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Posted to commits@spark.apache.org by we...@apache.org on 2017/02/15 16:21:55 UTC
spark git commit: [SPARK-17076][SQL] Cardinality estimation for join
based on basic column statistics
Repository: spark
Updated Branches:
refs/heads/master 8b75f8c1c -> 601b9c3e6
[SPARK-17076][SQL] Cardinality estimation for join based on basic column statistics
## What changes were proposed in this pull request?
Support cardinality estimation and stats propagation for all join types.
Limitations:
- For inner/outer joins without any equal condition, we estimate it like cartesian product.
- For left semi/anti joins, since we can't apply the heuristics for inner join to it, for now we just propagate the statistics from left side. We should support them when other advanced stats (e.g. histograms) are available in spark.
## How was this patch tested?
Add a new test suite.
Author: Zhenhua Wang <wz...@163.com>
Author: wangzhenhua <wa...@huawei.com>
Closes #16228 from wzhfy/joinEstimate.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/601b9c3e
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/601b9c3e
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/601b9c3e
Branch: refs/heads/master
Commit: 601b9c3e6821b533a76d538f7f26bb622fd026fc
Parents: 8b75f8c
Author: Zhenhua Wang <wz...@163.com>
Authored: Wed Feb 15 08:21:51 2017 -0800
Committer: Wenchen Fan <we...@databricks.com>
Committed: Wed Feb 15 08:21:51 2017 -0800
----------------------------------------------------------------------
.../plans/logical/basicLogicalOperators.scala | 26 +-
.../statsEstimation/EstimationUtils.scala | 20 +-
.../statsEstimation/JoinEstimation.scala | 307 +++++++++++++++++
.../plans/logical/statsEstimation/Range.scala | 116 +++++++
.../statsEstimation/JoinEstimationSuite.scala | 327 +++++++++++++++++++
.../ProjectEstimationSuite.scala | 21 +-
.../StatsEstimationTestBase.scala | 8 +-
7 files changed, 801 insertions(+), 24 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/601b9c3e/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala
index 4d696c0..af57632 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala
+++ b/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, ProjectEstimation}
+import org.apache.spark.sql.catalyst.plans.logical.statsEstimation.{AggregateEstimation, EstimationUtils, JoinEstimation, ProjectEstimation}
import org.apache.spark.sql.types._
import org.apache.spark.util.Utils
@@ -340,14 +340,22 @@ case class Join(
case _ => resolvedExceptNatural
}
- override def computeStats(conf: CatalystConf): Statistics = joinType match {
- case LeftAnti | LeftSemi =>
- // LeftSemi and LeftAnti won't ever be bigger than left
- left.stats(conf).copy()
- case _ =>
- // make sure we don't propagate isBroadcastable in other joins, because
- // they could explode the size.
- super.computeStats(conf).copy(isBroadcastable = false)
+ override def computeStats(conf: CatalystConf): Statistics = {
+ def simpleEstimation: Statistics = joinType match {
+ case LeftAnti | LeftSemi =>
+ // LeftSemi and LeftAnti won't ever be bigger than left
+ left.stats(conf)
+ case _ =>
+ // Make sure we don't propagate isBroadcastable in other joins, because
+ // they could explode the size.
+ super.computeStats(conf).copy(isBroadcastable = false)
+ }
+
+ if (conf.cboEnabled) {
+ JoinEstimation.estimate(conf, this).getOrElse(simpleEstimation)
+ } else {
+ simpleEstimation
+ }
}
}
http://git-wip-us.apache.org/repos/asf/spark/blob/601b9c3e/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/EstimationUtils.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/EstimationUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/EstimationUtils.scala
index e8b7942..4d18b28 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/EstimationUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/EstimationUtils.scala
@@ -17,10 +17,12 @@
package org.apache.spark.sql.catalyst.plans.logical.statsEstimation
+import scala.math.BigDecimal.RoundingMode
+
import org.apache.spark.sql.catalyst.CatalystConf
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap}
-import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, LogicalPlan}
-import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, LogicalPlan, Statistics}
+import org.apache.spark.sql.types.{DataType, StringType}
object EstimationUtils {
@@ -29,6 +31,20 @@ object EstimationUtils {
def rowCountsExist(conf: CatalystConf, plans: LogicalPlan*): Boolean =
plans.forall(_.stats(conf).rowCount.isDefined)
+ /** Check if each attribute has column stat in the corresponding statistics. */
+ def columnStatsExist(statsAndAttr: (Statistics, Attribute)*): Boolean = {
+ statsAndAttr.forall { case (stats, attr) =>
+ stats.attributeStats.contains(attr)
+ }
+ }
+
+ def nullColumnStat(dataType: DataType, rowCount: BigInt): ColumnStat = {
+ ColumnStat(distinctCount = 0, min = None, max = None, nullCount = rowCount,
+ avgLen = dataType.defaultSize, maxLen = dataType.defaultSize)
+ }
+
+ def ceil(bigDecimal: BigDecimal): BigInt = bigDecimal.setScale(0, RoundingMode.CEILING).toBigInt()
+
/** Get column stats for output attributes. */
def getOutputMap(inputMap: AttributeMap[ColumnStat], output: Seq[Attribute])
: AttributeMap[ColumnStat] = {
http://git-wip-us.apache.org/repos/asf/spark/blob/601b9c3e/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala
new file mode 100644
index 0000000..982a5a8
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/JoinEstimation.scala
@@ -0,0 +1,307 @@
+/*
+ * 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.plans.logical.statsEstimation
+
+import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
+
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql.catalyst.CatalystConf
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference, Expression}
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, Join, Statistics}
+import org.apache.spark.sql.catalyst.plans.logical.statsEstimation.EstimationUtils._
+
+
+object JoinEstimation extends Logging {
+ /**
+ * Estimate statistics after join. Return `None` if the join type is not supported, or we don't
+ * have enough statistics for estimation.
+ */
+ def estimate(conf: CatalystConf, join: Join): Option[Statistics] = {
+ join.joinType match {
+ case Inner | Cross | LeftOuter | RightOuter | FullOuter =>
+ InnerOuterEstimation(conf, join).doEstimate()
+ case LeftSemi | LeftAnti =>
+ LeftSemiAntiEstimation(conf, join).doEstimate()
+ case _ =>
+ logDebug(s"[CBO] Unsupported join type: ${join.joinType}")
+ None
+ }
+ }
+}
+
+case class InnerOuterEstimation(conf: CatalystConf, join: Join) extends Logging {
+
+ private val leftStats = join.left.stats(conf)
+ private val rightStats = join.right.stats(conf)
+
+ /**
+ * Estimate output size and number of rows after a join operator, and update output column stats.
+ */
+ def doEstimate(): Option[Statistics] = join match {
+ case _ if !rowCountsExist(conf, join.left, join.right) =>
+ None
+
+ case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right) =>
+ // 1. Compute join selectivity
+ val joinKeyPairs = extractJoinKeysWithColStats(leftKeys, rightKeys)
+ val selectivity = joinSelectivity(joinKeyPairs)
+
+ // 2. Estimate the number of output rows
+ val leftRows = leftStats.rowCount.get
+ val rightRows = rightStats.rowCount.get
+ val innerJoinedRows = ceil(BigDecimal(leftRows * rightRows) * selectivity)
+
+ // Make sure outputRows won't be too small based on join type.
+ val outputRows = joinType match {
+ case LeftOuter =>
+ // All rows from left side should be in the result.
+ leftRows.max(innerJoinedRows)
+ case RightOuter =>
+ // All rows from right side should be in the result.
+ rightRows.max(innerJoinedRows)
+ case FullOuter =>
+ // T(A FOJ B) = T(A LOJ B) + T(A ROJ B) - T(A IJ B)
+ leftRows.max(innerJoinedRows) + rightRows.max(innerJoinedRows) - innerJoinedRows
+ case _ =>
+ // Don't change for inner or cross join
+ innerJoinedRows
+ }
+
+ // 3. Update statistics based on the output of join
+ val inputAttrStats = AttributeMap(
+ leftStats.attributeStats.toSeq ++ rightStats.attributeStats.toSeq)
+ val attributesWithStat = join.output.filter(a => inputAttrStats.contains(a))
+ val (fromLeft, fromRight) = attributesWithStat.partition(join.left.outputSet.contains(_))
+
+ val outputStats: Seq[(Attribute, ColumnStat)] = if (outputRows == 0) {
+ // The output is empty, we don't need to keep column stats.
+ Nil
+ } else if (innerJoinedRows == 0) {
+ joinType match {
+ // For outer joins, if the inner join part is empty, the number of output rows is the
+ // same as that of the outer side. And column stats of join keys from the outer side
+ // keep unchanged, while column stats of join keys from the other side should be updated
+ // based on added null values.
+ case LeftOuter =>
+ fromLeft.map(a => (a, inputAttrStats(a))) ++
+ fromRight.map(a => (a, nullColumnStat(a.dataType, leftRows)))
+ case RightOuter =>
+ fromRight.map(a => (a, inputAttrStats(a))) ++
+ fromLeft.map(a => (a, nullColumnStat(a.dataType, rightRows)))
+ case FullOuter =>
+ fromLeft.map { a =>
+ val oriColStat = inputAttrStats(a)
+ (a, oriColStat.copy(nullCount = oriColStat.nullCount + rightRows))
+ } ++ fromRight.map { a =>
+ val oriColStat = inputAttrStats(a)
+ (a, oriColStat.copy(nullCount = oriColStat.nullCount + leftRows))
+ }
+ case _ => Nil
+ }
+ } else {
+ val joinKeyStats = getIntersectedStats(joinKeyPairs)
+ join.joinType match {
+ // For outer joins, don't update column stats from the outer side.
+ case LeftOuter =>
+ fromLeft.map(a => (a, inputAttrStats(a))) ++
+ updateAttrStats(outputRows, fromRight, inputAttrStats, joinKeyStats)
+ case RightOuter =>
+ updateAttrStats(outputRows, fromLeft, inputAttrStats, joinKeyStats) ++
+ fromRight.map(a => (a, inputAttrStats(a)))
+ case FullOuter =>
+ inputAttrStats.toSeq
+ case _ =>
+ // Update column stats from both sides for inner or cross join.
+ updateAttrStats(outputRows, attributesWithStat, inputAttrStats, joinKeyStats)
+ }
+ }
+
+ val outputAttrStats = AttributeMap(outputStats)
+ Some(Statistics(
+ sizeInBytes = getOutputSize(join.output, outputRows, outputAttrStats),
+ rowCount = Some(outputRows),
+ attributeStats = outputAttrStats,
+ isBroadcastable = false))
+
+ case _ =>
+ // When there is no equi-join condition, we do estimation like cartesian product.
+ val inputAttrStats = AttributeMap(
+ leftStats.attributeStats.toSeq ++ rightStats.attributeStats.toSeq)
+ // Propagate the original column stats
+ val outputRows = leftStats.rowCount.get * rightStats.rowCount.get
+ Some(Statistics(
+ sizeInBytes = getOutputSize(join.output, outputRows, inputAttrStats),
+ rowCount = Some(outputRows),
+ attributeStats = inputAttrStats,
+ isBroadcastable = false))
+ }
+
+ // scalastyle:off
+ /**
+ * The number of rows of A inner join B on A.k1 = B.k1 is estimated by this basic formula:
+ * T(A IJ B) = T(A) * T(B) / max(V(A.k1), V(B.k1)), where V is the number of distinct values of
+ * that column. The underlying assumption for this formula is: each value of the smaller domain
+ * is included in the larger domain.
+ * Generally, inner join with multiple join keys can also be estimated based on the above
+ * formula:
+ * T(A IJ B) = T(A) * T(B) / (max(V(A.k1), V(B.k1)) * max(V(A.k2), V(B.k2)) * ... * max(V(A.kn), V(B.kn)))
+ * However, the denominator can become very large and excessively reduce the result, so we use a
+ * conservative strategy to take only the largest max(V(A.ki), V(B.ki)) as the denominator.
+ */
+ // scalastyle:on
+ def joinSelectivity(joinKeyPairs: Seq[(AttributeReference, AttributeReference)]): BigDecimal = {
+ var ndvDenom: BigInt = -1
+ var i = 0
+ while(i < joinKeyPairs.length && ndvDenom != 0) {
+ val (leftKey, rightKey) = joinKeyPairs(i)
+ // Check if the two sides are disjoint
+ val leftKeyStats = leftStats.attributeStats(leftKey)
+ val rightKeyStats = rightStats.attributeStats(rightKey)
+ val lRange = Range(leftKeyStats.min, leftKeyStats.max, leftKey.dataType)
+ val rRange = Range(rightKeyStats.min, rightKeyStats.max, rightKey.dataType)
+ if (Range.isIntersected(lRange, rRange)) {
+ // Get the largest ndv among pairs of join keys
+ val maxNdv = leftKeyStats.distinctCount.max(rightKeyStats.distinctCount)
+ if (maxNdv > ndvDenom) ndvDenom = maxNdv
+ } else {
+ // Set ndvDenom to zero to indicate that this join should have no output
+ ndvDenom = 0
+ }
+ i += 1
+ }
+
+ if (ndvDenom < 0) {
+ // There isn't join keys or column stats for any of the join key pairs, we do estimation like
+ // cartesian product.
+ 1
+ } else if (ndvDenom == 0) {
+ // One of the join key pairs is disjoint, thus the two sides of join is disjoint.
+ 0
+ } else {
+ 1 / BigDecimal(ndvDenom)
+ }
+ }
+
+ /**
+ * Propagate or update column stats for output attributes.
+ * 1. For cartesian product, all values are preserved, so there's no need to change column stats.
+ * 2. For other cases, a) update max/min of join keys based on their intersected range. b) update
+ * distinct count of other attributes based on output rows after join.
+ */
+ private def updateAttrStats(
+ outputRows: BigInt,
+ attributes: Seq[Attribute],
+ oldAttrStats: AttributeMap[ColumnStat],
+ joinKeyStats: AttributeMap[ColumnStat]): Seq[(Attribute, ColumnStat)] = {
+ val outputAttrStats = new ArrayBuffer[(Attribute, ColumnStat)]()
+ val leftRows = leftStats.rowCount.get
+ val rightRows = rightStats.rowCount.get
+ if (outputRows == leftRows * rightRows) {
+ // Cartesian product, just propagate the original column stats
+ attributes.foreach(a => outputAttrStats += a -> oldAttrStats(a))
+ } else {
+ val leftRatio =
+ if (leftRows != 0) BigDecimal(outputRows) / BigDecimal(leftRows) else BigDecimal(0)
+ val rightRatio =
+ if (rightRows != 0) BigDecimal(outputRows) / BigDecimal(rightRows) else BigDecimal(0)
+ attributes.foreach { a =>
+ // check if this attribute is a join key
+ if (joinKeyStats.contains(a)) {
+ outputAttrStats += a -> joinKeyStats(a)
+ } else {
+ val oldColStat = oldAttrStats(a)
+ val oldNdv = oldColStat.distinctCount
+ // We only change (scale down) the number of distinct values if the number of rows
+ // decreases after join, because join won't produce new values even if the number of
+ // rows increases.
+ val newNdv = if (join.left.outputSet.contains(a) && leftRatio < 1) {
+ ceil(BigDecimal(oldNdv) * leftRatio)
+ } else if (join.right.outputSet.contains(a) && rightRatio < 1) {
+ ceil(BigDecimal(oldNdv) * rightRatio)
+ } else {
+ oldNdv
+ }
+ // TODO: support nullCount updates for specific outer joins
+ outputAttrStats += a -> oldColStat.copy(distinctCount = newNdv)
+ }
+ }
+ }
+ outputAttrStats
+ }
+
+ /** Get intersected column stats for join keys. */
+ private def getIntersectedStats(joinKeyPairs: Seq[(AttributeReference, AttributeReference)])
+ : AttributeMap[ColumnStat] = {
+
+ val intersectedStats = new mutable.HashMap[Attribute, ColumnStat]()
+ joinKeyPairs.foreach { case (leftKey, rightKey) =>
+ val leftKeyStats = leftStats.attributeStats(leftKey)
+ val rightKeyStats = rightStats.attributeStats(rightKey)
+ val lRange = Range(leftKeyStats.min, leftKeyStats.max, leftKey.dataType)
+ val rRange = Range(rightKeyStats.min, rightKeyStats.max, rightKey.dataType)
+ // When we reach here, join selectivity is not zero, so each pair of join keys should be
+ // intersected.
+ assert(Range.isIntersected(lRange, rRange))
+
+ // Update intersected column stats
+ assert(leftKey.dataType.sameType(rightKey.dataType))
+ val minNdv = leftKeyStats.distinctCount.min(rightKeyStats.distinctCount)
+ val (newMin, newMax) = Range.intersect(lRange, rRange, leftKey.dataType)
+ intersectedStats.put(leftKey,
+ leftKeyStats.copy(distinctCount = minNdv, min = newMin, max = newMax, nullCount = 0))
+ intersectedStats.put(rightKey,
+ rightKeyStats.copy(distinctCount = minNdv, min = newMin, max = newMax, nullCount = 0))
+ }
+ AttributeMap(intersectedStats.toSeq)
+ }
+
+ private def extractJoinKeysWithColStats(
+ leftKeys: Seq[Expression],
+ rightKeys: Seq[Expression]): Seq[(AttributeReference, AttributeReference)] = {
+ leftKeys.zip(rightKeys).collect {
+ // Currently we don't deal with equal joins like key1 = key2 + 5.
+ // Note: join keys from EqualNullSafe also fall into this case (Coalesce), consider to
+ // support it in the future by using `nullCount` in column stats.
+ case (lk: AttributeReference, rk: AttributeReference)
+ if columnStatsExist((leftStats, lk), (rightStats, rk)) => (lk, rk)
+ }
+ }
+}
+
+case class LeftSemiAntiEstimation(conf: CatalystConf, join: Join) {
+ def doEstimate(): Option[Statistics] = {
+ // TODO: It's error-prone to estimate cardinalities for LeftSemi and LeftAnti based on basic
+ // column stats. Now we just propagate the statistics from left side. We should do more
+ // accurate estimation when advanced stats (e.g. histograms) are available.
+ if (rowCountsExist(conf, join.left)) {
+ val leftStats = join.left.stats(conf)
+ // Propagate the original column stats for cartesian product
+ val outputRows = leftStats.rowCount.get
+ Some(Statistics(
+ sizeInBytes = getOutputSize(join.output, outputRows, leftStats.attributeStats),
+ rowCount = Some(outputRows),
+ attributeStats = leftStats.attributeStats,
+ isBroadcastable = false))
+ } else {
+ None
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/601b9c3e/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/Range.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/Range.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/Range.scala
new file mode 100644
index 0000000..5aa6b93
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/statsEstimation/Range.scala
@@ -0,0 +1,116 @@
+/*
+ * 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.plans.logical.statsEstimation
+
+import java.math.{BigDecimal => JDecimal}
+import java.sql.{Date, Timestamp}
+
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.types.{BooleanType, DateType, TimestampType, _}
+
+
+/** Value range of a column. */
+trait Range
+
+/** For simplicity we use decimal to unify operations of numeric ranges. */
+case class NumericRange(min: JDecimal, max: JDecimal) extends Range
+
+/**
+ * This version of Spark does not have min/max for binary/string types, we define their default
+ * behaviors by this class.
+ */
+class DefaultRange extends Range
+
+/** This is for columns with only null values. */
+class NullRange extends Range
+
+object Range {
+ def apply(min: Option[Any], max: Option[Any], dataType: DataType): Range = dataType match {
+ case StringType | BinaryType => new DefaultRange()
+ case _ if min.isEmpty || max.isEmpty => new NullRange()
+ case _ => toNumericRange(min.get, max.get, dataType)
+ }
+
+ def isIntersected(r1: Range, r2: Range): Boolean = (r1, r2) match {
+ case (_, _: DefaultRange) | (_: DefaultRange, _) =>
+ // The DefaultRange represents string/binary types which do not have max/min stats,
+ // we assume they are intersected to be conservative on estimation
+ true
+ case (_, _: NullRange) | (_: NullRange, _) =>
+ false
+ case (n1: NumericRange, n2: NumericRange) =>
+ n1.min.compareTo(n2.max) <= 0 && n1.max.compareTo(n2.min) >= 0
+ }
+
+ /**
+ * Intersected results of two ranges. This is only for two overlapped ranges.
+ * The outputs are the intersected min/max values.
+ */
+ def intersect(r1: Range, r2: Range, dt: DataType): (Option[Any], Option[Any]) = {
+ (r1, r2) match {
+ case (_, _: DefaultRange) | (_: DefaultRange, _) =>
+ // binary/string types don't support intersecting.
+ (None, None)
+ case (n1: NumericRange, n2: NumericRange) =>
+ val newRange = NumericRange(n1.min.max(n2.min), n1.max.min(n2.max))
+ val (newMin, newMax) = fromNumericRange(newRange, dt)
+ (Some(newMin), Some(newMax))
+ }
+ }
+
+ /**
+ * For simplicity we use decimal to unify operations of numeric types, the two methods below
+ * are the contract of conversion.
+ */
+ private def toNumericRange(min: Any, max: Any, dataType: DataType): NumericRange = {
+ dataType match {
+ case _: NumericType =>
+ NumericRange(new JDecimal(min.toString), new JDecimal(max.toString))
+ case BooleanType =>
+ val min1 = if (min.asInstanceOf[Boolean]) 1 else 0
+ val max1 = if (max.asInstanceOf[Boolean]) 1 else 0
+ NumericRange(new JDecimal(min1), new JDecimal(max1))
+ case DateType =>
+ val min1 = DateTimeUtils.fromJavaDate(min.asInstanceOf[Date])
+ val max1 = DateTimeUtils.fromJavaDate(max.asInstanceOf[Date])
+ NumericRange(new JDecimal(min1), new JDecimal(max1))
+ case TimestampType =>
+ val min1 = DateTimeUtils.fromJavaTimestamp(min.asInstanceOf[Timestamp])
+ val max1 = DateTimeUtils.fromJavaTimestamp(max.asInstanceOf[Timestamp])
+ NumericRange(new JDecimal(min1), new JDecimal(max1))
+ }
+ }
+
+ private def fromNumericRange(n: NumericRange, dataType: DataType): (Any, Any) = {
+ dataType match {
+ case _: IntegralType =>
+ (n.min.longValue(), n.max.longValue())
+ case FloatType | DoubleType =>
+ (n.min.doubleValue(), n.max.doubleValue())
+ case _: DecimalType =>
+ (n.min, n.max)
+ case BooleanType =>
+ (n.min.longValue() == 1, n.max.longValue() == 1)
+ case DateType =>
+ (DateTimeUtils.toJavaDate(n.min.intValue()), DateTimeUtils.toJavaDate(n.max.intValue()))
+ case TimestampType =>
+ (DateTimeUtils.toJavaTimestamp(n.min.longValue()),
+ DateTimeUtils.toJavaTimestamp(n.max.longValue()))
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/601b9c3e/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/JoinEstimationSuite.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/JoinEstimationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/JoinEstimationSuite.scala
new file mode 100644
index 0000000..f62df84
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/JoinEstimationSuite.scala
@@ -0,0 +1,327 @@
+/*
+ * 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 scala.collection.mutable
+
+import org.apache.spark.sql.catalyst.expressions.{And, Attribute, AttributeMap, AttributeReference, EqualTo}
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, Join, Project, Statistics}
+import org.apache.spark.sql.catalyst.plans.logical.statsEstimation.EstimationUtils._
+import org.apache.spark.sql.types.{DateType, TimestampType, _}
+
+
+class JoinEstimationSuite extends StatsEstimationTestBase {
+
+ /** Set up tables and its columns for testing */
+ private val columnInfo: AttributeMap[ColumnStat] = AttributeMap(Seq(
+ attr("key-1-5") -> ColumnStat(distinctCount = 5, min = Some(1), max = Some(5), nullCount = 0,
+ avgLen = 4, maxLen = 4),
+ attr("key-5-9") -> ColumnStat(distinctCount = 5, min = Some(5), max = Some(9), nullCount = 0,
+ avgLen = 4, maxLen = 4),
+ attr("key-1-2") -> ColumnStat(distinctCount = 2, min = Some(1), max = Some(2), nullCount = 0,
+ avgLen = 4, maxLen = 4),
+ attr("key-2-4") -> ColumnStat(distinctCount = 3, min = Some(2), max = Some(4), nullCount = 0,
+ avgLen = 4, maxLen = 4),
+ attr("key-2-3") -> ColumnStat(distinctCount = 2, min = Some(2), max = Some(3), 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)
+
+ // Suppose table1 (key-1-5 int, key-5-9 int) has 5 records: (1, 9), (2, 8), (3, 7), (4, 6), (5, 5)
+ private val table1 = StatsTestPlan(
+ outputList = Seq("key-1-5", "key-5-9").map(nameToAttr),
+ rowCount = 5,
+ attributeStats = AttributeMap(Seq("key-1-5", "key-5-9").map(nameToColInfo)))
+
+ // Suppose table2 (key-1-2 int, key-2-4 int) has 3 records: (1, 2), (2, 3), (2, 4)
+ private val table2 = StatsTestPlan(
+ outputList = Seq("key-1-2", "key-2-4").map(nameToAttr),
+ rowCount = 3,
+ attributeStats = AttributeMap(Seq("key-1-2", "key-2-4").map(nameToColInfo)))
+
+ // Suppose table3 (key-1-2 int, key-2-3 int) has 2 records: (1, 2), (2, 3)
+ private val table3 = StatsTestPlan(
+ outputList = Seq("key-1-2", "key-2-3").map(nameToAttr),
+ rowCount = 2,
+ attributeStats = AttributeMap(Seq("key-1-2", "key-2-3").map(nameToColInfo)))
+
+ test("cross join") {
+ // table1 (key-1-5 int, key-5-9 int): (1, 9), (2, 8), (3, 7), (4, 6), (5, 5)
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ val join = Join(table1, table2, Cross, None)
+ val expectedStats = Statistics(
+ sizeInBytes = 5 * 3 * (8 + 4 * 4),
+ rowCount = Some(5 * 3),
+ // Keep the column stat from both sides unchanged.
+ attributeStats = AttributeMap(
+ Seq("key-1-5", "key-5-9", "key-1-2", "key-2-4").map(nameToColInfo)))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("disjoint inner join") {
+ // table1 (key-1-5 int, key-5-9 int): (1, 9), (2, 8), (3, 7), (4, 6), (5, 5)
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // key-5-9 and key-2-4 are disjoint
+ val join = Join(table1, table2, Inner,
+ Some(EqualTo(nameToAttr("key-5-9"), nameToAttr("key-2-4"))))
+ val expectedStats = Statistics(
+ sizeInBytes = 1,
+ rowCount = Some(0),
+ attributeStats = AttributeMap(Nil))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("disjoint left outer join") {
+ // table1 (key-1-5 int, key-5-9 int): (1, 9), (2, 8), (3, 7), (4, 6), (5, 5)
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // key-5-9 and key-2-4 are disjoint
+ val join = Join(table1, table2, LeftOuter,
+ Some(EqualTo(nameToAttr("key-5-9"), nameToAttr("key-2-4"))))
+ val expectedStats = Statistics(
+ sizeInBytes = 5 * (8 + 4 * 4),
+ rowCount = Some(5),
+ attributeStats = AttributeMap(Seq("key-1-5", "key-5-9").map(nameToColInfo) ++
+ // Null count for right side columns = left row count
+ Seq(nameToAttr("key-1-2") -> nullColumnStat(nameToAttr("key-1-2").dataType, 5),
+ nameToAttr("key-2-4") -> nullColumnStat(nameToAttr("key-2-4").dataType, 5))))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("disjoint right outer join") {
+ // table1 (key-1-5 int, key-5-9 int): (1, 9), (2, 8), (3, 7), (4, 6), (5, 5)
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // key-5-9 and key-2-4 are disjoint
+ val join = Join(table1, table2, RightOuter,
+ Some(EqualTo(nameToAttr("key-5-9"), nameToAttr("key-2-4"))))
+ val expectedStats = Statistics(
+ sizeInBytes = 3 * (8 + 4 * 4),
+ rowCount = Some(3),
+ attributeStats = AttributeMap(Seq("key-1-2", "key-2-4").map(nameToColInfo) ++
+ // Null count for left side columns = right row count
+ Seq(nameToAttr("key-1-5") -> nullColumnStat(nameToAttr("key-1-5").dataType, 3),
+ nameToAttr("key-5-9") -> nullColumnStat(nameToAttr("key-5-9").dataType, 3))))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("disjoint full outer join") {
+ // table1 (key-1-5 int, key-5-9 int): (1, 9), (2, 8), (3, 7), (4, 6), (5, 5)
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // key-5-9 and key-2-4 are disjoint
+ val join = Join(table1, table2, FullOuter,
+ Some(EqualTo(nameToAttr("key-5-9"), nameToAttr("key-2-4"))))
+ val expectedStats = Statistics(
+ sizeInBytes = (5 + 3) * (8 + 4 * 4),
+ rowCount = Some(5 + 3),
+ attributeStats = AttributeMap(
+ // Update null count in column stats.
+ Seq(nameToAttr("key-1-5") -> columnInfo(nameToAttr("key-1-5")).copy(nullCount = 3),
+ nameToAttr("key-5-9") -> columnInfo(nameToAttr("key-5-9")).copy(nullCount = 3),
+ nameToAttr("key-1-2") -> columnInfo(nameToAttr("key-1-2")).copy(nullCount = 5),
+ nameToAttr("key-2-4") -> columnInfo(nameToAttr("key-2-4")).copy(nullCount = 5))))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("inner join") {
+ // table1 (key-1-5 int, key-5-9 int): (1, 9), (2, 8), (3, 7), (4, 6), (5, 5)
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ val join = Join(table1, table2, Inner,
+ Some(EqualTo(nameToAttr("key-1-5"), nameToAttr("key-1-2"))))
+ // Update column stats for equi-join keys (key-1-5 and key-1-2).
+ val joinedColStat = ColumnStat(distinctCount = 2, min = Some(1), max = Some(2), nullCount = 0,
+ avgLen = 4, maxLen = 4)
+ // Update column stat for other column if #outputRow / #sideRow < 1 (key-5-9), or keep it
+ // unchanged (key-2-4).
+ val colStatForkey59 = nameToColInfo("key-5-9")._2.copy(distinctCount = 5 * 3 / 5)
+
+ val expectedStats = Statistics(
+ sizeInBytes = 3 * (8 + 4 * 4),
+ rowCount = Some(3),
+ attributeStats = AttributeMap(
+ Seq(nameToAttr("key-1-5") -> joinedColStat, nameToAttr("key-1-2") -> joinedColStat,
+ nameToAttr("key-5-9") -> colStatForkey59, nameToColInfo("key-2-4"))))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("inner join with multiple equi-join keys") {
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // table3 (key-1-2 int, key-2-3 int): (1, 2), (2, 3)
+ val join = Join(table2, table3, Inner, Some(
+ And(EqualTo(nameToAttr("key-1-2"), nameToAttr("key-1-2")),
+ EqualTo(nameToAttr("key-2-4"), nameToAttr("key-2-3")))))
+
+ // Update column stats for join keys.
+ val joinedColStat1 = ColumnStat(distinctCount = 2, min = Some(1), max = Some(2), nullCount = 0,
+ avgLen = 4, maxLen = 4)
+ val joinedColStat2 = ColumnStat(distinctCount = 2, min = Some(2), max = Some(3), nullCount = 0,
+ avgLen = 4, maxLen = 4)
+
+ val expectedStats = Statistics(
+ sizeInBytes = 2 * (8 + 4 * 4),
+ rowCount = Some(2),
+ attributeStats = AttributeMap(
+ Seq(nameToAttr("key-1-2") -> joinedColStat1, nameToAttr("key-1-2") -> joinedColStat1,
+ nameToAttr("key-2-4") -> joinedColStat2, nameToAttr("key-2-3") -> joinedColStat2)))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("left outer join") {
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // table3 (key-1-2 int, key-2-3 int): (1, 2), (2, 3)
+ val join = Join(table3, table2, LeftOuter,
+ Some(EqualTo(nameToAttr("key-2-3"), nameToAttr("key-2-4"))))
+ val joinedColStat = ColumnStat(distinctCount = 2, min = Some(2), max = Some(3), nullCount = 0,
+ avgLen = 4, maxLen = 4)
+
+ val expectedStats = Statistics(
+ sizeInBytes = 2 * (8 + 4 * 4),
+ rowCount = Some(2),
+ // Keep the column stat from left side unchanged.
+ attributeStats = AttributeMap(
+ Seq(nameToColInfo("key-1-2"), nameToColInfo("key-2-3"),
+ nameToColInfo("key-1-2"), nameToAttr("key-2-4") -> joinedColStat)))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("right outer join") {
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // table3 (key-1-2 int, key-2-3 int): (1, 2), (2, 3)
+ val join = Join(table2, table3, RightOuter,
+ Some(EqualTo(nameToAttr("key-2-4"), nameToAttr("key-2-3"))))
+ val joinedColStat = ColumnStat(distinctCount = 2, min = Some(2), max = Some(3), nullCount = 0,
+ avgLen = 4, maxLen = 4)
+
+ val expectedStats = Statistics(
+ sizeInBytes = 2 * (8 + 4 * 4),
+ rowCount = Some(2),
+ // Keep the column stat from right side unchanged.
+ attributeStats = AttributeMap(
+ Seq(nameToColInfo("key-1-2"), nameToAttr("key-2-4") -> joinedColStat,
+ nameToColInfo("key-1-2"), nameToColInfo("key-2-3"))))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("full outer join") {
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // table3 (key-1-2 int, key-2-3 int): (1, 2), (2, 3)
+ val join = Join(table2, table3, FullOuter,
+ Some(EqualTo(nameToAttr("key-2-4"), nameToAttr("key-2-3"))))
+
+ val expectedStats = Statistics(
+ sizeInBytes = 3 * (8 + 4 * 4),
+ rowCount = Some(3),
+ // Keep the column stat from both sides unchanged.
+ attributeStats = AttributeMap(Seq(nameToColInfo("key-1-2"), nameToColInfo("key-2-4"),
+ nameToColInfo("key-1-2"), nameToColInfo("key-2-3"))))
+ assert(join.stats(conf) == expectedStats)
+ }
+
+ test("left semi/anti join") {
+ // table2 (key-1-2 int, key-2-4 int): (1, 2), (2, 3), (2, 4)
+ // table3 (key-1-2 int, key-2-3 int): (1, 2), (2, 3)
+ Seq(LeftSemi, LeftAnti).foreach { jt =>
+ val join = Join(table2, table3, jt,
+ Some(EqualTo(nameToAttr("key-2-4"), nameToAttr("key-2-3"))))
+ // For now we just propagate the statistics from left side for left semi/anti join.
+ val expectedStats = Statistics(
+ sizeInBytes = 3 * (8 + 4 * 2),
+ rowCount = Some(3),
+ attributeStats = AttributeMap(Seq(nameToColInfo("key-1-2"), nameToColInfo("key-2-4"))))
+ assert(join.stats(conf) == expectedStats)
+ }
+ }
+
+ test("test join keys of different types") {
+ /** Columns in a table with only one row */
+ def genColumnData: mutable.LinkedHashMap[Attribute, ColumnStat] = {
+ val dec = new java.math.BigDecimal("1.000000000000000000")
+ val date = Date.valueOf("2016-05-08")
+ val timestamp = Timestamp.valueOf("2016-05-08 00:00:01")
+ mutable.LinkedHashMap[Attribute, ColumnStat](
+ AttributeReference("cbool", BooleanType)() -> ColumnStat(distinctCount = 1,
+ min = Some(false), max = Some(false), nullCount = 0, avgLen = 1, maxLen = 1),
+ AttributeReference("cbyte", ByteType)() -> ColumnStat(distinctCount = 1,
+ min = Some(1L), max = Some(1L), nullCount = 0, avgLen = 1, maxLen = 1),
+ AttributeReference("cshort", ShortType)() -> ColumnStat(distinctCount = 1,
+ min = Some(1L), max = Some(1L), nullCount = 0, avgLen = 2, maxLen = 2),
+ AttributeReference("cint", IntegerType)() -> ColumnStat(distinctCount = 1,
+ min = Some(1L), max = Some(1L), nullCount = 0, avgLen = 4, maxLen = 4),
+ AttributeReference("clong", LongType)() -> ColumnStat(distinctCount = 1,
+ min = Some(1L), max = Some(1L), nullCount = 0, avgLen = 8, maxLen = 8),
+ AttributeReference("cdouble", DoubleType)() -> ColumnStat(distinctCount = 1,
+ min = Some(1.0), max = Some(1.0), nullCount = 0, avgLen = 8, maxLen = 8),
+ AttributeReference("cfloat", FloatType)() -> ColumnStat(distinctCount = 1,
+ min = Some(1.0), max = Some(1.0), nullCount = 0, avgLen = 4, maxLen = 4),
+ AttributeReference("cdec", DecimalType.SYSTEM_DEFAULT)() -> ColumnStat(distinctCount = 1,
+ min = Some(dec), max = Some(dec), nullCount = 0, avgLen = 16, maxLen = 16),
+ AttributeReference("cstring", StringType)() -> ColumnStat(distinctCount = 1,
+ min = None, max = None, nullCount = 0, avgLen = 3, maxLen = 3),
+ AttributeReference("cbinary", BinaryType)() -> ColumnStat(distinctCount = 1,
+ min = None, max = None, nullCount = 0, avgLen = 3, maxLen = 3),
+ AttributeReference("cdate", DateType)() -> ColumnStat(distinctCount = 1,
+ min = Some(date), max = Some(date), nullCount = 0, avgLen = 4, maxLen = 4),
+ AttributeReference("ctimestamp", TimestampType)() -> ColumnStat(distinctCount = 1,
+ min = Some(timestamp), max = Some(timestamp), nullCount = 0, avgLen = 8, maxLen = 8)
+ )
+ }
+
+ val columnInfo1 = genColumnData
+ val columnInfo2 = genColumnData
+ val table1 = StatsTestPlan(
+ outputList = columnInfo1.keys.toSeq,
+ rowCount = 1,
+ attributeStats = AttributeMap(columnInfo1.toSeq))
+ val table2 = StatsTestPlan(
+ outputList = columnInfo2.keys.toSeq,
+ rowCount = 1,
+ attributeStats = AttributeMap(columnInfo2.toSeq))
+ val joinKeys = table1.output.zip(table2.output)
+ joinKeys.foreach { case (key1, key2) =>
+ withClue(s"For data type ${key1.dataType}") {
+ // All values in two tables are the same, so column stats after join are also the same.
+ val join = Join(Project(Seq(key1), table1), Project(Seq(key2), table2), Inner,
+ Some(EqualTo(key1, key2)))
+ val expectedStats = Statistics(
+ sizeInBytes = 1 * (8 + 2 * getColSize(key1, columnInfo1(key1))),
+ rowCount = Some(1),
+ attributeStats = AttributeMap(Seq(key1 -> columnInfo1(key1), key2 -> columnInfo1(key1))))
+ assert(join.stats(conf) == expectedStats)
+ }
+ }
+ }
+
+ test("join with null column") {
+ val (nullColumn, nullColStat) = (attr("cnull"),
+ ColumnStat(distinctCount = 0, min = None, max = None, nullCount = 1, avgLen = 4, maxLen = 4))
+ val nullTable = StatsTestPlan(
+ outputList = Seq(nullColumn),
+ rowCount = 1,
+ attributeStats = AttributeMap(Seq(nullColumn -> nullColStat)))
+ val join = Join(table1, nullTable, Inner, Some(EqualTo(nameToAttr("key-1-5"), nullColumn)))
+ val expectedStats = Statistics(
+ sizeInBytes = 1,
+ rowCount = Some(0),
+ attributeStats = AttributeMap(Nil))
+ assert(join.stats(conf) == expectedStats)
+ }
+}
http://git-wip-us.apache.org/repos/asf/spark/blob/601b9c3e/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/ProjectEstimationSuite.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/ProjectEstimationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/ProjectEstimationSuite.scala
index ae102a4..f408dc4 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/ProjectEstimationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/ProjectEstimationSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.statsEstimation
import java.sql.{Date, Timestamp}
-import org.apache.spark.sql.catalyst.expressions.{Alias, AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{Alias, Attribute, AttributeMap, AttributeReference}
import org.apache.spark.sql.catalyst.plans.logical._
import org.apache.spark.sql.types._
@@ -95,12 +95,7 @@ class ProjectEstimationSuite extends StatsEstimationTestBase {
AttributeReference("ctimestamp", TimestampType)() -> ColumnStat(distinctCount = 2,
min = Some(t1), max = Some(t2), nullCount = 0, avgLen = 8, maxLen = 8)
))
- val columnSizes = columnInfo.map { case (attr, colStat) =>
- (attr, attr.dataType match {
- case StringType => colStat.avgLen + 8 + 4
- case _ => colStat.avgLen
- })
- }
+ val columnSizes: Map[Attribute, Long] = columnInfo.map(kv => (kv._1, getColSize(kv._1, kv._2)))
val child = StatsTestPlan(
outputList = columnInfo.keys.toSeq,
rowCount = 2,
@@ -108,11 +103,13 @@ class ProjectEstimationSuite extends StatsEstimationTestBase {
// Row with single column
columnInfo.keys.foreach { attr =>
- checkProjectStats(
- child = child,
- projectAttrMap = AttributeMap(attr -> columnInfo(attr) :: Nil),
- expectedSize = 2 * (8 + columnSizes(attr)),
- expectedRowCount = 2)
+ withClue(s"For data type ${attr.dataType}") {
+ checkProjectStats(
+ child = child,
+ projectAttrMap = AttributeMap(attr -> columnInfo(attr) :: Nil),
+ expectedSize = 2 * (8 + columnSizes(attr)),
+ expectedRowCount = 2)
+ }
}
// Row with multiple columns
http://git-wip-us.apache.org/repos/asf/spark/blob/601b9c3e/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/StatsEstimationTestBase.scala
----------------------------------------------------------------------
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/StatsEstimationTestBase.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/StatsEstimationTestBase.scala
index a5fac4b..c56b41c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/StatsEstimationTestBase.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/statsEstimation/StatsEstimationTestBase.scala
@@ -21,7 +21,7 @@ import org.apache.spark.SparkFunSuite
import org.apache.spark.sql.catalyst.{CatalystConf, SimpleCatalystConf}
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference}
import org.apache.spark.sql.catalyst.plans.logical.{ColumnStat, LeafNode, LogicalPlan, Statistics}
-import org.apache.spark.sql.types.IntegerType
+import org.apache.spark.sql.types.{IntegerType, StringType}
class StatsEstimationTestBase extends SparkFunSuite {
@@ -29,6 +29,12 @@ class StatsEstimationTestBase extends SparkFunSuite {
/** Enable stats estimation based on CBO. */
protected val conf = SimpleCatalystConf(caseSensitiveAnalysis = true, cboEnabled = true)
+ def getColSize(attribute: Attribute, colStat: ColumnStat): Long = attribute.dataType match {
+ // For UTF8String: base + offset + numBytes
+ case StringType => colStat.avgLen + 8 + 4
+ case _ => colStat.avgLen
+ }
+
def attr(colName: String): AttributeReference = AttributeReference(colName, IntegerType)()
/** Convert (column name, column stat) pairs to an AttributeMap based on plan output. */
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