rdblue commented on a change in pull request #29066: URL: https://github.com/apache/spark/pull/29066#discussion_r500641525
########## File path: sql/core/src/test/scala/org/apache/spark/sql/connector/WriteDistributionAndOrderingSuite.scala ########## @@ -0,0 +1,594 @@ +/* + * 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.connector + +import java.util +import java.util.Collections + +import org.scalatest.BeforeAndAfter + +import org.apache.spark.sql.{catalyst, DataFrame, QueryTest} +import org.apache.spark.sql.catalyst.analysis.{TableAlreadyExistsException, UnresolvedAttribute} +import org.apache.spark.sql.catalyst.plans.physical +import org.apache.spark.sql.catalyst.plans.physical.{HashPartitioning, RangePartitioning, UnknownPartitioning} +import org.apache.spark.sql.connector.catalog.{Identifier, Table} +import org.apache.spark.sql.connector.distributions.{Distribution, Distributions} +import org.apache.spark.sql.connector.expressions.{Expression, FieldReference, NullOrdering, SortDirection, SortOrder, Transform} +import org.apache.spark.sql.connector.expressions.LogicalExpressions._ +import org.apache.spark.sql.connector.write.{BatchWrite, LogicalWriteInfo, RequiresDistributionAndOrdering, SupportsDynamicOverwrite, SupportsOverwrite, SupportsTruncate, Write, WriteBuilder} +import org.apache.spark.sql.execution.{QueryExecution, SortExec, SparkPlan} +import org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec +import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec +import org.apache.spark.sql.functions.lit +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.sources.Filter +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types.{IntegerType, StringType, StructType} +import org.apache.spark.sql.util.{CaseInsensitiveStringMap, QueryExecutionListener} + +class WriteDistributionAndOrderingSuite + extends QueryTest with SharedSparkSession with BeforeAndAfter { + + import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._ + + before { + spark.conf.set("spark.sql.catalog.testcat", classOf[ExtendedInMemoryTableCatalog].getName) + } + + after { + spark.sessionState.catalogManager.reset() + spark.sessionState.conf.clear() + } + + private val writeOperations = Seq("append", "overwrite", "overwriteDynamic") + + private val namespace = Array("ns1") + private val ident = Identifier.of(namespace, "test_table") + private val tableNameAsString = "testcat." + ident.toString + private val emptyProps = Collections.emptyMap[String, String] + private val schema = new StructType() + .add("id", IntegerType) + .add("data", StringType) + + writeOperations.foreach { operation => + test(s"ordered distribution and sort with same exprs ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.ordered(ordering) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + UnresolvedAttribute("data"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = RangePartitioning(writeOrdering, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"clustered distribution and sort with same exprs ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.DESCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val clustering = Array[Expression](FieldReference("data"), FieldReference("id")) + val distribution = Distributions.clustered(clustering) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + attr("data"), + catalyst.expressions.Descending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + attr("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val writePartitioningExprs = Seq(attr("data"), attr("id")) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = HashPartitioning(writePartitioningExprs, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"clustered distribution and sort with extended exprs ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.DESCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val clustering = Array[Expression](FieldReference("data")) + val distribution = Distributions.clustered(clustering) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + attr("data"), + catalyst.expressions.Descending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + attr("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val writePartitioningExprs = Seq(attr("data")) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = HashPartitioning(writePartitioningExprs, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"unspecified distribution and local sort ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.DESCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.unspecified() + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + attr("data"), + catalyst.expressions.Descending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val writePartitioning = UnknownPartitioning(0) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"unspecified distribution and no sort ($operation)") { + val ordering = Array.empty[SortOrder] + val distribution = Distributions.unspecified() + + val writeOrdering = Seq.empty[catalyst.expressions.SortOrder] + val writePartitioning = UnknownPartitioning(0) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"ordered distribution and sort with manual global sort ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.ordered(ordering) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + UnresolvedAttribute("data"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + UnresolvedAttribute("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = RangePartitioning(writeOrdering, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeTransform = df => df.orderBy("data", "id"), + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"ordered distribution and sort with incompatible global sort ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.ordered(ordering) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + UnresolvedAttribute("data"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + UnresolvedAttribute("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = RangePartitioning(writeOrdering, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeTransform = df => df.orderBy(df("data").desc, df("id").asc), + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"ordered distribution and sort with manual local sort ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.ordered(ordering) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + UnresolvedAttribute("data"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + UnresolvedAttribute("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = RangePartitioning(writeOrdering, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeTransform = df => df.sortWithinPartitions("data", "id"), + writeOperation = operation + ) + } + } + + // TODO: do we need to dedup repartitions too? RepartitionByExpr -> Projects -> RepartitionByExpr + writeOperations.foreach { operation => + ignore(s"ordered distribution and sort with manual repartition ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.ordered(ordering) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + UnresolvedAttribute("data"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + UnresolvedAttribute("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = RangePartitioning(writeOrdering, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeTransform = df => df.repartitionByRange(df("data"), df("id")), + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"clustered distribution and local sort with manual global sort ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.DESCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.clustered(Array(FieldReference("data"))) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + UnresolvedAttribute("data"), + catalyst.expressions.Descending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + UnresolvedAttribute("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val writePartitioningExprs = Seq(attr("data")) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = HashPartitioning(writePartitioningExprs, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeTransform = df => df.orderBy("data", "id"), + writeOperation = operation + ) + } + } + + writeOperations.foreach { operation => + test(s"clustered distribution and local sort with manual local sort ($operation)") { + val ordering = Array[SortOrder]( + sort(FieldReference("data"), SortDirection.DESCENDING, NullOrdering.NULLS_FIRST), + sort(FieldReference("id"), SortDirection.ASCENDING, NullOrdering.NULLS_FIRST) + ) + val distribution = Distributions.clustered(Array(FieldReference("data"))) + + val writeOrdering = Seq( + catalyst.expressions.SortOrder( + UnresolvedAttribute("data"), + catalyst.expressions.Descending, + catalyst.expressions.NullsFirst, + Set.empty + ), + catalyst.expressions.SortOrder( + UnresolvedAttribute("id"), + catalyst.expressions.Ascending, + catalyst.expressions.NullsFirst, + Set.empty + ) + ) + val writePartitioningExprs = Seq(attr("data")) + val numShufflePartitions = SQLConf.get.numShufflePartitions + val writePartitioning = HashPartitioning(writePartitioningExprs, numShufflePartitions) + + checkWriteRequirements( + tableDistribution = distribution, + tableOrdering = ordering, + expectedWritePartitioning = writePartitioning, + expectedWriteOrdering = writeOrdering, + writeTransform = df => df.orderBy("data", "id"), + writeOperation = operation + ) + } + } + + private def checkWriteRequirements( + tableDistribution: Distribution, + tableOrdering: Array[SortOrder], + expectedWritePartitioning: physical.Partitioning, + expectedWriteOrdering: Seq[catalyst.expressions.SortOrder], + writeTransform: DataFrame => DataFrame = df => df, + writeOperation: String = "append"): Unit = { + + catalog.createTable(ident, schema, Array.empty, emptyProps, tableDistribution, tableOrdering) + + val df = spark.createDataFrame(Seq((1L, "a"), (2L, "b"), (3L, "c"))).toDF("id", "data") + val writer = writeTransform(df).writeTo(tableNameAsString) + val executedPlan = writeOperation match { + case "append" => execute(writer.append()) + case "overwrite" => execute(writer.overwrite(lit(true))) + case "overwriteDynamic" => execute(writer.overwritePartitions()) + } + + checkPartitioningAndOrdering(executedPlan, expectedWritePartitioning, expectedWriteOrdering) + + checkAnswer(spark.table(tableNameAsString), df) + } + + private def checkPartitioningAndOrdering( + plan: SparkPlan, + partitioning: physical.Partitioning, + ordering: Seq[catalyst.expressions.SortOrder]): Unit = { + + val sorts = plan.collect { case s: SortExec => s } + assert(sorts.size <= 1, "must be at most one sort") + val shuffles = plan.collect { case s: ShuffleExchangeExec => s } + assert(shuffles.size <= 1, "must be at most one shuffle") + + val actualPartitioning = plan.outputPartitioning + val expectedPartitioning = partitioning match { + case p: physical.RangePartitioning => + val resolvedOrdering = p.ordering.map(resolveAttrs(_, plan)) + p.copy(ordering = resolvedOrdering.asInstanceOf[Seq[catalyst.expressions.SortOrder]]) + case p: physical.HashPartitioning => + val resolvedExprs = p.expressions.map(resolveAttrs(_, plan)) + p.copy(expressions = resolvedExprs) + case other => other + } + // TODO: can be compatible, does not have to match 100% + assert(actualPartitioning == expectedPartitioning, "partitioning must match") + + val actualOrdering = plan.outputOrdering + val expectedOrdering = ordering.map(resolveAttrs(_, plan)) + // TODO: can be compatible, does not have to match 100% + assert(actualOrdering == expectedOrdering, "ordering must match") + } + + private def resolveAttrs( + expr: catalyst.expressions.Expression, + plan: SparkPlan): catalyst.expressions.Expression = { + + expr.transform { + case UnresolvedAttribute(parts) => + val attrName = parts.mkString(",") + plan.output.find(a => a.name == attrName).get + } + } + + private def attr(name: String): UnresolvedAttribute = { + UnresolvedAttribute(name) + } + + private def catalog: ExtendedInMemoryTableCatalog = { + val catalog = spark.sessionState.catalogManager.catalog("testcat") + catalog.asTableCatalog.asInstanceOf[ExtendedInMemoryTableCatalog] + } + + // executes a write operation and keeps the executed physical plan + private def execute(writeFunc: => Unit): SparkPlan = { + var executedPlan: SparkPlan = null + + val listener = new QueryExecutionListener { + override def onSuccess(funcName: String, qe: QueryExecution, durationNs: Long): Unit = { + executedPlan = qe.executedPlan + } + override def onFailure(funcName: String, qe: QueryExecution, exception: Exception): Unit = { + } + } + spark.listenerManager.register(listener) + + writeFunc + + sparkContext.listenerBus.waitUntilEmpty() + + executedPlan.asInstanceOf[WriteToDataSourceV2Exec].query + } +} + +class ExtendedInMemoryTableCatalog extends InMemoryTableCatalog { + + import org.apache.spark.sql.connector.catalog.CatalogV2Implicits._ + + def createTable( + ident: Identifier, + schema: StructType, + partitions: Array[Transform], + properties: util.Map[String, String], + distribution: Distribution, + ordering: Array[SortOrder]): Table = { + + if (tables.containsKey(ident)) { + throw new TableAlreadyExistsException(ident) + } + + InMemoryTableCatalog.maybeSimulateFailedTableCreation(properties) + + val table = new ExtendedInMemoryTable( + s"$name.${ident.quoted}", schema, partitions, properties, distribution, ordering) + tables.put(ident, table) + namespaces.putIfAbsent(ident.namespace.toList, Map()) + table + } +} + +class ExtendedInMemoryTable( Review comment: Why not add the distribution and order to the existing `InMemoryTable` and set default args that disable sorting and distribution? 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