1. 究竟是怎么运行的？
很多的博客里大量的讲了什么是RDD, Dependency, Shuffle... 但是究竟那些Executor是怎么运行你提交的代码段的？
下面是一个日志分析的例子，来自Spark的example
def main(args: Array[String]) {
val sparkConf = new SparkConf().setAppName("Log Query")
val sc = new SparkContext(sparkConf)
val dataSet =
if (args.length == 1) sc.textFile(args(0)) else sc.parallelize(exampleApacheLogs)
// scalastyle:off
val apacheLogRegex =
"""^([\d.]+) (\S+) (\S+) \[([\w\d:/]+\s[+\-]\d{4})\] "(.+?)" (\d{3}) ([\d\-]+) "([^"]+)" "([^"]+)".*""".r
// scalastyle:on
/** Tracks the total query count and number of aggregate bytes for a particular group. */
class Stats(val count: Int, val numBytes: Int) extends Serializable {
def merge(other: Stats): Stats = {
new Stats(count + other.count, numBytes + other.numBytes)
}
override def toString: String = "bytes=%s\tn=%s".format(numBytes, count)
}
def extractKey(line: String): (String, String, String) = {
apacheLogRegex.findFirstIn(line) match {
case Some(apacheLogRegex(ip, _, user, dateTime, query, status, bytes, referer, ua)) =>
if (user != "\"-\"") (ip, user, query)
else (null, null, null)
case _ => (null, null, null)
}
}
def extractStats(line: String): Stats = {
apacheLogRegex.findFirstIn(line) match {
case Some(apacheLogRegex(ip, _, user, dateTime, query, status, bytes, referer, ua)) =>
new Stats(1, bytes.toInt)
case _ => new Stats(1, 0)
}
}
dataSet.map(line => (extractKey(line), extractStats(line)))
.reduceByKey((c, d) => c.merge(d))
.collect().foreach{
case (user, query) => println("%s\t%s".format(user, query))}
sc.stop()
}
在map的RDD算子里，自定义了extractKey, extractStats函数，而在reduceByKey的RDD又自定义了一个相同的key的merge函数
这些函数是如何被传递到executor里并且进行运算的呢？
1.1 RDD,ShuffleDependency
在前面的博文（
Executor上是如何launch task的）中，已经讨论过如何获取到Driver的RDD, Dependency, 那么RDD如何能够运行这些函数呢？
Execute获取的DAG里提交的ShuffleMapTask是在TaskDecription中serializedTask中反序列化出来
ShuffleMapTask的RunTask的方法
override def runTask(context: TaskContext): MapStatus = { // Deserialize the RDD using the broadcast variable. val threadMXBean = ManagementFactory.getThreadMXBean val deserializeStartTime = System.currentTimeMillis() val deserializeStartCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) { threadMXBean.getCurrentThreadCpuTime } else 0L val ser = SparkEnv.get.closureSerializer.newInstance() val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])]( ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader) _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime _executorDeserializeCpuTime = if (threadMXBean.isCurrentThreadCpuTimeSupported) { threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime } else 0L var writer: ShuffleWriter[Any, Any] = null try { val manager = SparkEnv.get.shuffleManager writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context) writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]]) writer.stop(success = true).get } catch { case e: Exception => try { if (writer != null) { writer.stop(success = false) } } catch { case e: Exception => log.debug("Could not stop writer", e) } throw e } }看到了通过shufflewrite去写迭代的rdd数据
1.1.1 ShuffleWrite
ShuffleWrite的构建是通过shuffleManager来获取的，在SortShuffleManager.scala中
/** Get a writer for a given partition. Called on executors by map tasks. */ override def getWriter[K, V]( handle: ShuffleHandle, mapId: Int, context: TaskContext): ShuffleWriter[K, V] = { numMapsForShuffle.putIfAbsent( handle.shuffleId, handle.asInstanceOf[BaseShuffleHandle[_, _, _]].numMaps) val env = SparkEnv.get handle match { case unsafeShuffleHandle: SerializedShuffleHandle[K @unchecked, V @unchecked] => new UnsafeShuffleWriter( env.blockManager, shuffleBlockResolver.asInstanceOf[IndexShuffleBlockResolver], context.taskMemoryManager(), unsafeShuffleHandle, mapId, context, env.conf) case bypassMergeSortHandle: BypassMergeSortShuffleHandle[K @unchecked, V @unchecked] => new BypassMergeSortShuffleWriter( env.blockManager, shuffleBlockResolver.asInstanceOf[IndexShuffleBlockResolver], bypassMergeSortHandle, mapId, context, env.conf) case other: BaseShuffleHandle[K @unchecked, V @unchecked, _] => new SortShuffleWriter(shuffleBlockResolver, other, mapId, context) } }在ShuffleDependency中保存着ShuffleHandle, ShuffleHandle中也保存着Dependency
- 在Driver DAG 中registerShuffle中dependency决定着使用什么ShuffleHandle
- 在Executor的shuffleManager中是由dependency中的ShuffleHandle来决定什么ShuffleWrite
在日志分析的这个代码案例中，返回的是SortShuffleWriter
1.1.2 RDD.iterator
在ShuffleMapTask中的runTask方法
writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
final def iterator(split: Partition, context: TaskContext): Iterator[T] = { if (storageLevel != StorageLevel.NONE) { getOrCompute(split, context) } else { computeOrReadCheckpoint(split, context) } }
Map的rdd的构造迭代器MapPartitionsRDD，MapPartitionsRDD并没有设置缓存或者存储，StorageLevel是NONE，调用computerOrReadCheckpoint方法
/** * Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing. */ private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] = { if (isCheckpointedAndMaterialized) { firstParent[T].iterator(split, context) } else { compute(split, context) } }也没有做过checkpointed ，调用compute方法
override def compute(split: Partition, context: TaskContext): Iterator[U] = f(context, split.index, firstParent[T].iterator(split, context))先来看fistParent
/** Returns the first parent RDD */ protected[spark] def firstParent[U: ClassTag]: RDD[U] = { dependencies.head.rdd.asInstanceOf[RDD[U]] }每个RDD都会保存一个Dependency的数组，Dependency里有RDD的属性，而Dependency数组的头一个dependency的RDD，就是处理数据的首个RDD，也就是如下的代码里的dataSet
val dataSet = if (args.length == 1) sc.textFile(args(0)) else sc.parallelize(exampleApacheLogs)我们以parallelize为例子，所对应的RDD就是ParallelCollectionRDD
回到
firstParent[T].iterator(split, context))iterator函数就是前面的RDD函数，StorageLevel依然是NONE，也没有做过checkpointed，依然还是调用compute的方法
override def compute(s: Partition, context: TaskContext): Iterator[T] = { new InterruptibleIterator(context, s.asInstanceOf[ParallelCollectionPartition[T]].iterator) }生成了一个
InterruptibleIterator迭代器，迭代器本质只是一个代理的迭代器
@DeveloperApiclass InterruptibleIterator[+T](val context: TaskContext, val delegate: Iterator[T]) extends Iterator[T] { def hasNext: Boolean = { // TODO(aarondav/rxin): Check Thread.interrupted instead of context.interrupted if interrupt // is allowed. The assumption is that Thread.interrupted does not have a memory fence in read // (just a volatile field in C), while context.interrupted is a volatile in the JVM, which // introduces an expensive read fence. if (context.isInterrupted) { throw new TaskKilledException } else { delegate.hasNext } } def next(): T = delegate.next()}当发现有打断命令的时候，直接抛出TaskKilledException的异常，其所代理的iterator 是
s.asInstanceOf[ParallelCollectionPartition[T]].iterator
ParallelCollectionRDD的Partition就是ParallelCollectionPartition
private[spark] class ParallelCollectionPartition[T: ClassTag]( var rddId: Long, var slice: Int, var values: Seq[T] ) extends Partition with Serializable { def iterator: Iterator[T] = values.iterator .......}Values是需要支持序列化的数组，在Driver端ParallelCollectionRDD中将数据Data进行了ParallelCollectionPartition的分片，分片的数据Values被保存在了ParallelCollectionPartition里，数据并没有被保存在ParallelCollectionRDD中，
所以进行计算的数据并不是通过RDD传递过来的，而是通过反序列化ShuffleMapTask获得的，走的是直接的rpc通道
private[spark] class ShuffleMapTask( stageId: Int, stageAttemptId: Int, taskBinary: Broadcast[Array[Byte]], partition: Partition, @transient private var locs: Seq[TaskLocation], metrics: TaskMetrics, localProperties: Properties, jobId: Option[Int] = None, appId: Option[String] = None, appAttemptId: Option[String] = None) extends Task[MapStatus](stageId, stageAttemptId, partition.index, metrics, localProperties, jobId, appId, appAttemptId)
回到MapPartitionsRDD原来的函数中去：
override def compute(split: Partition, context: TaskContext): Iterator[U] = f(context, split.index, firstParent[T].iterator(split, context))要看看f是什么？RDD.map函数
def map[U: ClassTag](f: T => U): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF)) }
我们在看看我们是如何调用map函数的：
dataSet.map(line => (extractKey(line), extractStats(line)))
def map[B](f: A => B): Iterator[B] = new AbstractIterator[B] { def hasNext = self.hasNext def next() = f(self.next()) }返回的可以简单的认为AbstractIterator，self 指向的是InterruptibleIterator，f 就是 line => (extractKey(line), extractStats(line))
我们来看ExternalSorter.scala通过迭代器获取Partiton的数据并进行运算的代码
while (records.hasNext) { addElementsRead() kv = records.next() map.changeValue((getPartition(kv._1), kv._1), update) maybeSpillCollection(usingMap = true) }
- AbstractIterator.hasNext -> InterruptibleIterator.hasNext -> Elements( Seq.interator).hasNext -> def hasNext: Boolean = index < end
- AbstractIterator.next() -> InterruptibleIterator.next() -> Elements( Seq.interator).next(). -> f(InterruptibleIterator.next()) ->(extractKey(InterruptibleIterator.next()), extractStats(InterruptibleIterator.next()))
运算extractKey, extractStats后返回的是一个
Product2[Tuple3(String,String,String),Stats] KV值
还记得executor会loadDriver的jar么？虽然在scala里所定义函数都默认支持反序列化，但是在运行方法并不需要反序列化，只要加载jar包，classload 这个我们写的driver的类就可以了。
1.1.3 reduceByKey算子
在LogQuery中
.reduceByKey((c, d) => c.merge(d))
我们来看PairRDDFunction.scala中的reduceByKey，为什么PairRDDFunction不是RDD在前面的博客已经描述过
def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope { combineByKeyWithClassTag[V]((v: V) => v, func, func, partitioner) }combineByKeyWithClassTag函数中
def combineByKeyWithClassTag[C]( createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, partitioner: Partitioner, mapSideCombine: Boolean = true, serializer: Serializer = null)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope { require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0 if (keyClass.isArray) { if (mapSideCombine) { throw new SparkException("Cannot use map-side combining with array keys.") } if (partitioner.isInstanceOf[HashPartitioner]) { throw new SparkException("HashPartitioner cannot partition array keys.") } } val aggregator = new Aggregator[K, V, C]( self.context.clean(createCombiner), self.context.clean(mergeValue), self.context.clean(mergeCombiners)) if (self.partitioner == Some(partitioner)) { self.mapPartitions(iter => { val context = TaskContext.get() new InterruptibleIterator(context, aggregator.combineValuesByKey(iter, context)) }, preservesPartitioning = true) } else { new ShuffledRDD[K, V, C](self, partitioner) .setSerializer(serializer) .setAggregator(aggregator) .setMapSideCombine(mapSideCombine) } }
在以前都没有介绍过Aggregator，我们来介绍一下这个Aggregator，Aggregator有三个关键函数
- createCombiner: 通过Map获得的新KV, 在Key不存在的情况下将V转化为C
- mergeValue: 通过Map获得的新KV, 在已经存在相同的Key情况下，将新获得的V聚合到C
- mergeCombiners: 分布式计算的时候，最后要每个RDD的分区最后汇总，汇总的时候对相同的Key，已经聚合的C和另一个分区已经聚合的C再次聚合
还是回到ExternalSorter.scala的insertAll中
val mergeValue = aggregator.get.mergeValue val createCombiner = aggregator.get.createCombiner var kv: Product2[K, V] = null val update = (hadValue: Boolean, oldValue: C) => { if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2) } while (records.hasNext) { addElementsRead() kv = records.next() map.changeValue((getPartition(kv._1), kv._1), update) maybeSpillCollection(usingMap = true) }我们看到在map.changeValue的时候，通过update的方法更新相同的key
val update = (hadValue: Boolean, oldValue: C) => { if (hadValue) mergeValue(oldValue, kv._2) else createCombiner(kv._2) }mergeValue,createCombiner就是从Aggregator中获取到的，而Aggregator被保存在ShuffledRDD和ShuffledDependency中，ShuffledDependency是通过Driver RPC传递给Executor的，所以可以从ShuffledDependency获取到Aggregator，通过Aggregator里指定的算法进行KV的操作，而mergeValue就是Driver中的c.merge(d)，因为c 是stats 对象
class Stats(val count: Int, val numBytes: Int) extends Serializable { def merge(other: Stats): Stats = { new Stats(count + other.count, numBytes + other.numBytes) } override def toString: String = "bytes=%s\tn=%s".format(numBytes, count) }
调用了Stats.merge的方法
2. 总结
- 通过反序列化RDD(不是ShuffleRDD)，通过Dependency的列表获的最初获取数据的RDD的迭代器A
- Map算子对迭代器A重新封装AbstractIterator，在迭代器A获取结果后进行Map算子里的函数调用line => (extractKey(line), extractStats(line)),返回KV的结果
- reduceByKey算子里的函数传递是通过ShuffledDependency里的aggregator进行传递
- Executor 只要对迭代器AbstractIterator进行迭代获取KV，调用aggregator里的方法进行相同的K对V进行操作，完成Driver里面的main函数定义的RDD运算。