[SPARK-17076] [SQL] Cardinality estimation for join based on basic column statistics #16228
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| /* | ||
| * 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. | ||
| */ | ||
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| package org.apache.spark.sql.catalyst.plans.logical.statsEstimation | ||
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| import scala.collection.mutable | ||
| import scala.collection.mutable.ArrayBuffer | ||
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| 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._ | ||
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| 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 | ||
| } | ||
| } | ||
| } | ||
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| case class InnerOuterEstimation(conf: CatalystConf, join: Join) extends Logging { | ||
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| private val leftStats = join.left.stats(conf) | ||
| private val rightStats = join.right.stats(conf) | ||
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| /** | ||
| * 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 | ||
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| case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right) => | ||
| // 1. Compute join selectivity | ||
| val joinKeyPairs = extractJoinKeysWithColStats(leftKeys, rightKeys) | ||
| val selectivity = joinSelectivity(joinKeyPairs) | ||
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| // 2. Estimate the number of output rows | ||
| val leftRows = leftStats.rowCount.get | ||
| val rightRows = rightStats.rowCount.get | ||
| val innerJoinedRows = ceil(BigDecimal(leftRows * rightRows) * selectivity) | ||
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| // 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 | ||
| } | ||
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| // 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(_)) | ||
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| 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) | ||
| } | ||
| } | ||
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| val outputAttrStats = AttributeMap(outputStats) | ||
| Some(Statistics( | ||
| sizeInBytes = getOutputSize(join.output, outputRows, outputAttrStats), | ||
| rowCount = Some(outputRows), | ||
| attributeStats = outputAttrStats, | ||
| isBroadcastable = false)) | ||
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| 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)) | ||
| } | ||
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| // scalastyle:off | ||
| /** | ||
| * The number of rows of A inner join B on A.k1 = B.k1 is estimated by this basic formula: | ||
|
Contributor
There was a problem hiding this comment. outer join will also hit this branch, right?
Contributor
Author
There was a problem hiding this comment. This is only for estimating the inner joined part.
Contributor
There was a problem hiding this comment. look at the code path:
Contributor
Author
There was a problem hiding this comment. yes, I mean it computes the inner join part in the outer join. |
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| * 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 | ||
| } | ||
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| 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) | ||
| } | ||
| } | ||
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| /** | ||
| * 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 | ||
| } | ||
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| /** Get intersected column stats for join keys. */ | ||
| private def getIntersectedStats(joinKeyPairs: Seq[(AttributeReference, AttributeReference)]) | ||
| : AttributeMap[ColumnStat] = { | ||
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| 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)) | ||
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| // 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) | ||
| } | ||
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| 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) | ||
| } | ||
| } | ||
| } | ||
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| 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 | ||
| } | ||
| } | ||
| } | ||
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not related to this PR, but
super.computeStats(conf)won't produce a stats withisBroadcastable = trueThere was a problem hiding this comment.
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We can't make sure we won't change the behavior of super.computeStats(conf), so I think it's safer to do this here.