c21 commented on a change in pull request #29804:
URL: https://github.com/apache/spark/pull/29804#discussion_r495758770
##########
File path:
sql/core/src/test/scala/org/apache/spark/sql/sources/BucketedReadSuite.scala
##########
@@ -1012,4 +1014,182 @@ abstract class BucketedReadSuite extends QueryTest with
SQLTestUtils {
}
}
}
+
+ private def checkDisableBucketedScan(
+ query: String,
+ expectedNumBucketedScanEnabled: Int,
Review comment:
@maropu - sure, updated.
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##########
@@ -951,6 +951,14 @@ object SQLConf {
.checkValue(_ > 0, "the value of spark.sql.sources.bucketing.maxBuckets
must be greater than 0")
.createWithDefault(100000)
+ val AUTO_BUCKETED_SCAN_ENABLED =
+ buildConf("spark.sql.sources.bucketing.autoBucketedScan.enabled")
+ .doc("When true, decide whether to do bucketed scan on input tables
based on query plan " +
Review comment:
@maropu - sure, updated.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/bucketing/DisableUnnecessaryBucketedScan.scala
##########
@@ -0,0 +1,147 @@
+/*
+ * 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.execution.bucketing
+
+import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution,
HashClusteredDistribution}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.{FileSourceScanExec, FilterExec,
ProjectExec, SortExec, SparkPlan}
+import org.apache.spark.sql.execution.aggregate.BaseAggregateExec
+import org.apache.spark.sql.execution.exchange.Exchange
+import org.apache.spark.sql.internal.SQLConf
+
+/**
+ * Disable unnecessary bucketed table scan based on actual physical query plan.
+ * NOTE: this rule is designed to be applied right after
[[EnsureRequirements]],
+ * where all [[ShuffleExchangeExec]] and [[SortExec]] have been added to plan
properly.
+ *
+ * When BUCKETING_ENABLED and AUTO_BUCKETED_SCAN_ENABLED are set to true, go
through
+ * query plan to check where bucketed table scan is unnecessary, and disable
bucketed table
+ * scan if:
+ *
+ * 1.The sub-plan from the nearest downstream [[hasInterestingPartition]]
operator
+ * to the bucketed table scan, contains only [[isAllowedUnaryExecNode]]
operators
+ * and at least one [[Exchange]].
+ *
+ * 2.The sub-plan from root to bucketed table scan, does not contain
+ * [[hasInterestingPartition]] operator.
+ *
+ * Examples:
+ * 1.join:
+ * SortMergeJoin(t1.i = t2.j)
+ * / \
+ * Sort(i) Sort(j)
+ * / \
+ * Shuffle(i) Scan(t2: i, j)
+ * / (bucketed on column j, enable bucketed scan)
+ * Scan(t1: i, j)
+ * (bucketed on column j, DISABLE bucketed scan)
+ *
+ * 2.aggregate:
+ * HashAggregate(i, ..., Final)
+ * |
+ * Shuffle(i)
+ * |
+ * HashAggregate(i, ..., Partial)
+ * |
+ * Filter
+ * |
+ * Scan(t1: i, j)
+ * (bucketed on column j, DISABLE bucketed scan)
+ *
+ * The idea of [[hasInterestingPartition]] is inspired from "interesting
order" in
+ * the paper "Access Path Selection in a Relational Database Management System"
+ * (https://dl.acm.org/doi/10.1145/582095.582099).
+ */
+case class DisableUnnecessaryBucketedScan(conf: SQLConf) extends
Rule[SparkPlan] {
+
+ /**
+ * Disable bucketed table scan with pre-order traversal of plan.
+ *
+ * @param withInterestingPartition The traversed plan has operator with
interesting partition.
+ * @param withExchange The traversed plan has [[Exchange]] operator.
+ * @param withAllowedNode The traversed plan has only
[[isAllowedUnaryExecNode]] operators.
+ */
+ private def disableBucketWithInterestingPartition(
+ plan: SparkPlan,
+ withInterestingPartition: Boolean,
+ withExchange: Boolean,
+ withAllowedNode: Boolean): SparkPlan = {
+ plan match {
+ case p if hasInterestingPartition(p) =>
+ // Operator with interesting partition, propagates
`withInterestingPartition` as true
+ // to its children, and resets `withExchange` and `withAllowedNode`.
+ p.mapChildren(disableBucketWithInterestingPartition(_, true, false,
true))
+ case exchange: Exchange =>
+ // Exchange operator propagates `withExchange` as true to its child.
+ exchange.mapChildren(disableBucketWithInterestingPartition(
+ _, withInterestingPartition, true, withAllowedNode))
+ case scan: FileSourceScanExec =>
+ if (isBucketedScanWithoutFilter(scan)) {
+ if (!withInterestingPartition || (withExchange && withAllowedNode)) {
+ scan.copy(disableBucketedScan = true)
+ } else {
+ scan
+ }
+ } else {
+ scan
+ }
+ case o =>
+ o.mapChildren(disableBucketWithInterestingPartition(
+ _,
+ withInterestingPartition,
+ withExchange,
+ withAllowedNode && isAllowedUnaryExecNode(o)))
+ }
+ }
+
+ private def hasInterestingPartition(plan: SparkPlan): Boolean = {
+ plan.requiredChildDistribution.exists {
+ case _: ClusteredDistribution | _: HashClusteredDistribution => true
+ case _ => false
+ }
+ }
+
+ private def isAllowedUnaryExecNode(plan: SparkPlan): Boolean = {
Review comment:
@viirya - this is good question. I agree we can be more bold and we
probably don't need a whitelist operators here, e.g. SMJ - shuffle - BHJ -
scan, SMJ - Shuffle - union - Scan (and another scan) should also work, but my
feeling is to start with more confidence change first and improve later. With a
whitelist operators here, we have a high confidence that this feature should
work without introducing regression, but much less confidence if we allow
arbitrary operators in the middle (at least for me). For now, to be honest, I
cannot find a case why arbitrary operators cannot work. But I want to play
safer in the beginning and any future improvement for this is much welcomed. cc
@cloud-fan and @maropu for thoughts.
Added a comment for now.
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##########
@@ -951,6 +951,14 @@ object SQLConf {
.checkValue(_ > 0, "the value of spark.sql.sources.bucketing.maxBuckets
must be greater than 0")
.createWithDefault(100000)
+ val AUTO_BUCKETED_SCAN_ENABLED =
+ buildConf("spark.sql.sources.bucketing.autoBucketedScan.enabled")
+ .doc("When true, decide whether to do bucketed scan on input tables
based on query plan " +
+ "automatically.")
+ .version("3.1.0")
+ .booleanConf
+ .createWithDefault(true)
Review comment:
@viirya - this config will not take any effect in that case, updated.
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##########
@@ -951,6 +951,14 @@ object SQLConf {
.checkValue(_ > 0, "the value of spark.sql.sources.bucketing.maxBuckets
must be greater than 0")
.createWithDefault(100000)
+ val AUTO_BUCKETED_SCAN_ENABLED =
+ buildConf("spark.sql.sources.bucketing.autoBucketedScan.enabled")
+ .doc("When true, decide whether to do bucketed scan on input tables
based on query plan " +
+ "automatically.")
+ .version("3.1.0")
Review comment:
@maropu - sure, updated.
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