Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/17546#discussion_r110309359
--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/StarJoinCostBasedReorderSuite.scala
---
@@ -0,0 +1,428 @@
+/*
+ * 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.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,
LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.RuleExecutor
+import
org.apache.spark.sql.catalyst.statsEstimation.{StatsEstimationTestBase,
StatsTestPlan}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.internal.SQLConf._
+
+
+class StarJoinCostBasedReorderSuite extends PlanTest with
StatsEstimationTestBase {
+
+ override val conf = new SQLConf().copy(
+ CASE_SENSITIVE -> true,
+ CBO_ENABLED -> true,
+ JOIN_REORDER_ENABLED -> true,
+ STARSCHEMA_DETECTION -> true,
+ JOIN_REORDER_DP_STAR_FILTER -> true)
+
+ object Optimize extends RuleExecutor[LogicalPlan] {
+ val batches =
+ Batch("Operator Optimizations", FixedPoint(100),
+ CombineFilters,
+ PushDownPredicate,
+ ReorderJoin(conf),
+ PushPredicateThroughJoin,
+ ColumnPruning,
+ CollapseProject) ::
+ Batch("Join Reorder", Once,
+ CostBasedJoinReorder(conf)) :: Nil
+ }
+
+ private val columnInfo: AttributeMap[ColumnStat] = AttributeMap(Seq(
+ // F1 (fact table)
+ attr("f1_fk1") -> ColumnStat(distinctCount = 100, min = Some(1), max =
Some(100),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("f1_fk2") -> ColumnStat(distinctCount = 100, min = Some(1), max =
Some(100),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("f1_fk3") -> ColumnStat(distinctCount = 100, min = Some(1), max =
Some(100),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("f1_c1") -> ColumnStat(distinctCount = 100, min = Some(1), max =
Some(100),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("f1_c2") -> ColumnStat(distinctCount = 100, min = Some(1), max =
Some(100),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+
+ // D1 (dimension)
+ attr("d1_pk") -> ColumnStat(distinctCount = 100, min = Some(1), max =
Some(100),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("d1_c2") -> ColumnStat(distinctCount = 50, min = Some(1), max =
Some(50),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("d1_c3") -> ColumnStat(distinctCount = 50, min = Some(1), max =
Some(50),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+
+ // D2 (dimension)
+ attr("d2_pk") -> ColumnStat(distinctCount = 20, min = Some(1), max =
Some(20),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("d2_c2") -> ColumnStat(distinctCount = 10, min = Some(1), max =
Some(10),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("d2_c3") -> ColumnStat(distinctCount = 10, min = Some(1), max =
Some(10),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+
+ // D3 (dimension)
+ attr("d3_pk") -> ColumnStat(distinctCount = 10, min = Some(1), max =
Some(10),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("d3_c2") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+ attr("d3_c3") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 0, avgLen = 4, maxLen = 4),
+
+ // T1 (regular table i.e. outside star)
+ attr("t1_c1") -> ColumnStat(distinctCount = 20, min = Some(1), max =
Some(20),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t1_c2") -> ColumnStat(distinctCount = 10, min = Some(1), max =
Some(10),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t1_c3") -> ColumnStat(distinctCount = 10, min = Some(1), max =
Some(10),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+
+ // T2 (regular table)
+ attr("t2_c1") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t2_c2") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t2_c3") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+
+ // T3 (regular table)
+ attr("t3_c1") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t3_c2") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t3_c3") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+
+ // T4 (regular table)
+ attr("t4_c1") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t4_c2") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t4_c3") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+
+ // T5 (regular table)
+ attr("t5_c1") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t5_c2") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t5_c3") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+
+ // T6 (regular table)
+ attr("t6_c1") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t6_c2") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, avgLen = 4, maxLen = 4),
+ attr("t6_c3") -> ColumnStat(distinctCount = 5, min = Some(1), max =
Some(5),
+ nullCount = 1, 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_c1",
"f1_c2").map(nameToAttr),
+ rowCount = 1000,
+ size = Some(1000 * (8 + 4 * 5)),
+ attributeStats = AttributeMap(Seq("f1_fk1", "f1_fk2", "f1_fk3",
"f1_c1", "f1_c2")
+ .map(nameToColInfo)))
+
+ // To control the layout of the join plans, keep the size for the
non-fact tables constant
+ // and vary the rowcount and the number of distinct values of the join
columns.
+ private val d1 = StatsTestPlan(
+ outputList = Seq("d1_pk", "d1_c2", "d1_c3").map(nameToAttr),
+ rowCount = 100,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("d1_pk", "d1_c2",
"d1_c3").map(nameToColInfo)))
+
+ private val d2 = StatsTestPlan(
+ outputList = Seq("d2_pk", "d2_c2", "d2_c3").map(nameToAttr),
+ rowCount = 20,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("d2_pk", "d2_c2",
"d2_c3").map(nameToColInfo)))
+
+ private val d3 = StatsTestPlan(
+ outputList = Seq("d3_pk", "d3_c2", "d3_c3").map(nameToAttr),
+ rowCount = 10,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("d3_pk", "d3_c2",
"d3_c3").map(nameToColInfo)))
+
+ private val t1 = StatsTestPlan(
+ outputList = Seq("t1_c1", "t1_c2", "t1_c3").map(nameToAttr),
+ rowCount = 50,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("t1_c1", "t1_c2",
"t1_c3").map(nameToColInfo)))
+
+ private val t2 = StatsTestPlan(
+ outputList = Seq("t2_c1", "t2_c2", "t2_c3").map(nameToAttr),
+ rowCount = 10,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("t2_c1", "t2_c2",
"t2_c3").map(nameToColInfo)))
+
+ private val t3 = StatsTestPlan(
+ outputList = Seq("t3_c1", "t3_c2", "t3_c3").map(nameToAttr),
+ rowCount = 10,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("t3_c1", "t3_c2",
"t3_c3").map(nameToColInfo)))
+
+ private val t4 = StatsTestPlan(
+ outputList = Seq("t4_c1", "t4_c2", "t4_c3").map(nameToAttr),
+ rowCount = 10,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("t4_c1", "t4_c2",
"t4_c3").map(nameToColInfo)))
+
+ private val t5 = StatsTestPlan(
+ outputList = Seq("t5_c1", "t5_c2", "t5_c3").map(nameToAttr),
+ rowCount = 10,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("t5_c1", "t5_c2",
"t5_c3").map(nameToColInfo)))
+
+ private val t6 = StatsTestPlan(
+ outputList = Seq("t6_c1", "t6_c2", "t6_c3").map(nameToAttr),
+ rowCount = 10,
+ size = Some(3000),
+ attributeStats = AttributeMap(Seq("t6_c1", "t6_c2",
"t6_c3").map(nameToColInfo)))
+
+ test("Test 1: Star query with two dimensions and two regular tables") {
+
+ // d1 t1
+ // \ /
+ // f1
+ // / \
+ // d2 t2
+ //
+ // star: {f1, d1, d2}
+ // non-star: {t1, t2}
+ //
+ // level 0: (t2 ), (d2 ), (f1 ), (d1 ), (t1 )
+ // level 1: {f1 d1 }, {d2 f1 }
+ // level 2: {d2 f1 d1 }
+ // level 3: {t2 d1 d2 f1 }, {t1 d1 d2 f1 }
+ // level 4: {f1 t1 t2 d1 d2 }
--- End diff --
Does the order of items in this representation matter? If so, `{f1 t1 t2 d1
d2 }` is confusing because it looks like `f1` will join with `t1`, `t2` first.
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