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[spark] Support push down aggregate (#4259)
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44 changes: 44 additions & 0 deletions
44
...on-spark/paimon-spark-common/src/main/scala/org/apache/paimon/spark/PaimonLocalScan.scala
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| @@ -0,0 +1,44 @@ | ||
| /* | ||
| * 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.paimon.spark | ||
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| import org.apache.paimon.table.Table | ||
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| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.connector.read.LocalScan | ||
| import org.apache.spark.sql.sources.Filter | ||
| import org.apache.spark.sql.types.StructType | ||
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| /** A scan does not require [[RDD]] to execute */ | ||
| case class PaimonLocalScan( | ||
| rows: Array[InternalRow], | ||
| readSchema: StructType, | ||
| table: Table, | ||
| filters: Array[Filter]) | ||
| extends LocalScan { | ||
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| override def description(): String = { | ||
| val pushedFiltersStr = if (filters.nonEmpty) { | ||
| ", PushedFilters: [" + filters.mkString(",") + "]" | ||
| } else { | ||
| "" | ||
| } | ||
| s"PaimonLocalScan: [${table.name}]" + pushedFiltersStr | ||
| } | ||
| } |
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109 changes: 109 additions & 0 deletions
109
...aimon-spark-common/src/main/scala/org/apache/paimon/spark/aggregate/LocalAggregator.scala
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| Original file line number | Diff line number | Diff line change |
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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.paimon.spark.aggregate | ||
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| import org.apache.paimon.manifest.PartitionEntry | ||
| import org.apache.paimon.table.{DataTable, Table} | ||
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| import org.apache.spark.sql.catalyst.InternalRow | ||
| import org.apache.spark.sql.connector.expressions.aggregate.{AggregateFunc, Aggregation, CountStar} | ||
| import org.apache.spark.sql.types.{DataType, LongType, StructField, StructType} | ||
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| class LocalAggregator(table: Table) { | ||
| private var aggFuncEvaluator: Seq[AggFuncEvaluator[_]] = _ | ||
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| private def initialize(aggregation: Aggregation): Unit = { | ||
| aggFuncEvaluator = aggregation.aggregateExpressions().map { | ||
| case _: CountStar => new CountStarEvaluator() | ||
| case _ => throw new UnsupportedOperationException() | ||
| } | ||
| } | ||
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| private def supportAggregateFunction(func: AggregateFunc): Boolean = { | ||
| func match { | ||
| case _: CountStar => true | ||
| case _ => false | ||
| } | ||
| } | ||
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| def pushAggregation(aggregation: Aggregation): Boolean = { | ||
| if ( | ||
| !table.isInstanceOf[DataTable] || | ||
| !table.primaryKeys.isEmpty | ||
| ) { | ||
| return false | ||
| } | ||
| if (table.asInstanceOf[DataTable].coreOptions.deletionVectorsEnabled) { | ||
| return false | ||
| } | ||
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| if ( | ||
| aggregation.groupByExpressions().nonEmpty || | ||
| aggregation.aggregateExpressions().isEmpty || | ||
| aggregation.aggregateExpressions().exists(!supportAggregateFunction(_)) | ||
| ) { | ||
| return false | ||
| } | ||
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| initialize(aggregation) | ||
| true | ||
| } | ||
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| def update(partitionEntry: PartitionEntry): Unit = { | ||
| assert(aggFuncEvaluator != null) | ||
| aggFuncEvaluator.foreach(_.update(partitionEntry)) | ||
| } | ||
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| def result(): Array[InternalRow] = { | ||
| assert(aggFuncEvaluator != null) | ||
| Array(InternalRow.fromSeq(aggFuncEvaluator.map(_.result()))) | ||
| } | ||
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| def resultSchema(): StructType = { | ||
| assert(aggFuncEvaluator != null) | ||
| val fields = aggFuncEvaluator.zipWithIndex.map { | ||
| case (evaluator, i) => | ||
| // Note that, Spark will re-assign the attribute name to original name, | ||
| // so here we just return an arbitrary name | ||
| StructField(s"${evaluator.prettyName}_$i", evaluator.resultType) | ||
| } | ||
| StructType.apply(fields) | ||
| } | ||
| } | ||
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| trait AggFuncEvaluator[T] { | ||
| def update(partitionEntry: PartitionEntry): Unit | ||
| def result(): T | ||
| def resultType: DataType | ||
| def prettyName: String | ||
| } | ||
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| class CountStarEvaluator extends AggFuncEvaluator[Long] { | ||
| private var _result: Long = 0L | ||
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| override def update(partitionEntry: PartitionEntry): Unit = { | ||
| _result += partitionEntry.recordCount() | ||
| } | ||
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| override def result(): Long = _result | ||
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| override def resultType: DataType = LongType | ||
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| override def prettyName: String = "count_star" | ||
| } |
99 changes: 99 additions & 0 deletions
99
...imon-spark-common/src/test/scala/org/apache/paimon/spark/sql/PushDownAggregatesTest.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| /* | ||
| * 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.paimon.spark.sql | ||
|
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| import org.apache.paimon.spark.PaimonSparkTestBase | ||
|
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| import org.apache.spark.sql.Row | ||
| import org.apache.spark.sql.catalyst.plans.logical.Aggregate | ||
| import org.apache.spark.sql.execution.LocalTableScanExec | ||
| import org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanHelper | ||
| import org.apache.spark.sql.execution.aggregate.BaseAggregateExec | ||
|
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| class PushDownAggregatesTest extends PaimonSparkTestBase with AdaptiveSparkPlanHelper { | ||
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| private def runAndCheckAggregate( | ||
| query: String, | ||
| expectedRows: Seq[Row], | ||
| expectedNumAggregates: Int): Unit = { | ||
| val df = spark.sql(query) | ||
| checkAnswer(df, expectedRows) | ||
| assert(df.schema.names.toSeq == df.queryExecution.executedPlan.output.map(_.name)) | ||
| assert(df.queryExecution.analyzed.find(_.isInstanceOf[Aggregate]).isDefined) | ||
| val numAggregates = collect(df.queryExecution.executedPlan) { | ||
| case agg: BaseAggregateExec => agg | ||
| }.size | ||
| assert(numAggregates == expectedNumAggregates, query) | ||
| if (numAggregates == 0) { | ||
| assert(collect(df.queryExecution.executedPlan) { | ||
| case scan: LocalTableScanExec => scan | ||
| }.size == 1) | ||
| } | ||
| } | ||
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| test("Push down aggregate - append table") { | ||
| withTable("T") { | ||
| spark.sql("CREATE TABLE T (c1 INT, c2 STRING) PARTITIONED BY(day STRING)") | ||
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| runAndCheckAggregate("SELECT COUNT(*) FROM T", Row(0) :: Nil, 0) | ||
| // This query does not contain aggregate due to AQE optimize it to empty relation. | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T GROUP BY c1", Nil, 0) | ||
| runAndCheckAggregate("SELECT COUNT(c1) FROM T", Row(0) :: Nil, 2) | ||
| runAndCheckAggregate("SELECT COUNT(*), COUNT(c1) FROM T", Row(0, 0) :: Nil, 2) | ||
| runAndCheckAggregate("SELECT COUNT(*), COUNT(*) + 1 FROM T", Row(0, 1) :: Nil, 0) | ||
| runAndCheckAggregate("SELECT COUNT(*) as c FROM T WHERE day='a'", Row(0) :: Nil, 0) | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T WHERE c1=1", Row(0) :: Nil, 2) | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T WHERE day='a' and c1=1", Row(0) :: Nil, 2) | ||
|
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| spark.sql( | ||
| "INSERT INTO T VALUES(1, 'x', 'a'), (2, 'x', 'a'), (3, 'x', 'b'), (3, 'x', 'c'), (null, 'x', 'a')") | ||
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| runAndCheckAggregate("SELECT COUNT(*) FROM T", Row(5) :: Nil, 0) | ||
| runAndCheckAggregate( | ||
| "SELECT COUNT(*) FROM T GROUP BY c1", | ||
| Row(1) :: Row(1) :: Row(1) :: Row(2) :: Nil, | ||
| 2) | ||
| runAndCheckAggregate("SELECT COUNT(c1) FROM T", Row(4) :: Nil, 2) | ||
| runAndCheckAggregate("SELECT COUNT(*), COUNT(c1) FROM T", Row(5, 4) :: Nil, 2) | ||
| runAndCheckAggregate("SELECT COUNT(*), COUNT(*) + 1 FROM T", Row(5, 6) :: Nil, 0) | ||
| runAndCheckAggregate("SELECT COUNT(*) as c FROM T WHERE day='a'", Row(3) :: Nil, 0) | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T WHERE c1=1", Row(1) :: Nil, 2) | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T WHERE day='a' and c1=1", Row(1) :: Nil, 2) | ||
| } | ||
| } | ||
|
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| test("Push down aggregate - primary table") { | ||
| withTable("T") { | ||
| spark.sql("CREATE TABLE T (c1 INT, c2 STRING) TBLPROPERTIES ('primary-key' = 'c1')") | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T", Row(0) :: Nil, 2) | ||
| spark.sql("INSERT INTO T VALUES(1, 'x'), (2, 'x'), (3, 'x'), (3, 'x')") | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T", Row(3) :: Nil, 2) | ||
| } | ||
| } | ||
|
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| test("Push down aggregate - enable deletion vector") { | ||
| withTable("T") { | ||
| spark.sql( | ||
| "CREATE TABLE T (c1 INT, c2 STRING) TBLPROPERTIES('deletion-vectors.enabled' = 'true')") | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T", Row(0) :: Nil, 2) | ||
| spark.sql("INSERT INTO T VALUES(1, 'x'), (2, 'x'), (3, 'x'), (3, 'x')") | ||
| runAndCheckAggregate("SELECT COUNT(*) FROM T", Row(4) :: Nil, 2) | ||
| } | ||
| } | ||
| } |