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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.catalyst.optimizer

import org.scalatest.Matchers._

import org.apache.spark.api.python.PythonEvalType
import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.expressions.PythonUDF
import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
import org.apache.spark.sql.catalyst.rules.RuleExecutor
import org.apache.spark.sql.internal.SQLConf._
import org.apache.spark.sql.types.BooleanType

class PullOutPythonUDFInJoinConditionSuite extends PlanTest {

object Optimize extends RuleExecutor[LogicalPlan] {
val batches =
Batch("Extract PythonUDF From JoinCondition", Once,
PullOutPythonUDFInJoinCondition) ::
Batch("Check Cartesian Products", Once,
CheckCartesianProducts) :: Nil
}

val testRelationLeft = LocalRelation('a.int, 'b.int)
val testRelationRight = LocalRelation('c.int, 'd.int)

// Dummy python UDF for testing. Unable to execute.
val pythonUDF = PythonUDF("pythonUDF", null,
BooleanType,
Seq.empty,
PythonEvalType.SQL_BATCHED_UDF,
udfDeterministic = true)

val unsupportedJoinTypes = Seq(LeftOuter, RightOuter, FullOuter, LeftAnti)

private def comparePlanWithCrossJoinEnable(query: LogicalPlan, expected: LogicalPlan): Unit = {
// AnalysisException thrown by CheckCartesianProducts while spark.sql.crossJoin.enabled=false
val exception = intercept[AnalysisException] {
Optimize.execute(query.analyze)
}
assert(exception.message.startsWith("Detected implicit cartesian product"))

// pull out the python udf while set spark.sql.crossJoin.enabled=true
withSQLConf(CROSS_JOINS_ENABLED.key -> "true") {
val optimized = Optimize.execute(query.analyze)
comparePlans(optimized, expected)
}
}

test("inner join condition with python udf only") {
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sorry, probably I was not clear enough in my previous comment. This UT and the following differ only for the join type. We can dedup them by doing something like:

Seq(Inner, LeftSemi).foreach { joinType =>
  test(...) { ...}
}

PS nit: maybe we can also define a new val supportedJoinTypes = Seq(Inner, LeftSemi)...

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I'm sorry for lacking of comments to your previous comment they differ only by the join type..., they differ not only the type, but also the expected plan.

val query = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = Some(pythonUDF))
val expected = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = None).where(pythonUDF).analyze
comparePlanWithCrossJoinEnable(query, expected)
}

test("left semi join condition with python udf only") {
val query = testRelationLeft.join(
testRelationRight,
joinType = LeftSemi,
condition = Some(pythonUDF))
val expected = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = None).where(pythonUDF).select('a, 'b).analyze
comparePlanWithCrossJoinEnable(query, expected)
}

test("python udf and common condition") {
val query = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = Some(pythonUDF && 'a.attr === 'c.attr))
val expected = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = Some('a.attr === 'c.attr)).where(pythonUDF).analyze
val optimized = Optimize.execute(query.analyze)
comparePlans(optimized, expected)
}

test("python udf or common condition") {
val query = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = Some(pythonUDF || 'a.attr === 'c.attr))
val expected = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = None).where(pythonUDF || 'a.attr === 'c.attr).analyze
comparePlanWithCrossJoinEnable(query, expected)
}

test("pull out whole complex condition with multiple python udf") {
val pythonUDF1 = PythonUDF("pythonUDF1", null,
BooleanType,
Seq.empty,
PythonEvalType.SQL_BATCHED_UDF,
udfDeterministic = true)
val condition = (pythonUDF || 'a.attr === 'c.attr) && pythonUDF1

val query = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = Some(condition))
val expected = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = None).where(condition).analyze
comparePlanWithCrossJoinEnable(query, expected)
}

test("partial pull out complex condition with multiple python udf") {
val pythonUDF1 = PythonUDF("pythonUDF1", null,
BooleanType,
Seq.empty,
PythonEvalType.SQL_BATCHED_UDF,
udfDeterministic = true)
val condition = (pythonUDF || pythonUDF1) && 'a.attr === 'c.attr

val query = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = Some(condition))
val expected = testRelationLeft.join(
testRelationRight,
joinType = Inner,
condition = Some('a.attr === 'c.attr)).where(pythonUDF || pythonUDF1).analyze
val optimized = Optimize.execute(query.analyze)
comparePlans(optimized, expected)
}

test("throw an exception for not support join type") {
for (joinType <- unsupportedJoinTypes) {
val thrownException = the [AnalysisException] thrownBy {
val query = testRelationLeft.join(
testRelationRight,
joinType,
condition = Some(pythonUDF))
Optimize.execute(query.analyze)
}
assert(thrownException.message.contentEquals(
s"Using PythonUDF in join condition of join type $joinType is not supported."))
}
}
}