)。Spark能够选用ZooKeeper来实现HA。

     ZooKeeper提供了一个Leader Election机制,利用这个机制能够保证尽管集群存在多个Master可是唯独一个是Active的。其它的都是Standby。当Active的Master出现问题时,另外的一个Standby Master会被选举出来。

因为集群的信息,包含Worker。 Driver和Application的信息都已经持久化到文件系统,因此在切换的过程中只会影响新Job的提交,对于正在进行的Job没有不论什么的影响。加入ZooKeeper的集群总体架构例如以下图所看到的。

技术分享


1. Master的重新启动策略

Master在启动时。会依据启动參数来决定不同的Master故障重新启动策略:

  1. ZOOKEEPER实现HA
  2. FILESYSTEM:实现Master无数据丢失重新启动,集群的执行时数据会保存到本地/网络文件系统上
  3. 丢弃全部原来的数据重新启动

Master::preStart()能够看出这三种不同逻辑的实现。

override def preStart() {
    logInfo("Starting Spark master at " + masterUrl)
    ...
    //persistenceEngine是持久化Worker,Driver和Application信息的,这样在Master又一次启动时不会影响
    //已经提交Job的执行
    persistenceEngine = RECOVERY_MODE match {
      case "ZOOKEEPER" =>
        logInfo("Persisting recovery state to ZooKeeper")
        new ZooKeeperPersistenceEngine(SerializationExtension(context.system), conf)
      case "FILESYSTEM" =>
        logInfo("Persisting recovery state to directory: " + RECOVERY_DIR)
        new FileSystemPersistenceEngine(RECOVERY_DIR, SerializationExtension(context.system))
      case _ =>
        new BlackHolePersistenceEngine()
    }
    //leaderElectionAgent负责Leader的选取。
    leaderElectionAgent = RECOVERY_MODE match {
        case "ZOOKEEPER" =>
          context.actorOf(Props(classOf[ZooKeeperLeaderElectionAgent], self, masterUrl, conf))
        case _ => // 唯独一个Master的集群。那么当前的Master就是Active的
          context.actorOf(Props(classOf[MonarchyLeaderAgent], self))
      }
  }

RECOVERY_MODE是一个字符串,能够从spark-env.sh中去设置。

val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE")

假设不设置spark.deploy.recoveryMode的话,那么集群的全部执行数据在Master重新启动是都会丢失。这个结论是从BlackHolePersistenceEngine的实现得出的。

private[spark] class BlackHolePersistenceEngine extends PersistenceEngine {
  override def addApplication(app: ApplicationInfo) {}
  override def removeApplication(app: ApplicationInfo) {}
  override def addWorker(worker: WorkerInfo) {}
  override def removeWorker(worker: WorkerInfo) {}
  override def addDriver(driver: DriverInfo) {}
  override def removeDriver(driver: DriverInfo) {}

  override def readPersistedData() = (Nil, Nil, Nil)
}

它把全部的接口实现为空。

PersistenceEngine是一个trait。作为对照,能够看一下ZooKeeper的实现。

class ZooKeeperPersistenceEngine(serialization: Serialization, conf: SparkConf)
  extends PersistenceEngine
  with Logging
{
  val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/master_status"
  val zk: CuratorFramework = SparkCuratorUtil.newClient(conf)

  SparkCuratorUtil.mkdir(zk, WORKING_DIR)
  // 将app的信息序列化到文件WORKING_DIR/app_{app.id}中
  override def addApplication(app: ApplicationInfo) {
    serializeIntoFile(WORKING_DIR + "/app_" + app.id, app)
  }

  override def removeApplication(app: ApplicationInfo) {
    zk.delete().forPath(WORKING_DIR + "/app_" + app.id)
  }

Spark使用的并非ZooKeeper的API,而是使用的org.apache.curator.framework.CuratorFramework 和 org.apache.curator.framework.recipes.leader.{LeaderLatchListener, LeaderLatch} 。Curator在ZooKeeper上做了一层非常友好的封装。


2. 集群启动參数的配置

简单总结一下參数的设置,通过上述代码的分析。我们知道为了使用ZooKeeper至少应该设置一下參数(实际上,只须要设置这些參数。通过设置spark-env.sh:

spark.deploy.recoveryMode=ZOOKEEPER
spark.deploy.zookeeper.url=zk_server_1:2181,zk_server_2:2181
spark.deploy.zookeeper.dir=/dir   
// OR 通过一下方式设置
export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER "
export SPARK_DAEMON_JAVA_OPTS="${SPARK_DAEMON_JAVA_OPTS} -Dspark.deploy.zookeeper.url=zk_server1:2181,zk_server_2:2181"

各个參数的意义:

參数
默认值
含义
spark.deploy.recoveryMode
NONE
恢复模式(Master又一次启动的模式)。有三种:1, ZooKeeper, 2。 FileSystem, 3 NONE
spark.deploy.zookeeper.url

ZooKeeper的Server地址
spark.deploy.zookeeper.dir
/spark
ZooKeeper 保存集群元数据信息的文件文件夹,包含Worker,Driver和Application。


3. CuratorFramework简单介绍

CuratorFramework极大的简化了ZooKeeper的使用,它提供了high-level的API,而且基于ZooKeeper加入了非常多特性,包含

  • 自己主动连接管理:连接到ZooKeeper的Client有可能会连接中断。Curator处理了这样的情况。对于Client来说自己主动重连是透明的。
  • 简洁的API:简化了原生态的ZooKeeper的方法,事件等。提供了一个简单易用的接口。
  • Recipe的实现(很多其它介绍请点击Recipes):
    • Leader的选择
    • 共享锁
    • 缓存和监控
    • 分布式的队列
    • 分布式的优先队列


CuratorFrameworks通过CuratorFrameworkFactory来创建线程安全的ZooKeeper的实例。

CuratorFrameworkFactory.newClient()提供了一个简单的方式来创建ZooKeeper的实例,能够传入不同的參数来对实例进行全然的控制。获取实例后,必须通过start()来启动这个实例。在结束时,须要调用close()。

/**
     * Create a new client
     *
     *
     * @param connectString list of servers to connect to
     * @param sessionTimeoutMs session timeout
     * @param connectionTimeoutMs connection timeout
     * @param retryPolicy retry policy to use
     * @return client
     */
    public static CuratorFramework newClient(String connectString, int sessionTimeoutMs, int connectionTimeoutMs, RetryPolicy retryPolicy)
    {
        return builder().
            connectString(connectString).
            sessionTimeoutMs(sessionTimeoutMs).
            connectionTimeoutMs(connectionTimeoutMs).
            retryPolicy(retryPolicy).
            build();
    }

须要关注的还有两个Recipe:org.apache.curator.framework.recipes.leader.{LeaderLatchListener, LeaderLatch}。

首先看一下LeaderlatchListener。它在LeaderLatch状态变化的时候被通知:

  1. 在该节点被选为Leader的时候,接口isLeader()会被调用
  2. 在节点被剥夺Leader的时候,接口notLeader()会被调用

因为通知是异步的。因此有可能在接口被调用的时候。这个状态是准确的。须要确认一下LeaderLatch的hasLeadership()是否的确是true/false。这一点在接下来Spark的实现中能够得到体现。

/**
* LeaderLatchListener can be used to be notified asynchronously about when the state of the LeaderLatch has changed.
*
* Note that just because you are in the middle of one of these method calls, it does not necessarily mean that
* hasLeadership() is the corresponding true/false value. It is possible for the state to change behind the scenes
* before these methods get called. The contract is that if that happens, you should see another call to the other
* method pretty quickly.
*/
public interface LeaderLatchListener
{
  /**
* This is called when the LeaderLatch‘s state goes from hasLeadership = false to hasLeadership = true.
*
* Note that it is possible that by the time this method call happens, hasLeadership has fallen back to false. If
* this occurs, you can expect {@link #notLeader()} to also be called.
*/
  public void isLeader();

  /**
* This is called when the LeaderLatch‘s state goes from hasLeadership = true to hasLeadership = false.
*
* Note that it is possible that by the time this method call happens, hasLeadership has become true. If
* this occurs, you can expect {@link #isLeader()} to also be called.
*/
  public void notLeader();
}

LeaderLatch负责在众多连接到ZooKeeper Cluster的竞争者中选择一个Leader。

Leader的选择机制能够看ZooKeeper的详细实现,LeaderLatch这是完毕了非常好的封装。我们只须要要知道在初始化它的实例后。须要通过

public class LeaderLatch implements Closeable
{
    private final Logger log = LoggerFactory.getLogger(getClass());
    private final CuratorFramework client;
    private final String latchPath;
    private final String id;
    private final AtomicReference<State> state = new AtomicReference<State>(State.LATENT);
    private final AtomicBoolean hasLeadership = new AtomicBoolean(false);
    private final AtomicReference<String> ourPath = new AtomicReference<String>();
    private final ListenerContainer<LeaderLatchListener> listeners = new ListenerContainer<LeaderLatchListener>();
    private final CloseMode closeMode;
    private final AtomicReference<Future<?>> startTask = new AtomicReference<Future<?

>>(); . . . /** * Attaches a listener to this LeaderLatch * <p/> * Attaching the same listener multiple times is a noop from the second time on. * <p/> * All methods for the listener are run using the provided Executor. It is common to pass in a single-threaded * executor so that you can be certain that listener methods are called in sequence, but if you are fine with * them being called out of order you are welcome to use multiple threads. * * @param listener the listener to attach */ public void addListener(LeaderLatchListener listener) { listeners.addListener(listener); }


通过addListener能够将我们实现的Listener加入到LeaderLatch。在Listener里。我们在两个接口里实现了被选为Leader或者被剥夺Leader角色时的逻辑就可以。


4. ZooKeeperLeaderElectionAgent的实现

实际上因为有Curator的存在。Spark实现Master的HA就变得非常easy了,ZooKeeperLeaderElectionAgent实现了接口LeaderLatchListener。在isLeader()确认所属的Master被选为Leader后,向Master发送消息ElectedLeader,Master会将自己的状态改为ALIVE。

当noLeader()被调用时,它会向Master发送消息RevokedLeadership时,Master会关闭。


private[spark] class ZooKeeperLeaderElectionAgent(val masterActor: ActorRef,
    masterUrl: String, conf: SparkConf)
  extends LeaderElectionAgent with LeaderLatchListener with Logging  {
  val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/leader_election"
  // zk是通过CuratorFrameworkFactory创建的ZooKeeper实例
  private var zk: CuratorFramework = _
  // leaderLatch:Curator负责选出Leader。

  private var leaderLatch: LeaderLatch = _   private var status = LeadershipStatus.NOT_LEADER   override def preStart() {     logInfo("Starting ZooKeeper LeaderElection agent")     zk = SparkCuratorUtil.newClient(conf)     leaderLatch = new LeaderLatch(zk, WORKING_DIR)     leaderLatch.addListener(this)     leaderLatch.start()   }


在prestart中,启动了leaderLatch来处理选举ZK中的Leader。就如在上节分析的,基本的逻辑在isLeader和noLeader中。

  override def isLeader() {
    synchronized {
      // could have lost leadership by now.
      //如今leadership可能已经被剥夺了。

。详情參见Curator的实现。 if (!leaderLatch.hasLeadership) { return } logInfo("We have gained leadership") updateLeadershipStatus(true) } } override def notLeader() { synchronized { // 如今可能赋予leadership了。详情參见Curator的实现。 if (leaderLatch.hasLeadership) { return } logInfo("We have lost leadership") updateLeadershipStatus(false) } }


updateLeadershipStatus的逻辑非常easy,就是向Master发送消息。

def updateLeadershipStatus(isLeader: Boolean) {
    if (isLeader && status == LeadershipStatus.NOT_LEADER) {
      status = LeadershipStatus.LEADER
      masterActor ! ElectedLeader
    } else if (!isLeader && status == LeadershipStatus.LEADER) {
      status = LeadershipStatus.NOT_LEADER
      masterActor ! RevokedLeadership
    }
  }

5. 设计理念

为了解决Standalone模式下的Master的SPOF,Spark採用了ZooKeeper提供的选举功能。Spark并没有採用ZooKeeper原生的Java API,而是採用了Curator,一个对ZooKeeper进行了封装的框架。採用了Curator后,Spark不用管理与ZooKeeper的连接,这些对于Spark来说都是透明的。

Spark只使用了100行代码,就实现了Master的HA。当然了,Spark是站在的巨人的肩膀上。谁又会去反复发明轮子呢?


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Spark技术内幕:Master基于ZooKeeper的High Availability(HA)源代码实现