Returns the advisory partition size.

Cancels the shuffle job.

Cleans up the resources used by the physical operator (if any). In general, all the resources should be cleaned up when the task finishes but operators like SortMergeJoinExec and LimitExec may want eager cleanup to free up tight resources (e.g., memory).

Produces the result of the query as an RDD[InternalRow]

Overridden by concrete implementations of SparkPlan.

Produces the result of the query as a broadcast variable.

Overridden by concrete implementations of SparkPlan.

Produces the result of the query as an RDD[ColumnarBatch] if supportsColumnar returns true. By convention the executor that creates a ColumnarBatch is responsible for closing it when it is no longer needed. This allows input formats to be able to reuse batches if needed.

Produces the result of the writes as an RDD[WriterCommitMessage]

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.

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

Concrete implementations of SparkPlan should override doExecute.

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

Concrete implementations of SparkPlan should override doExecuteBroadcast.

Runs this query returning the result as an array.

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

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.

Executes a query after preparing the query and adding query plan information to created RDDs for visualization.

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.

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.

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

Returns the result of writes as an RDD[WriterCommitMessage] variable by delegating to doExecuteWrite after preparations.

Concrete implementations of SparkPlan should override doExecuteWrite.

Returns the shuffle RDD with specified partition specs.

Exposes status if the materialization is started

Overridden make copy also propagates sqlContext to copied plan.

This flag aims to detect if the stage materialization is started. This helps to avoid unnecessary AQE stage materialization when the stage is canceled.

Returns the number of mappers of this shuffle.

Returns the shuffle partition number.

Specifies how data is partitioned across different nodes in the cluster. Note this method may fail if it is invoked before EnsureRequirements is applied since PartitioningCollection requires all its partitionings to have the same number of partitions.

Prepares this SparkPlan for execution. It's idempotent.

Finds scalar subquery expressions in this plan node and starts evaluating them.

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.

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

Resets all the metrics.

Returns the runtime statistics after shuffle materialization.

Set logical plan link recursively if unset.

A ShuffleDependency that will partition rows of its child based on the partitioning scheme defined in newPartitioning. Those partitions of the returned ShuffleDependency will be the input of shuffle.

The shuffle ID.

The origin of this shuffle operator.

Submits the shuffle job.

Return true if this stage of the plan supports columnar execution. A plan can also support row-based execution (see supportsRowBased). Spark will decide which execution to be called during query planning.

Return true if this stage of the plan supports row-based execution. A plan can also support columnar execution (see supportsColumnar). Spark will decide which execution to be called during query planning.

Converts the output of this plan to row-based if it is columnar plan.

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.

Blocks the thread until all subqueries finish evaluation and update the results.