Spark-SparkSQL深入学习系列七（转自OopsOutOfMemory）

/** Spark SQL源码分析系列文章*/

接上一篇文章Spark
SQL Catalyst源码分析之Physical Plan，本文将介绍Physical Plan的toRDD的具体实现细节：
我们都知道一段sql，真正的执行是当你调用它的collect()方法才会执行Spark Job，最后计算得到RDD。

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lazy val toRdd: RDD[Row] = executedPlan.execute()

Spark Plan基本包含4种操作类型，即BasicOperator基本类型，还有就是Join、Aggregate和Sort这种稍复杂的。

如图：

一、BasicOperator

1.1、Project

Project 的大致含义是：传入一系列表达式Seq[NamedExpression]，给定输入的Row，经过Convert（Expression的计算eval）操作，生成一个新的Row。
Project的实现是调用其child.execute()方法，然后调用mapPartitions对每一个Partition进行操作。

这个f函数其实是new了一个MutableProjection，然后循环的对每个partition进行Convert。

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case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {

override def output = projectList.map(_.toAttribute)

override def execute() = child.execute().mapPartitions { iter => //对每个分区进行f映射

@transient val reusableProjection = new MutableProjection(projectList)

iter.map(reusableProjection)

}

}

通过观察MutableProjection的定义，可以发现，就是bind references to a schema 和 eval的过程：
将一个Row转换为另一个已经定义好schema column的Row。

如果输入的Row已经有Schema了，则传入的Seq[Expression]也会bound到当前的Schema。

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case class MutableProjection(expressions: Seq[Expression]) extends (Row => Row) {

def this(expressions: Seq[Expression], inputSchema: Seq[Attribute]) =

this(expressions.map(BindReferences.bindReference(_, inputSchema))) //bound schema

private[this] val exprArray = expressions.toArray

private[this] val mutableRow = new GenericMutableRow(exprArray.size) //新的Row

def currentValue: Row = mutableRow

def apply(input: Row): Row = {

var i = 0

while (i < exprArray.length) {

mutableRow(i) = exprArray(i).eval(input) //根据输入的input，即一个Row，计算生成的Row

i += 1

}

mutableRow //返回新的Row

}

}

1.2、Filter

Filter的具体实现是传入的condition进行对input row的eval计算，最后返回的是一个Boolean类型，
如果表达式计算成功，返回true，则这个分区的这条数据就会保存下来，否则会过滤掉。

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case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {

override def output = child.output

override def execute() = child.execute().mapPartitions { iter =>

iter.filter(condition.eval(_).asInstanceOf[Boolean]) //计算表达式 eval(input row)

}

}

1.3、Sample

Sample取样操作其实是调用了child.execute()的结果后，返回的是一个RDD，对这个RDD调用其sample函数，原生方法。

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case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: SparkPlan)

extends UnaryNode

{

override def output = child.output

// TODO: How to pick seed?

override def execute() = child.execute().sample(withReplacement, fraction, seed)

}

1.4、Union

Union操作支持多个子查询的Union，所以传入的child是一个Seq[SparkPlan]
execute()方法的实现是对其所有的children，每一个进行execute()，即select查询的结果集合RDD。
通过调用SparkContext的union方法，将所有子查询的结果合并起来。

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case class Union(children: Seq[SparkPlan])(@transient sqlContext: SQLContext) extends SparkPlan {

// TODO: attributes output by union should be distinct for nullability purposes

override def output = children.head.output

override def execute() = sqlContext.sparkContext.union(children.map(_.execute())) //子查询的结果进行union

override def otherCopyArgs = sqlContext :: Nil

}

1.5、Limit

Limit操作在RDD的原生API里也有，即take().
但是Limit的实现分2种情况：
第一种是 limit作为结尾的操作符，即select xxx from yyy limit zzz。 并且是被executeCollect调用，则直接在driver里使用take方法。
第二种是 limit不是作为结尾的操作符，即limit后面还有查询，那么就在每个分区调用limit，最后repartition到一个分区来计算global limit.

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case class Limit(limit: Int, child: SparkPlan)(@transient sqlContext: SQLContext)

extends UnaryNode {

// TODO: Implement a partition local limit, and use a strategy to generate the proper limit plan:

// partition local limit -> exchange into one partition -> partition local limit again

override def otherCopyArgs = sqlContext :: Nil

override def output = child.output

override def executeCollect() = child.execute().map(_.copy()).take(limit) //直接在driver调用take

override def execute() = {

val rdd = child.execute().mapPartitions { iter =>

val mutablePair = new MutablePair[Boolean, Row]()

iter.take(limit).map(row => mutablePair.update(false, row)) //每个分区先计算limit

}

val part = new HashPartitioner(1)

val shuffled = new ShuffledRDD[Boolean, Row, Row, MutablePair[Boolean, Row]](rdd, part) //需要shuffle，来repartition

shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false)))

shuffled.mapPartitions(_.take(limit).map(_._2)) //最后单独一个partition来take limit

}

}

1.6、TakeOrdered

TakeOrdered是经过排序后的limit N，一般是用在sort by 操作符后的limit。
可以简单理解为TopN操作符。

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case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan)

(@transient sqlContext: SQLContext) extends UnaryNode {

override def otherCopyArgs = sqlContext :: Nil

override def output = child.output

@transient

lazy val ordering = new RowOrdering(sortOrder) //这里是通过RowOrdering来实现排序的

override def executeCollect() = child.execute().map(_.copy()).takeOrdered(limit)(ordering)

// TODO: Terminal split should be implemented differently from non-terminal split.

// TODO: Pick num splits based on |limit|.

override def execute() = sqlContext.sparkContext.makeRDD(executeCollect(), 1)

}

1.7、Sort

Sort也是通过RowOrdering这个类来实现排序的，child.execute()对每个分区进行map，每个分区根据RowOrdering的order来进行排序，生成一个新的有序集合。
也是通过调用Spark RDD的sorted方法来实现的。

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case class Sort(

sortOrder: Seq[SortOrder],

global: Boolean,

child: SparkPlan)

extends UnaryNode {

override def requiredChildDistribution =

if (global) OrderedDistribution(sortOrder) :: Nil else UnspecifiedDistribution :: Nil

@transient

lazy val ordering = new RowOrdering(sortOrder) //排序顺序

override def execute() = attachTree(this, "sort") {

// TODO: Optimize sorting operation?

child.execute()

.mapPartitions(

iterator => iterator.map(_.copy()).toArray.sorted(ordering).iterator, //每个分区调用sorted方法，传入<span style="font-family: Arial, Helvetica, sans-serif;">ordering排序规则，进行排序</span>

preservesPartitioning = true)

}

override def output = child.output

}

1.8、ExistingRdd

ExistingRdd是

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object ExistingRdd {

def convertToCatalyst(a: Any): Any = a match {

case o: Option[_] => o.orNull

case s: Seq[Any] => s.map(convertToCatalyst)

case p: Product => new GenericRow(p.productIterator.map(convertToCatalyst).toArray)

case other => other

}

def productToRowRdd[A <: Product](data: RDD[A]): RDD[Row] = {

data.mapPartitions { iterator =>

if (iterator.isEmpty) {

Iterator.empty

} else {

val bufferedIterator = iterator.buffered

val mutableRow = new GenericMutableRow(bufferedIterator.head.productArity)

bufferedIterator.map { r =>

var i = 0

while (i < mutableRow.length) {

mutableRow(i) = convertToCatalyst(r.productElement(i))

i += 1

}

mutableRow

}

}

}

}

def fromProductRdd[A <: Product : TypeTag](productRdd: RDD[A]) = {

ExistingRdd(ScalaReflection.attributesFor[A], productToRowRdd(productRdd))

}

}

二、 Join Related Operators

HashJoin：

在讲解Join Related Operator之前，有必要了解一下HashJoin这个位于execution包下的joins.scala文件里的trait。
Join操作主要包含BroadcastHashJoin、LeftSemiJoinHash、ShuffledHashJoin均实现了HashJoin这个trait.
主要类图如下:



HashJoin这个trait的主要成员有：
buildSide是左连接还是右连接，有一种基准的意思。
leftKeys是左孩子的expressions, rightKeys是右孩子的expressions。
left是左孩子物理计划，right是右孩子物理计划。
buildSideKeyGenerator是一个Projection是根据传入的Row对象来计算buildSide的Expression的。
streamSideKeyGenerator是一个MutableProjection是根据传入的Row对象来计算streamSide的Expression的。
这里buildSide如果是left的话，可以理解为buildSide是左表，那么去连接这个左表的右表就是streamSide。



HashJoin关键的操作是joinIterators，简单来说就是join两个表，把每个表看着Iterators[Row].
方式：
1、首先遍历buildSide，计算buildKeys然后利用一个HashMap，形成 (buildKeys, Iterators[Row])的格式。
2、遍历StreamedSide，计算streamedKey，去HashMap里面去匹配key，来进行join
3、最后生成一个joinRow，这个将2个row对接。
见代码注释：

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trait HashJoin {

val leftKeys: Seq[Expression]

val rightKeys: Seq[Expression]

val buildSide: BuildSide

val left: SparkPlan

val right: SparkPlan

lazy val (buildPlan, streamedPlan) = buildSide match { //模式匹配，将physical plan封装形成Tuple2，如果是buildLeft，那么就是(left,right)，否则是(right,left)

case BuildLeft => (left, right)

case BuildRight => (right, left)

}

lazy val (buildKeys, streamedKeys) = buildSide match { //模式匹配，将expression进行封装<span style="font-family: Arial, Helvetica, sans-serif;">Tuple2</span>

case BuildLeft => (leftKeys, rightKeys)

case BuildRight => (rightKeys, leftKeys)

}

def output = left.output ++ right.output

@transient lazy val buildSideKeyGenerator = new Projection(buildKeys, buildPlan.output) //生成buildSideKey来根据Expression来计算Row返回结果

@transient lazy val streamSideKeyGenerator = //<span style="font-family: Arial, Helvetica, sans-serif;">生成</span><span style="font-family: Arial, Helvetica, sans-serif;">streamSideKeyGenerator</span><span style="font-family: Arial, Helvetica, sans-serif;">来根据Expression来计算Row返回结果</span>

() => new MutableProjection(streamedKeys, streamedPlan.output)

def joinIterators(buildIter: Iterator[Row], streamIter: Iterator[Row]): Iterator[Row] = { //把build表的Iterator[Row]和streamIterator[Row]进行join操作返回Join后的Iterator[Row]

// TODO: Use Spark's HashMap implementation.

val hashTable = new java.util.HashMap[Row, ArrayBuffer[Row]]() //匹配主要使用HashMap实现

var currentRow: Row = null

// Create a mapping of buildKeys -> rows

while (buildIter.hasNext) { //目前只对build Iterator进行迭代，形成rowKey，Rows，类似wordCount，但是这里不是累加Value，而是Row的集合。

currentRow = buildIter.next()

val rowKey = buildSideKeyGenerator(currentRow) //计算rowKey作为HashMap的key

if(!rowKey.anyNull) {

val existingMatchList = hashTable.get(rowKey)

val matchList = if (existingMatchList == null) {

val newMatchList = new ArrayBuffer[Row]()

hashTable.put(rowKey, newMatchList) //(rowKey, matchedRowList)

newMatchList

} else {

existingMatchList

}

matchList += currentRow.copy() //返回matchList

}

}

new Iterator[Row] { //最后用streamedRow的Key来匹配buildSide端的HashMap

private[this] var currentStreamedRow: Row = _

private[this] var currentHashMatches: ArrayBuffer[Row] = _

private[this] var currentMatchPosition: Int = -1

// Mutable per row objects.

private[this] val joinRow = new JoinedRow

private[this] val joinKeys = streamSideKeyGenerator()

override final def hasNext: Boolean =

(currentMatchPosition != -1 && currentMatchPosition < currentHashMatches.size) ||

(streamIter.hasNext && fetchNext())

override final def next() = {

val ret = buildSide match {

case BuildRight => joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition)) //右连接的话，streamedRow放左边，匹配到的key的Row放到右表

case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow) //左连接的话，相反。

}

currentMatchPosition += 1

ret

}

/**

* Searches the streamed iterator for the next row that has at least one match in hashtable.

*

* @return true if the search is successful, and false if the streamed iterator runs out of

* tuples.

*/

private final def fetchNext(): Boolean = {

currentHashMatches = null

currentMatchPosition = -1

while (currentHashMatches == null && streamIter.hasNext) {

currentStreamedRow = streamIter.next()

if (!joinKeys(currentStreamedRow).anyNull) {

currentHashMatches = hashTable.get(joinKeys.currentValue) //streamedRow从buildSide里的HashTable里面匹配rowKey

}

}

if (currentHashMatches == null) {

false

} else {

currentMatchPosition = 0

true

}

}

}

}

}

joinRow的实现，实现2个Row对接:
实际上就是生成一个新的Array，将2个Array合并。

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class JoinedRow extends Row {

private[this] var row1: Row = _

private[this] var row2: Row = _

.........

def copy() = {

val totalSize = row1.size + row2.size

val copiedValues = new Array[Any](totalSize)

var i = 0

while(i < totalSize) {

copiedValues(i) = apply(i)

i += 1

}

new GenericRow(copiedValues) //返回一个新的合并后的Row

}

2.1、LeftSemiJoinHash

left semi join，不多说了，hive早期版本里替代IN和EXISTS 的版本。
将右表的join keys放到HashSet里，然后遍历左表，查找左表的join key是否能匹配。

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case class LeftSemiJoinHash(

leftKeys: Seq[Expression],

rightKeys: Seq[Expression],

left: SparkPlan,

right: SparkPlan) extends BinaryNode with HashJoin {

val buildSide = BuildRight //buildSide是以右表为基准

override def requiredChildDistribution =

ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil

override def output = left.output

def execute() = {

buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) => //右表的物理计划执行后生成RDD，利用zipPartitions对Partition进行合并。然后用上述方法实现。

val hashSet = new java.util.HashSet[Row]()

var currentRow: Row = null

// Create a Hash set of buildKeys

while (buildIter.hasNext) {

currentRow = buildIter.next()

val rowKey = buildSideKeyGenerator(currentRow)

if(!rowKey.anyNull) {

val keyExists = hashSet.contains(rowKey)

if (!keyExists) {

hashSet.add(rowKey)

}

}

}

val joinKeys = streamSideKeyGenerator()

streamIter.filter(current => {

!joinKeys(current).anyNull && hashSet.contains(joinKeys.currentValue)

})

}

}

}

2.2、BroadcastHashJoin

名约： 广播HashJoin，呵呵。
是InnerHashJoin的实现。这里用到了concurrent并发里的future，异步的广播buildPlan的表执行后的的RDD。
如果接收到了广播后的表，那么就用streamedPlan来匹配这个广播的表。
实现是RDD的mapPartitions和HashJoin里的joinIterators最后生成join的结果。

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case class BroadcastHashJoin(

leftKeys: Seq[Expression],

rightKeys: Seq[Expression],

buildSide: BuildSide,

left: SparkPlan,

right: SparkPlan)(@transient sqlContext: SQLContext) extends BinaryNode with HashJoin {

override def otherCopyArgs = sqlContext :: Nil

override def outputPartitioning: Partitioning = left.outputPartitioning

override def requiredChildDistribution =

UnspecifiedDistribution :: UnspecifiedDistribution :: Nil

@transient

lazy val broadcastFuture = future { //利用SparkContext广播表

sqlContext.sparkContext.broadcast(buildPlan.executeCollect())

}

def execute() = {

val broadcastRelation = Await.result(broadcastFuture, 5.minute)

streamedPlan.execute().mapPartitions { streamedIter =>

joinIterators(broadcastRelation.value.iterator, streamedIter) //调用joinIterators对每个分区map

}

}

}

2.3、ShuffleHashJoin

ShuffleHashJoin顾名思义就是需要shuffle数据，outputPartitioning是左孩子的的Partitioning。
会根据这个Partitioning进行shuffle。然后利用SparkContext里的zipPartitions方法对每个分区进行zip。
这里的requiredChildDistribution，的是ClusteredDistribution，这个会在HashPartitioning里面进行匹配。
关于这里面的分区这里不赘述，可以去org.apache.spark.sql.catalyst.plans.physical下的partitioning里面去查看。

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case class ShuffledHashJoin(

leftKeys: Seq[Expression],

rightKeys: Seq[Expression],

buildSide: BuildSide,

left: SparkPlan,

right: SparkPlan) extends BinaryNode with HashJoin {

override def outputPartitioning: Partitioning = left.outputPartitioning

override def requiredChildDistribution =

ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil

def execute() = {

buildPlan.execute().zipPartitions(streamedPlan.execute()) {

(buildIter, streamIter) => joinIterators(buildIter, streamIter)

}

}

}

未完待续 :)

原创文章，转载请注明：

转载自：OopsOutOfMemory盛利的Blog，作者： OopsOutOfMemory

本文链接地址：http://blog.csdn.net/oopsoom/article/details/38274621

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