LuciferYang commented on code in PR #55579: URL: https://github.com/apache/spark/pull/55579#discussion_r4454064947
########## sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/RuntimePartitionPruningFileScan.scala: ########## @@ -0,0 +1,136 @@ +/* + * 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.Locale +import java.util.concurrent.atomic.AtomicReference + +import org.apache.spark.SparkException +import org.apache.spark.sql.catalyst.expressions.{And, Attribute, BoundReference, Expression, Predicate} +import org.apache.spark.sql.connector.expressions.{FieldReference, NamedReference} +import org.apache.spark.sql.connector.read.InputPartition +import org.apache.spark.sql.execution.datasources.FilePartition +import org.apache.spark.sql.internal.connector.SupportsRuntimeCatalystFiltering + +/** + * A [[FileScan]] that prunes its partition directories with runtime filters, the DPP filters and + * scalar subquery filters Spark derives over its partition columns (SPARK-30628). The built-in + * file formats mix this in; a [[FileScan]] that does not extend one of their scans keeps its + * previous behavior. + * + * The runtime-filter path returns plain [[FilePartition]]s packed from what `partitions` planned, + * so a scan that overrides `planInputPartitions()` or reports a partitioning or an ordering should + * not mix this in. + */ +trait RuntimePartitionPruningFileScan extends FileScan with SupportsRuntimeCatalystFiltering { + + @transient private lazy val plannedPartitionsRef = new AtomicReference[Seq[FilePartition]]() + + /** + * The partitions `partitions` planned, stored once per scan instance. Both the plain read path + * and the runtime-filter path go through this, so a scan node that gets runtime filters lists the + * files once and filters that listing, rather than reading the index twice and risking two + * snapshots. A subclass still customizes `partitions`. + * + * `partitions` runs outside the scan's monitor, which a `lazy val` would hold while it runs. Two + * threads asking first at once may both list; both get the result that was stored. + */ + private def plannedPartitions: Seq[FilePartition] = { + val ref = plannedPartitionsRef + Option(ref.get()).getOrElse { + ref.compareAndSet(null, partitions) + ref.get() + } + } + + override def planInputPartitions(): Array[InputPartition] = { + plannedPartitions.toArray + } + + /** + * The partition columns Spark can derive a runtime filter on, restricted to the ones + * `readSchema()` still exposes: a reference missing from the scan relation output fails to + * resolve, and a pushed-down aggregate keeps only the partition columns it groups by. + * + * A filter over one of them is evaluated against each file's partition values, so the scan + * evaluates it in full and Spark does not evaluate it again after the scan -- + * `FileScanBuilder.pushFilters` already keeps compile-time partition filters out of the post-scan + * filters for the same reason. + */ + override def filterAttributes(): Array[NamedReference] = { + val readFields = readSchema().fieldNames.map(normalizePartitionName).toSet + readPartitionSchema.fieldNames + .filter(name => readFields.contains(normalizePartitionName(name))) + .map(FieldReference.column) + } + + override def fullyPushedFilterAttributes(): Array[NamedReference] = filterAttributes() + + /** + * Plans the files whose partition values satisfy `expressions`, taken from what `partitions` + * planned and bin-packed again for the surviving set. + * + * Filtering what was planned rather than listing the files again keeps one listing per scan, and + * the files a subclass excluded in `partitions` stay excluded. How the survivors group into + * partitions is decided here again, for their own size. Where each file was cut into splits is + * not: `partitions` cut them for the whole set it listed, before these filters existed. + * + * A partition value is fixed within a file, so every row of every partition returned satisfies + * the expressions even though one partition can still hold files from several partition + * directories. + */ + override def planInputPartitionsWithRuntimeFilters( + expressions: Array[Expression]): Array[InputPartition] = { + val fieldIndex = readPartitionSchema.fieldNames.zipWithIndex + .map { case (name, i) => normalizePartitionName(name) -> i }.toMap + // Evaluating these is the scan's only chance to apply them, since the attributes are declared + // fully pushed and Spark drops the post-scan filter. A reference outside the read partition + // schema has no value to evaluate against, so fail loudly rather than return wrong rows. Spark + // screens against `filterAttributes()` before it gets here, which is a subset of that schema. + val notApplicable = expressions.filterNot( + _.references.forall(a => fieldIndex.contains(normalizePartitionName(a.name)))) + if (notApplicable.nonEmpty) { + throw SparkException.internalError("A file scan can only apply a runtime filter over its " + + s"read partition columns ${readPartitionSchema.fieldNames.mkString("[", ", ", "]")}, " + + s"got ${notApplicable.mkString(", ")}") + } + val bound = expressions.reduce(And).transform { + case a: Attribute => + BoundReference(fieldIndex(normalizePartitionName(a.name)), a.dataType, a.nullable) Review Comment: I'd rather keep the copy. Kyuubi's `HiveScan` extends `FileScan` and defines its own `private def normalizeName`. If the one in `FileScan` becomes `protected` or package-private, scalac 2.13 rejects that private method with "weaker access privileges in overriding", so Kyuubi would stop compiling. ########## sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/RuntimePartitionPruningFileScan.scala: ########## @@ -0,0 +1,136 @@ +/* + * 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.Locale +import java.util.concurrent.atomic.AtomicReference + +import org.apache.spark.SparkException +import org.apache.spark.sql.catalyst.expressions.{And, Attribute, BoundReference, Expression, Predicate} +import org.apache.spark.sql.connector.expressions.{FieldReference, NamedReference} +import org.apache.spark.sql.connector.read.InputPartition +import org.apache.spark.sql.execution.datasources.FilePartition +import org.apache.spark.sql.internal.connector.SupportsRuntimeCatalystFiltering + +/** + * A [[FileScan]] that prunes its partition directories with runtime filters, the DPP filters and + * scalar subquery filters Spark derives over its partition columns (SPARK-30628). The built-in + * file formats mix this in; a [[FileScan]] that does not extend one of their scans keeps its + * previous behavior. + * + * The runtime-filter path returns plain [[FilePartition]]s packed from what `partitions` planned, + * so a scan that overrides `planInputPartitions()` or reports a partitioning or an ordering should + * not mix this in. + */ +trait RuntimePartitionPruningFileScan extends FileScan with SupportsRuntimeCatalystFiltering { + + @transient private lazy val plannedPartitionsRef = new AtomicReference[Seq[FilePartition]]() + + /** + * The partitions `partitions` planned, stored once per scan instance. Both the plain read path + * and the runtime-filter path go through this, so a scan node that gets runtime filters lists the + * files once and filters that listing, rather than reading the index twice and risking two + * snapshots. A subclass still customizes `partitions`. + * + * `partitions` runs outside the scan's monitor, which a `lazy val` would hold while it runs. Two + * threads asking first at once may both list; both get the result that was stored. + */ + private def plannedPartitions: Seq[FilePartition] = { + val ref = plannedPartitionsRef + Option(ref.get()).getOrElse { + ref.compareAndSet(null, partitions) + ref.get() + } + } + + override def planInputPartitions(): Array[InputPartition] = { + plannedPartitions.toArray + } + + /** + * The partition columns Spark can derive a runtime filter on, restricted to the ones + * `readSchema()` still exposes: a reference missing from the scan relation output fails to + * resolve, and a pushed-down aggregate keeps only the partition columns it groups by. + * + * A filter over one of them is evaluated against each file's partition values, so the scan + * evaluates it in full and Spark does not evaluate it again after the scan -- + * `FileScanBuilder.pushFilters` already keeps compile-time partition filters out of the post-scan + * filters for the same reason. + */ + override def filterAttributes(): Array[NamedReference] = { + val readFields = readSchema().fieldNames.map(normalizePartitionName).toSet + readPartitionSchema.fieldNames + .filter(name => readFields.contains(normalizePartitionName(name))) + .map(FieldReference.column) + } + + override def fullyPushedFilterAttributes(): Array[NamedReference] = filterAttributes() + + /** + * Plans the files whose partition values satisfy `expressions`, taken from what `partitions` + * planned and bin-packed again for the surviving set. + * + * Filtering what was planned rather than listing the files again keeps one listing per scan, and + * the files a subclass excluded in `partitions` stay excluded. How the survivors group into + * partitions is decided here again, for their own size. Where each file was cut into splits is + * not: `partitions` cut them for the whole set it listed, before these filters existed. + * + * A partition value is fixed within a file, so every row of every partition returned satisfies + * the expressions even though one partition can still hold files from several partition + * directories. + */ + override def planInputPartitionsWithRuntimeFilters( + expressions: Array[Expression]): Array[InputPartition] = { + val fieldIndex = readPartitionSchema.fieldNames.zipWithIndex + .map { case (name, i) => normalizePartitionName(name) -> i }.toMap + // Evaluating these is the scan's only chance to apply them, since the attributes are declared + // fully pushed and Spark drops the post-scan filter. A reference outside the read partition + // schema has no value to evaluate against, so fail loudly rather than return wrong rows. Spark + // screens against `filterAttributes()` before it gets here, which is a subset of that schema. + val notApplicable = expressions.filterNot( + _.references.forall(a => fieldIndex.contains(normalizePartitionName(a.name)))) + if (notApplicable.nonEmpty) { + throw SparkException.internalError("A file scan can only apply a runtime filter over its " + + s"read partition columns ${readPartitionSchema.fieldNames.mkString("[", ", ", "]")}, " + + s"got ${notApplicable.mkString(", ")}") + } + val bound = expressions.reduce(And).transform { + case a: Attribute => + BoundReference(fieldIndex(normalizePartitionName(a.name)), a.dataType, a.nullable) + } + val predicate = Predicate.createInterpreted(bound) + val keptFiles = plannedPartitions + .flatMap(_.files.filter(file => predicate.eval(file.partitionValues))) + .sortBy(_.length)(implicitly[Ordering[Long]].reverse) + // Pack the survivors instead of handing back the planned partitions with files removed: + // `partitions` sized those bins for the whole file set, so a scan that pruned most of its files + // would run the same few tasks over a fraction of the data. V1 packs the pruned set the same + // way, from `dynamicallySelectedPartitions`. + val openCostInBytes = conf.filesOpenCostInBytes + val maxSplitBytes = FilePartition.maxSplitBytes( + sparkSession, keptFiles.map(_.length + openCostInBytes).sum) + FilePartition.getFilePartitions(sparkSession, keptFiles, maxSplitBytes).toArray + } + + // Read when the scan is built, as `FileScan` reads it for the names `partitions` matches, so the + // runtime filters bind the same way even if the session's setting changes before they arrive. + private val isCaseSensitive = conf.caseSensitiveAnalysis + + private def normalizePartitionName(name: String): String = { + if (isCaseSensitive) name else name.toLowerCase(Locale.ROOT) + } Review Comment: Done in e721ae7. "DPP and a scalar subquery prune input partitions at runtime for v2 text" uses its own single-column text fixture, and `AvroV2Suite` has "SPARK-30628: Avro source v2: runtime filters prune partitions". Both check that only partition 7 is read. ########## sql/core/src/test/scala/org/apache/spark/sql/connector/DataSourceV2FileSourceDPPSuite.scala: ########## @@ -0,0 +1,787 @@ +/* + * 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.io.File + +import scala.collection.mutable + +import org.apache.spark.{SparkException, SparkThrowable} +import org.apache.spark.sql.{DataFrame, QueryTest, Row} +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, DynamicPruningExpression, DynamicPruningSubquery, EqualTo, Expression, Literal} +import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan} +import org.apache.spark.sql.connector.read.InputPartition +import org.apache.spark.sql.execution.FilterExec +import org.apache.spark.sql.execution.adaptive.{AdaptiveSparkPlanHelper, DisableAdaptiveExecutionSuite, EnableAdaptiveExecutionSuite} +import org.apache.spark.sql.execution.datasources.FilePartition +import org.apache.spark.sql.execution.datasources.v2.{BatchScanExec, DataSourceV2ScanRelation, FileScan, RuntimePartitionPruningFileScan} +import org.apache.spark.sql.execution.datasources.v2.parquet.ParquetScan +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSparkSession +import org.apache.spark.sql.types.{IntegerType, LongType} + +/** + * Tests for SPARK-30628: subquery partition pruning and DPP for V2 file sources. + * + * These tests exercise V2 file scans (e.g. ParquetScan) -- distinct from the in-memory V2 + * catalog used by [[org.apache.spark.sql.DynamicPartitionPruningV2Suite]] and from the V2 + * iterative-pushdown work covered by [[DataSourceV2EnhancedRuntimePartitionFilterSuite]]. + */ +abstract class DataSourceV2FileSourceDPPSuiteBase extends QueryTest + with SharedSparkSession + with AdaptiveSparkPlanHelper { + + // Standard V2-only conf for DPP tests on file sources. AQE is set by the concrete subclasses, + // which mix in DisableAdaptiveExecutionSuite / EnableAdaptiveExecutionSuite: the DPP subquery is + // planned by PlanDynamicPruningFilters without AQE and by PlanAdaptiveDynamicPruningFilters with + // it, and those are two separate rules. + private def withDppV2Conf[T](thunk: => T): T = { + withSQLConf( + SQLConf.USE_V1_SOURCE_LIST.key -> "", + SQLConf.DYNAMIC_PARTITION_PRUNING_ENABLED.key -> "true", + SQLConf.DYNAMIC_PARTITION_PRUNING_REUSE_BROADCAST_ONLY.key -> "false", + SQLConf.DYNAMIC_PARTITION_PRUNING_FALLBACK_FILTER_RATIO.key -> "2")(thunk) + } + + // Writes a partitioned `fact` and an unpartitioned `dim` table in `format` and registers them + // as temp views. `fact` has 100 rows across 10 partitions (10 rows each); `dim` has 10 rows + // with `dim_id` and `dim_val` both in [0, 10). The read schema is given explicitly because csv + // infers strings, and a cast on the join key changes whether DPP fires at all. + private def writeFactAndDim(dir: File, format: String = "parquet"): Unit = { + val factPath = new File(dir, "fact").getCanonicalPath + val dimPath = new File(dir, "dim").getCanonicalPath + spark.range(100) + .selectExpr("id", "id % 10 AS part") + .write.format(format).partitionBy("part").save(factPath) + spark.read.format(format).schema("id long, part int").load(factPath) + .createOrReplaceTempView("fact") + spark.range(10) + .selectExpr("id AS dim_id", "id AS dim_val") + .write.format(format).save(dimPath) + spark.read.format(format).schema("dim_id long, dim_val long").load(dimPath) + .createOrReplaceTempView("dim") + } + + // The BatchScanExec reading `fact`, the only partitioned table of the two. The lookup descends + // into AQE query stages, so it finds the scan whichever stage it ended up in. + private def factScanOf(df: DataFrame): BatchScanExec = { + val executedPlan = df.queryExecution.executedPlan + collectFirst(executedPlan) { + case b: BatchScanExec if b.scan.isInstanceOf[FileScan] && + b.scan.asInstanceOf[FileScan].readPartitionSchema.nonEmpty => b + }.getOrElse(fail("no fact BatchScanExec found in:\n" + executedPlan.treeString)) + } + + private def fileScanOf(df: DataFrame): RuntimePartitionPruningFileScan = + factScanOf(df).scan.asInstanceOf[RuntimePartitionPruningFileScan] + + private def collectDppFilters(plan: LogicalPlan): Seq[Expression] = { + plan.collect { + case f: Filter if f.condition.exists(_.isInstanceOf[DynamicPruningSubquery]) => f.condition + } + } + + test("DPP inserts DynamicPruningSubquery for V2 parquet partitioned table") { + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql( + """SELECT f.id FROM fact f JOIN dim d + |ON f.part = d.dim_id WHERE d.dim_val = 7""".stripMargin) + val optimized = df.queryExecution.optimizedPlan + assert(collectDppFilters(optimized).nonEmpty, + "expected DynamicPruningSubquery in optimized plan over V2 parquet, got plan:\n" + + optimized.treeString) + } + } + } + + test("DPP does not fire when join key is a data column (non-partition)") { + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + // Join on f.id (data column), not f.part (partition column). `f.part` stays in the + // projection so the fact scan still reads a partition column: without it the scan declares + // nothing filterable for a reason that has nothing to do with the join key. + val df = sql( + """SELECT f.id, f.part FROM fact f JOIN dim d + |ON f.id = d.dim_id WHERE d.dim_val = 7""".stripMargin) + val optimized = df.queryExecution.optimizedPlan + assert(collectDppFilters(optimized).isEmpty, + "DPP should not fire on non-partition join keys, got plan:\n" + + optimized.treeString) + assert(df.collect().length === 1) + // And the scan is one that could have been pruned, had the join key been its partition + // column: a filterAttributes() reporting every read column would make DPP fire above, and + // the fact scan would read fewer than all 100 rows. + assert(fileScanOf(df).filterAttributes().flatMap(_.fieldNames).toSeq === Seq("part")) + val numOutputRows = factScanOf(df).metrics("numOutputRows").value + assert(numOutputRows === 100, + s"expected the fact scan to read every row, got $numOutputRows") + } + } + } + + test("DPP fires when partitioned fact is on the right side of the join") { + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + // dim on left, fact on right; join on partition column + val df = sql( + """SELECT f.id FROM dim d JOIN fact f + |ON d.dim_id = f.part WHERE d.dim_val = 7""".stripMargin) + val optimized = df.queryExecution.optimizedPlan + assert(collectDppFilters(optimized).nonEmpty, + "expected DPP on right-side partitioned fact, got plan:\n" + + optimized.treeString) + checkAnswer(df, (0 until 10).map(i => Row(i * 10 + 7))) + val numOutputRows = factScanOf(df).metrics("numOutputRows").value + assert(numOutputRows === 10, + s"expected the fact scan to read only partition 7, got $numOutputRows rows") + } + } + } + + test("DPP filter reaches BatchScanExec.runtimeFilters as DynamicPruningExpression") { + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql( + """SELECT f.id FROM fact f JOIN dim d + |ON f.part = d.dim_id WHERE d.dim_val = 7""".stripMargin) + df.collect() + val factScan = factScanOf(df) + // A DPP filter that degraded to true is also a DynamicPruningExpression, so match on the + // payload: only a subquery child means the filter can actually prune anything. + val dppFilters = factScan.runtimeFilters.collect { + case d @ DynamicPruningExpression(child) if !child.isInstanceOf[Literal] => d + } + assert(dppFilters.nonEmpty, + "expected the fact scan's runtimeFilters to carry a DynamicPruningExpression over a " + + s"subquery, got ${factScan.runtimeFilters}") + } + } + } + + test("DPP prunes input partitions at runtime for v2 parquet") { + // Fact has 100 rows across 10 partitions (10 rows each). DPP filter d.dim_val = 7 selects + // exactly partition 7 -> 10 rows. Without runtime pruning, all 100 rows are read. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql( + """SELECT f.id FROM fact f JOIN dim d + |ON f.part = d.dim_id WHERE d.dim_val = 7""".stripMargin) + val rows = df.collect() + // Correctness: 10 fact rows match (partition 7), each multiplied by 1 dim row. + assert(rows.length == 10, + s"expected 10 rows after join+filter, got ${rows.length}") + // Pruning: fact scan should have read only the 10 rows in partition 7. + val numOutputRows = factScanOf(df).metrics("numOutputRows").value + assert(numOutputRows == 10, + s"expected fact scan to read 10 rows after DPP, got $numOutputRows. plan:\n" + + df.queryExecution.executedPlan.treeString) + } + } + } + + test("DPP that prunes every partition leaves the scan with no input partition") { + // `fact5` has partitions 0..4 while the dim row selects 7, so the build side is not empty and + // the surviving partition set is. That combination is what reaches the zero-partition + // DataSourceRDD: BatchScanExec's EmptyRDDWithPartitions branch keys off SinglePartition, which + // a file scan never reports. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val factPath = new File(dir, "fact5").getCanonicalPath + spark.range(50).selectExpr("id", "id % 5 AS part") + .write.format("parquet").partitionBy("part").save(factPath) + spark.read.format("parquet").schema("id long, part int").load(factPath) + .createOrReplaceTempView("fact5") + val df = sql( + """SELECT f.id FROM fact5 f JOIN dim d + |ON f.part = d.dim_id WHERE d.dim_val = 7""".stripMargin) + assert(df.collect().isEmpty) + val factScan = factScanOf(df) + assert(factScan.filteredPartitions.flatten.isEmpty, + s"expected no input partition, got ${factScan.filteredPartitions.flatten.size}") + // What the assertion above does not see: BatchScanExec turning an empty partition set into + // an RDD with one empty partition still satisfies it. + assert(factScan.inputRDD.partitions.isEmpty, + s"expected an RDD with no partition, got ${factScan.inputRDD.partitions.length}") + val numOutputRows = factScan.metrics("numOutputRows").value + assert(numOutputRows == 0, + s"expected the fact scan to read nothing, got $numOutputRows rows") + } + } + } + + test("a DPP filter a union branch cannot apply is dropped instead of failing the query") { + // PartitionPruning resolves a union key through the first branch only, and the filter it + // inserts is then pushed positionally into every branch. So the unpartitioned branch's scan + // is offered a filter over a column it never declared filterable. Dropping it costs nothing, + // since that branch cannot prune on it and the join re-applies it; letting it through means an + // INTERNAL_ERROR from a query that runs today. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val otherPath = new File(dir, "fact_other").getCanonicalPath + spark.range(100).selectExpr("id", "id % 10 AS other") + .write.format("parquet").save(otherPath) + spark.read.format("parquet").schema("id long, other long").load(otherPath) + .createOrReplaceTempView("fact_other") + val df = sql( + """SELECT u.id FROM ( + | SELECT id, part FROM fact UNION ALL SELECT id, other FROM fact_other + |) u JOIN dim d ON u.part = d.dim_id WHERE d.dim_val = 7""".stripMargin) + val expected = (0 until 10).map(i => Row(i * 10 + 7)) + checkAnswer(df, expected ++ expected) + // The filter did reach the branch that cannot use it in the optimized plan, which is what + // makes this a regression test rather than a query that happens to work. + val optimizedBranchFilters = df.queryExecution.optimizedPlan.collect { + case f: Filter if f.condition.exists(_.isInstanceOf[DynamicPruningSubquery]) && + f.child.output.exists(_.name == "other") => f + } + assert(optimizedBranchFilters.size == 1, + "expected the DPP filter over the branch that cannot apply it, got plan:\n" + + df.queryExecution.optimizedPlan.treeString) + // The strategy dropped it at planning, so the scan of that branch has no runtime filter. + val otherScans = collect(df.queryExecution.executedPlan) { + case b: BatchScanExec if b.output.exists(_.name == "other") => b + } + assert(otherScans.size == 1, s"expected one scan of fact_other, got $otherScans") + assert(otherScans.head.runtimeFilters.isEmpty, + s"expected no runtime filter on fact_other, got ${otherScans.head.runtimeFilters}") + // And the branch that can use it still pruned. + val numOutputRows = factScanOf(df).metrics("numOutputRows").value + assert(numOutputRows == 10, + s"expected the partitioned branch to read 10 rows, got $numOutputRows") + } + } + } + + test("a partition value the runtime filter cannot evaluate fails the query") { + // A partition value the filter cannot be evaluated against has to fail the query: keeping the + // file would return rows that nothing filters, since the filter is declared fully pushed. What + // this pins is that the failure is not swallowed. The exposure is the same one a compile-time + // partition filter over the same column has, and it is why this path must not gain the + // eval-failure tolerance the iterative PartitionPredicate path has. That no FilterExec is left + // above the scan is pinned separately. + withDppV2Conf { + withSQLConf(SQLConf.ANSI_ENABLED.key -> "true") { + withTempDir { dir => + writeFactAndDim(dir) + val strPath = new File(dir, "fact_str").getCanonicalPath + spark.range(10) + .selectExpr("id", "CASE WHEN id = 3 THEN 'abc' ELSE CAST(id AS STRING) END AS part") + .write.format("parquet").partitionBy("part").save(strPath) + spark.read.format("parquet").schema("id long, part string").load(strPath) + .createOrReplaceTempView("fact_str") + val e = intercept[SparkThrowable] { + sql( + """SELECT f.id FROM fact_str f + |WHERE f.part = (SELECT max(dim_id) FROM dim WHERE dim_val = 7)""".stripMargin) + .collect() + } + assert(e.getCondition === "CAST_INVALID_INPUT", + s"expected the failing cast to surface, got ${e.getCondition}") + } + } + } + } + + test("a subclass that narrows the file set in partitions keeps it under a runtime filter") { + // The runtime-filter path filters the files `partitions` returned, so a file an override of it + // drops stays dropped. Listing the files again instead would hand it back as soon as a filter + // fired, and with the filter declared fully pushed nothing above the scan would remove it. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql("SELECT f.id, f.part FROM fact f") + df.collect() + val scan = fileScanOf(df).asInstanceOf[ParquetScan] + // Keeps only partition directory 3's files and counts how often the scan asks for its + // partitions. + var partitionsCalls = 0 + val narrowed = new ParquetScan(scan.sparkSession, scan.hadoopConf, scan.fileIndex, + scan.dataSchema, scan.readDataSchema, scan.readPartitionSchema, scan.pushedFilters, + scan.options, scan.pushedAggregate, scan.partitionFilters, scan.dataFilters, + scan.pushedVariantExtractions) { + override protected def partitions: Seq[FilePartition] = { + partitionsCalls += 1 + super.partitions.map { part => + part.copy(files = part.files.filter(_.partitionValues.getInt(0) == 3)) + }.filter(_.files.nonEmpty) + } + } + def partValues(parts: Array[InputPartition]): Seq[Int] = + parts.flatMap(_.asInstanceOf[FilePartition].files.map(_.partitionValues.getInt(0))) + .distinct.toSeq + val partAttr = AttributeReference("part", IntegerType)() + // The plain read path first, as planning does through `supportsColumnar`. + assert(partValues(narrowed.planInputPartitions()) === Seq(3)) + // A filter selecting a directory the override dropped comes back with nothing, and the + // calls after it, plain and filtered, still find the directory it kept: no call narrows + // what the next one starts from. + assert(narrowed.planInputPartitionsWithRuntimeFilters( + Array(EqualTo(partAttr, Literal(7)))).isEmpty) + assert(partValues(narrowed.planInputPartitions()) === Seq(3)) + assert(partValues(narrowed.planInputPartitionsWithRuntimeFilters( + Array(EqualTo(partAttr, Literal(3))))) === Seq(3)) + // All four calls above shared one listing, which is what keeps the plan and the execution + // on one snapshot of the file index. + assert(partitionsCalls === 1, s"expected one listing, got $partitionsCalls") + } + } + } + + test("partitions runs without holding the scan's monitor") { + // An override of `partitions` may hand work to another thread and wait for it. If the memo + // held the scan's monitor meanwhile, that thread would block when it tried to initialize one + // of the scan's lazy vals. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql("SELECT f.id, f.part FROM fact f") + df.collect() + val scan = fileScanOf(df).asInstanceOf[ParquetScan] + val partAttr = AttributeReference("part", IntegerType)() + Seq[ParquetScan => Array[InputPartition]]( + _.planInputPartitions(), + _.planInputPartitionsWithRuntimeFilters(Array(EqualTo(partAttr, Literal(3)))) + ).foreach { firstCall => + val heldLock = mutable.ArrayBuffer.empty[Boolean] + val probe = new ParquetScan(scan.sparkSession, scan.hadoopConf, scan.fileIndex, + scan.dataSchema, scan.readDataSchema, scan.readPartitionSchema, scan.pushedFilters, + scan.options, scan.pushedAggregate, scan.partitionFilters, scan.dataFilters, + scan.pushedVariantExtractions) { + override protected def partitions: Seq[FilePartition] = { + heldLock += Thread.holdsLock(this) + super.partitions + } + } + firstCall(probe) + assert(heldLock.toSeq === Seq(false)) + } + } + } + } + + test("scalar subquery on partition column of v2 parquet prunes partitions") { + // Scalar subquery (SELECT max(dim_id) FROM dim WHERE dim_val = 7) evaluates to 7. + // WHERE f.part = 7 should select exactly partition 7 -> 10 rows. Without partition + // pruning, the scan reads all 100 rows and the post-filter keeps 10. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql( + """SELECT f.id FROM fact f + |WHERE f.part = (SELECT max(dim_id) FROM dim WHERE dim_val = 7)""".stripMargin) + val rows = df.collect() + assert(rows.length == 10, + s"expected 10 rows after scalar-subquery filter, got ${rows.length}") + val numOutputRows = factScanOf(df).metrics("numOutputRows").value + assert(numOutputRows == 10, + s"expected fact scan to read 10 rows after scalar-subquery pruning, got " + + s"$numOutputRows. plan:\n" + df.queryExecution.executedPlan.treeString) + } + } + } + + test("filterAttributes reports the read partition columns") { + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql("SELECT f.id, f.part FROM fact f") + df.collect() + val declared = fileScanOf(df).filterAttributes().flatMap(_.fieldNames).toSeq + assert(declared == Seq("part"), + s"expected the partition column to be declared runtime-filterable, got $declared") + } + } + } + + test("filterAttributes reports nothing once a pushed aggregate drops the partition columns") { + // With an aggregate pushed down, ParquetScan.readSchema() returns only the aggregate schema, so + // the partition columns are not in the scan relation output. Spark resolves every declared + // runtime-filter attribute against that output and fails the query if one is missing, so + // filterAttributes() must not report them. + withDppV2Conf { + withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true") { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql("SELECT count(part) FROM fact") + checkAnswer(df, Row(100)) + val declared = fileScanOf(df).filterAttributes().flatMap(_.fieldNames).toSeq + assert(declared.isEmpty, + s"expected no runtime-filterable attribute under a pushed aggregate, got $declared") + } + } + } + } + + test("filterAttributes keeps a partition column a pushed aggregate groups by") { + // Aggregate pushdown only happens when the GROUP BY set covers every partition column, and + // those columns go into the pushed-down schema, so they stay in the scan relation output and + // stay runtime-filterable. This is the other side of the intersection from the test above. + withDppV2Conf { + withSQLConf(SQLConf.PARQUET_AGGREGATE_PUSHDOWN_ENABLED.key -> "true") { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql("SELECT part, count(*) FROM fact GROUP BY part") + checkAnswer(df, (0 until 10).map(p => Row(p, 10))) + val scan = fileScanOf(df) + // Without the pushdown the assertion below says nothing: `part` is in the read partition + // schema either way. Pin that the aggregate really went into the scan. + assert(scan.asInstanceOf[ParquetScan].pushedAggregate.isDefined, + s"expected the aggregate to be pushed into the scan, got ${scan.description()}") + val declared = scan.filterAttributes().flatMap(_.fieldNames).toSeq + assert(declared == Seq("part"), + s"expected the group-by partition column to stay declared, got $declared") + } + } + } + } + + test("scalar subquery on a partition column is not re-evaluated after the scan") { + // The file scan declares its partition columns in fullyPushedFilterAttributes: applying such a + // filter selects partition directories, so every row the scan returns satisfies it and Spark + // drops the post-scan FilterExec, the same treatment a compile-time partition filter gets. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql( + """SELECT f.id FROM fact f + |WHERE f.part = (SELECT max(dim_id) FROM dim WHERE dim_val = 7)""".stripMargin) + val rows = df.collect() + assert(rows.length == 10, s"expected 10 rows, got ${rows.length}") + val executedPlan = df.queryExecution.executedPlan + assert(collect(executedPlan) { case f: FilterExec => f }.isEmpty, + "a fully pushed partition filter should leave no FilterExec above the scan, got:\n" + + executedPlan.treeString) + } + } + } + + test("a runtime filter is ANDed with the compile-time partition filters, not substituted") { + // The two halves are applied in different places: `partitions` puts `partitionFilters` through + // `fileIndex.listFiles`, and `planInputPartitionsWithRuntimeFilters` filters what that planned. + // Both are the scan's only evaluator: FileScanBuilder.pushFilters returns just the data filters + // as post-scan filters, and a partition-column runtime filter is declared fully pushed, so + // dropping either half returns rows that nothing filters out. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + // min(dim_id) over dim_val > 6 is 7, so this keeps partitions 7 and 8: 7 from the runtime + // filter's lower bound, 8 from the compile-time upper bound. + val df = sql( + """SELECT f.id FROM fact f + |WHERE f.part <= 8 + | AND f.part >= (SELECT min(dim_id) FROM dim WHERE dim_val > 6)""".stripMargin) + checkAnswer(df, (0 until 10).flatMap(i => Seq(Row(i * 10 + 7), Row(i * 10 + 8)))) + val numOutputRows = factScanOf(df).metrics("numOutputRows").value + assert(numOutputRows == 20, + s"expected the fact scan to read only partitions 7 and 8, got $numOutputRows rows") + } + } + } + + test("runtime partition pruning survives a mixed-case partition column reference") { + // `f.PART` is resolved to the schema's `part` before it ever becomes a runtime filter, so this + // pins the end-to-end path rather than any name matching: what the scan receives is identical + // to the lower-case query's. The test below pins how the scan matches the name. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql( + """SELECT f.id FROM fact f + |WHERE f.PART = (SELECT max(dim_id) FROM dim WHERE dim_val = 7)""".stripMargin) + val rows = df.collect() + assert(rows.length == 10, s"expected 10 rows, got ${rows.length}") + val numOutputRows = factScanOf(df).metrics("numOutputRows").value + assert(numOutputRows == 10, + s"expected the fact scan to read 10 rows, got $numOutputRows") + } + } + } + + test("a runtime filter the scan cannot apply is rejected rather than silently dropped") { + // Unreachable through SQL: PushDownUtils screens a filter against the attributes the scan + // declared before handing it over. Calling the scan directly is what pins the guard, which has + // to fail rather than drop the filter, since a fully pushed predicate dropped here would be + // evaluated nowhere. The reference is matched the way the rest of the scan matches partition + // column names, so a mixed-case reference binds rather than being rejected. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql("SELECT f.id, f.part FROM fact f") + df.collect() + val scan = fileScanOf(df) + val e = intercept[SparkException] { + scan.planInputPartitionsWithRuntimeFilters( + Array(EqualTo(AttributeReference("id", LongType)(), Literal(1L)))) + } + assert(e.getCondition === "INTERNAL_ERROR") + assert(e.getMessage.contains("can only apply a runtime filter over its read partition"), + s"unexpected message: ${e.getMessage}") + + val mixedCase = scan.planInputPartitionsWithRuntimeFilters( + Array(EqualTo(AttributeReference("PART", IntegerType)(), Literal(7)))) + assert(mixedCase.flatMap( + _.asInstanceOf[FilePartition].files.map(_.partitionValues.getInt(0))).distinct.toSeq + === Seq(7)) + } + } + } + + test("a fully pushed filter stays exact when one FilePartition spans partition directories") { + // The fully-pushed contract used to ask for the attribute's value to be fixed within every + // InputPartition. A file scan does not satisfy that: FilePartition packs by size and can hold + // files from several partition directories. What makes the declaration sound is that the + // predicate chose those directories, so every row in the partition satisfies it even though the + // value varies. Pack the surviving directories into one FilePartition to exercise that. + withDppV2Conf { + withSQLConf( + SQLConf.FILES_MAX_PARTITION_BYTES.key -> "1g", + SQLConf.FILES_MIN_PARTITION_NUM.key -> "1", + SQLConf.FILES_OPEN_COST_IN_BYTES.key -> "0") { + withTempDir { dir => + writeFactAndDim(dir) + // min(dim_id) over dim_val > 7 is 8, so this keeps partitions 8 and 9. + val df = sql( + """SELECT f.id FROM fact f + |WHERE f.part >= (SELECT min(dim_id) FROM dim WHERE dim_val > 7)""".stripMargin) + checkAnswer(df, (0 until 10).flatMap(i => Seq(Row(i * 10 + 8), Row(i * 10 + 9)))) + val factScan = factScanOf(df) + assert(factScan.filteredPartitions.flatten.size == 1, + "expected both surviving partition directories to pack into one FilePartition, got " + + s"${factScan.filteredPartitions.flatten.size}") + val numOutputRows = factScan.metrics("numOutputRows").value + assert(numOutputRows == 20, + s"expected that one FilePartition to carry both directories' 20 rows, got " + + s"$numOutputRows") + assert(collect(df.queryExecution.executedPlan) { case f: FilterExec => f }.isEmpty, + "expected the partition filter to be fully pushed, leaving no FilterExec") + } + } + } + } + + test("runtime pruning packs the surviving files for their own size") { + // `partitions` sized its bins for the whole file set, so handing those bins back with files + // removed leaves a heavily pruned scan running the few tasks the unpruned set needed. Selecting + // the same partitions at compile time packs for the pruned set, so the two layouts must agree. + withDppV2Conf { + withSQLConf( + SQLConf.FILES_MAX_PARTITION_BYTES.key -> "1g", + SQLConf.FILES_MIN_PARTITION_NUM.key -> "3", + SQLConf.FILES_OPEN_COST_IN_BYTES.key -> "0") { + withTempDir { dir => + writeFactAndDim(dir) + val expected = (0 until 10).flatMap(i => Seq(Row(i * 10 + 8, 8), Row(i * 10 + 9, 9))) + // min(dim_id) over dim_val > 7 is 8, so this keeps partitions 8 and 9. + val runtime = sql( + """SELECT f.id, f.part FROM fact f + |WHERE f.part >= (SELECT min(dim_id) FROM dim WHERE dim_val > 7)""".stripMargin) + checkAnswer(runtime, expected) + val compileTime = sql("SELECT f.id, f.part FROM fact f WHERE f.part >= 8") + checkAnswer(compileTime, expected) + // What gives the comparison teeth: the whole file set packs two or more of the surviving + // files into one bin, so handing those bins back with the other files removed cannot + // produce as many partitions as packing the survivors does. That needs the fixture to + // write more than one file per partition directory, and this is the assertion that fails + // if it stops doing so. + val unpruned = sql("SELECT f.id, f.part FROM fact f") + unpruned.collect() + val unprunedPartitions = factScanOf(unpruned).filteredPartitions.flatten + .map(_.asInstanceOf[FilePartition]) + val binsHoldingSurvivors = + unprunedPartitions.count(_.files.exists(_.partitionValues.getInt(0) >= 8)) + val survivorsWhenUnpruned = + unprunedPartitions.flatMap(_.files).count(_.partitionValues.getInt(0) >= 8) + assert(binsHoldingSurvivors < survivorsWhenUnpruned, + s"expected the whole set to pack $survivorsWhenUnpruned surviving files into fewer " + + s"than $survivorsWhenUnpruned bins, got $binsHoldingSurvivors") + val compileTimePartitions = factScanOf(compileTime).filteredPartitions.flatten + .map(_.asInstanceOf[FilePartition]) + val runtimePartitions = factScanOf(runtime).filteredPartitions.flatten + .map(_.asInstanceOf[FilePartition]) + val runtimeFiles = runtimePartitions.flatMap(_.files).size + // The teeth above were measured on the unpruned scan; this is what ties them to this arm. + assert(runtimeFiles === survivorsWhenUnpruned, + s"expected this arm to read the $survivorsWhenUnpruned files the unpruned packing " + + s"held, got $runtimeFiles") + // Both arms must see the same files: the runtime path cannot re-split, so a compile-time + // maxSplitBytes below one file's length would cut that arm's files finer and the counts + // would differ for a reason this test is not about. + assert(runtimeFiles === compileTimePartitions.flatMap(_.files).size, + s"expected both arms to read $runtimeFiles files, got " + + s"${compileTimePartitions.flatMap(_.files).size} at compile time") + // The two arms pack from differently ordered input: `partitions` sorts within a partition + // directory, this path sorts the survivors globally, and Next Fit Decreasing is sensitive + // to that. What makes the counts comparable is that under this conf no two surviving + // files fit in one bin, so both arms give one partition per file whatever the order. + assert(runtimePartitions.size === runtimeFiles, + s"expected one partition per surviving file, got ${runtimePartitions.size} " + + s"for $runtimeFiles files") + assert(runtimePartitions.size === compileTimePartitions.size, + "expected the runtime-pruned scan to be packed like the compile-time one " + + s"(${compileTimePartitions.size} partitions), got ${runtimePartitions.size}") + } + } + } + } + + test("a DPP filter degraded to true leaves the v2 file scan unpruned") { + // With reuse-only DPP and no broadcast to reuse, the subquery is replaced by true before + // execution. What this pins is that such a filter still reaches the scan and the query stays + // correct; the row count only records that nothing was pruned, which is what a true predicate + // does whether or not `catalystRuntimeFilters` drops it. + withDppV2Conf { + withSQLConf( + SQLConf.DYNAMIC_PARTITION_PRUNING_REUSE_BROADCAST_ONLY.key -> "true", + SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") { + withTempDir { dir => + writeFactAndDim(dir) + val df = sql( + """SELECT f.id FROM fact f JOIN dim d + |ON f.part = d.dim_id WHERE d.dim_val = 7""".stripMargin) + val rows = df.collect() + assert(rows.length == 10, s"expected 10 rows after join+filter, got ${rows.length}") + val factScan = factScanOf(df) + assert(factScan.runtimeFilters.exists { + case DynamicPruningExpression(l: Literal) => l == Literal.TrueLiteral + case _ => false + }, s"expected a degraded DPP filter, got ${factScan.runtimeFilters}") + val numOutputRows = factScan.metrics("numOutputRows").value + assert(numOutputRows == 100, + s"expected the fact scan to read all 100 rows, got $numOutputRows") + } + } + } + } + + test("a runtime filter binds to the read partition columns when only some of them are read") { + // `partitions` projects each file's partition values down to `readPartitionSchema` when the + // query reads a strict subset of the partition columns, so a filter on `p2` has to read slot 0 + // of those values. Binding by the table's partition schema would read slot 1, which in the + // projected values holds `p3`: rows that satisfy the filter are lost, and with the filter + // fully pushed, rows that do not are returned. + withDppV2Conf { + withTempDir { dir => + val tPath = new File(dir, "t").getCanonicalPath + spark.range(100).selectExpr("id", "id % 2 AS p1", "id % 5 AS p2", "id % 3 AS p3") + .write.format("parquet").partitionBy("p1", "p2", "p3").save(tPath) + spark.read.format("parquet").schema("id long, p1 int, p2 int, p3 int").load(tPath) + .createOrReplaceTempView("t") + writeFactAndDim(dir) + val expected = (0 until 100).filter(_ % 5 == 2).map(i => Row(i.toLong, i % 3)) + Seq( + """SELECT id, p3 FROM t + |WHERE p2 = (SELECT max(dim_id) FROM dim WHERE dim_val = 2)""".stripMargin, + """SELECT t.id, t.p3 FROM t JOIN dim d + |ON t.p2 = d.dim_id WHERE d.dim_val = 2""".stripMargin).foreach { query => + val df = sql(query) + checkAnswer(df, expected) + val scan = factScanOf(df) + assert(scan.scan.asInstanceOf[FileScan].readPartitionSchema.fieldNames.toSeq === + Seq("p2", "p3"), "the scan must read a strict subset of the partition columns") + val numOutputRows = scan.metrics("numOutputRows").value + assert(numOutputRows == expected.size, + s"expected the scan to read ${expected.size} rows, got $numOutputRows") + } + } + } + } + + test("SPARK-38148: no DPP over a partition column a static equality already fixes") { + // `FileScanBuilder` pushes the static partition filter fully into the scan, so no `Filter` + // above the scan carries it; the cleanup has to read it from the scan relation's pushed + // filters, as it reads it from the `Filter` over a V1 relation. V1 is the reference. + withDppV2Conf { + withTempDir { dir => + writeFactAndDim(dir) + // An int join key, so the pruning key is `part` itself rather than a cast of it. + val dimIntPath = new File(dir, "dim_int").getCanonicalPath + spark.range(10).selectExpr("CAST(id AS INT) AS dim_id", "CAST(id AS INT) AS dim_val") + .write.format("parquet").save(dimIntPath) + // (condition, the partitions it keeps, whether DPP stays, whether V1 is checked). The first + // six are the SPARK-38148 cases. The last two do not fix the key, so V2 keeps the DPP; they + // are checked under V2 only, because V1 reads an equality anywhere in its filter and drops + // the DPP. + Seq[(String, Int => Boolean, Boolean, Boolean)]( + ("f.part = 3", _ == 3, false, true), + ("3 = f.part", _ == 3, false, true), + ("f.part <=> 3", _ == 3, false, true), + ("3 <=> f.part", _ == 3, false, true), + ("f.part > 3", _ > 3, true, true), + ("5 > f.part", _ < 5, true, true), + ("f.part <> 3", _ != 3, true, false), + ("(f.part = 3 OR f.part = 4)", p => p == 3 || p == 4, true, false) + ).foreach { case (condition, keeps, hasDpp, checkV1) => + val query = + s"""SELECT /*+ broadcast(d) */ f.id FROM fact f JOIN dim_int d + |ON f.part = d.dim_id WHERE $condition AND d.dim_val > 2""".stripMargin + val expected = (0 until 100).filter { i => + val part = i % 10 + part > 2 && keeps(part) + }.map(i => Row(i.toLong)) + (if (checkV1) Seq("parquet", "") else Seq("")).foreach { v1Sources => + withSQLConf(SQLConf.USE_V1_SOURCE_LIST.key -> v1Sources) { + // A temp view keeps the relation it was created with, so create both under this conf. + spark.read.format("parquet").schema("id long, part int") + .load(new File(dir, "fact").getCanonicalPath).createOrReplaceTempView("fact") + spark.read.format("parquet").load(dimIntPath).createOrReplaceTempView("dim_int") + val df = sql(query) + val optimized = df.queryExecution.optimizedPlan + assert(optimized.exists(_.isInstanceOf[DataSourceV2ScanRelation]) === + v1Sources.isEmpty, s"wrong source version for '$v1Sources':\n$optimized") + assert(collectDppFilters(optimized).nonEmpty === hasDpp, + s"`$condition` with v1 sources '$v1Sources': expected DPP $hasDpp, got\n$optimized") + checkAnswer(df, expected) + } + } + } + } + } + } + + Seq("orc", "json", "csv").foreach { format => Review Comment: Done in e721ae7. `DataSourceV2FileSourceDPPSuite` clears the temp views after each test, and `withFactAndDim` in `DataSourceV2FileScanSubclassSuite` uses `withTempView`. -- This is an automated message from the Apache Git Service. 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