标签：broadcast 源码 spark 大数据 apache
最近工作上忙死了……广播变量这一块其实早就看过了，一直没有贴出来。
本文基于Spark 1.0源码分析，主要探讨广播变量的初始化、创建、读取以及清除。
BroadcastManager类中包含一个BroadcastFactory对象的引用。大部分操作通过调用BroadcastFactory中的方法来实现。
BroadcastFactory是一个Trait，有两个直接子类TorrentBroadcastFactory、HttpBroadcastFactory。这两个子类实现了对HttpBroadcast、TorrentBroadcast的封装，而后面两个又同时集成了Broadcast抽象类。
图……就不画了
SparkContext初始化时会创建SparkEnv对象env，这个过程中会调用BroadcastManager的构造方法返回一个对象作为env的成员变量存在：
val broadcastManager = new BroadcastManager(isDriver, conf, securityManager)
构造BroadcastManager对象时会调用initialize方法，主要根据配置初始化broadcastFactory成员变量，并调用其initialize方法。
val broadcastFactoryClass =
conf.get("spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory")
broadcastFactory =
Class.forName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory]
// Initialize appropriate BroadcastFactory and BroadcastObject
broadcastFactory.initialize(isDriver, conf, securityManager)
两个工厂类的initialize方法都是对其相应实体类的initialize方法的调用，下面分开两个类来看。
def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) {
synchronized {
if (!initialized) {
bufferSize = conf.getInt("spark.buffer.size", 65536)
compress = conf.getBoolean("spark.broadcast.compress", true)
securityManager = securityMgr
if (isDriver) {
createServer(conf)
conf.set("spark.httpBroadcast.uri", serverUri)
}
serverUri = conf.get("spark.httpBroadcast.uri")
cleaner = new MetadataCleaner(MetadataCleanerType.HTTP_BROADCAST, cleanup, conf)
compressionCodec = CompressionCodec.createCodec(conf)
initialized = true
}
}
}
除了一些变量的初始化外，主要做两件事情，一是createServer（只有在Driver端会做），其次是创建一个MetadataCleaner对象。
private def createServer(conf: SparkConf) {
broadcastDir = Utils.createTempDir(Utils.getLocalDir(conf))
server = new HttpServer(broadcastDir, securityManager)
server.start()
serverUri = server.uri
logInfo("Broadcast server started at " + serverUri)
}
首先创建一个存放广播变量的目录，默认是
conf.get("spark.local.dir", System.getProperty("java.io.tmpdir")).split(',')(0)
然后初始化一个HttpServer对象并启动（封装了jetty），启动过程中包括加载资源文件，起端口和线程用来监控请求等。这部分的细节在org.apache.spark.HttpServer类中，此处不做展开。
一个MetadataCleaner对象包装了一个定时计划Timer，每隔一段时间执行一个回调函数，此处传入的回调函数为cleanup：
private def cleanup(cleanupTime: Long) {
val iterator = files.internalMap.entrySet().iterator()
while(iterator.hasNext) {
val entry = iterator.next()
val (file, time) = (entry.getKey, entry.getValue)
if (time < cleanupTime) {
iterator.remove()
deleteBroadcastFile(file)
}
}
}
即清楚存在吵过一定时长的broadcast文件。在时长未设定（默认情况）时，不清除：
if (delaySeconds > 0) {
logDebug(
"Starting metadata cleaner for " + name + " with delay of " + delaySeconds + " seconds " +
"and period of " + periodSeconds + " secs")
timer.schedule(task, periodSeconds * 1000, periodSeconds * 1000)
}
def initialize(_isDriver: Boolean, conf: SparkConf) {
TorrentBroadcast.conf = conf // TODO: we might have to fix it in tests
synchronized {
if (!initialized) {
initialized = true
}
}
}
Torrent在此处没做什么，这也可以看出和Http的区别，Torrent的处理方式就是p2p，去中心化。而Http是中心化服务，需要启动服务来接受请求。
调用SparkContext中的 def broadcast[T: ClassTag](value: T): Broadcast[T]方法来初始化一个广播变量，实现如下：
def broadcast[T: ClassTag](value: T): Broadcast[T] = {
val bc = env.broadcastManager.newBroadcast[T](value, isLocal)
cleaner.foreach(_.registerBroadcastForCleanup(bc))
bc
}
即调用broadcastManager的newBroadcast方法：
def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean) = {
broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement())
}
再调用工厂类的newBroadcast方法，此处返回的是一个Broadcast对象。
def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
new HttpBroadcast[T](value_, isLocal, id)
即创建一个新的HttpBroadcast对象并返回。
构造对象时主要做两件事情：
HttpBroadcast.synchronized {
SparkEnv.get.blockManager.putSingle(
blockId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
}
if (!isLocal) {
HttpBroadcast.write(id, value_)
}
1.将变量id和值放入blockManager，但并不通知master
2.调用伴生对象的write方法
def write(id: Long, value: Any) {
val file = getFile(id)
val out: OutputStream = {
if (compress) {
compressionCodec.compressedOutputStream(new FileOutputStream(file))
} else {
new BufferedOutputStream(new FileOutputStream(file), bufferSize)
}
}
val ser = SparkEnv.get.serializer.newInstance()
val serOut = ser.serializeStream(out)
serOut.writeObject(value)
serOut.close()
files += file
}
write方法将对象值按照指定的压缩、序列化写入指定的文件。这个文件所在的目录即是HttpServer的资源目录，文件名和id的对应关系为：
case class BroadcastBlockId(broadcastId: Long, field: String = "") extends BlockId {
def name = "broadcast_" + broadcastId + (if (field == "") "" else "_" + field)
}
def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) =
new TorrentBroadcast[T](value_, isLocal, id)
同样是创建一个TorrentBroadcast对象，并返回。
TorrentBroadcast.synchronized {
SparkEnv.get.blockManager.putSingle(
broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
}
if (!isLocal) {
sendBroadcast()
}
做两件事情，第一步和Http一样，第二步：
def sendBroadcast() {
val tInfo = TorrentBroadcast.blockifyObject(value_)
totalBlocks = tInfo.totalBlocks
totalBytes = tInfo.totalBytes
hasBlocks = tInfo.totalBlocks
// Store meta-info
val metaId = BroadcastBlockId(id, "meta")
val metaInfo = TorrentInfo(null, totalBlocks, totalBytes)
TorrentBroadcast.synchronized {
SparkEnv.get.blockManager.putSingle(
metaId, metaInfo, StorageLevel.MEMORY_AND_DISK, tellMaster = true)
}
// Store individual pieces
for (i <- 0 until totalBlocks) {
val pieceId = BroadcastBlockId(id, "piece" + i)
TorrentBroadcast.synchronized {
SparkEnv.get.blockManager.putSingle(
pieceId, tInfo.arrayOfBlocks(i), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
}
}
}
可以看出，先将元数据信息缓存到blockManager，再将块信息缓存过去。开头可以看到有一个分块动作，是调用伴生对象的blockifyObject方法：
def blockifyObject[T](obj: T): TorrentInfo
此方法将对象obj分块（默认块大小为4M），返回一个TorrentInfo对象，第一个参数为一个TorrentBlock对象（包含blockID和block字节数组）、块数量以及obj的字节流总长度。
元数据信息中的blockId为广播变量id+后缀，value为总块数和总字节数。
数据信息是分块缓存，每块的id为广播变量id加后缀及块变好，数据位一个TorrentBlock对象
通过调用bc.value来取得广播变量的值，其主要实现在反序列化方法readObject中
HttpBroadcast.synchronized {
SparkEnv.get.blockManager.getSingle(blockId) match {
case Some(x) => value_ = x.asInstanceOf[T]
case None => {
logInfo("Started reading broadcast variable " + id)
val start = System.nanoTime
value_ = HttpBroadcast.read[T](id)
/*
* We cache broadcast data in the BlockManager so that subsequent tasks using it
* do not need to re-fetch. This data is only used locally and no other node
* needs to fetch this block, so we don't notify the master.
*/
SparkEnv.get.blockManager.putSingle(
blockId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
val time = (System.nanoTime - start) / 1e9
logInfo("Reading broadcast variable " + id + " took " + time + " s")
}
}
}
首先查看blockManager中是否已有，如有则直接取值，否则调用伴生对象的read方法进行读取：
def read[T: ClassTag](id: Long): T = {
logDebug("broadcast read server: " + serverUri + " id: broadcast-" + id)
val url = serverUri + "/" + BroadcastBlockId(id).name
var uc: URLConnection = null
if (securityManager.isAuthenticationEnabled()) {
logDebug("broadcast security enabled")
val newuri = Utils.constructURIForAuthentication(new URI(url), securityManager)
uc = newuri.toURL.openConnection()
uc.setAllowUserInteraction(false)
} else {
logDebug("broadcast not using security")
uc = new URL(url).openConnection()
}
val in = {
uc.setReadTimeout(httpReadTimeout)
val inputStream = uc.getInputStream
if (compress) {
compressionCodec.compressedInputStream(inputStream)
} else {
new BufferedInputStream(inputStream, bufferSize)
}
}
val ser = SparkEnv.get.serializer.newInstance()
val serIn = ser.deserializeStream(in)
val obj = serIn.readObject[T]()
serIn.close()
obj
}
使用serverUri和block id对应的文件名直接开启一个HttpConnection将中心服务器上相应的数据取过来，使用配置的压缩和序列化机制进行解压和反序列化。
这里可以看到，所有需要用到广播变量值的executor都需要去driver上pull广播变量的内容。
取到值后，缓存到blockManager中，以便下次使用。
private def readObject(in: ObjectInputStream) {
in.defaultReadObject()
TorrentBroadcast.synchronized {
SparkEnv.get.blockManager.getSingle(broadcastId) match {
case Some(x) =>
value_ = x.asInstanceOf[T]
case None =>
val start = System.nanoTime
logInfo("Started reading broadcast variable " + id)
// Initialize @transient variables that will receive garbage values from the master.
resetWorkerVariables()
if (receiveBroadcast()) {
value_ = TorrentBroadcast.unBlockifyObject[T](arrayOfBlocks, totalBytes, totalBlocks)
/* Store the merged copy in cache so that the next worker doesn't need to rebuild it.
* This creates a trade-off between memory usage and latency. Storing copy doubles
* the memory footprint; not storing doubles deserialization cost. Also,
* this does not need to be reported to BlockManagerMaster since other executors
* does not need to access this block (they only need to fetch the chunks,
* which are reported).
*/
SparkEnv.get.blockManager.putSingle(
broadcastId, value_, StorageLevel.MEMORY_AND_DISK, tellMaster = false)
// Remove arrayOfBlocks from memory once value_ is on local cache
resetWorkerVariables()
} else {
logError("Reading broadcast variable " + id + " failed")
}
val time = (System.nanoTime - start) / 1e9
logInfo("Reading broadcast variable " + id + " took " + time + " s")
}
}
}
和Http一样，都是先查看blockManager中是否已经缓存，若没有，则调用receiveBroadcast方法：
def receiveBroadcast(): Boolean = {
// Receive meta-info about the size of broadcast data,
// the number of chunks it is divided into, etc.
val metaId = BroadcastBlockId(id, "meta")
var attemptId = 10
while (attemptId > 0 && totalBlocks == -1) {
TorrentBroadcast.synchronized {
SparkEnv.get.blockManager.getSingle(metaId) match {
case Some(x) =>
val tInfo = x.asInstanceOf[TorrentInfo]
totalBlocks = tInfo.totalBlocks
totalBytes = tInfo.totalBytes
arrayOfBlocks = new Array[TorrentBlock](totalBlocks)
hasBlocks = 0
case None =>
Thread.sleep(500)
}
}
attemptId -= 1
}
if (totalBlocks == -1) {
return false
}
/*
* Fetch actual chunks of data. Note that all these chunks are stored in
* the BlockManager and reported to the master, so that other executors
* can find out and pull the chunks from this executor.
*/
val recvOrder = new Random().shuffle(Array.iterate(0, totalBlocks)(_ + 1).toList)
for (pid <- recvOrder) {
val pieceId = BroadcastBlockId(id, "piece" + pid)
TorrentBroadcast.synchronized {
SparkEnv.get.blockManager.getSingle(pieceId) match {
case Some(x) =>
arrayOfBlocks(pid) = x.asInstanceOf[TorrentBlock]
hasBlocks += 1
SparkEnv.get.blockManager.putSingle(
pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, tellMaster = true)
case None =>
throw new SparkException("Failed to get " + pieceId + " of " + broadcastId)
}
}
}
hasBlocks == totalBlocks
}
和写数据一样，同样是分成两个部分，首先取元数据信息，再根据元数据信息读取实际的block信息。注意这里都是从blockManager中读取的，这里贴出blockManager.getSingle的分析。
调用栈中最后到BlockManager.doGetRemote方法，中间有一条语句：
val locations = Random.shuffle(master.getLocations(blockId))
即将存有这个block的节点信息随机打乱，然后使用：
val data = BlockManagerWorker.syncGetBlock(
GetBlock(blockId), ConnectionManagerId(loc.host, loc.port))
来获取。
从这里可以看出，Torrent方法首先将广播变量数据分块，并存到BlockManager中；每个节点需要读取广播变量时，是分块读取，对每一块都读取其位置信息，然后随机选一个存有此块数据的节点进行get；每个节点读取后会将包含的快信息报告给BlockManagerMaster，这样本地节点也成为了这个广播网络中的一个peer。
与Http方式形成鲜明对比，这是一个去中心化的网络，只需要保持一个tracker即可，这就是p2p的思想。
广播变量被创建时，紧接着有这样一句代码：
cleaner.foreach(_.registerBroadcastForCleanup(bc))
cleaner是一个ContextCleaner对象，会将刚刚创建的广播变量注册到其中，调用栈为：
def registerBroadcastForCleanup[T](broadcast: Broadcast[T]) {
registerForCleanup(broadcast, CleanBroadcast(broadcast.id))
} private def registerForCleanup(objectForCleanup: AnyRef, task: CleanupTask) {
referenceBuffer += new CleanupTaskWeakReference(task, objectForCleanup, referenceQueue)
}
等出现广播变量被弱引用时（关于弱引用，可以参考：http://blog.csdn.net/lyfi01/article/details/6415726），则会执行
cleaner.foreach(_.start())
start方法中会调用keepCleaning方法，会遍历注册的清理任务（包括RDD、shuffle和broadcast），依次进行清理：
private def keepCleaning(): Unit = Utils.logUncaughtExceptions {
while (!stopped) {
try {
val reference = Option(referenceQueue.remove(ContextCleaner.REF_QUEUE_POLL_TIMEOUT))
.map(_.asInstanceOf[CleanupTaskWeakReference])
reference.map(_.task).foreach { task =>
logDebug("Got cleaning task " + task)
referenceBuffer -= reference.get
task match {
case CleanRDD(rddId) =>
doCleanupRDD(rddId, blocking = blockOnCleanupTasks)
case CleanShuffle(shuffleId) =>
doCleanupShuffle(shuffleId, blocking = blockOnCleanupTasks)
case CleanBroadcast(broadcastId) =>
doCleanupBroadcast(broadcastId, blocking = blockOnCleanupTasks)
}
}
} catch {
case e: Exception => logError("Error in cleaning thread", e)
}
}
}
doCleanupBroadcast调用以下语句：
broadcastManager.unbroadcast(broadcastId, true, blocking)
然后是：
def unbroadcast(id: Long, removeFromDriver: Boolean, blocking: Boolean) {
broadcastFactory.unbroadcast(id, removeFromDriver, blocking)
}
每个工厂类调用其对应实体类的伴生对象的unbroadcast方法。
def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = synchronized {
SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
if (removeFromDriver) {
val file = getFile(id)
files.remove(file)
deleteBroadcastFile(file)
}
}
1是删除blockManager中的缓存，2是删除本地持久化的文件
def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = synchronized {
SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking)
}
Broadcast可以使用在executor端多次使用某个数据的场景（比如说字典），Http和Torrent两种方式对应传统的CS访问方式和P2P访问方式，当广播变量较大或者使用较频繁时，采用后者可以减少driver端的压力。
BlockManager在此处充当P2P中的tracker角色，没有展开描述，后续会开专题讲这个部分。
声明：本文为原创，禁止用于任何商业目的，转载请注明出处：http://blog.csdn.net/asongoficeandfire/article/details/37584643
Spark大师之路：广播变量（Broadcast）源码分析,布布扣,bubuko.com
标签：broadcast 源码 spark 大数据 apache
原文地址：http://blog.csdn.net/asongoficeandfire/article/details/37584643
