Spark SQL(9)-Spark SQL JOIN操作源码总结
Spark SQL(9)-Spark SQL JOIN操作源码总结
本文主要总结下spark sql join操作的实现，本文会根据spark sql 的源码来总结其具体的实现；大体流程还是从sql语句到逻辑算子树再到analyzed-> optimized -> 物理计划及其处理逻辑进行大致的总结。
Join逻辑算子树
先来一个sql：
SELECT NAME FROM NAME LEFT JOIN NAME2 ON NAME = NAME JOIN NAME3 ON NAME = NAME
这条sql形成的逻辑算子树为：
上图的树结构的生成；主要关注join部分就可以；其源码在AstBuilder中：
override def visitFromClause(ctx: FromClauseContext): LogicalPlan = withOrigin(ctx) {
val from = ctx.relation.asScala.foldLeft(null: LogicalPlan) { (left, relation) =>
val right = plan(relation.relationPrimary)
val join = right.optionalMap(left)(Join(_, _, Inner, None))
withJoinRelations(join, relation)
}
ctx.lateralView.asScala.foldLeft(from)(withGenerate)
}
private def withJoinRelations(base: LogicalPlan, ctx: RelationContext): LogicalPlan = {
val pp = ctx.joinRelation
pp.asScala.foldLeft(base) { (left, join) =>
withOrigin(join) {
val baseJoinType = join.joinType match {
case null => Inner
case jt if jt.CROSS != null => Cross
case jt if jt.FULL != null => FullOuter
case jt if jt.SEMI != null => LeftSemi
case jt if jt.ANTI != null => LeftAnti
case jt if jt.LEFT != null => LeftOuter
case jt if jt.RIGHT != null => RightOuter
case _ => Inner
}
// Resolve the join type and join condition
val (joinType, condition) = Option(join.joinCriteria) match {
case Some(c) if c.USING != null =>
(UsingJoin(baseJoinType, c.identifier.asScala.map(_.getText)), None)
case Some(c) if c.booleanExpression != null =>
(baseJoinType, Option(expression(c.booleanExpression)))
case None if join.NATURAL != null =>
if (baseJoinType == Cross) {
throw new ParseException("NATURAL CROSS JOIN is not supported", ctx)
}
(NaturalJoin(baseJoinType), None)
case None =>
(baseJoinType, None)
}
Join(left, plan(join.right), joinType, condition)
}
}
}
从上图可以看出来对于join的操作，形成的树结构里面，保存的join关系是一个list<JoinReleation>,每个joinRelation包含了JoinType、relationPrimary以及joinCriteria;其中joinCriteria相当于是booleanExpression操作。
之后就是Join Analyzed 以及optimized 操作，在这里俩步主要操作就是添加子查询别名等操作，之后在优化阶段算子下推、消除子查询别名等优化；这里面涉及的规则比较多，感兴趣的同学可以查看源码多研究研究；
物理计划阶段
这一步主要涉及到 SparkPlanner 中配置的各种strategies，在这些策略中主要关注JoinSelection部分就行，他的apply方如下：
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
// --- BroadcastHashJoin --------------------------------------------------------------------
// broadcast hints were specified
case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
if canBroadcastByHints(joinType, left, right) =>
val buildSide = broadcastSideByHints(joinType, left, right)
Seq(joins.BroadcastHashJoinExec(
leftKeys, rightKeys, joinType, buildSide, condition, planLater(left), planLater(right)))
// broadcast hints were not specified, so need to infer it from size and configuration.
case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
if canBroadcastBySizes(joinType, left, right) =>
val buildSide = broadcastSideBySizes(joinType, left, right)
Seq(joins.BroadcastHashJoinExec(
leftKeys, rightKeys, joinType, buildSide, condition, planLater(left), planLater(right)))
// --- ShuffledHashJoin ---------------------------------------------------------------------
case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
if !conf.preferSortMergeJoin && canBuildRight(joinType) && canBuildLocalHashMap(right)
&& muchSmaller(right, left) ||
!RowOrdering.isOrderable(leftKeys) =>
Seq(joins.ShuffledHashJoinExec(
leftKeys, rightKeys, joinType, BuildRight, condition, planLater(left), planLater(right)))
case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
if !conf.preferSortMergeJoin && canBuildLeft(joinType) && canBuildLocalHashMap(left)
&& muchSmaller(left, right) ||
!RowOrdering.isOrderable(leftKeys) =>
Seq(joins.ShuffledHashJoinExec(
leftKeys, rightKeys, joinType, BuildLeft, condition, planLater(left), planLater(right)))
// --- SortMergeJoin ------------------------------------------------------------
case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
if RowOrdering.isOrderable(leftKeys) =>
joins.SortMergeJoinExec(
leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil
// --- Without joining keys ------------------------------------------------------------
// Pick BroadcastNestedLoopJoin if one side could be broadcast
case j @ logical.Join(left, right, joinType, condition)
if canBroadcastByHints(joinType, left, right) =>
val buildSide = broadcastSideByHints(joinType, left, right)
joins.BroadcastNestedLoopJoinExec(
planLater(left), planLater(right), buildSide, joinType, condition) :: Nil
case j @ logical.Join(left, right, joinType, condition)
if canBroadcastBySizes(joinType, left, right) =>
val buildSide = broadcastSideBySizes(joinType, left, right)
joins.BroadcastNestedLoopJoinExec(
planLater(left), planLater(right), buildSide, joinType, condition) :: Nil
// Pick CartesianProduct for InnerJoin
case logical.Join(left, right, _: InnerLike, condition) =>
joins.CartesianProductExec(planLater(left), planLater(right), condition) :: Nil
case logical.Join(left, right, joinType, condition) =>
val buildSide = broadcastSide(
left.stats.hints.broadcast, right.stats.hints.broadcast, left, right)
// This join could be very slow or OOM
joins.BroadcastNestedLoopJoinExec(
planLater(left), planLater(right), buildSide, joinType, condition) :: Nil
// --- Cases where this strategy does not apply ---------------------------------------------
case _ => Nil
}
}
从上面的代码可以看出其根据不同的条件生成不同的join操作：BroadcastHashJoinExec、ShuffledHashJoinExec、SortMergeJoinExec、BroadcastNestedLoopJoinExec；
在介绍在四个操作之前，先介绍下join操作实现的大体思想：
假设有俩张表，在spark中进行操作的时候；
一张表为流表；一张表为构建表；默认的大表为流表，小表为构建表；基于流表的迭代，然后和构建表进行匹配，生成join之后的行数据。其实可以想象一种极端情况；大表特别的大有几百万行数据，小表数据只有10行，这个时候只需要迭代遍历流表，然后去小表（构建表）去匹配数据，匹配到之后生成join完成之后的行；
在spark中join的大体实现是分流表和构建表；基于这俩个角色来实现join操作。接下来简单介绍下上面的几种join操作：
1、BroadcastHashJoinExec主要通过广播形式实现join操作；其生成的条件是：一种是标记了hint；并且可以创建构建右表或者构建左表；另外一种是小表小于配置的spark.sql.autoBroadcastJoinThreshold参数的大小，则会进行基于广播的join；这里面spark会先将构建表的数据拉倒driver端，之后再分发到各个worker节点，所以这一步如果构建表比较大的情况下对spark的driver节点来说可能会有压力。
2、ShuffledHashJoinExec 通过shuffle之后在内存中保存join构建表来实现join操作；其生成的条件是：可以构建左表或者右表，其次表的大小小于分区数和配置的广播参数的乘积（保证可以加载到本地内存进行计算），并且打开了优先考虑基于hash join的开关、其次需要保证构建表足够小（构建表*3小于流表）；其主要思想就是对流表进行迭代，之后和内存中的构建表数据匹配生成join之后的行数据。
3、SortMergeJoinExec 通过shuffle操作之后进行排序，再然后进行基于排序的join操作；如果上述俩个都不满足的情况就会进行就排序的join(前提是可以排序)；排序的join就是先对数据进行shuffle分区，保证相同的key分到相同的分区，之后进行排序操作，保证数据有序，之后进行merge join操作，同时读取流表和构建表；因为数据有序，所以只要顺序遍历流表和构建表；匹配生成join行数据就行
4、BroadcastNestedLoopJoinExec 主要针对的是没有join条件的连接操作；暂时不做研究；
接下来主要总结下hashJoin和SortMergeJoinExec的实现逻辑；
ShuffledHashJoinExec
private def buildHashedRelation(iter: Iterator[InternalRow]): HashedRelation = {
val buildDataSize = longMetric("buildDataSize")
val buildTime = longMetric("buildTime")
val start = System.nanoTime()
val context = TaskContext.get()
val relation = HashedRelation(iter, buildKeys, taskMemoryManager = context.taskMemoryManager())
buildTime += (System.nanoTime() - start) / 1000000
buildDataSize += relation.estimatedSize
// This relation is usually used until the end of task.
context.addTaskCompletionListener(_ => relation.close())
relation
}
protected override def doExecute(): RDD[InternalRow] = {
val numOutputRows = longMetric("numOutputRows")
val avgHashProbe = longMetric("avgHashProbe")
streamedPlan.execute().zipPartitions(buildPlan.execute()) { (streamIter, buildIter) =>
val hashed = buildHashedRelation(buildIter)
join(streamIter, hashed, numOutputRows, avgHashProbe)
}
}
先看上面的doExecute方法，一般物理计划都是触发这个方法来执行的，这里主要的逻辑是调用了buildHashedRelation方法，在这个方法中主要关注HashedRelation就行：
private[execution] object HashedRelation {
/**
* Create a HashedRelation from an Iterator of InternalRow.
*/
def apply(
input: Iterator[InternalRow],
key: Seq[Expression],
sizeEstimate: Int = 64,
taskMemoryManager: TaskMemoryManager = null): HashedRelation = {
val mm = Option(taskMemoryManager).getOrElse {
new TaskMemoryManager(
new StaticMemoryManager(
new SparkConf().set(MEMORY_OFFHEAP_ENABLED.key, "false"),
Long.MaxValue,
Long.MaxValue,
1),
0)
}
if (key.length == 1 && key.head.dataType == LongType) {
LongHashedRelation(input, key, sizeEstimate, mm)
} else {
UnsafeHashedRelation(input, key, sizeEstimate, mm)
}
}
}
这里面根据类型dataType如果是long那么就生成LongHashedRelation（基于LongToUnsafeRowMap实现），如果不是就是UnsafeHashedRelation（基于BytesToBytesMap实现）这里主要关注UnsafeHashedRelation就行：
private[joins] object UnsafeHashedRelation {
def apply(
input: Iterator[InternalRow],
key: Seq[Expression],
sizeEstimate: Int,
taskMemoryManager: TaskMemoryManager): HashedRelation = {
val pageSizeBytes = Option(SparkEnv.get).map(_.memoryManager.pageSizeBytes)
.getOrElse(new SparkConf().getSizeAsBytes("spark.buffer.pageSize", "16m"))
val binaryMap = new BytesToBytesMap(
taskMemoryManager,
// Only 70% of the slots can be used before growing, more capacity help to reduce collision
(sizeEstimate * 1.5 + 1).toInt,
pageSizeBytes,
true)
// Create a mapping of buildKeys -> rows
val keyGenerator = UnsafeProjection.create(key)
var numFields = 0
while (input.hasNext) {
val row = input.next().asInstanceOf[UnsafeRow]
numFields = row.numFields()
val key = keyGenerator(row)
if (!key.anyNull) {
val loc = binaryMap.lookup(key.getBaseObject, key.getBaseOffset, key.getSizeInBytes)
val success = loc.append(
key.getBaseObject, key.getBaseOffset, key.getSizeInBytes,
row.getBaseObject, row.getBaseOffset, row.getSizeInBytes)
if (!success) {
binaryMap.free()
throw new SparkException("There is no enough memory to build hash map")
}
}
}
new UnsafeHashedRelation(numFields, binaryMap)
}
从上面的代码可以看出，这里主要是根据从ShuffledHashJoinExec传过来的buildKeys，构建一个基于buildKeys和rows的映射表，其实就是上面提到的构建表。这里准备好构建表之后，回到上面提到的ShuffledHashJoinExec.doExecute中可以看到：
protected override def doExecute(): RDD[InternalRow] = {
val numOutputRows = longMetric("numOutputRows")
val avgHashProbe = longMetric("avgHashProbe")
streamedPlan.execute().zipPartitions(buildPlan.execute()) { (streamIter, buildIter) =>
val hashed = buildHashedRelation(buildIter)
join(streamIter, hashed, numOutputRows, avgHashProbe)
}
}
可以看到基于streamIter（流表）、hashed（构建表）构成了一个join操作：
protected def join(
streamedIter: Iterator[InternalRow],
hashed: HashedRelation,
numOutputRows: SQLMetric,
avgHashProbe: SQLMetric): Iterator[InternalRow] = {
val joinedIter = joinType match {
case _: InnerLike =>
innerJoin(streamedIter, hashed)
case LeftOuter | RightOuter =>
outerJoin(streamedIter, hashed)
case LeftSemi =>
semiJoin(streamedIter, hashed)
case LeftAnti =>
antiJoin(streamedIter, hashed)
case j: ExistenceJoin =>
existenceJoin(streamedIter, hashed)
case x =>
throw new IllegalArgumentException(
s"BroadcastHashJoin should not take $x as the JoinType")
}
// At the end of the task, we update the avg hash probe.
TaskContext.get().addTaskCompletionListener(_ =>
avgHashProbe.set(hashed.getAverageProbesPerLookup))
val resultProj = createResultProjection
joinedIter.map { r =>
numOutputRows += 1
resultProj(r)
}
}
这里可以看看innerJoin的操作：
private def innerJoin(
streamIter: Iterator[InternalRow],
hashedRelation: HashedRelation): Iterator[InternalRow] = {
val joinRow = new JoinedRow
val joinKeys = streamSideKeyGenerator()
streamIter.flatMap { srow =>
joinRow.withLeft(srow)
val matches = hashedRelation.get(joinKeys(srow))
if (matches != null) {
matches.map(joinRow.withRight(_)).filter(boundCondition)
} else {
Seq.empty
}
}
}
可以看出，遍历流表，从构建表获取相同的key,如果不为空就构建joinRow，并应用join的条件进行筛选。到这里整个hash join的实现就算是完成了。对于其他类型的join可以自己跟代码阅读。
SortMergeJoinExec
doExecute方法如下：
protected override def doExecute(): RDD[InternalRow] = {
val numOutputRows = longMetric("numOutputRows")
val spillThreshold = getSpillThreshold
val inMemoryThreshold = getInMemoryThreshold
left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
val boundCondition: (InternalRow) => Boolean = {
condition.map { cond =>
newPredicate(cond, left.output ++ right.output).eval _
}.getOrElse {
(r: InternalRow) => true
}
}
// An ordering that can be used to compare keys from both sides.
val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
val resultProj: InternalRow => InternalRow = UnsafeProjection.create(output, output)
joinType match {
case _: InnerLike =>
new RowIterator {
private[this] var currentLeftRow: InternalRow = _
private[this] var currentRightMatches: ExternalAppendOnlyUnsafeRowArray = _
private[this] var rightMatchesIterator: Iterator[UnsafeRow] = null
private[this] val smjScanner = new SortMergeJoinScanner(
createLeftKeyGenerator(),
createRightKeyGenerator(),
keyOrdering,
RowIterator.fromScala(leftIter),
RowIterator.fromScala(rightIter),
inMemoryThreshold,
spillThreshold
)
private[this] val joinRow = new JoinedRow
if (smjScanner.findNextInnerJoinRows()) {
currentRightMatches = smjScanner.getBufferedMatches
currentLeftRow = smjScanner.getStreamedRow
rightMatchesIterator = currentRightMatches.generateIterator()
}
override def advanceNext(): Boolean = {
while (rightMatchesIterator != null) {
if (!rightMatchesIterator.hasNext) {
if (smjScanner.findNextInnerJoinRows()) {
currentRightMatches = smjScanner.getBufferedMatches
currentLeftRow = smjScanner.getStreamedRow
rightMatchesIterator = currentRightMatches.generateIterator()
} else {
currentRightMatches = null
currentLeftRow = null
rightMatchesIterator = null
return false
}
}
joinRow(currentLeftRow, rightMatchesIterator.next())
if (boundCondition(joinRow)) {
numOutputRows += 1
return true
}
}
false
}
override def getRow: InternalRow = resultProj(joinRow)
}.toScala
case LeftOuter =>
val smjScanner = new SortMergeJoinScanner(
streamedKeyGenerator = createLeftKeyGenerator(),
bufferedKeyGenerator = createRightKeyGenerator(),
keyOrdering,
streamedIter = RowIterator.fromScala(leftIter),
bufferedIter = RowIterator.fromScala(rightIter),
inMemoryThreshold,
spillThreshold
)
val rightNullRow = new GenericInternalRow(right.output.length)
new LeftOuterIterator(
smjScanner, rightNullRow, boundCondition, resultProj, numOutputRows).toScala
case RightOuter =>
val smjScanner = new SortMergeJoinScanner(
streamedKeyGenerator = createRightKeyGenerator(),
bufferedKeyGenerator = createLeftKeyGenerator(),
keyOrdering,
streamedIter = RowIterator.fromScala(rightIter),
bufferedIter = RowIterator.fromScala(leftIter),
inMemoryThreshold,
spillThreshold
)
val leftNullRow = new GenericInternalRow(left.output.length)
new RightOuterIterator(
smjScanner, leftNullRow, boundCondition, resultProj, numOutputRows).toScala
case FullOuter =>
val leftNullRow = new GenericInternalRow(left.output.length)
val rightNullRow = new GenericInternalRow(right.output.length)
val smjScanner = new SortMergeFullOuterJoinScanner(
leftKeyGenerator = createLeftKeyGenerator(),
rightKeyGenerator = createRightKeyGenerator(),
keyOrdering,
leftIter = RowIterator.fromScala(leftIter),
rightIter = RowIterator.fromScala(rightIter),
boundCondition,
leftNullRow,
rightNullRow)
new FullOuterIterator(
smjScanner,
resultProj,
numOutputRows).toScala
case LeftSemi =>
new RowIterator {
private[this] var currentLeftRow: InternalRow = _
private[this] val smjScanner = new SortMergeJoinScanner(
createLeftKeyGenerator(),
createRightKeyGenerator(),
keyOrdering,
RowIterator.fromScala(leftIter),
RowIterator.fromScala(rightIter),
inMemoryThreshold,
spillThreshold
)
private[this] val joinRow = new JoinedRow
override def advanceNext(): Boolean = {
while (smjScanner.findNextInnerJoinRows()) {
val currentRightMatches = smjScanner.getBufferedMatches
currentLeftRow = smjScanner.getStreamedRow
if (currentRightMatches != null && currentRightMatches.length > 0) {
val rightMatchesIterator = currentRightMatches.generateIterator()
while (rightMatchesIterator.hasNext) {
joinRow(currentLeftRow, rightMatchesIterator.next())
if (boundCondition(joinRow)) {
numOutputRows += 1
return true
}
}
}
}
false
}
override def getRow: InternalRow = currentLeftRow
}.toScala
case LeftAnti =>
new RowIterator {
private[this] var currentLeftRow: InternalRow = _
private[this] val smjScanner = new SortMergeJoinScanner(
createLeftKeyGenerator(),
createRightKeyGenerator(),
keyOrdering,
RowIterator.fromScala(leftIter),
RowIterator.fromScala(rightIter),
inMemoryThreshold,
spillThreshold
)
private[this] val joinRow = new JoinedRow
override def advanceNext(): Boolean = {
while (smjScanner.findNextOuterJoinRows()) {
currentLeftRow = smjScanner.getStreamedRow
val currentRightMatches = smjScanner.getBufferedMatches
if (currentRightMatches == null || currentRightMatches.length == 0) {
numOutputRows += 1
return true
}
var found = false
val rightMatchesIterator = currentRightMatches.generateIterator()
while (!found && rightMatchesIterator.hasNext) {
joinRow(currentLeftRow, rightMatchesIterator.next())
if (boundCondition(joinRow)) {
found = true
}
}
if (!found) {
numOutputRows += 1
return true
}
}
false
}
override def getRow: InternalRow = currentLeftRow
}.toScala
case j: ExistenceJoin =>
new RowIterator {
private[this] var currentLeftRow: InternalRow = _
private[this] val result: InternalRow = new GenericInternalRow(Array[Any](null))
private[this] val smjScanner = new SortMergeJoinScanner(
createLeftKeyGenerator(),
createRightKeyGenerator(),
keyOrdering,
RowIterator.fromScala(leftIter),
RowIterator.fromScala(rightIter),
inMemoryThreshold,
spillThreshold
)
private[this] val joinRow = new JoinedRow
override def advanceNext(): Boolean = {
while (smjScanner.findNextOuterJoinRows()) {
currentLeftRow = smjScanner.getStreamedRow
val currentRightMatches = smjScanner.getBufferedMatches
var found = false
if (currentRightMatches != null && currentRightMatches.length > 0) {
val rightMatchesIterator = currentRightMatches.generateIterator()
while (!found && rightMatchesIterator.hasNext) {
joinRow(currentLeftRow, rightMatchesIterator.next())
if (boundCondition(joinRow)) {
found = true
}
}
}
result.setBoolean(0, found)
numOutputRows += 1
return true
}
false
}
override def getRow: InternalRow = resultProj(joinRow(currentLeftRow, result))
}.toScala
case x =>
throw new IllegalArgumentException(
s"SortMergeJoin should not take $x as the JoinType")
}
}
}
这里首先看下InnerLike分支下的实现：
具体逻辑很简单：
实例化了一个SortMergeJoinScanner，具体实现可以看实现的advanceNext方法，调用findNextInnerJoinRows找到下一行可以join的数据；这里面：
1、currentLeftRow相当于是流表数据，触发是：smjScanner.getStreamedRow
2、currentRightMatches相当于是构建表数据，触发是：smjScanner.getBufferedMatches
3、advanceNext这里面主要就是findNextInnerJoinRows方法，如果返回true那么就是有新行，直接重置1、2的值，然后构建joinRow，之后再应用过滤条件
4、findNextInnerJoinRows：
final def findNextInnerJoinRows(): Boolean = {
while (advancedStreamed() && streamedRowKey.anyNull) {
// Advance the streamed side of the join until we find the next row whose join key contains
// no nulls or we hit the end of the streamed iterator.
}
if (streamedRow == null) {
// We have consumed the entire streamed iterator, so there can be no more matches.
matchJoinKey = null
bufferedMatches.clear()
false
} else if (matchJoinKey != null && keyOrdering.compare(streamedRowKey, matchJoinKey) == 0) {
// The new streamed row has the same join key as the previous row, so return the same matches.
true
} else if (bufferedRow == null) {
// The streamed row's join key does not match the current batch of buffered rows and there are
// no more rows to read from the buffered iterator, so there can be no more matches.
matchJoinKey = null
bufferedMatches.clear()
false
} else {
// Advance both the streamed and buffered iterators to find the next pair of matching rows.
var comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
do {
if (streamedRowKey.anyNull) {
advancedStreamed()
} else {
assert(!bufferedRowKey.anyNull)
comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
if (comp > 0) advancedBufferedToRowWithNullFreeJoinKey()
else if (comp < 0) advancedStreamed()
}
} while (streamedRow != null && bufferedRow != null && comp != 0)
if (streamedRow == null || bufferedRow == null) {
// We have either hit the end of one of the iterators, so there can be no more matches.
matchJoinKey = null
bufferedMatches.clear()
false
} else {
// The streamed row's join key matches the current buffered row's join, so walk through the
// buffered iterator to buffer the rest of the matching rows.
assert(comp == 0)
bufferMatchingRows()
true
}
}
}
主要逻辑如下：
如果流表为空直接返回，
如何流表的行可以和当前的缓存matchJoinKey对应上，则返回true；
如果构建表为空，直接返回false；
之后具体逻辑在do while中，首先还是校验；之后对流表和构建表数据的key进行比对，如果大于0；则重新拿构建表的数据，如果小于0，就拿流表的数据，如果不是就循环，直到俩个key相同，或者俩个表为空；之后会一直添加bufferedMatches（相当于对拥有同一个key的构建表数据进行append操作，加入bufferedMatches中）；
其次在bufferMatchingRows方法中记录了matchJoinKey，之后再调用findNextInnerJoinRows的时候，如果发现新的流表key和matchJoinKey相同直接返回true，进行join操作。
关于LeftOuter和RightOuter主要实现是基于LeftOuterIterator和RightOuterIterator，这俩个是OneSideOuterIterator的具体实现，其实依赖SortMergeJoinScanner.findNextOuterJoinRows来判断流表和构建表的key，然后进行相应的处理；这俩个主要实现setBufferedSideOutput和setStreamSideOutput这俩个方法，之后的逻辑都在advanceStream中。
对于FullOuter主要实现就是FullOuterIterator，这里：
private class FullOuterIterator(
smjScanner: SortMergeFullOuterJoinScanner,
resultProj: InternalRow => InternalRow,
numRows: SQLMetric) extends RowIterator {
private[this] val joinedRow: JoinedRow = smjScanner.getJoinedRow()
override def advanceNext(): Boolean = {
val r = smjScanner.advanceNext()
if (r) numRows += 1
r
}
override def getRow: InternalRow = resultProj(joinedRow)
}
这么看FullOuter的实现倒是最简单的；
因为返回的是一个迭代器，所以在查看源码的时候，主要关注advanceNext方法的实现，根据这个可以追溯到整个的join的过程。
总结，这里主要简单总结了下spark join的实现思想。具体的实现细节还是要深入代码去了解，比如SortMergeJoinExec中，他的溢出是基于什么的？这个其实在SortMergeJoinScanner
中的ExternalAppendOnlyUnsafeRowArray，他基于UnsafeExternalSorter来实现对应的溢写操作。
