本帖最后由 xioaxu790 于 2014-12-15 13:55 编辑 问题导读 1、什么是Physical Plan？ 2、如何理解Optimizer工作流程？ 3、 TakeOrdered的作用是什么？ Spark SQL Catalyst源码分析之Analyzer（1） Spark SQL Catalyst源码分析之TreeNode Library（2） Spark SQL Catalyst源码分析之Optimizer（3）
一、SparkPlanner lazy val optimizedPlan = optimizer(analyzed)
// TODO: Don't just pick the first one...
lazy val sparkPlan = planner(optimizedPlan).next() 复制代码 @transient
protected[sql] val planner = new SparkPlanner
protected[sql] class SparkPlanner extends SparkStrategies {
val sparkContext: SparkContext = self.sparkContext
val sqlContext: SQLContext = self
def numPartitions = self.numShufflePartitions //partitions的个数
val strategies: Seq[Strategy] = //策略的集合
CommandStrategy(self) ::
TakeOrdered ::
PartialAggregation ::
LeftSemiJoin ::
HashJoin ::
InMemoryScans ::
ParquetOperations ::
BasicOperators ::
CartesianProduct ::
BroadcastNestedLoopJoin :: Nil
etc......
} 复制代码 abstract class QueryPlanner[PhysicalPlan <: TreeNode[PhysicalPlan]] {
/** A list of execution strategies that can be used by the planner */
def strategies: Seq[Strategy]
/**
* Given a [[plans.logical.LogicalPlan LogicalPlan]], returns a list of `PhysicalPlan`s that can
* be used for execution. If this strategy does not apply to the give logical operation then an
* empty list should be returned.
*/
abstract protected class Strategy extends Logging {
def apply(plan: LogicalPlan): Seq[PhysicalPlan] //接受一个logical plan，返回Seq[PhysicalPlan]
}
/**
* Returns a placeholder for a physical plan that executes `plan`. This placeholder will be
* filled in automatically by the QueryPlanner using the other execution strategies that are
* available.
*/
protected def planLater(plan: LogicalPlan) = apply(plan).next() //返回一个占位符，占位符会自动被QueryPlanner用其它的strategies apply
def apply(plan: LogicalPlan): Iterator[PhysicalPlan] = {
// Obviously a lot to do here still...
val iter = strategies.view.flatMap(_(plan)).toIterator //整合所有的Strategy，_(plan)每个Strategy应用plan上，得到所有Strategies执行完后生成的所有Physical Plan的集合，一个iter
assert(iter.hasNext, s"No plan for $plan")
iter //返回所有物理计划
}
} 复制代码
二、Spark Plan @transient
protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] {
val batches =
Batch("Add exchange", Once, AddExchange(self)) :: //添加shuffler操作如果必要的话
Batch("Prepare Expressions", Once, new BindReferences[SparkPlan]) :: Nil //Bind references
} 复制代码 abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging {
self: Product =>
// TODO: Move to `DistributedPlan`
/** Specifies how data is partitioned across different nodes in the cluster. */
def outputPartitioning: Partitioning = UnknownPartitioning(0) // TODO: WRONG WIDTH!
/** Specifies any partition requirements on the input data for this operator. */
def requiredChildDistribution: Seq[Distribution] =
Seq.fill(children.size)(UnspecifiedDistribution)
/**
* Runs this query returning the result as an RDD.
*/
def execute(): RDD[Row] //真正执行查询的方法execute，返回的是一个RDD
/**
* Runs this query returning the result as an array.
*/
def executeCollect(): Array[Row] = execute().map(_.copy()).collect() //exe & collect
protected def buildRow(values: Seq[Any]): Row = //根据当前的值，生成Row对象，其实是一个封装了Array的对象。
new GenericRow(values.toArray)
} 复制代码
三、Strategies case object Inner extends JoinType
case object LeftOuter extends JoinType
case object RightOuter extends JoinType
case object FullOuter extends JoinType
case object LeftSemi extends JoinType 复制代码 object LeftSemiJoin extends Strategy with PredicateHelper {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
// Find left semi joins where at least some predicates can be evaluated by matching join keys
case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right) =>
val semiJoin = execution.LeftSemiJoinHash( //根据解析后的Join，实例化execution.LeftSemiJoinHash这个Spark Plan 返回
leftKeys, rightKeys, planLater(left), planLater(right))
condition.map(Filter(_, semiJoin)).getOrElse(semiJoin) :: Nil
// no predicate can be evaluated by matching hash keys
case logical.Join(left, right, LeftSemi, condition) => //没有Join key的，即非等值join连接的，返回LeftSemiJoinBNL这个Spark Plan
execution.LeftSemiJoinBNL(
planLater(left), planLater(right), condition)(sqlContext) :: Nil
case _ => Nil
}
} 复制代码 object HashJoin extends Strategy with PredicateHelper {
private[this] def broadcastHashJoin(
leftKeys: Seq[Expression],
rightKeys: Seq[Expression],
left: LogicalPlan,
right: LogicalPlan,
condition: Option[Expression],
side: BuildSide) = {
val broadcastHashJoin = execution.BroadcastHashJoin(
leftKeys, rightKeys, side, planLater(left), planLater(right))(sqlContext)
condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
}
def broadcastTables: Seq[String] = sqlContext.joinBroadcastTables.split(",").toBuffer //获取需要广播的表
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case ExtractEquiJoinKeys(
Inner,
leftKeys,
rightKeys,
condition,
left,
right @ PhysicalOperation(_, _, b: BaseRelation))
if broadcastTables.contains(b.tableName) => //如果右孩子是广播的表，则buildSide取BuildRight
broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildRight)
case ExtractEquiJoinKeys(
Inner,
leftKeys,
rightKeys,
condition,
left @ PhysicalOperation(_, _, b: BaseRelation),
right)
if broadcastTables.contains(b.tableName) =>//如果左孩子是广播的表，则buildSide取BuildLeft
broadcastHashJoin(leftKeys, rightKeys, left, right, condition, BuildLeft)
case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) =>
val hashJoin =
execution.ShuffledHashJoin( //根据hash key shuffle的 Hash Join
leftKeys, rightKeys, BuildRight, planLater(left), planLater(right))
condition.map(Filter(_, hashJoin)).getOrElse(hashJoin) :: Nil
case _ => Nil
}
} 复制代码 object PartialAggregation extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case logical.Aggregate(groupingExpressions, aggregateExpressions, child) =>
// Collect all aggregate expressions.
val allAggregates =
aggregateExpressions.flatMap(_ collect { case a: AggregateExpression => a })
// Collect all aggregate expressions that can be computed partially.
val partialAggregates =
aggregateExpressions.flatMap(_ collect { case p: PartialAggregate => p })
// Only do partial aggregation if supported by all aggregate expressions.
if (allAggregates.size == partialAggregates.size) {
// Create a map of expressions to their partial evaluations for all aggregate expressions.
val partialEvaluations: Map[Long, SplitEvaluation] =
partialAggregates.map(a => (a.id, a.asPartial)).toMap
// We need to pass all grouping expressions though so the grouping can happen a second
// time. However some of them might be unnamed so we alias them allowing them to be
// referenced in the second aggregation.
val namedGroupingExpressions: Map[Expression, NamedExpression] = groupingExpressions.map {
case n: NamedExpression => (n, n)
case other => (other, Alias(other, "PartialGroup")())
}.toMap
// Replace aggregations with a new expression that computes the result from the already
// computed partial evaluations and grouping values.
val rewrittenAggregateExpressions = aggregateExpressions.map(_.transformUp {
case e: Expression if partialEvaluations.contains(e.id) =>
partialEvaluations(e.id).finalEvaluation
case e: Expression if namedGroupingExpressions.contains(e) =>
namedGroupingExpressions(e).toAttribute
}).asInstanceOf[Seq[NamedExpression]]
val partialComputation =
(namedGroupingExpressions.values ++
partialEvaluations.values.flatMap(_.partialEvaluations)).toSeq
// Construct two phased aggregation.
execution.Aggregate( //返回execution.Aggregate这个Spark Plan
partial = false,
namedGroupingExpressions.values.map(_.toAttribute).toSeq,
rewrittenAggregateExpressions,
execution.Aggregate(
partial = true,
groupingExpressions,
partialComputation,
planLater(child))(sqlContext))(sqlContext) :: Nil
} else {
Nil
}
case _ => Nil
}
} 复制代码 object BroadcastNestedLoopJoin extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case logical.Join(left, right, joinType, condition) =>
execution.BroadcastNestedLoopJoin(
planLater(left), planLater(right), joinType, condition)(sqlContext) :: Nil
case _ => Nil
}
} 复制代码 if (!matched && (joinType == LeftOuter || joinType == FullOuter)) { //LeftOuter or FullOuter
matchedRows += buildRow(streamedRow ++ Array.fill(right.output.size)(null))
}
}
Iterator((matchedRows, includedBroadcastTuples))
}
val includedBroadcastTuples = streamedPlusMatches.map(_._2)
val allIncludedBroadcastTuples =
if (includedBroadcastTuples.count == 0) {
new scala.collection.mutable.BitSet(broadcastedRelation.value.size)
} else {
streamedPlusMatches.map(_._2).reduce(_ ++ _)
}
val rightOuterMatches: Seq[Row] =
if (joinType == RightOuter || joinType == FullOuter) { //RightOuter or FullOuter
broadcastedRelation.value.zipWithIndex.filter {
case (row, i) => !allIncludedBroadcastTuples.contains(i)
}.map {
// TODO: Use projection.
case (row, _) => buildRow(Vector.fill(left.output.size)(null) ++ row)
}
} else {
Vector()
} 复制代码 笛卡尔积的Join，有待过滤条件的Join。
主要是利用RDD的cartesian实现的。
object CartesianProduct extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case logical.Join(left, right, _, None) =>
execution.CartesianProduct(planLater(left), planLater(right)) :: Nil
case logical.Join(left, right, Inner, Some(condition)) =>
execution.Filter(condition,
execution.CartesianProduct(planLater(left), planLater(right))) :: Nil
case _ => Nil
}
} 复制代码 object TakeOrdered extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case logical.Limit(IntegerLiteral(limit), logical.Sort(order, child)) =>
execution.TakeOrdered(limit, order, planLater(child))(sqlContext) :: Nil
case _ => Nil
}
} 复制代码 object ParquetOperations extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
// TODO: need to support writing to other types of files. Unify the below code paths.
case logical.WriteToFile(path, child) =>
val relation =
ParquetRelation.create(path, child, sparkContext.hadoopConfiguration)
// Note: overwrite=false because otherwise the metadata we just created will be deleted
InsertIntoParquetTable(relation, planLater(child), overwrite=false)(sqlContext) :: Nil
case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) =>
InsertIntoParquetTable(table, planLater(child), overwrite)(sqlContext) :: Nil
case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) =>
val prunePushedDownFilters =
if (sparkContext.conf.getBoolean(ParquetFilters.PARQUET_FILTER_PUSHDOWN_ENABLED, true)) {
(filters: Seq[Expression]) => {
filters.filter { filter =>
// Note: filters cannot be pushed down to Parquet if they contain more complex
// expressions than simple "Attribute cmp Literal" comparisons. Here we remove
// all filters that have been pushed down. Note that a predicate such as
// "(A AND B) OR C" can result in "A OR C" being pushed down.
val recordFilter = ParquetFilters.createFilter(filter)
if (!recordFilter.isDefined) {
// First case: the pushdown did not result in any record filter.
true
} else {
// Second case: a record filter was created; here we are conservative in
// the sense that even if "A" was pushed and we check for "A AND B" we
// still want to keep "A AND B" in the higher-level filter, not just "B".
!ParquetFilters.findExpression(recordFilter.get, filter).isDefined
}
}
}
} else {
identity[Seq[Expression]] _
}
pruneFilterProject(
projectList,
filters,
prunePushedDownFilters,
ParquetTableScan(_, relation, filters)(sqlContext)) :: Nil
case _ => Nil
}
} 复制代码 object InMemoryScans extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case PhysicalOperation(projectList, filters, mem: InMemoryRelation) =>
pruneFilterProject(
projectList,
filters,
identity[Seq[Expression]], // No filters are pushed down.
InMemoryColumnarTableScan(_, mem)) :: Nil
case _ => Nil
}
} 复制代码 object BasicOperators extends Strategy {
def numPartitions = self.numPartitions
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case logical.Distinct(child) =>
execution.Aggregate(
partial = false, child.output, child.output, planLater(child))(sqlContext) :: Nil
case logical.Sort(sortExprs, child) =>
// This sort is a global sort. Its requiredDistribution will be an OrderedDistribution.
execution.Sort(sortExprs, global = true, planLater(child)):: Nil
case logical.SortPartitions(sortExprs, child) =>
// This sort only sorts tuples within a partition. Its requiredDistribution will be
// an UnspecifiedDistribution.
execution.Sort(sortExprs, global = false, planLater(child)) :: Nil
case logical.Project(projectList, child) =>
execution.Project(projectList, planLater(child)) :: Nil
case logical.Filter(condition, child) =>
execution.Filter(condition, planLater(child)) :: Nil
case logical.Aggregate(group, agg, child) =>
execution.Aggregate(partial = false, group, agg, planLater(child))(sqlContext) :: Nil
case logical.Sample(fraction, withReplacement, seed, child) =>
execution.Sample(fraction, withReplacement, seed, planLater(child)) :: Nil
case logical.LocalRelation(output, data) =>
val dataAsRdd =
sparkContext.parallelize(data.map(r =>
new GenericRow(r.productIterator.map(convertToCatalyst).toArray): Row))
execution.ExistingRdd(output, dataAsRdd) :: Nil
case logical.Limit(IntegerLiteral(limit), child) =>
execution.Limit(limit, planLater(child))(sqlContext) :: Nil
case Unions(unionChildren) =>
execution.Union(unionChildren.map(planLater))(sqlContext) :: Nil
case logical.Generate(generator, join, outer, _, child) =>
execution.Generate(generator, join = join, outer = outer, planLater(child)) :: Nil
case logical.NoRelation =>
execution.ExistingRdd(Nil, singleRowRdd) :: Nil
case logical.Repartition(expressions, child) =>
execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil
case SparkLogicalPlan(existingPlan, _) => existingPlan :: Nil
case _ => Nil
}
} 复制代码 case class CommandStrategy(context: SQLContext) extends Strategy {
def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
case logical.SetCommand(key, value) =>
Seq(execution.SetCommand(key, value, plan.output)(context))
case logical.ExplainCommand(logicalPlan) =>
Seq(execution.ExplainCommand(logicalPlan, plan.output)(context))
case logical.CacheCommand(tableName, cache) =>
Seq(execution.CacheCommand(tableName, cache)(context))
case _ => Nil
}
} 复制代码 四、Execution 五、总结 
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