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org.apache.spark.sql.execution

BlockingOperatorWithCodegen

trait BlockingOperatorWithCodegen extends SparkPlan with CodegenSupport

A special kind of operators which support whole stage codegen. Blocking means these operators will consume all the inputs first, before producing output. Typical blocking operators are sort and aggregate.

Linear Supertypes
CodegenSupport, SparkPlan, Serializable, Serializable, Logging, QueryPlan[SparkPlan], TreeNode[SparkPlan], Product, Equals, AnyRef, Any
Known Subclasses
SortExec, HashAggregateExec
Ordering
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Inherited
  1. BlockingOperatorWithCodegen
  2. CodegenSupport
  3. SparkPlan
  4. Serializable
  5. Serializable
  6. Logging
  7. QueryPlan
  8. TreeNode
  9. Product
  10. Equals
  11. AnyRef
  12. Any
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Visibility
  1. Public
  2. All

Abstract Value Members

  1. abstract def canEqual(that: Any): Boolean
    Definition Classes
    Equals
  2. abstract def children: Seq[SparkPlan]
    Definition Classes
    TreeNode
  3. abstract def inputRDDs(): Seq[RDD[InternalRow]]

    Returns all the RDDs of InternalRow which generates the input rows.

    Returns all the RDDs of InternalRow which generates the input rows.

    Definition Classes
    CodegenSupport
    Note

    Right now we support up to two RDDs

  4. abstract def output: Seq[Attribute]
    Definition Classes
    QueryPlan
  5. abstract def productArity: Int
    Definition Classes
    Product
  6. abstract def productElement(n: Int): Any
    Definition Classes
    Product

Concrete Value Members

  1. lazy val allAttributes: AttributeSeq
    Definition Classes
    QueryPlan
  2. def apply(number: Int): TreeNode[_]
    Definition Classes
    TreeNode
  3. def argString(maxFields: Int): String
    Definition Classes
    TreeNode
  4. def asCode: String
    Definition Classes
    TreeNode
  5. final lazy val canonicalized: SparkPlan
    Definition Classes
    QueryPlan
    Annotations
    @transient()
  6. def clone(): SparkPlan
    Definition Classes
    TreeNode → AnyRef
  7. def collect[B](pf: PartialFunction[SparkPlan, B]): Seq[B]
    Definition Classes
    TreeNode
  8. def collectFirst[B](pf: PartialFunction[SparkPlan, B]): Option[B]
    Definition Classes
    TreeNode
  9. def collectLeaves(): Seq[SparkPlan]
    Definition Classes
    TreeNode
  10. def collectWithSubqueries[B](f: PartialFunction[SparkPlan, B]): Seq[B]
    Definition Classes
    QueryPlan
  11. def conf: SQLConf
    Definition Classes
    QueryPlan
  12. final def consume(ctx: CodegenContext, outputVars: Seq[ExprCode], row: String = null): String

    Consume the generated columns or row from current SparkPlan, call its parent's doConsume().

    Consume the generated columns or row from current SparkPlan, call its parent's doConsume().

    Note that outputVars and row can't both be null.

    Definition Classes
    CodegenSupport
  13. lazy val containsChild: Set[TreeNode[_]]
    Definition Classes
    TreeNode
  14. def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String

    Generate the Java source code to process the rows from child SparkPlan.

    Generate the Java source code to process the rows from child SparkPlan. This should only be called from consume.

    This should be override by subclass to support codegen.

    Note: The operator should not assume the existence of an outer processing loop, which it can jump from with "continue;"!

    For example, filter could generate this: # code to evaluate the predicate expression, result is isNull1 and value2 if (!isNull1 && value2) { # call consume(), which will call parent.doConsume() }

    Note: A plan can either consume the rows as UnsafeRow (row), or a list of variables (input). When consuming as a listing of variables, the code to produce the input is already generated and CodegenContext.currentVars is already set. When consuming as UnsafeRow, implementations need to put row.code in the generated code and set CodegenContext.INPUT_ROW manually. Some plans may need more tweaks as they have different inputs(join build side, aggregate buffer, etc.), or other special cases.

    Definition Classes
    CodegenSupport
  15. final def execute(): RDD[InternalRow]

    Returns the result of this query as an RDD[InternalRow] by delegating to doExecute after preparations.

    Returns the result of this query as an RDD[InternalRow] by delegating to doExecute after preparations.

    Concrete implementations of SparkPlan should override doExecute.

    Definition Classes
    SparkPlan
  16. final def executeBroadcast[T](): Broadcast[T]

    Returns the result of this query as a broadcast variable by delegating to doExecuteBroadcast after preparations.

    Returns the result of this query as a broadcast variable by delegating to doExecuteBroadcast after preparations.

    Concrete implementations of SparkPlan should override doExecuteBroadcast.

    Definition Classes
    SparkPlan
  17. def executeCollect(): Array[InternalRow]

    Runs this query returning the result as an array.

    Runs this query returning the result as an array.

    Definition Classes
    SparkPlan
  18. def executeCollectPublic(): Array[Row]

    Runs this query returning the result as an array, using external Row format.

    Runs this query returning the result as an array, using external Row format.

    Definition Classes
    SparkPlan
  19. final def executeColumnar(): RDD[ColumnarBatch]

    Returns the result of this query as an RDD[ColumnarBatch] by delegating to doColumnarExecute after preparations.

    Returns the result of this query as an RDD[ColumnarBatch] by delegating to doColumnarExecute after preparations.

    Concrete implementations of SparkPlan should override doColumnarExecute if supportsColumnar returns true.

    Definition Classes
    SparkPlan
  20. def executeTail(n: Int): Array[InternalRow]

    Runs this query returning the last n rows as an array.

    Runs this query returning the last n rows as an array.

    This is modeled after RDD.take but never runs any job locally on the driver.

    Definition Classes
    SparkPlan
  21. def executeTake(n: Int): Array[InternalRow]

    Runs this query returning the first n rows as an array.

    Runs this query returning the first n rows as an array.

    This is modeled after RDD.take but never runs any job locally on the driver.

    Definition Classes
    SparkPlan
  22. def executeToIterator(): Iterator[InternalRow]

    Runs this query returning the result as an iterator of InternalRow.

    Runs this query returning the result as an iterator of InternalRow.

    Definition Classes
    SparkPlan
    Note

    Triggers multiple jobs (one for each partition).

  23. final def expressions: Seq[Expression]
    Definition Classes
    QueryPlan
  24. def fastEquals(other: TreeNode[_]): Boolean
    Definition Classes
    TreeNode
  25. def find(f: (SparkPlan) ⇒ Boolean): Option[SparkPlan]
    Definition Classes
    TreeNode
  26. def flatMap[A](f: (SparkPlan) ⇒ TraversableOnce[A]): Seq[A]
    Definition Classes
    TreeNode
  27. def foreach(f: (SparkPlan) ⇒ Unit): Unit
    Definition Classes
    TreeNode
  28. def foreachUp(f: (SparkPlan) ⇒ Unit): Unit
    Definition Classes
    TreeNode
  29. def generateTreeString(depth: Int, lastChildren: Seq[Boolean], append: (String) ⇒ Unit, verbose: Boolean, prefix: String, addSuffix: Boolean, maxFields: Int, printNodeId: Boolean): Unit
    Definition Classes
    TreeNode
  30. def getTagValue[T](tag: TreeNodeTag[T]): Option[T]
    Definition Classes
    TreeNode
  31. def hashCode(): Int
    Definition Classes
    TreeNode → AnyRef → Any
  32. val id: Int
    Definition Classes
    SparkPlan
  33. def innerChildren: Seq[QueryPlan[_]]
    Definition Classes
    QueryPlan → TreeNode
  34. def inputSet: AttributeSet
    Definition Classes
    QueryPlan
  35. def limitNotReachedChecks: Seq[String]

    A sequence of checks which evaluate to true if the downstream Limit operators have not received enough records and reached the limit.

    A sequence of checks which evaluate to true if the downstream Limit operators have not received enough records and reached the limit. If current node is a data producing node, it can leverage this information to stop producing data and complete the data flow earlier. Common data producing nodes are leaf nodes like Range and Scan, and blocking nodes like Sort and Aggregate. These checks should be put into the loop condition of the data producing loop.

    Definition Classes
    BlockingOperatorWithCodegen → CodegenSupport
  36. final def limitNotReachedCond: String

    A helper method to generate the data producing loop condition according to the limit-not-reached checks.

    A helper method to generate the data producing loop condition according to the limit-not-reached checks.

    Definition Classes
    CodegenSupport
  37. def logicalLink: Option[LogicalPlan]

    returns

    The logical plan this plan is linked to.

    Definition Classes
    SparkPlan
  38. def longMetric(name: String): SQLMetric

    returns

    SQLMetric for the name.

    Definition Classes
    SparkPlan
  39. def makeCopy(newArgs: Array[AnyRef]): SparkPlan

    Overridden make copy also propagates sqlContext to copied plan.

    Overridden make copy also propagates sqlContext to copied plan.

    Definition Classes
    SparkPlan → TreeNode
  40. def map[A](f: (SparkPlan) ⇒ A): Seq[A]
    Definition Classes
    TreeNode
  41. def mapChildren(f: (SparkPlan) ⇒ SparkPlan): SparkPlan
    Definition Classes
    TreeNode
  42. def mapExpressions(f: (Expression) ⇒ Expression): BlockingOperatorWithCodegen.this.type
    Definition Classes
    QueryPlan
  43. def metricTerm(ctx: CodegenContext, name: String): String

    Creates a metric using the specified name.

    Creates a metric using the specified name.

    returns

    name of the variable representing the metric

    Definition Classes
    CodegenSupport
  44. def metrics: Map[String, SQLMetric]

    returns

    All metrics containing metrics of this SparkPlan.

    Definition Classes
    SparkPlan
  45. final def missingInput: AttributeSet
    Definition Classes
    QueryPlan
  46. def needCopyResult: Boolean

    Whether or not the result rows of this operator should be copied before putting into a buffer.

    Whether or not the result rows of this operator should be copied before putting into a buffer.

    If any operator inside WholeStageCodegen generate multiple rows from a single row (for example, Join), this should be true.

    If an operator starts a new pipeline, this should be false.

    Definition Classes
    BlockingOperatorWithCodegen → CodegenSupport
  47. def needStopCheck: Boolean

    Whether or not the children of this operator should generate a stop check when consuming input rows.

    Whether or not the children of this operator should generate a stop check when consuming input rows. This is used to suppress shouldStop() in a loop of WholeStageCodegen.

    This should be false if an operator starts a new pipeline, which means it consumes all rows produced by children but doesn't output row to buffer by calling append(), so the children don't require shouldStop() in the loop of producing rows.

    Definition Classes
    BlockingOperatorWithCodegen → CodegenSupport
  48. def nodeName: String
    Definition Classes
    TreeNode
  49. def numberedTreeString: String
    Definition Classes
    TreeNode
  50. val origin: Origin
    Definition Classes
    TreeNode
  51. def outputOrdering: Seq[SortOrder]

    Specifies how data is ordered in each partition.

    Specifies how data is ordered in each partition.

    Definition Classes
    SparkPlan
  52. def outputPartitioning: Partitioning

    Specifies how data is partitioned across different nodes in the cluster.

    Specifies how data is partitioned across different nodes in the cluster.

    Definition Classes
    SparkPlan
  53. lazy val outputSet: AttributeSet
    Definition Classes
    QueryPlan
    Annotations
    @transient()
  54. def p(number: Int): SparkPlan
    Definition Classes
    TreeNode
  55. final def prepare(): Unit

    Prepares this SparkPlan for execution.

    Prepares this SparkPlan for execution. It's idempotent.

    Definition Classes
    SparkPlan
  56. def prettyJson: String
    Definition Classes
    TreeNode
  57. def printSchema(): Unit
    Definition Classes
    QueryPlan
  58. final def produce(ctx: CodegenContext, parent: CodegenSupport): String

    Returns Java source code to process the rows from input RDD.

    Returns Java source code to process the rows from input RDD.

    Definition Classes
    CodegenSupport
  59. def producedAttributes: AttributeSet
    Definition Classes
    QueryPlan
  60. def productIterator: Iterator[Any]
    Definition Classes
    Product
  61. def productPrefix: String
    Definition Classes
    Product
  62. lazy val references: AttributeSet
    Definition Classes
    QueryPlan
    Annotations
    @transient()
  63. def requiredChildDistribution: Seq[Distribution]

    Specifies the data distribution requirements of all the children for this operator.

    Specifies the data distribution requirements of all the children for this operator. By default it's UnspecifiedDistribution for each child, which means each child can have any distribution.

    If an operator overwrites this method, and specifies distribution requirements(excluding UnspecifiedDistribution and BroadcastDistribution) for more than one child, Spark guarantees that the outputs of these children will have same number of partitions, so that the operator can safely zip partitions of these children's result RDDs. Some operators can leverage this guarantee to satisfy some interesting requirement, e.g., non-broadcast joins can specify HashClusteredDistribution(a,b) for its left child, and specify HashClusteredDistribution(c,d) for its right child, then it's guaranteed that left and right child are co-partitioned by a,b/c,d, which means tuples of same value are in the partitions of same index, e.g., (a=1,b=2) and (c=1,d=2) are both in the second partition of left and right child.

    Definition Classes
    SparkPlan
  64. def requiredChildOrdering: Seq[Seq[SortOrder]]

    Specifies sort order for each partition requirements on the input data for this operator.

    Specifies sort order for each partition requirements on the input data for this operator.

    Definition Classes
    SparkPlan
  65. def resetMetrics(): Unit

    Resets all the metrics.

    Resets all the metrics.

    Definition Classes
    SparkPlan
  66. final def sameResult(other: SparkPlan): Boolean
    Definition Classes
    QueryPlan
  67. lazy val schema: StructType
    Definition Classes
    QueryPlan
  68. def schemaString: String
    Definition Classes
    QueryPlan
  69. final def semanticHash(): Int
    Definition Classes
    QueryPlan
  70. def setLogicalLink(logicalPlan: LogicalPlan): Unit

    Set logical plan link recursively if unset.

    Set logical plan link recursively if unset.

    Definition Classes
    SparkPlan
  71. def setTagValue[T](tag: TreeNodeTag[T], value: T): Unit
    Definition Classes
    TreeNode
  72. def shouldStopCheckCode: String

    Helper default should stop check code.

    Helper default should stop check code.

    Definition Classes
    CodegenSupport
  73. def simpleString(maxFields: Int): String
    Definition Classes
    QueryPlan → TreeNode
  74. def simpleStringWithNodeId(): String
    Definition Classes
    QueryPlan → TreeNode
  75. final val sqlContext: SQLContext

    A handle to the SQL Context that was used to create this plan.

    A handle to the SQL Context that was used to create this plan. Since many operators need access to the sqlContext for RDD operations or configuration this field is automatically populated by the query planning infrastructure.

    Definition Classes
    SparkPlan
  76. def subqueries: Seq[SparkPlan]
    Definition Classes
    QueryPlan
  77. def subqueriesAll: Seq[SparkPlan]
    Definition Classes
    QueryPlan
  78. def supportCodegen: Boolean

    Whether this SparkPlan supports whole stage codegen or not.

    Whether this SparkPlan supports whole stage codegen or not.

    Definition Classes
    CodegenSupport
  79. def supportsColumnar: Boolean

    Return true if this stage of the plan supports columnar execution.

    Return true if this stage of the plan supports columnar execution.

    Definition Classes
    SparkPlan
  80. def toJSON: String
    Definition Classes
    TreeNode
  81. def toString(): String
    Definition Classes
    TreeNode → AnyRef → Any
  82. def transform(rule: PartialFunction[SparkPlan, SparkPlan]): SparkPlan
    Definition Classes
    TreeNode
  83. def transformAllExpressions(rule: PartialFunction[Expression, Expression]): BlockingOperatorWithCodegen.this.type
    Definition Classes
    QueryPlan
  84. def transformDown(rule: PartialFunction[SparkPlan, SparkPlan]): SparkPlan
    Definition Classes
    TreeNode
  85. def transformExpressions(rule: PartialFunction[Expression, Expression]): BlockingOperatorWithCodegen.this.type
    Definition Classes
    QueryPlan
  86. def transformExpressionsDown(rule: PartialFunction[Expression, Expression]): BlockingOperatorWithCodegen.this.type
    Definition Classes
    QueryPlan
  87. def transformExpressionsUp(rule: PartialFunction[Expression, Expression]): BlockingOperatorWithCodegen.this.type
    Definition Classes
    QueryPlan
  88. def transformUp(rule: PartialFunction[SparkPlan, SparkPlan]): SparkPlan
    Definition Classes
    TreeNode
  89. def treeString(append: (String) ⇒ Unit, verbose: Boolean, addSuffix: Boolean, maxFields: Int, printOperatorId: Boolean): Unit
    Definition Classes
    TreeNode
  90. final def treeString(verbose: Boolean, addSuffix: Boolean, maxFields: Int, printOperatorId: Boolean): String
    Definition Classes
    TreeNode
  91. final def treeString: String
    Definition Classes
    TreeNode
  92. def unsetTagValue[T](tag: TreeNodeTag[T]): Unit
    Definition Classes
    TreeNode
  93. def usedInputs: AttributeSet

    The subset of inputSet those should be evaluated before this plan.

    The subset of inputSet those should be evaluated before this plan.

    We will use this to insert some code to access those columns that are actually used by current plan before calling doConsume().

    Definition Classes
    CodegenSupport
  94. def vectorTypes: Option[Seq[String]]

    The exact java types of the columns that are output in columnar processing mode.

    The exact java types of the columns that are output in columnar processing mode. This is a performance optimization for code generation and is optional.

    Definition Classes
    SparkPlan
  95. def verboseString(maxFields: Int): String
    Definition Classes
    QueryPlan → TreeNode
  96. def verboseStringWithOperatorId(): String
    Definition Classes
    QueryPlan
  97. def verboseStringWithSuffix(maxFields: Int): String
    Definition Classes
    TreeNode
  98. def withNewChildren(newChildren: Seq[SparkPlan]): SparkPlan
    Definition Classes
    TreeNode