lazy val optimizedPlan = optimizer(analyzed)
    // TODO: Don't just pick the first one...
    lazy val sparkPlan = planner(optimizedPlan).next()

  @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......
	 }

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
  }
}

 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：

  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

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