[SPARK-17075][SQL] implemented filter estimation

## What changes were proposed in this pull request?

We traverse predicate and evaluate the logical expressions to compute the selectivity of a FILTER operator.

## How was this patch tested?

We add a new test suite to test various logical operators.

Author: Ron Hu <ron.hu@huawei.com>

Closes #16395 from ron8hu/filterSelectivity.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala

@@ -23,7 +23,7 @@ import org.apache.spark.sql.catalyst.catalog.{CatalogTable, CatalogTypes}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression
 import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.statsEstimation.{AggregateEstimation, EstimationUtils, JoinEstimation, ProjectEstimation}
+import org.apache.spark.sql.catalyst.plans.logical.statsEstimation._
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -129,6 +129,14 @@ case class Filter(condition: Expression, child: LogicalPlan)
       .filterNot(SubqueryExpression.hasCorrelatedSubquery)
     child.constraints.union(predicates.toSet)
   }
+
+  override def computeStats(conf: CatalystConf): Statistics = {
+    if (conf.cboEnabled) {
+      FilterEstimation(this, conf).estimate.getOrElse(super.computeStats(conf))
+    } else {
+      super.computeStats(conf)
+    }
+  }
 }
 
 abstract class SetOperation(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/Range.scala

@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.plans.logical.statsEstimation
 import java.math.{BigDecimal => JDecimal}
 import java.sql.{Date, Timestamp}
 
+import org.apache.spark.sql.catalyst.expressions.Literal
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types.{BooleanType, DateType, TimestampType, _}
 
@@ -57,6 +58,20 @@ object Range {
       n1.min.compareTo(n2.max) <= 0 && n1.max.compareTo(n2.min) >= 0
   }
 
+  def rangeContainsLiteral(r: Range, lit: Literal): Boolean = r match {
+    case _: DefaultRange => true
+    case _: NullRange => false
+    case n: NumericRange =>
+      val literalValue = if (lit.dataType.isInstanceOf[BooleanType]) {
+        if (lit.value.asInstanceOf[Boolean]) new JDecimal(1) else new JDecimal(0)
+      } else {
+        assert(lit.dataType.isInstanceOf[NumericType] || lit.dataType.isInstanceOf[DateType] ||
+          lit.dataType.isInstanceOf[TimestampType])
+        new JDecimal(lit.value.toString)
+      }
+      n.min.compareTo(literalValue) <= 0 && n.max.compareTo(literalValue) >= 0
+  }
+
   /**
    * Intersected results of two ranges. This is only for two overlapped ranges.
    * The outputs are the intersected min/max values.
@@ -113,4 +128,5 @@ object Range {
           DateTimeUtils.toJavaTimestamp(n.max.longValue()))
     }
   }
+
 }

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/FilterEstimationSuite.scala

+/*
+ * 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.statsEstimation
+
+import java.sql.{Date, Timestamp}
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.plans.logical.statsEstimation.EstimationUtils._
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types._
+
+/**
+ * In this test suite, we test predicates containing the following operators:
+ * =, <, <=, >, >=, AND, OR, IS NULL, IS NOT NULL, IN, NOT IN
+ */
+class FilterEstimationSuite extends StatsEstimationTestBase {
+
+  // Suppose our test table has 10 rows and 6 columns.
+  // First column cint has values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
+  // Hence, distinctCount:10, min:1, max:10, nullCount:0, avgLen:4, maxLen:4
+  val arInt = AttributeReference("cint", IntegerType)()
+  val childColStatInt = ColumnStat(distinctCount = 10, min = Some(1), max = Some(10),
+    nullCount = 0, avgLen = 4, maxLen = 4)
+
+  // Second column cdate has 10 values from 2017-01-01 through 2017-01-10.
+  val dMin = Date.valueOf("2017-01-01")
+  val dMax = Date.valueOf("2017-01-10")
+  val arDate = AttributeReference("cdate", DateType)()
+  val childColStatDate = ColumnStat(distinctCount = 10, min = Some(dMin), max = Some(dMax),
+    nullCount = 0, avgLen = 4, maxLen = 4)
+
+  // Third column ctimestamp has 10 values from "2017-01-01 01:00:00" through
+  // "2017-01-01 10:00:00" for 10 distinct timestamps (or hours).
+  val tsMin = Timestamp.valueOf("2017-01-01 01:00:00")
+  val tsMax = Timestamp.valueOf("2017-01-01 10:00:00")
+  val arTimestamp = AttributeReference("ctimestamp", TimestampType)()
+  val childColStatTimestamp = ColumnStat(distinctCount = 10, min = Some(tsMin), max = Some(tsMax),
+    nullCount = 0, avgLen = 8, maxLen = 8)
+
+  // Fourth column cdecimal has 4 values from 0.20 through 0.80 at increment of 0.20.
+  val decMin = new java.math.BigDecimal("0.200000000000000000")
+  val decMax = new java.math.BigDecimal("0.800000000000000000")
+  val arDecimal = AttributeReference("cdecimal", DecimalType(18, 18))()
+  val childColStatDecimal = ColumnStat(distinctCount = 4, min = Some(decMin), max = Some(decMax),
+    nullCount = 0, avgLen = 8, maxLen = 8)
+
+  // Fifth column cdouble has 10 double values: 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0
+  val arDouble = AttributeReference("cdouble", DoubleType)()
+  val childColStatDouble = ColumnStat(distinctCount = 10, min = Some(1.0), max = Some(10.0),
+    nullCount = 0, avgLen = 8, maxLen = 8)
+
+  // Sixth column cstring has 10 String values:
+  // "A0", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9"
+  val arString = AttributeReference("cstring", StringType)()
+  val childColStatString = ColumnStat(distinctCount = 10, min = None, max = None,
+    nullCount = 0, avgLen = 2, maxLen = 2)
+
+  test("cint = 2") {
+    validateEstimatedStats(
+      arInt,
+      Filter(EqualTo(arInt, Literal(2)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 1, min = Some(2), max = Some(2),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(1L)
+    )
+  }
+
+  test("cint = 0") {
+    // This is an out-of-range case since 0 is outside the range [min, max]
+    validateEstimatedStats(
+      arInt,
+      Filter(EqualTo(arInt, Literal(0)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 10, min = Some(1), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(0L)
+    )
+  }
+
+  test("cint < 3") {
+    validateEstimatedStats(
+      arInt,
+      Filter(LessThan(arInt, Literal(3)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 2, min = Some(1), max = Some(3),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(3L)
+    )
+  }
+
+  test("cint < 0") {
+    // This is a corner case since literal 0 is smaller than min.
+    validateEstimatedStats(
+      arInt,
+      Filter(LessThan(arInt, Literal(0)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 10, min = Some(1), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(0L)
+    )
+  }
+
+  test("cint <= 3") {
+    validateEstimatedStats(
+      arInt,
+      Filter(LessThanOrEqual(arInt, Literal(3)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 2, min = Some(1), max = Some(3),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(3L)
+    )
+  }
+
+  test("cint > 6") {
+    validateEstimatedStats(
+      arInt,
+      Filter(GreaterThan(arInt, Literal(6)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 4, min = Some(6), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(5L)
+    )
+  }
+
+  test("cint > 10") {
+    // This is a corner case since max value is 10.
+    validateEstimatedStats(
+      arInt,
+      Filter(GreaterThan(arInt, Literal(10)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 10, min = Some(1), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(0L)
+    )
+  }
+
+  test("cint >= 6") {
+    validateEstimatedStats(
+      arInt,
+      Filter(GreaterThanOrEqual(arInt, Literal(6)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 4, min = Some(6), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(5L)
+    )
+  }
+
+  test("cint IS NULL") {
+    validateEstimatedStats(
+      arInt,
+      Filter(IsNull(arInt), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 0, min = None, max = None,
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(0L)
+    )
+  }
+
+  test("cint IS NOT NULL") {
+    validateEstimatedStats(
+      arInt,
+      Filter(IsNotNull(arInt), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 10, min = Some(1), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(10L)
+    )
+  }
+
+  test("cint > 3 AND cint <= 6") {
+    val condition = And(GreaterThan(arInt, Literal(3)), LessThanOrEqual(arInt, Literal(6)))
+    validateEstimatedStats(
+      arInt,
+      Filter(condition, childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 3, min = Some(3), max = Some(6),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(4L)
+    )
+  }
+
+  test("cint = 3 OR cint = 6") {
+    val condition = Or(EqualTo(arInt, Literal(3)), EqualTo(arInt, Literal(6)))
+    validateEstimatedStats(
+      arInt,
+      Filter(condition, childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 10, min = Some(1), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(2L)
+    )
+  }
+
+  test("cint IN (3, 4, 5)") {
+    validateEstimatedStats(
+      arInt,
+      Filter(InSet(arInt, Set(3, 4, 5)), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 3, min = Some(3), max = Some(5),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(3L)
+    )
+  }
+
+  test("cint NOT IN (3, 4, 5)") {
+    validateEstimatedStats(
+      arInt,
+      Filter(Not(InSet(arInt, Set(3, 4, 5))), childStatsTestPlan(Seq(arInt), 10L)),
+      ColumnStat(distinctCount = 10, min = Some(1), max = Some(10),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(7L)
+    )
+  }
+
+  test("cdate = cast('2017-01-02' AS DATE)") {
+    val d20170102 = Date.valueOf("2017-01-02")
+    validateEstimatedStats(
+      arDate,
+      Filter(EqualTo(arDate, Literal(d20170102)),
+        childStatsTestPlan(Seq(arDate), 10L)),
+      ColumnStat(distinctCount = 1, min = Some(d20170102), max = Some(d20170102),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(1L)
+    )
+  }
+
+  test("cdate < cast('2017-01-03' AS DATE)") {
+    val d20170103 = Date.valueOf("2017-01-03")
+    validateEstimatedStats(
+      arDate,
+      Filter(LessThan(arDate, Literal(d20170103)),
+        childStatsTestPlan(Seq(arDate), 10L)),
+      ColumnStat(distinctCount = 2, min = Some(dMin), max = Some(d20170103),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(3L)
+    )
+  }
+
+  test("""cdate IN ( cast('2017-01-03' AS DATE),
+      cast('2017-01-04' AS DATE), cast('2017-01-05' AS DATE) )""") {
+    val d20170103 = Date.valueOf("2017-01-03")
+    val d20170104 = Date.valueOf("2017-01-04")
+    val d20170105 = Date.valueOf("2017-01-05")
+    validateEstimatedStats(
+      arDate,
+      Filter(In(arDate, Seq(Literal(d20170103), Literal(d20170104), Literal(d20170105))),
+        childStatsTestPlan(Seq(arDate), 10L)),
+      ColumnStat(distinctCount = 3, min = Some(d20170103), max = Some(d20170105),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(3L)
+    )
+  }
+
+  test("ctimestamp = cast('2017-01-01 02:00:00' AS TIMESTAMP)") {
+    val ts2017010102 = Timestamp.valueOf("2017-01-01 02:00:00")
+    validateEstimatedStats(
+      arTimestamp,
+      Filter(EqualTo(arTimestamp, Literal(ts2017010102)),
+        childStatsTestPlan(Seq(arTimestamp), 10L)),
+      ColumnStat(distinctCount = 1, min = Some(ts2017010102), max = Some(ts2017010102),
+        nullCount = 0, avgLen = 8, maxLen = 8),
+      Some(1L)
+    )
+  }
+
+  test("ctimestamp < cast('2017-01-01 03:00:00' AS TIMESTAMP)") {
+    val ts2017010103 = Timestamp.valueOf("2017-01-01 03:00:00")
+    validateEstimatedStats(
+      arTimestamp,
+      Filter(LessThan(arTimestamp, Literal(ts2017010103)),
+        childStatsTestPlan(Seq(arTimestamp), 10L)),
+      ColumnStat(distinctCount = 2, min = Some(tsMin), max = Some(ts2017010103),
+        nullCount = 0, avgLen = 8, maxLen = 8),
+      Some(3L)
+    )
+  }
+
+  test("cdecimal = 0.400000000000000000") {
+    val dec_0_40 = new java.math.BigDecimal("0.400000000000000000")
+    validateEstimatedStats(
+      arDecimal,
+      Filter(EqualTo(arDecimal, Literal(dec_0_40)),
+        childStatsTestPlan(Seq(arDecimal), 4L)),
+      ColumnStat(distinctCount = 1, min = Some(dec_0_40), max = Some(dec_0_40),
+        nullCount = 0, avgLen = 8, maxLen = 8),
+      Some(1L)
+    )
+  }
+
+  test("cdecimal < 0.60 ") {
+    val dec_0_60 = new java.math.BigDecimal("0.600000000000000000")
+    validateEstimatedStats(
+      arDecimal,
+      Filter(LessThan(arDecimal, Literal(dec_0_60, DecimalType(12, 2))),
+        childStatsTestPlan(Seq(arDecimal), 4L)),
+      ColumnStat(distinctCount = 3, min = Some(decMin), max = Some(dec_0_60),
+        nullCount = 0, avgLen = 8, maxLen = 8),
+      Some(3L)
+    )
+  }
+
+  test("cdouble < 3.0") {
+    validateEstimatedStats(
+      arDouble,
+      Filter(LessThan(arDouble, Literal(3.0)), childStatsTestPlan(Seq(arDouble), 10L)),
+      ColumnStat(distinctCount = 2, min = Some(1.0), max = Some(3.0),
+        nullCount = 0, avgLen = 8, maxLen = 8),
+      Some(3L)
+    )
+  }
+
+  test("cstring = 'A2'") {
+    validateEstimatedStats(
+      arString,
+      Filter(EqualTo(arString, Literal("A2")), childStatsTestPlan(Seq(arString), 10L)),
+      ColumnStat(distinctCount = 1, min = None, max = None,
+        nullCount = 0, avgLen = 2, maxLen = 2),
+      Some(1L)
+    )
+  }
+
+  // There is no min/max statistics for String type.  We estimate 10 rows returned.
+  test("cstring < 'A2'") {
+    validateEstimatedStats(
+      arString,
+      Filter(LessThan(arString, Literal("A2")), childStatsTestPlan(Seq(arString), 10L)),
+      ColumnStat(distinctCount = 10, min = None, max = None,
+        nullCount = 0, avgLen = 2, maxLen = 2),
+      Some(10L)
+    )
+  }
+
+  // This is a corner test case.  We want to test if we can handle the case when the number of
+  // valid values in IN clause is greater than the number of distinct values for a given column.
+  // For example, column has only 2 distinct values 1 and 6.
+  // The predicate is: column IN (1, 2, 3, 4, 5).
+  test("cint IN (1, 2, 3, 4, 5)") {
+    val cornerChildColStatInt = ColumnStat(distinctCount = 2, min = Some(1), max = Some(6),
+      nullCount = 0, avgLen = 4, maxLen = 4)
+    val cornerChildStatsTestplan = StatsTestPlan(
+      outputList = Seq(arInt),
+      rowCount = 2L,
+      attributeStats = AttributeMap(Seq(arInt -> cornerChildColStatInt))
+    )
+    validateEstimatedStats(
+      arInt,
+      Filter(InSet(arInt, Set(1, 2, 3, 4, 5)), cornerChildStatsTestplan),
+      ColumnStat(distinctCount = 2, min = Some(1), max = Some(5),
+        nullCount = 0, avgLen = 4, maxLen = 4),
+      Some(2L)
+    )
+  }
+
+  private def childStatsTestPlan(outList: Seq[Attribute], tableRowCount: BigInt): StatsTestPlan = {
+    StatsTestPlan(
+      outputList = outList,
+      rowCount = tableRowCount,
+      attributeStats = AttributeMap(Seq(
+        arInt -> childColStatInt,
+        arDate -> childColStatDate,
+        arTimestamp -> childColStatTimestamp,
+        arDecimal -> childColStatDecimal,
+        arDouble -> childColStatDouble,
+        arString -> childColStatString
+      ))
+    )
+  }
+
+  private def validateEstimatedStats(
+      ar: AttributeReference,
+      filterNode: Filter,
+      expectedColStats: ColumnStat,
+      rowCount: Option[BigInt] = None)
+    : Unit = {
+
+    val expectedRowCount: BigInt = rowCount.getOrElse(0L)
+    val expectedAttrStats = toAttributeMap(Seq(ar.name -> expectedColStats), filterNode)
+    val expectedSizeInBytes = getOutputSize(filterNode.output, expectedRowCount, expectedAttrStats)
+
+    val filteredStats = filterNode.stats(conf)
+    assert(filteredStats.sizeInBytes == expectedSizeInBytes)
+    assert(filteredStats.rowCount == rowCount)
+    ar.dataType match {
+      case DecimalType() =>
+        // Due to the internal transformation for DecimalType within engine, the new min/max
+        // in ColumnStat may have a different structure even it contains the right values.
+        // We convert them to Java BigDecimal values so that we can compare the entire object.
+        val generatedColumnStats = filteredStats.attributeStats(ar)
+        val newMax = new java.math.BigDecimal(generatedColumnStats.max.getOrElse(0).toString)
+        val newMin = new java.math.BigDecimal(generatedColumnStats.min.getOrElse(0).toString)
+        val outputColStats = generatedColumnStats.copy(min = Some(newMin), max = Some(newMax))
+        assert(outputColStats == expectedColStats)
+      case _ =>
+        // For all other SQL types, we compare the entire object directly.
+        assert(filteredStats.attributeStats(ar) == expectedColStats)
+    }
+  }
+
+}