viirya commented on a change in pull request #29066: URL: https://github.com/apache/spark/pull/29066#discussion_r539063039
########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2Writes.scala ########## @@ -0,0 +1,185 @@ +/* + * 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.datasources.v2 + +import java.util.UUID + +import org.apache.spark.SparkException +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst +import org.apache.spark.sql.catalyst.analysis.Resolver +import org.apache.spark.sql.catalyst.expressions.{NamedExpression, PredicateHelper, SortOrder} +import org.apache.spark.sql.catalyst.plans.logical.{AppendData, LogicalPlan, OverwriteByExpression, OverwritePartitionsDynamic, RepartitionByExpression, Sort} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.connector.catalog.Table +import org.apache.spark.sql.connector.distributions.{ClusteredDistribution, OrderedDistribution, UnspecifiedDistribution} +import org.apache.spark.sql.connector.expressions.{Expression, FieldReference, IdentityTransform, NullOrdering, SortDirection, SortValue} +import org.apache.spark.sql.connector.write.{LogicalWriteInfoImpl, RequiresDistributionAndOrdering, SupportsDynamicOverwrite, SupportsOverwrite, SupportsTruncate, Write, WriteBuilder} +import org.apache.spark.sql.execution.datasources.DataSourceStrategy +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.sources.{AlwaysTrue, Filter} + +/** + * A rule that constructs [[Write]]s. + * + * This rule does resolution in the optimizer because some nodes like [[OverwriteByExpression]] + * must undergo the expression optimization before we can construct a logical write. + */ +object V2Writes extends Rule[LogicalPlan] with PredicateHelper { + + import DataSourceV2Implicits._ + + override def apply(plan: LogicalPlan): LogicalPlan = plan transformDown { + case a @ AppendData(r: DataSourceV2Relation, query, options, _, None) => + val writeBuilder = newWriteBuilder(r.table, query, options) + val write = writeBuilder.build() + a.copy(write = Some(write), query = addDistributionAndOrdering(write, query)) + + case o @ OverwriteByExpression(r: DataSourceV2Relation, deleteExpr, query, options, _, None) => + // fail if any filter cannot be converted. correctness depends on removing all matching data. + val filters = splitConjunctivePredicates(deleteExpr).flatMap { p => + val filter = DataSourceStrategy.translateFilter(p, supportNestedPredicatePushdown = true) + if (filter.isEmpty) { + throw new AnalysisException(s"Cannot translate expression to source filter: $p") + } + filter + }.toArray + + val table = r.table + val writeBuilder = newWriteBuilder(table, query, options) + val write = writeBuilder match { + case builder: SupportsTruncate if isTruncate(filters) => + builder.truncate().build() + case builder: SupportsOverwrite => + builder.overwrite(filters).build() + case _ => + throw new SparkException(s"Table does not support overwrite by expression: $table") + } + + o.copy(write = Some(write), query = addDistributionAndOrdering(write, query)) + + case o @ OverwritePartitionsDynamic(r: DataSourceV2Relation, query, options, _, None) => + val table = r.table + val writeBuilder = newWriteBuilder(table, query, options) + val write = writeBuilder match { + case builder: SupportsDynamicOverwrite => + builder.overwriteDynamicPartitions().build() + case _ => + throw new SparkException(s"Table does not support dynamic partition overwrite: $table") + } + o.copy(write = Some(write), query = addDistributionAndOrdering(write, query)) + } + + private def newWriteBuilder( + table: Table, + query: LogicalPlan, + writeOptions: Map[String, String]): WriteBuilder = { + + val info = LogicalWriteInfoImpl( + queryId = UUID.randomUUID().toString, + query.schema, + writeOptions.asOptions) + table.asWritable.newWriteBuilder(info) + } + + private def isTruncate(filters: Array[Filter]): Boolean = { + filters.length == 1 && filters(0).isInstanceOf[AlwaysTrue] + } + + private def addDistributionAndOrdering( + write: Write, + query: LogicalPlan): LogicalPlan = write match { + + case write: RequiresDistributionAndOrdering => + val sqlConf = SQLConf.get + val resolver = sqlConf.resolver + + val distribution = write.requiredDistribution match { + case d: OrderedDistribution => + d.ordering.map(e => toCatalyst(e, query, resolver)) + case d: ClusteredDistribution => + d.clustering.map(e => toCatalyst(e, query, resolver)) + case _: UnspecifiedDistribution => + Array.empty[catalyst.expressions.Expression] + } + + val queryWithDistribution = if (distribution.nonEmpty) { + val numShufflePartitions = sqlConf.numShufflePartitions + // the conversion to catalyst expressions above produces SortOrder expressions + // for OrderedDistribution and generic expressions for ClusteredDistribution + // this allows RepartitionByExpression to pick either range or hash partitioning + RepartitionByExpression(distribution, query, numShufflePartitions) Review comment: Is it possible the required distribution be changed later by other optimization? The distribution requirement from data source is a hard requirement? Once if the distribution is changed and not matched the requirement, how will data source react to it? ########## File path: sql/catalyst/src/main/scala/org/apache/spark/sql/connector/expressions/expressions.scala ########## @@ -261,3 +340,20 @@ private[sql] object FieldReference { LogicalExpressions.parseReference(column) } } + +private[sql] final case class SortValue( Review comment: nit: `SortValue` sounds somehow confusing to me. Affected by the catalyst `SortOrder`, seems `SortOrder` sounds better. However you already define `SortOrder` as interface. Not strong option, can be ignored if you think it's okay. ########## File path: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2Writes.scala ########## @@ -0,0 +1,185 @@ +/* + * 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.datasources.v2 + +import java.util.UUID + +import org.apache.spark.SparkException +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst +import org.apache.spark.sql.catalyst.analysis.Resolver +import org.apache.spark.sql.catalyst.expressions.{NamedExpression, PredicateHelper, SortOrder} +import org.apache.spark.sql.catalyst.plans.logical.{AppendData, LogicalPlan, OverwriteByExpression, OverwritePartitionsDynamic, RepartitionByExpression, Sort} +import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.connector.catalog.Table +import org.apache.spark.sql.connector.distributions.{ClusteredDistribution, OrderedDistribution, UnspecifiedDistribution} +import org.apache.spark.sql.connector.expressions.{Expression, FieldReference, IdentityTransform, NullOrdering, SortDirection, SortValue} +import org.apache.spark.sql.connector.write.{LogicalWriteInfoImpl, RequiresDistributionAndOrdering, SupportsDynamicOverwrite, SupportsOverwrite, SupportsTruncate, Write, WriteBuilder} +import org.apache.spark.sql.execution.datasources.DataSourceStrategy +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.sources.{AlwaysTrue, Filter} + +/** + * A rule that constructs [[Write]]s. + * + * This rule does resolution in the optimizer because some nodes like [[OverwriteByExpression]] + * must undergo the expression optimization before we can construct a logical write. + */ +object V2Writes extends Rule[LogicalPlan] with PredicateHelper { + + import DataSourceV2Implicits._ + + override def apply(plan: LogicalPlan): LogicalPlan = plan transformDown { + case a @ AppendData(r: DataSourceV2Relation, query, options, _, None) => + val writeBuilder = newWriteBuilder(r.table, query, options) + val write = writeBuilder.build() + a.copy(write = Some(write), query = addDistributionAndOrdering(write, query)) + + case o @ OverwriteByExpression(r: DataSourceV2Relation, deleteExpr, query, options, _, None) => + // fail if any filter cannot be converted. correctness depends on removing all matching data. + val filters = splitConjunctivePredicates(deleteExpr).flatMap { p => + val filter = DataSourceStrategy.translateFilter(p, supportNestedPredicatePushdown = true) + if (filter.isEmpty) { + throw new AnalysisException(s"Cannot translate expression to source filter: $p") + } + filter + }.toArray + + val table = r.table + val writeBuilder = newWriteBuilder(table, query, options) + val write = writeBuilder match { + case builder: SupportsTruncate if isTruncate(filters) => + builder.truncate().build() + case builder: SupportsOverwrite => + builder.overwrite(filters).build() + case _ => + throw new SparkException(s"Table does not support overwrite by expression: $table") + } + + o.copy(write = Some(write), query = addDistributionAndOrdering(write, query)) + + case o @ OverwritePartitionsDynamic(r: DataSourceV2Relation, query, options, _, None) => + val table = r.table + val writeBuilder = newWriteBuilder(table, query, options) + val write = writeBuilder match { + case builder: SupportsDynamicOverwrite => + builder.overwriteDynamicPartitions().build() + case _ => + throw new SparkException(s"Table does not support dynamic partition overwrite: $table") + } + o.copy(write = Some(write), query = addDistributionAndOrdering(write, query)) + } + + private def newWriteBuilder( + table: Table, + query: LogicalPlan, + writeOptions: Map[String, String]): WriteBuilder = { + + val info = LogicalWriteInfoImpl( + queryId = UUID.randomUUID().toString, + query.schema, + writeOptions.asOptions) + table.asWritable.newWriteBuilder(info) + } + + private def isTruncate(filters: Array[Filter]): Boolean = { + filters.length == 1 && filters(0).isInstanceOf[AlwaysTrue] + } + + private def addDistributionAndOrdering( + write: Write, + query: LogicalPlan): LogicalPlan = write match { + + case write: RequiresDistributionAndOrdering => + val sqlConf = SQLConf.get + val resolver = sqlConf.resolver + + val distribution = write.requiredDistribution match { + case d: OrderedDistribution => + d.ordering.map(e => toCatalyst(e, query, resolver)) + case d: ClusteredDistribution => + d.clustering.map(e => toCatalyst(e, query, resolver)) + case _: UnspecifiedDistribution => + Array.empty[catalyst.expressions.Expression] + } + + val queryWithDistribution = if (distribution.nonEmpty) { + val numShufflePartitions = sqlConf.numShufflePartitions + // the conversion to catalyst expressions above produces SortOrder expressions + // for OrderedDistribution and generic expressions for ClusteredDistribution + // this allows RepartitionByExpression to pick either range or hash partitioning + RepartitionByExpression(distribution, query, numShufflePartitions) + } else { + query + } + + val ordering = write.requiredOrdering.toSeq + .map(e => toCatalyst(e, query, resolver)) + .asInstanceOf[Seq[catalyst.expressions.SortOrder]] + + val queryWithDistributionAndOrdering = if (ordering.nonEmpty) { + Sort(ordering, global = false, queryWithDistribution) + } else { + queryWithDistribution + } + + queryWithDistributionAndOrdering + case _ => + query + } + + private def toCatalyst( + expr: Expression, + query: LogicalPlan, + resolver: Resolver): catalyst.expressions.Expression = { + def resolve(ref: FieldReference): NamedExpression = { + // this part is controversial as we perform resolution in the optimizer + // we cannot perform this step in the analyzer since we need to optimize expressions + // in nodes like OverwriteByExpression before constructing a logical write Review comment: If we resolve it in the analyzer, cannot we optimize the resolved expressions later in the optimizer? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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