Spark SQL Catalyst源码分析之Physical Plan

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：

一、SparkPlanner

话接上回，Optimizer接受输入的Analyzed Logical Plan后，会有SparkPlanner来对Optimized Logical Plan进行转换，生成Physical plans。

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]。

SparkPlanner继承了SparkStrategies，SparkStrategies继承了QueryPlanner。

SparkStrategies包含了一系列特定的Strategies，这些Strategies是继承自QueryPlanner中定义的Strategy，它定义接受一个Logical Plan，生成一系列的Physical Plan

  @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
  }
}

继承关系：

二、Spark Plan

Spark Plan是Catalyst里经过所有Strategies apply 的最终的物理执行计划的抽象类，它只是用来执行spark job的。

 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)
}

关于Spark Plan的继承关系，如图：

三、Strategies

Strategy，注意这里Strategy是在execution包下的，在SparkPlanner里定义了目前的几种策略：

LeftSemiJoin、HashJoin、PartialAggregation、BroadcastNestedLoopJoin、CartesianProduct、TakeOrdered、ParquetOperations、InMemoryScans、BasicOperators、CommandStrategy

3.1、LeftSemiJoin

Join分为好几种类型：

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，

这里ExtractEquiJoinKeys是一个pattern定义在patterns.scala里，主要是做模式匹配用的。

这里匹配只要是等值的join操作，都会封装为ExtractEquiJoinKeys对象，它会解析当前join，最后返回(joinType, rightKeys, leftKeys, condition, leftChild, rightChild)的格式。

最后返回一个execution.LeftSemiJoinHash这个Spark Plan，可见Spark Plan的类图继承关系图。

 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
    }
  }

3.2、HashJoin

HashJoin是我们最见的操作，innerJoin类型，里面提供了2种Spark Plan，BroadcastHashJoin 和 ShuffledHashJoin

BroadcastHashJoin的实现是一种广播变量的实现方法，如果设置了spark.sql.join.broadcastTables这个参数的表（表面逗号隔开）

就会用spark的Broadcast Variables方式先将一张表给查询出来，然后广播到各个机器中，相当于Hive中的map join。

ShuffledHashJoin是一种最传统的默认的join方式，会根据shuffle key进行shuffle的hash join。

 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
    }
  }

3.3、PartialAggregation

PartialAggregation是一个部分聚合的策略，即有些聚合操作可以在local里面完成的，就在local data里完成，而不必要的去shuffle所有的字段。

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
    }
  }

3.4、BroadcastNestedLoopJoin

BroadcastNestedLoopJoin是用于Left Outer Join， RightOuter， FullOuter这三种类型的join

而上述的Hash Join仅仅用于InnerJoin，这点要区分开来。

  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()
      }

3.5、CartesianProduct

  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
    }
  }

3.6、TakeOrdered

TakeOrdered是用于Limit操作的，如果有Limit和Sort操作。

则返回一个TakeOrdered的Spark Plan。

主要也是利用RDD的takeOrdered方法来实现的排序后取TopN。

  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
    }
  }

3.7、ParquetOperations

支持ParquetOperations的读写，插入Table等。

  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
    }
  }

3.8、InMemoryScans

InMemoryScans主要是对InMemoryRelation这个Logical Plan操作。

调用的其实是Spark Planner里的pruneFilterProject这个方法。

 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
    }
  }

3.9、BasicOperators

所有定义在org.apache.spark.sql.execution里的基本的Spark Plan，它们都在org.apache.spark.sql.execution包下basicOperators.scala内的

有Project、Filter、Sample、Union、Limit、TakeOrdered、Sort、ExistingRdd。

这些是基本元素，实现都相对简单，基本上都是RDD里的方法来实现的。

 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
    }
  }

3.10 CommandStrategy

CommandStrategy是专门针对Command类型的Logical Plan

即set key = value 、 explain sql、 cache table xxx 这类操作

SetCommand主要实现方式是SparkContext的参数

ExplainCommand主要实现方式是利用executed Plan打印出tree string

CacheCommand主要实现方式SparkContext的cache table和uncache table

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
    }
  }

四、Execution

Spark Plan的Execution方式均为调用其execute()方法生成RDD，除了简单的基本操作例如上面的basic operator实现比较简单，其它的实现都比较复杂，大致的实现我都在上面介绍了，本文就不详细讨论了。

五、总结

本文从介绍了Spark SQL的Catalyst框架的Physical plan以及其如何从Optimized Logical Plan转化为Spark Plan的过程，这个过程用到了很多的物理计划策略Strategies，每个Strategies最后还是在RuleExecutor里面被执行，最后生成一系列物理计划Executed Spark Plans。

Spark Plan是执行前最后一种计划，当生成executed spark plan后，就可以调用collect()方法来启动Spark Job来进行Spark SQL的真正执行了。

——EOF——

原创文章，转载请注明出自：http://blog.csdn.net/oopsoom/article/details/38235247

Spark SQL Catalyst源码分析之Physical Plan