huaxingao commented on code in PR #6252: URL: https://github.com/apache/iceberg/pull/6252#discussion_r1030822401
########## spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/source/SparkPushedDownAggregateUtil.java: ########## @@ -0,0 +1,373 @@ +/* + * 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.iceberg.spark.source; + +import java.math.BigDecimal; +import java.nio.ByteBuffer; +import java.util.List; +import org.apache.iceberg.MetadataTableType; +import org.apache.iceberg.MetricsConfig; +import org.apache.iceberg.MetricsModes; +import org.apache.iceberg.Table; +import org.apache.iceberg.expressions.AggregateUtil; +import org.apache.iceberg.expressions.BoundAggregate; +import org.apache.iceberg.expressions.Expression; +import org.apache.iceberg.expressions.ExpressionVisitors; +import org.apache.iceberg.expressions.Literal; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.spark.SparkTableUtil; +import org.apache.iceberg.types.Conversions; +import org.apache.iceberg.types.Type; +import org.apache.iceberg.types.Types; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SparkSession; +import org.apache.spark.sql.catalyst.InternalRow; +import org.apache.spark.sql.types.Decimal; +import scala.collection.JavaConverters; + +/** Helper methods for working with Spark aggregate push down. */ +public class SparkPushedDownAggregateUtil { + + private SparkPushedDownAggregateUtil() {} + + public static boolean metricsModeSupportsAggregatePushDown( + Table table, List<Expression> aggregates) { + MetricsConfig config = MetricsConfig.forTable(table); + for (Expression aggregate : aggregates) { + String colName = AggregateUtil.getAggregateColumnName(aggregate); + if (!colName.equals("*")) { + MetricsModes.MetricsMode mode = config.columnMode(colName); + if (mode.toString().equals("none")) { + return false; + } else if (mode.toString().equals("counts")) { + if (aggregate.op() == Expression.Operation.MAX + || aggregate.op() == Expression.Operation.MIN) { + return false; + } + } else if (mode.toString().contains("truncate")) { + if (AggregateUtil.getAggregateType(aggregate).typeId() == Type.TypeID.STRING) { + if (aggregate.op() == Expression.Operation.MAX + || aggregate.op() == Expression.Operation.MIN) { + return false; + } + } + } + } + } + + return true; + } + + public static InternalRow[] constructInternalRowForPushedDownAggregate( + SparkSession spark, Table table, List<Expression> aggregates, List<Integer> indexInTable) { + List<Object> valuesInSparkInternalRow = Lists.newArrayList(); + Row[] row = SparkPushedDownAggregateUtil.getStatisticRow(spark, table); + for (int index = 0; index < aggregates.size(); index++) { + Expression aggregate = aggregates.get(index); + Type type = AggregateUtil.getAggregateType(aggregate); + valuesInSparkInternalRow.add( + SparkPushedDownAggregateUtil.getAggregateValue( + aggregate, row, indexInTable.get(index), type)); + } + + InternalRow[] rows = new InternalRow[1]; + rows[0] = InternalRow.fromSeq(JavaConverters.asScalaBuffer(valuesInSparkInternalRow).toSeq()); + return rows; + } + + public static Row[] getStatisticRow(SparkSession spark, Table table) { + Dataset<Row> metadataRows = + SparkTableUtil.loadMetadataTable(spark, table, MetadataTableType.DATA_FILES); Review Comment: https://github.com/apache/iceberg/pull/5872#discussion_r990821627 When push down aggregates, we don't need to do a job planning because we only need a `LocalScan` on Spark driver. I guess maybe we don't need a flag to enable/disable running a parallel operation from within job planning? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
