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。
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：
- ZOOKEEPER实现HA
- 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
- 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：
- 在该节点被选为Leader的时候。接口isLeader()会被调用
- 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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