Github user cloud-fan commented on a diff in the pull request:

    https://github.com/apache/spark/pull/15363#discussion_r106846685
  
    --- Diff: 
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/StarJoinReorderSuite.scala
 ---
    @@ -0,0 +1,580 @@
    +/*
    + * 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.apache.spark.sql.catalyst.SimpleCatalystConf
    +import org.apache.spark.sql.catalyst.dsl.expressions._
    +import org.apache.spark.sql.catalyst.dsl.plans._
    +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap}
    +import org.apache.spark.sql.catalyst.plans.{Inner, PlanTest}
    +import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, 
LocalRelation, LogicalPlan}
    +import org.apache.spark.sql.catalyst.rules.RuleExecutor
    +import 
org.apache.spark.sql.catalyst.statsEstimation.{StatsEstimationTestBase, 
StatsTestPlan}
    +
    +
    +class StarJoinReorderSuite extends PlanTest with StatsEstimationTestBase {
    +
    +  override val conf = SimpleCatalystConf(
    +    caseSensitiveAnalysis = true, starSchemaDetection = true)
    +
    +  object Optimize extends RuleExecutor[LogicalPlan] {
    +    val batches =
    +      Batch("Operator Optimizations", FixedPoint(100),
    +        CombineFilters,
    +        PushDownPredicate,
    +        ReorderJoin(conf),
    +        PushPredicateThroughJoin,
    +        ColumnPruning,
    +        CollapseProject) :: Nil
    +  }
    +
    +  // Table setup using star schema relationships:
    +  //
    +  // d1 - f1 - d2
    +  //      |
    +  //      d3 - s3
    +  //
    +  // Table f1 is the fact table. Tables d1, d2, and d3 are the dimension 
tables.
    +  // Dimension d3 is further joined/normalized into table s3.
    +  // Tables' cardinality: f1 > d3 > d1 > d2 > s3
    +  private val columnInfo: AttributeMap[ColumnStat] = AttributeMap(Seq(
    +    // F1
    +    attr("f1_fk1") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("f1_fk2") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("f1_fk3") -> ColumnStat(distinctCount = 4, min = Some(1), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("f1_c4") -> ColumnStat(distinctCount = 4, min = Some(1), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    // D1
    +    attr("d1_pk1") -> ColumnStat(distinctCount = 4, min = Some(1), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d1_c2") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d1_c3") -> ColumnStat(distinctCount = 4, min = Some(1), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d1_c4") -> ColumnStat(distinctCount = 2, min = Some(2), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    // D2
    +    attr("d2_c2") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 1, avgLen = 4, maxLen = 4),
    +    attr("d2_pk1") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d2_c3") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d2_c4") -> ColumnStat(distinctCount = 2, min = Some(3), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    // D3
    +    attr("d3_fk1") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d3_c2") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d3_pk1") -> ColumnStat(distinctCount = 5, min = Some(1), max = 
Some(5),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("d3_c4") -> ColumnStat(distinctCount = 2, min = Some(2), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    // S3
    +    attr("s3_pk1") -> ColumnStat(distinctCount = 2, min = Some(1), max = 
Some(2),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("s3_c2") -> ColumnStat(distinctCount = 1, min = Some(3), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("s3_c3") -> ColumnStat(distinctCount = 1, min = Some(3), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("s3_c4") -> ColumnStat(distinctCount = 2, min = Some(3), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    // F11
    +    attr("f11_fk1") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("f11_fk2") -> ColumnStat(distinctCount = 3, min = Some(1), max = 
Some(3),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("f11_fk3") -> ColumnStat(distinctCount = 4, min = Some(1), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4),
    +    attr("f11_c4") -> ColumnStat(distinctCount = 4, min = Some(1), max = 
Some(4),
    +      nullCount = 0, avgLen = 4, maxLen = 4)
    +  ))
    +
    +  private val nameToAttr: Map[String, Attribute] = columnInfo.map(kv => 
kv._1.name -> kv._1)
    +  private val nameToColInfo: Map[String, (Attribute, ColumnStat)] =
    +    columnInfo.map(kv => kv._1.name -> kv)
    +
    +  private val f1 = StatsTestPlan(
    +    outputList = Seq("f1_fk1", "f1_fk2", "f1_fk3", 
"f1_c4").map(nameToAttr),
    +    rowCount = 6,
    +    size = Some(48),
    +    attributeStats = AttributeMap(Seq("f1_fk1", "f1_fk2", "f1_fk3", 
"f1_c4").map(nameToColInfo)))
    +
    +  private val d1 = StatsTestPlan(
    +    outputList = Seq("d1_pk1", "d1_c2", "d1_c3", "d1_c4").map(nameToAttr),
    +    rowCount = 4,
    +    size = Some(32),
    +    attributeStats = AttributeMap(Seq("d1_pk1", "d1_c2", "d1_c3", 
"d1_c4").map(nameToColInfo)))
    +
    +  private val d2 = StatsTestPlan(
    +    outputList = Seq("d2_c2", "d2_pk1", "d2_c3", "d2_c4").map(nameToAttr),
    +    rowCount = 3,
    +    size = Some(24),
    +    attributeStats = AttributeMap(Seq("d2_c2", "d2_pk1", "d2_c3", 
"d2_c4").map(nameToColInfo)))
    +
    +  private val d3 = StatsTestPlan(
    +    outputList = Seq("d3_fk1", "d3_c2", "d3_pk1", "d3_c4").map(nameToAttr),
    +    rowCount = 5,
    +    size = Some(40),
    +    attributeStats = AttributeMap(Seq("d3_fk1", "d3_c2", "d3_pk1", 
"d3_c4").map(nameToColInfo)))
    +
    +  private val s3 = StatsTestPlan(
    +    outputList = Seq("s3_pk1", "s3_c2", "s3_c3", "s3_c4").map(nameToAttr),
    +    rowCount = 2,
    +    size = Some(17),
    +    attributeStats = AttributeMap(Seq("s3_pk1", "s3_c2", "s3_c3", 
"s3_c4").map(nameToColInfo)))
    +
    +  private val d3_ns = LocalRelation('d3_fk1.int, 'd3_c2.int, 'd3_pk1.int, 
'd3_c4.int)
    +
    +  private val f11 = StatsTestPlan(
    +    outputList = Seq("f11_fk1", "f11_fk2", "f11_fk3", 
"f11_c4").map(nameToAttr),
    +    rowCount = 6,
    +    size = Some(48),
    +    attributeStats = AttributeMap(Seq("f11_fk1", "f11_fk2", "f11_fk3", 
"f11_c4")
    +      .map(nameToColInfo)))
    +
    +  private val subq = d3.select(sum('d3_fk1).as('col))
    +
    +  test("Test 1: Selective star-join on all dimensions") {
    +    // Star join:
    +    //   (=)  (=)
    +    // d1 - f1 - d2
    +    //      | (=)
    +    //      s3 - d3
    +    //
    +    // Query:
    +    //  select f1_fk1, f1_fk3
    +    //  from d1, d2, f1, d3, s3
    +    //  where f1_fk2 = d2_pk1 and d2_c2 < 2
    +    //  and f1_fk1 = d1_pk1
    +    //  and f1_fk3 = d3_pk1
    +    //  and d3_fk1 = s3_pk1
    +    //
    +    // Positional join reordering: d1, f1, d2, d3, s3
    +    // Star join reordering: f1, d2, d1, d3, s3
    +    val query =
    +      d1.join(d2).join(f1).join(d3).join(s3)
    +        .where((nameToAttr("f1_fk2") === nameToAttr("d2_pk1")) &&
    +          (nameToAttr("d2_c2") === 2) &&
    +          (nameToAttr("f1_fk1") === nameToAttr("d1_pk1")) &&
    +          (nameToAttr("f1_fk3") === nameToAttr("d3_pk1")) &&
    +          (nameToAttr("d3_fk1") === nameToAttr("s3_pk1")))
    +
    +    val expected =
    +      f1.join(d2.where(nameToAttr("d2_c2") === 2), Inner,
    +          Some(nameToAttr("f1_fk2") === nameToAttr("d2_pk1")))
    +        .join(d1, Inner, Some(nameToAttr("f1_fk1") === 
nameToAttr("d1_pk1")))
    +        .join(d3, Inner, Some(nameToAttr("f1_fk3") === 
nameToAttr("d3_pk1")))
    +        .join(s3, Inner, Some(nameToAttr("d3_fk1") === 
nameToAttr("s3_pk1")))
    +
    +    assertEqualPlans(query, expected)
    +  }
    +
    +  test("Test 2: Star join on a subset of dimensions due to inequality 
joins") {
    +    // Star join:
    +    //   (=)  (<)
    +    // d1 - f1 - d2
    +    //      |
    +    //      | (=)
    +    //      d3 - s3
    +    //        (=)
    +    //
    +    // Query:
    +    //  select f1_fk1, f1_fk3
    +    //  from d1, f1, d2, s3, d3
    +    //  where f1_fk2 < d2_pk1
    +    //  and f1_fk1 = d1_pk1 and d1_c2 = 2
    +    //  and f1_fk3 = d3_pk1
    +    //  and d3_fk1 = s3_pk1
    +    //
    +    // Default join reordering: d1, f1, d2, d3, s3
    +    // Star join reordering: f1, d1, d3, d2,, d3
    +
    +    val query =
    +      d1.join(f1).join(d2).join(s3).join(d3)
    +        .where((nameToAttr("f1_fk2") < nameToAttr("d2_pk1")) &&
    +          (nameToAttr("f1_fk1") === nameToAttr("d1_pk1")) &&
    +          (nameToAttr("d1_c2") === 2) &&
    +          (nameToAttr("f1_fk3") === nameToAttr("d3_pk1")) &&
    +          (nameToAttr("d3_fk1") === nameToAttr("s3_pk1")))
    +
    +    val expected =
    +      f1.join(d1.where(nameToAttr("d1_c2") === 2), Inner,
    +          Some(nameToAttr("f1_fk1") === nameToAttr("d1_pk1")))
    +        .join(d3, Inner, Some(nameToAttr("f1_fk3") === 
nameToAttr("d3_pk1")))
    +        .join(d2, Inner, Some(nameToAttr("f1_fk2") < nameToAttr("d2_pk1")))
    +        .join(s3, Inner, Some(nameToAttr("d3_fk1") === 
nameToAttr("s3_pk1")))
    +
    +    assertEqualPlans(query, expected)
    +  }
    +
    +  test("Test 3:  Star join on a subset of dimensions since join column is 
not unique") {
    +    // Star join:
    +    //   (=)  (=)
    +    // d1 - f1 - d2
    +    //      | (=)
    +    //      d3 - s3
    +    //
    +    // Query:
    +    //  select f1_fk1, f1_fk3
    +    //  from d1, f1, d2, s3, d3
    +    //  where f1_fk2 = d2_c4
    +    //  and f1_fk1 = d1_pk1 and d1_c2 = 2
    +    //  and f1_fk3 = d3_pk1
    +    //  and d3_fk1 = s3_pk1
    +    //
    +    // Default join reordering: d1, f1, d2, d3, s3
    +    // Star join reordering: f1, d1, d3, d2, d3
    --- End diff --
    
    the last `d3` should be `s3`


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