标签：spark sql catalyst sql spark shark
前面几篇文章主要介绍的是spark sql包里的的spark sql执行流程，以及Catalyst包内的SqlParser，Analyzer和Optimizer，最后要介绍一下Catalyst里最后的一个Plan了，即Physical Plan。物理计划是Spark SQL执行Spark job的前置，也是最后一道计划。
如图：
lazy val optimizedPlan = optimizer(analyzed)
// TODO: Don't just pick the first one...
lazy val sparkPlan = planner(optimizedPlan).next() SparkPlanner的apply方法，会返回一个Iterator[PhysicalPlan]。 @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......
}QueryPlanner 是SparkPlanner的基类，定义了一系列的关键点，如Strategy，planLater和apply。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
}
}
lazy val executedPlan: SparkPlan = prepareForExecution(sparkPlan)prepareForExecution其实是一个RuleExecutor[SparkPlan]，当然这里的Rule就是SparkPlan了。
@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
}Spark Plan继承Query Plan[Spark Plan]，里面定义的partition，requiredChildDistribution以及spark sql启动执行的execute方法。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)
}
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如果Logical Plan里的Join是joinType为LeftSemi的话，就会执行这种策略，
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
}
}
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
}
}
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
}
}
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
}
}部分代码； 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()
}
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
}
}
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
}
}
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
}
}
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
}
}
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
}
}
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
}
}
Spark SQL Catalyst源码分析之Physical Plan,布布扣,bubuko.com
Spark SQL Catalyst源码分析之Physical Plan
标签：spark sql catalyst sql spark shark
原文地址：http://blog.csdn.net/oopsoom/article/details/38235247
