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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.execution.python
import scala.collection.mutable
import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Expression}
import org.apache.spark.sql.catalyst.rules.Rule
import org.apache.spark.sql.execution
import org.apache.spark.sql.execution.SparkPlan
/**
* Extracts PythonUDFs from operators, rewriting the query plan so that the UDF can be evaluated
* alone in a batch.
*
* Only extracts the PythonUDFs that could be evaluated in Python (the single child is PythonUDFs
* or all the children could be evaluated in JVM).
*
* This has the limitation that the input to the Python UDF is not allowed include attributes from
* multiple child operators.
*/
private[spark] object ExtractPythonUDFs extends Rule[SparkPlan] {
private def hasPythonUDF(e: Expression): Boolean = {
e.find(_.isInstanceOf[PythonUDF]).isDefined
}
private def canEvaluateInPython(e: PythonUDF): Boolean = {
e.children match {
// single PythonUDF child could be chained and evaluated in Python
case Seq(u: PythonUDF) => canEvaluateInPython(u)
// Python UDF can't be evaluated directly in JVM
case children => !children.exists(hasPythonUDF)
}
}
private def collectEvaluatableUDF(expr: Expression): Seq[PythonUDF] = expr match {
case udf: PythonUDF if canEvaluateInPython(udf) => Seq(udf)
case e => e.children.flatMap(collectEvaluatableUDF)
}
def apply(plan: SparkPlan): SparkPlan = plan transformUp {
case plan: SparkPlan => extract(plan)
}
/**
* Extract all the PythonUDFs from the current operator.
*/
def extract(plan: SparkPlan): SparkPlan = {
val udfs = plan.expressions.flatMap(collectEvaluatableUDF)
if (udfs.isEmpty) {
// If there aren't any, we are done.
plan
} else {
val attributeMap = mutable.HashMap[PythonUDF, Expression]()
// Rewrite the child that has the input required for the UDF
val newChildren = plan.children.map { child =>
// Pick the UDF we are going to evaluate
val validUdfs = udfs.filter { case udf =>
// Check to make sure that the UDF can be evaluated with only the input of this child.
udf.references.subsetOf(child.outputSet)
}
if (validUdfs.nonEmpty) {
val resultAttrs = udfs.zipWithIndex.map { case (u, i) =>
AttributeReference(s"pythonUDF$i", u.dataType)()
}
val evaluation = BatchPythonEvaluation(validUdfs, child.output ++ resultAttrs, child)
attributeMap ++= validUdfs.zip(resultAttrs)
evaluation
} else {
child
}
}
// Other cases are disallowed as they are ambiguous or would require a cartesian
// product.
udfs.filterNot(attributeMap.contains).foreach { udf =>
if (udf.references.subsetOf(plan.inputSet)) {
sys.error(s"Invalid PythonUDF $udf, requires attributes from more than one child.")
} else {
sys.error(s"Unable to evaluate PythonUDF $udf. Missing input attributes.")
}
}
val rewritten = plan.transformExpressions {
case p: PythonUDF if attributeMap.contains(p) =>
attributeMap(p)
}.withNewChildren(newChildren)
// extract remaining python UDFs recursively
val newPlan = extract(rewritten)
if (newPlan.output != plan.output) {
// Trim away the new UDF value if it was only used for filtering or something.
execution.Project(plan.output, newPlan)
} else {
newPlan
}
}
}
}
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