org.apache.spark.sql.execution
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package execution
the generator expression
when true, each output row is implicitly joined with the input tuple that produced it.
when true, each input row will be output at least once, even if the output of the
given generator is empty. outer has no effect when join is false.
the output attributes of this node, which constructed in analysis phase, and we can not change it, as the parent node bound with it already.
Specifies whether this operator is capable of processing Java-object-based Rows (i.e.
Specifies whether this operator is capable of processing Java-object-based Rows (i.e. rows that are not UnsafeRows).
Specifies whether this operator is capable of processing UnsafeRows
Specifies whether this operator is capable of processing UnsafeRows
Overridden by concrete implementations of SparkPlan.
Overridden by concrete implementations of SparkPlan.
Overridden by concrete implementations of SparkPlan. It is guaranteed to run before any
execute of SparkPlan. This is helpful if we want to set up some state before executing the
query, e.g., BroadcastHashJoin uses it to broadcast asynchronously.
Note: the prepare method has already walked down the tree, so the implementation doesn't need to call children's prepare methods.
Returns the result of this query as an RDD[InternalRow] by delegating to doExecute after adding query plan information to created RDDs for visualization.
Returns the result of this query as an RDD[InternalRow] by delegating to doExecute after adding query plan information to created RDDs for visualization. Concrete implementations of SparkPlan should override doExecute instead.
Runs this query returning the result as an array.
Runs this query returning the result as an array.
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.
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.
the generator expression
when true, each output row is implicitly joined with the input tuple that produced it.
Overridden make copy also propogates sqlContext to copied plan.
Overridden make copy also propogates sqlContext to copied plan.
Creates a row ordering for the given schema, in natural ascending order.
Creates a row ordering for the given schema, in natural ascending order.
when true, each input row will be output at least once, even if the output of the
given generator is empty.
when true, each input row will be output at least once, even if the output of the
given generator is empty. outer has no effect when join is false.
the output attributes of this node, which constructed in analysis phase, and we can not change it, as the parent node bound with it already.
the output attributes of this node, which constructed in analysis phase, and we can not change it, as the parent node bound with it already.
Specifies how data is ordered in each partition.
Specifies how data is ordered in each partition.
Specifies how data is partitioned across different nodes in the cluster.
Specifies how data is partitioned across different nodes in the cluster.
Specifies whether this operator outputs UnsafeRows
Specifies whether this operator outputs UnsafeRows
Prepare a SparkPlan for execution.
Prepare a SparkPlan for execution. It's idempotent.
Specifies any partition requirements on the input data for this operator.
Specifies any partition requirements on the input data for this operator.
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.
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.
Applies a Generator to a stream of input rows, combining the output of each into a new stream of rows. This operation is similar to a
flatMapin functional programming with one important additional feature, which allows the input rows to be joined with their output.the generator expression
when true, each output row is implicitly joined with the input tuple that produced it.
when true, each input row will be output at least once, even if the output of the given
generatoris empty.outerhas no effect whenjoinis false.the output attributes of this node, which constructed in analysis phase, and we can not change it, as the parent node bound with it already.