org.apache.spark.sql.catalyst.plans.Inner Scala Examples

package org.apache.spark.sql.catalyst.optimizer

import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.{Inner, PlanTest}
import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
import org.apache.spark.sql.catalyst.rules.RuleExecutor

class ReorderAssociativeOperatorSuite extends PlanTest {

  object Optimize extends RuleExecutor[LogicalPlan] {
    val batches =
      Batch("ReorderAssociativeOperator", Once,
        ReorderAssociativeOperator) :: Nil
  }

  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)

  test("Reorder associative operators") {
    val originalQuery =
      testRelation
        .select(
          (Literal(3) + ((Literal(1) + 'a) + 2)) + 4,
          'b * 1 * 2 * 3 * 4,
          ('b + 1) * 2 * 3 * 4,
          'a + 1 + 'b + 2 + 'c + 3,
          'a + 1 + 'b * 2 + 'c + 3,
          Rand(0) * 1 * 2 * 3 * 4)

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer =
      testRelation
        .select(
          ('a + 10).as("((3 + ((1 + a) + 2)) + 4)"),
          ('b * 24).as("((((b * 1) * 2) * 3) * 4)"),
          (('b + 1) * 24).as("((((b + 1) * 2) * 3) * 4)"),
          ('a + 'b + 'c + 6).as("(((((a + 1) + b) + 2) + c) + 3)"),
          ('a + 'b * 2 + 'c + 4).as("((((a + 1) + (b * 2)) + c) + 3)"),
          Rand(0) * 1 * 2 * 3 * 4)
        .analyze

    comparePlans(optimized, correctAnswer)
  }

  test("nested expression with aggregate operator") {
    val originalQuery =
      testRelation.as("t1")
        .join(testRelation.as("t2"), Inner, Some("t1.a".attr === "t2.a".attr))
        .groupBy("t1.a".attr + 1, "t2.a".attr + 1)(
          (("t1.a".attr + 1) + ("t2.a".attr + 1)).as("col"))

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer = originalQuery.analyze

    comparePlans(optimized, correctAnswer)
  }
} 

package org.apache.spark.sql.catalyst.optimizer

import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.{Inner, PlanTest}
import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
import org.apache.spark.sql.catalyst.rules.RuleExecutor

class ReorderAssociativeOperatorSuite extends PlanTest {

  object Optimize extends RuleExecutor[LogicalPlan] {
    val batches =
      Batch("ReorderAssociativeOperator", Once,
        ReorderAssociativeOperator) :: Nil
  }

  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)

  test("Reorder associative operators") {
    val originalQuery =
      testRelation
        .select(
          (Literal(3) + ((Literal(1) + 'a) + 2)) + 4,
          'b * 1 * 2 * 3 * 4,
          ('b + 1) * 2 * 3 * 4,
          'a + 1 + 'b + 2 + 'c + 3,
          'a + 1 + 'b * 2 + 'c + 3,
          Rand(0) * 1 * 2 * 3 * 4)

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer =
      testRelation
        .select(
          ('a + 10).as("((3 + ((1 + a) + 2)) + 4)"),
          ('b * 24).as("((((b * 1) * 2) * 3) * 4)"),
          (('b + 1) * 24).as("((((b + 1) * 2) * 3) * 4)"),
          ('a + 'b + 'c + 6).as("(((((a + 1) + b) + 2) + c) + 3)"),
          ('a + 'b * 2 + 'c + 4).as("((((a + 1) + (b * 2)) + c) + 3)"),
          Rand(0) * 1 * 2 * 3 * 4)
        .analyze

    comparePlans(optimized, correctAnswer)
  }

  test("nested expression with aggregate operator") {
    val originalQuery =
      testRelation.as("t1")
        .join(testRelation.as("t2"), Inner, Some("t1.a".attr === "t2.a".attr))
        .groupBy("t1.a".attr + 1, "t2.a".attr + 1)(
          (("t1.a".attr + 1) + ("t2.a".attr + 1)).as("col"))

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer = originalQuery.analyze

    comparePlans(optimized, correctAnswer)
  }
} 

package org.apache.spark.sql.catalyst.optimizer

import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.{Inner, PlanTest}
import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
import org.apache.spark.sql.catalyst.rules.RuleExecutor

class ReorderAssociativeOperatorSuite extends PlanTest {

  object Optimize extends RuleExecutor[LogicalPlan] {
    val batches =
      Batch("ReorderAssociativeOperator", Once,
        ReorderAssociativeOperator) :: Nil
  }

  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)

  test("Reorder associative operators") {
    val originalQuery =
      testRelation
        .select(
          (Literal(3) + ((Literal(1) + 'a) + 2)) + 4,
          'b * 1 * 2 * 3 * 4,
          ('b + 1) * 2 * 3 * 4,
          'a + 1 + 'b + 2 + 'c + 3,
          'a + 1 + 'b * 2 + 'c + 3,
          Rand(0) * 1 * 2 * 3 * 4)

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer =
      testRelation
        .select(
          ('a + 10).as("((3 + ((1 + a) + 2)) + 4)"),
          ('b * 24).as("((((b * 1) * 2) * 3) * 4)"),
          (('b + 1) * 24).as("((((b + 1) * 2) * 3) * 4)"),
          ('a + 'b + 'c + 6).as("(((((a + 1) + b) + 2) + c) + 3)"),
          ('a + 'b * 2 + 'c + 4).as("((((a + 1) + (b * 2)) + c) + 3)"),
          Rand(0) * 1 * 2 * 3 * 4)
        .analyze

    comparePlans(optimized, correctAnswer)
  }

  test("nested expression with aggregate operator") {
    val originalQuery =
      testRelation.as("t1")
        .join(testRelation.as("t2"), Inner, Some("t1.a".attr === "t2.a".attr))
        .groupBy("t1.a".attr + 1, "t2.a".attr + 1)(
          (("t1.a".attr + 1) + ("t2.a".attr + 1)).as("col"))

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer = originalQuery.analyze

    comparePlans(optimized, correctAnswer)
  }
} 

package org.apache.carbondata.view.plans

import org.apache.carbondata.mv.dsl.Plans._
import org.apache.carbondata.view.testutil.ModularPlanTest
import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
import org.apache.spark.sql.catalyst.plans.{Inner, _}

class ExtractJoinConditionsSuite extends ModularPlanTest {
  val testRelation0 = LocalRelation('a.int, 'b.int, 'c.int)
  val testRelation1 = LocalRelation('d.int)
  val testRelation2 = LocalRelation('b.int,'c.int,'e.int)
  
  test("join only") {
    val left = testRelation0.where('a === 1)
    val right = testRelation1
    val originalQuery =
      left.join(right, condition = Some("d".attr === "b".attr || "d".attr === "c".attr)).analyze
    val modularPlan = originalQuery.modularize 
    val extracted = modularPlan.extractJoinConditions(modularPlan.children(0),modularPlan.children(1))
    
    val correctAnswer = originalQuery match {
      case logical.Join(logical.Filter(cond1,MatchLocalRelation(tbl1,_)),MatchLocalRelation(tbl2,_),Inner,Some(cond2)) =>
        Seq(cond2)
    }
    
    compareExpressions(correctAnswer, extracted)
  }
  
  test("join and filter") {
    val left = testRelation0.where('b === 2).subquery('l)
    val right = testRelation2.where('b === 2).subquery('r)
    val originalQuery =
      left.join(right,condition = Some("r.b".attr === 2 && "l.c".attr === "r.c".attr)).analyze
    val modularPlan = originalQuery.modularize
    val extracted = modularPlan.extractJoinConditions(modularPlan.children(0),modularPlan.children(1))
    
    val originalQuery1 =
      left.join(right,condition = Some("l.c".attr === "r.c".attr)).analyze
      
    val correctAnswer = originalQuery1 match {
      case logical.Join(logical.Filter(cond1,MatchLocalRelation(tbl1,_)),logical.Filter(cond2,MatchLocalRelation(tbl2,_)),Inner,Some(cond3)) =>
        Seq(cond3)
    }    
    
    compareExpressions(correctAnswer, extracted)
  }
} 

package org.apache.spark.sql.catalyst.optimizer

import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.{Inner, PlanTest}
import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
import org.apache.spark.sql.catalyst.rules.RuleExecutor

class ReorderAssociativeOperatorSuite extends PlanTest {

  object Optimize extends RuleExecutor[LogicalPlan] {
    val batches =
      Batch("ReorderAssociativeOperator", Once,
        ReorderAssociativeOperator) :: Nil
  }

  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)

  test("Reorder associative operators") {
    val originalQuery =
      testRelation
        .select(
          (Literal(3) + ((Literal(1) + 'a) + 2)) + 4,
          'b * 1 * 2 * 3 * 4,
          ('b + 1) * 2 * 3 * 4,
          'a + 1 + 'b + 2 + 'c + 3,
          'a + 1 + 'b * 2 + 'c + 3,
          Rand(0) * 1 * 2 * 3 * 4)

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer =
      testRelation
        .select(
          ('a + 10).as("((3 + ((1 + a) + 2)) + 4)"),
          ('b * 24).as("((((b * 1) * 2) * 3) * 4)"),
          (('b + 1) * 24).as("((((b + 1) * 2) * 3) * 4)"),
          ('a + 'b + 'c + 6).as("(((((a + 1) + b) + 2) + c) + 3)"),
          ('a + 'b * 2 + 'c + 4).as("((((a + 1) + (b * 2)) + c) + 3)"),
          Rand(0) * 1 * 2 * 3 * 4)
        .analyze

    comparePlans(optimized, correctAnswer)
  }

  test("nested expression with aggregate operator") {
    val originalQuery =
      testRelation.as("t1")
        .join(testRelation.as("t2"), Inner, Some("t1.a".attr === "t2.a".attr))
        .groupBy("t1.a".attr + 1, "t2.a".attr + 1)(
          (("t1.a".attr + 1) + ("t2.a".attr + 1)).as("col"))

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer = originalQuery.analyze

    comparePlans(optimized, correctAnswer)
  }
} 

package org.apache.spark.sql.execution.joins

import org.apache.spark.sql.{SQLConf, DataFrame, Row}
import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
import org.apache.spark.sql.catalyst.plans.Inner
import org.apache.spark.sql.catalyst.plans.logical.Join
import org.apache.spark.sql.catalyst.expressions.{And, LessThan, Expression}
import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
//半连接测试套件
class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {

  private lazy val left = ctx.createDataFrame(
    ctx.sparkContext.parallelize(Seq(
      Row(1, 2.0),
      Row(1, 2.0),
      Row(2, 1.0),
      Row(2, 1.0),
      Row(3, 3.0),
      Row(null, null),
      Row(null, 5.0),
      Row(6, null)
    )), new StructType().add("a", IntegerType).add("b", DoubleType))

  private lazy val right = ctx.createDataFrame(
    ctx.sparkContext.parallelize(Seq(
      Row(2, 3.0),
      Row(2, 3.0),
      Row(3, 2.0),
      Row(4, 1.0),
      Row(null, null),
      Row(null, 5.0),
      Row(6, null)
    )), new StructType().add("c", IntegerType).add("d", DoubleType))

  private lazy val condition = {
    And((left.col("a") === right.col("c")).expr,
      LessThan(left.col("b").expr, right.col("d").expr))
  }

  // Note: the input dataframes and expression must be evaluated lazily because
  // the SQLContext should be used only within a test to keep SQL tests stable
  private def testLeftSemiJoin(
      testName: String,
      leftRows: => DataFrame,
      rightRows: => DataFrame,
      condition: => Expression,
      expectedAnswer: Seq[Product]): Unit = {

    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
      ExtractEquiJoinKeys.unapply(join)
    }

    test(s"$testName using LeftSemiJoinHash") {
      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
            EnsureRequirements(left.sqlContext).apply(
              LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
            expectedAnswer.map(Row.fromTuple),
            sortAnswers = true)
        }
      }
    }

    test(s"$testName using BroadcastLeftSemiJoinHash") {
      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
            BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
            expectedAnswer.map(Row.fromTuple),
            sortAnswers = true)
        }
      }
    }

    test(s"$testName using LeftSemiJoinBNL") {
      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
          LeftSemiJoinBNL(left, right, Some(condition)),
          expectedAnswer.map(Row.fromTuple),
          sortAnswers = true)
      }
    }
  }
  //测试左半连接
  testLeftSemiJoin(
    "basic test",
    left,
    right,
    condition,
    Seq(
      (2, 1.0),
      (2, 1.0)
    )
  )
} 

package org.apache.spark.sql.catalyst.analysis

import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
import org.apache.spark.sql.catalyst.plans.Inner
import org.apache.spark.sql.catalyst.plans.logical._

class ResolveHintsSuite extends AnalysisTest {
  import org.apache.spark.sql.catalyst.analysis.TestRelations._

  test("invalid hints should be ignored") {
    checkAnalysis(
      UnresolvedHint("some_random_hint_that_does_not_exist", Seq("TaBlE"), table("TaBlE")),
      testRelation,
      caseSensitive = false)
  }

  test("case-sensitive or insensitive parameters") {
    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("TaBlE"), table("TaBlE")),
      ResolvedHint(testRelation, HintInfo(broadcast = true)),
      caseSensitive = false)

    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("table"), table("TaBlE")),
      ResolvedHint(testRelation, HintInfo(broadcast = true)),
      caseSensitive = false)

    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("TaBlE"), table("TaBlE")),
      ResolvedHint(testRelation, HintInfo(broadcast = true)),
      caseSensitive = true)

    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("table"), table("TaBlE")),
      testRelation,
      caseSensitive = true)
  }

  test("multiple broadcast hint aliases") {
    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("table", "table2"), table("table").join(table("table2"))),
      Join(ResolvedHint(testRelation, HintInfo(broadcast = true)),
        ResolvedHint(testRelation2, HintInfo(broadcast = true)), Inner, None),
      caseSensitive = false)
  }

  test("do not traverse past existing broadcast hints") {
    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("table"),
        ResolvedHint(table("table").where('a > 1), HintInfo(broadcast = true))),
      ResolvedHint(testRelation.where('a > 1), HintInfo(broadcast = true)).analyze,
      caseSensitive = false)
  }

  test("should work for subqueries") {
    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("tableAlias"), table("table").as("tableAlias")),
      ResolvedHint(testRelation, HintInfo(broadcast = true)),
      caseSensitive = false)

    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("tableAlias"), table("table").subquery('tableAlias)),
      ResolvedHint(testRelation, HintInfo(broadcast = true)),
      caseSensitive = false)

    // Negative case: if the alias doesn't match, don't match the original table name.
    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("table"), table("table").as("tableAlias")),
      testRelation,
      caseSensitive = false)
  }

  test("do not traverse past subquery alias") {
    checkAnalysis(
      UnresolvedHint("MAPJOIN", Seq("table"), table("table").where('a > 1).subquery('tableAlias)),
      testRelation.where('a > 1).analyze,
      caseSensitive = false)
  }

  test("should work for CTE") {
    checkAnalysis(
      CatalystSqlParser.parsePlan(
        """
          |WITH ctetable AS (SELECT * FROM table WHERE a > 1)
          |SELECT  * FROM ctetable
        """.stripMargin
      ),
      testRelation.where('a > 1).select('a).select('a).analyze,
      caseSensitive = false)
  }
} 

package org.apache.spark.sql.catalyst.optimizer

import org.apache.spark.sql.catalyst.dsl.expressions._
import org.apache.spark.sql.catalyst.dsl.plans._
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans.{Inner, PlanTest}
import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
import org.apache.spark.sql.catalyst.rules.RuleExecutor

class ReorderAssociativeOperatorSuite extends PlanTest {

  object Optimize extends RuleExecutor[LogicalPlan] {
    val batches =
      Batch("ReorderAssociativeOperator", Once,
        ReorderAssociativeOperator) :: Nil
  }

  val testRelation = LocalRelation('a.int, 'b.int, 'c.int)

  test("Reorder associative operators") {
    val originalQuery =
      testRelation
        .select(
          (Literal(3) + ((Literal(1) + 'a) + 2)) + 4,
          'b * 1 * 2 * 3 * 4,
          ('b + 1) * 2 * 3 * 4,
          'a + 1 + 'b + 2 + 'c + 3,
          'a + 1 + 'b * 2 + 'c + 3,
          Rand(0) * 1 * 2 * 3 * 4)

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer =
      testRelation
        .select(
          ('a + 10).as("((3 + ((1 + a) + 2)) + 4)"),
          ('b * 24).as("((((b * 1) * 2) * 3) * 4)"),
          (('b + 1) * 24).as("((((b + 1) * 2) * 3) * 4)"),
          ('a + 'b + 'c + 6).as("(((((a + 1) + b) + 2) + c) + 3)"),
          ('a + 'b * 2 + 'c + 4).as("((((a + 1) + (b * 2)) + c) + 3)"),
          Rand(0) * 1 * 2 * 3 * 4)
        .analyze

    comparePlans(optimized, correctAnswer)
  }

  test("nested expression with aggregate operator") {
    val originalQuery =
      testRelation.as("t1")
        .join(testRelation.as("t2"), Inner, Some("t1.a".attr === "t2.a".attr))
        .groupBy("t1.a".attr + 1, "t2.a".attr + 1)(
          (("t1.a".attr + 1) + ("t2.a".attr + 1)).as("col"))

    val optimized = Optimize.execute(originalQuery.analyze)

    val correctAnswer = originalQuery.analyze

    comparePlans(optimized, correctAnswer)
  }
} 

package org.apache.spark.sql.execution.joins

import org.apache.spark.sql.{SQLConf, DataFrame, Row}
import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
import org.apache.spark.sql.catalyst.plans.Inner
import org.apache.spark.sql.catalyst.plans.logical.Join
import org.apache.spark.sql.catalyst.expressions.{And, LessThan, Expression}
import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
import org.apache.spark.sql.test.SharedSQLContext
import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}

class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {

  private lazy val left = sqlContext.createDataFrame(
    sparkContext.parallelize(Seq(
      Row(1, 2.0),
      Row(1, 2.0),
      Row(2, 1.0),
      Row(2, 1.0),
      Row(3, 3.0),
      Row(null, null),
      Row(null, 5.0),
      Row(6, null)
    )), new StructType().add("a", IntegerType).add("b", DoubleType))

  private lazy val right = sqlContext.createDataFrame(
    sparkContext.parallelize(Seq(
      Row(2, 3.0),
      Row(2, 3.0),
      Row(3, 2.0),
      Row(4, 1.0),
      Row(null, null),
      Row(null, 5.0),
      Row(6, null)
    )), new StructType().add("c", IntegerType).add("d", DoubleType))

  private lazy val condition = {
    And((left.col("a") === right.col("c")).expr,
      LessThan(left.col("b").expr, right.col("d").expr))
  }

  // Note: the input dataframes and expression must be evaluated lazily because
  // the SQLContext should be used only within a test to keep SQL tests stable
  private def testLeftSemiJoin(
      testName: String,
      leftRows: => DataFrame,
      rightRows: => DataFrame,
      condition: => Expression,
      expectedAnswer: Seq[Product]): Unit = {

    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
      ExtractEquiJoinKeys.unapply(join)
    }

    test(s"$testName using LeftSemiJoinHash") {
      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
            EnsureRequirements(left.sqlContext).apply(
              LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
            expectedAnswer.map(Row.fromTuple),
            sortAnswers = true)
        }
      }
    }

    test(s"$testName using BroadcastLeftSemiJoinHash") {
      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
            BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
            expectedAnswer.map(Row.fromTuple),
            sortAnswers = true)
        }
      }
    }

    test(s"$testName using LeftSemiJoinBNL") {
      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
          LeftSemiJoinBNL(left, right, Some(condition)),
          expectedAnswer.map(Row.fromTuple),
          sortAnswers = true)
      }
    }
  }

  testLeftSemiJoin(
    "basic test",
    left,
    right,
    condition,
    Seq(
      (2, 1.0),
      (2, 1.0)
    )
  )
} 

package org.biodatageeks.sequila.rangejoins.common

import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.expressions._
import org.apache.spark.sql.catalyst.plans._
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.catalyst.plans.Inner


      //println(condition.head)
      if (condition.size!=0 && joinType == Inner) {
        condition.head match {
          case And(And(EqualTo(l3, r3), LessThanOrEqual(l1, g1)), LessThanOrEqual(l2, g2)) =>
            Some((joinType,
              getKeys(l1, l2, g1, g2, l3, r3, left, right),
              left, right))
          case And(And(EqualTo(l3, r3), GreaterThanOrEqual(g1, l1)), LessThanOrEqual(l2, g2)) =>
            Some((joinType,
              getKeys(l1, l2, g1, g2, l3, r3, left, right),
              left, right))
          case And(And(EqualTo(l3, r3), LessThanOrEqual(l1, g1)), GreaterThanOrEqual(g2, l2)) =>
            Some((joinType,
              getKeys(l1, l2, g1, g2, l3, r3, left, right),
              left, right))
          case And(And(EqualTo(l3, r3), GreaterThanOrEqual(g1, l1)), GreaterThanOrEqual(g2, l2)) =>
            Some((joinType,
              getKeys(l1, l2, g1, g2, l3, r3, left, right),
              left, right))
          case _ => None
        }
      } else {
        None
      }
    case _ =>
      None
  }

  def getKeys(l1:Expression,l2:Expression,g1:Expression,g2:Expression,l3:Expression,r3:Expression,left:LogicalPlan,right:LogicalPlan): Seq[Expression] ={
    var leftStart:Expression = null
    var leftEnd:Expression = null
    var rightStart:Expression = null
    var rightEnd:Expression = null
    var leftEquality:Expression = null
    var rightEquality:Expression = null

    if (canEvaluate(g1, right)) {
      if (canEvaluate(l1, left)) {
        leftStart=l1
        leftEnd=g2
        rightStart=l2
        rightEnd=g1
      } else {
        leftStart=l2
        leftEnd=g2
        rightStart=l1
        rightEnd=g1
      }
    } else {
      if (canEvaluate(l1, left)) {
        leftStart=l1
        leftEnd=g1
        rightStart=l2
        rightEnd=g2
      } else {
        leftStart=l2
        leftEnd=g1
        rightStart=l1
        rightEnd=g2
      }
    }

    if (canEvaluate(l3, left)) {
      leftEquality = l3
      rightEquality = r3
    } else {
      leftEquality = r3
      rightEquality = l3
    }

    List(leftStart, leftEnd, rightStart, rightEnd,leftEquality,rightEquality)
  }
}