org.apache.spark.sql.execution
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.
Runs this query returning the result as an RDD.
Runs this query returning the result as an array.
Runs this query returning the result as an array.
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.
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.
Specifies how data is partitioned across different nodes in the cluster.
Specifies how data is partitioned across different nodes in the cluster.
Specifies any partition requirements on the input data for this operator.
Specifies any 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.
:: DeveloperApi :: 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.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.