org.apache.spark.sql.execution.python
BatchEvalPythonExec
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package python
Overridden by concrete implementations of SparkPlan.
Overridden by concrete implementations of SparkPlan. Produces the result of the query as an RDD[InternalRow]
Overridden by concrete implementations of SparkPlan.
Overridden by concrete implementations of SparkPlan. Produces the result of the query as a broadcast variable.
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.
This will only be called once, protected by this.
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.
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.
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.
Execute a query after preparing the query and adding query plan information to created RDDs for visualization.
Execute a query after preparing the query and adding query plan information to created RDDs for visualization.
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.
Runs this query returning the result as an iterator of InternalRow.
Runs this query returning the result as an iterator of InternalRow.
Note: this will trigger multiple jobs (one for each partition).
Overridden make copy also propagates sqlContext to copied plan.
Overridden make copy also propagates 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.
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.
Prepare a SparkPlan for execution.
Prepare a SparkPlan for execution. It's idempotent.
Finds scalar subquery expressions in this plan node and starts evaluating them.
Finds scalar subquery expressions in this plan node and starts evaluating them. The list of subqueries are added to subqueryResults.
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.
Blocks the thread until all subqueries finish evaluation and update the results.
Blocks the thread until all subqueries finish evaluation and update the results.
A physical plan that evaluates a PythonUDF, one partition of tuples at a time.
Python evaluation works by sending the necessary (projected) input data via a socket to an external Python process, and combine the result from the Python process with the original row.
For each row we send to Python, we also put it in a queue. For each output row from Python, we drain the queue to find the original input row. Note that if the Python process is way too slow, this could lead to the queue growing unbounded and eventually run out of memory.