Spark-广播变量(Broadcast Variables)
1. 简单使用
def main(args: Array[String]): Unit = {
val sparkConf=new SparkConf()
.setAppName("BroadcastApp").setMaster("local[2]")
val sc= new SparkContext(sparkConf)
val data=sc.parallelize(Array(1,2,3,4,5))
//val broadcastdata=sc.broadcast(data)
//java.lang.IllegalArgumentException: requirement failed: Can not directly broadcast RDDs; instead, call collect() and broadcast the result.
//不能直接对RDD进行广播需要collect获取
//修改为
val broadcastdata=sc.broadcast(data.collect())
broadcastdata.value.foreach(println(_))//1 2 3 4 5
}
2. 广播流程
/**
* Broadcast a read-only variable to the cluster, returning a *********************变量只读**********************
* [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions.
* The variable will be sent to each cluster only once. ******************每个cluster只发送一次******************
*
* @param value value to broadcast to the Spark nodes
* @return `Broadcast` object, a read-only variable cached on each machine
*/
def broadcast[T: ClassTag](value: T): Broadcast[T] = {
assertNotStopped()
require(!classOf[RDD[_]].isAssignableFrom(classTag[T].runtimeClass),
"Can not directly broadcast RDDs; instead, call collect() and broadcast the result.")
// ***************调用broadcastManager的newBroadcast方法*****************
val bc = env.broadcastManager.newBroadcast[T](value, isLocal)
val callSite = getCallSite
logInfo("Created broadcast " + bc.id + " from " + callSite.shortForm)
cleaner.foreach(_.registerBroadcastForCleanup(bc))
bc
}
def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean): Broadcast[T] = {
broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement())
}
override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long): Broadcast[T] = {
new TorrentBroadcast[T](value_, id)
}
/**
* A BitTorrent-like implementation of [[org.apache.spark.broadcast.Broadcast]].
*
* The mechanism is as follows:
*
* The driver divides the serialized object into small chunks and
* stores those chunks in the BlockManager of the driver.
* 将广播对象分成若干个系列化大小的数据块(默认4m)，存储在BlockManager
* On each executor, the executor first attempts to fetch the object from its BlockManager. If
* it does not exist, it then uses remote fetches to fetch the small chunks from the driver and/or
* other executors if available. Once it gets the chunks, it puts the chunks in its own
* BlockManager, ready for other executors to fetch from.
* executor第一次请求对象不存在时，会去driver或者其他executors获得，一旦或得对象，便分块存储在自己BlockManager，并给其他executors提供获取对象的服务
这可以得出数据请求并非集中于Driver端从而避免了单点故障和Driver端网络磁盘IO过高。
* This prevents the driver from being the bottleneck in sending out multiple copies of the
* broadcast data (one per executor).
*
* When initialized, TorrentBroadcast objects read SparkEnv.get.conf.
*
* @param obj object to broadcast
* @param id A unique identifier for the broadcast variable.
*/
private[spark] class TorrentBroadcast[T: ClassTag](obj: T, id: Long)
extends Broadcast[T](id) with Logging with Serializable {
/**
* Value of the broadcast object on executors. This is reconstructed by [[readBroadcastBlock]],
* which builds this value by reading blocks from the driver and/or other executors.
*
* On the driver, if the value is required, it is read lazily from the block manager.
*/
lazy读取
会先从本地读取，对结果进行模式匹配，匹配到就本地读取，否则远程加载
@transient private lazy val _value: T = readBroadcastBlock()
是否压缩
/** The compression codec to use, or None if compression is disabled */
@transient private var compressionCodec: Option[CompressionCodec] = _
/** Size of each block. Default value is 4MB. This value is only read by the broadcaster. */
每个block大小默认是4
@transient private var blockSize: Int = _
private def setConf(conf: SparkConf) {
compressionCodec = if (conf.getBoolean("spark.broadcast.compress", true)) {
Some(CompressionCodec.createCodec(conf))
} else {
None
}
// Note: use getSizeAsKb (not bytes) to maintain compatibility if no units are provided
blockSize = conf.getSizeAsKb("spark.broadcast.blockSize", "4m").toInt * 1024
checksumEnabled = conf.getBoolean("spark.broadcast.checksum", true)
}
setConf(SparkEnv.get.conf)
/**
* Divide the object into multiple blocks and put those blocks in the block manager.
* 将该对象存储于本地BlockManager
* @param value the object to divide
* @return number of blocks this broadcast variable is divided into
*/
private def writeBlocks(value: T): Int = {
import StorageLevel._
// Store a copy of the broadcast variable in the driver so that tasks run on the driver
// do not create a duplicate copy of the broadcast variable's value.
val blockManager = SparkEnv.get.blockManager
if (!blockManager.putSingle(broadcastId, value, MEMORY_AND_DISK, tellMaster = false)) {
throw new SparkException(s"Failed to store $broadcastId in BlockManager")
}
/** Fetch torrent blocks from the driver and/or other executors. */
private def readBlocks(): Array[BlockData] = {
// Fetch chunks of data. Note that all these chunks are stored in the BlockManager and reported
// to the driver, so other executors can pull these chunks from this executor as well.
val blocks = new Array[BlockData](numBlocks)
val bm = SparkEnv.get.blockManager
......
// We found the block from remote executors/driver's BlockManager, so put the block
// in this executor's BlockManager.
//序列化存储到本地
if (!bm.putBytes(pieceId, b, StorageLevel.MEMORY_AND_DISK_SER, tellMaster = true)) {
throw new SparkException(
s"Failed to store $pieceId of $broadcastId in local BlockManager")
}
还原对象存储到本地，其他task在使用时不用重新获取
val obj = TorrentBroadcast.unBlockifyObject[T](
blocks.map(_.toInputStream()), SparkEnv.get.serializer, compressionCodec)
// Store the merged copy in BlockManager so other tasks on this executor don't
// need to re-fetch it.
val storageLevel = StorageLevel.MEMORY_AND_DISK
// Called by SparkContext or Executor before using Broadcast
//在使用广播之前由SparkContext或Executor调用
private def initialize() {
synchronized {
if (!initialized) {
broadcastFactory = new TorrentBroadcastFactory
broadcastFactory.initialize(isDriver, conf, securityManager)
initialized = true
}
}
}
3.总结
- 只读不可更改=>保证数据一致性
- 在每台机子缓存一个变量，不会每个task传输变量，减少了网络io
- 广播变量在运行task前会反序列化进行缓存
- 使用场景
1)广播的对象比较小
2)跨stage(多个stage公用)
3)一个executor运行多个task
