org.apache.spark.sql.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.
the qualified output attributes of the generator of this node, which constructed in analysis phase, and we can not change it, as the parent node bound with it already.
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().
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 be override by subclass to support codegen.
For example, Filter will generate the code like this:
# code to evaluate the predicate expression, result is isNull1 and value2 if (isNull1 || !value2) continue; # call consume(), which will call parent.doConsume()
Note: A plan can either consume the rows as UnsafeRow (row), or a list of variables (input).
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.
Generate the Java source code to process, should be overridden by subclass to support codegen.
Generate the Java source code to process, should be overridden by subclass to support codegen.
doProduce() usually generate the framework, for example, aggregation could generate this:
if (!initialized) { # create a hash map, then build the aggregation hash map # call child.produce() initialized = true; } while (hashmap.hasNext()) { row = hashmap.next(); # build the aggregation results # create variables for results # call consume(), which will call parent.doConsume() if (shouldStop()) return; }
Returns source code to evaluate the variables for required attributes, and clear the code of evaluated variables, to prevent them to be evaluated twice.
Returns source code to evaluate the variables for required attributes, and clear the code of evaluated variables, to prevent them to be evaluated twice.
Returns source code to evaluate all the variables, and clear the code of them, to prevent them to be evaluated twice.
Returns source code to evaluate all the variables, and clear the code of them, to prevent them to be evaluated twice.
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).
the generator expression
the qualified output attributes of the generator of this node, which constructed in analysis phase, and we can not change it, as the parent node bound with it already.
Returns all the RDDs of InternalRow which generates the input rows.
Returns all the RDDs of InternalRow which generates the input rows.
Note: right now we support up to two RDDs.
For optimization to suppress shouldStop() in a loop of WholeStageCodegen.
For optimization to suppress shouldStop() in a loop of WholeStageCodegen. Returning true means we need to insert shouldStop() into the loop producing rows, if any.
when true, each output row is implicitly joined with the input tuple that produced it.
Return a LongSQLMetric according to the name.
Return a LongSQLMetric according to the name.
Overridden make copy also propagates sqlContext to copied plan.
Overridden make copy also propagates sqlContext to copied plan.
Return all metadata that describes more details of this SparkPlan.
Return all metadata that describes more details of this SparkPlan.
Creates a metric using the specified name.
Creates a metric using the specified name.
name of the variable representing the metric
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.
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.
Which SparkPlan is calling produce() of this one.
Which SparkPlan is calling produce() of this one. It's itself for the first SparkPlan.
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.
Returns Java source code to process the rows from input RDD.
Returns Java source code to process the rows from input RDD.
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.
Reset all the metrics.
Reset all the metrics.
Set to false if this plan 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.
Set to false if this plan 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.
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.
Whether this SparkPlan support whole stage codegen or not.
Whether this SparkPlan support whole stage codegen or not.
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().
Blocks the thread until all subqueries finish evaluation and update the results.
Blocks the thread until all subqueries finish evaluation and update the results.
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.This operator supports whole stage code generation for generators that do not implement terminate().
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.the qualified output attributes of the generator of this node, which constructed in analysis phase, and we can not change it, as the parent node bound with it already.