huaxingao commented on code in PR #5872: URL: https://github.com/apache/iceberg/pull/5872#discussion_r988618305
########## spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/source/SparkPushedDownAggregateUtil.java: ########## @@ -0,0 +1,370 @@ +/* + * 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 java.util.Locale; +import java.util.Map; +import org.apache.iceberg.MetadataTableType; +import org.apache.iceberg.Schema; +import org.apache.iceberg.Table; +import org.apache.iceberg.expressions.Expression; +import org.apache.iceberg.expressions.Literal; +import org.apache.iceberg.expressions.UnboundAggregate; +import org.apache.iceberg.relocated.com.google.common.collect.Lists; +import org.apache.iceberg.spark.SparkSchemaUtil; +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.ArrayType; +import org.apache.spark.sql.types.DataType; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.Decimal; +import org.apache.spark.sql.types.DecimalType; +import org.apache.spark.sql.types.MapType; +import org.apache.spark.sql.types.Metadata; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import scala.collection.JavaConverters; + +/** Helper methods for working with Spark aggregate push down. */ +public class SparkPushedDownAggregateUtil { + + private SparkPushedDownAggregateUtil() {} + + // Build schema for pushed down aggregates. This schema will be used as the scan schema. + public static StructType buildSchemaForPushedDownAggregate( + List<Expression> aggregates, boolean caseSensitive, Schema schema) { + StructType finalSchema = new StructType(); + for (int index = 0; index < aggregates.size(); index++) { + if ((aggregates.get(index)).op().name().equals("COUNTSTAR")) { + StructField field = + new StructField("COUNT(*)", DataTypes.LongType, false, Metadata.empty()); + finalSchema = finalSchema.add(field); + } else { + String colName = ((UnboundAggregate) aggregates.get(index)).ref().name(); + DataType dataType = getDataTypeForAggregateColumn(colName, caseSensitive, schema); + if (dataType instanceof StructType + || dataType instanceof ArrayType + || dataType instanceof MapType) { + // not building pushed down aggregate schema for complex types to disable aggregate push + // down because the statistic info for complex are not available. + return finalSchema; + } + if ((aggregates.get(index)).op().name().equals("COUNT")) { + StructField field = + new StructField( + "COUNT(" + colName + ")", DataTypes.LongType, false, Metadata.empty()); + finalSchema = finalSchema.add(field); + } else if ((aggregates.get(index)).op().name().equals("MAX")) { + StructField field = + new StructField("MAX(" + colName + ")", dataType, false, Metadata.empty()); + finalSchema = finalSchema.add(field); + } else if ((aggregates.get(index)).op().name().equals("MIN")) { + StructField field = + new StructField("MIN(" + colName + ")", dataType, false, Metadata.empty()); + finalSchema = finalSchema.add(field); + } + } + } + return finalSchema; + } + + public static DataType getDataTypeForAggregateColumn( + String colName, boolean caseSensitive, Schema schema) { + Type type = null; + for (int i = 0; i < schema.columns().size(); i++) { + if ((caseSensitive && schema.columns().get(i).name().equals(colName)) + || (!caseSensitive && schema.columns().get(i).name().equalsIgnoreCase(colName))) { + type = schema.columns().get(i).type(); + } + } + return SparkSchemaUtil.convert(type); + } + + @SuppressWarnings("checkstyle:CyclomaticComplexity") + public static InternalRow[] constructInternalRowForPushedDownAggregate( + SparkSession spark, Table table, StructType pushedAggregateSchema, boolean caseSensitive) { + // get the statistics info from DATA_FILES, calculate the aggregate values (min/max/count) + // and use these value to build an internalRow. + Dataset<Row> metadataRows = + SparkTableUtil.loadMetadataTable(spark, table, MetadataTableType.DATA_FILES); + + Dataset dataset = + metadataRows.selectExpr( + "lower_bounds", "upper_bounds", "record_count", "null_value_counts"); + Row[] staticticRows = (Row[]) dataset.collect(); Review Comment: Please let me know if there is a better way to do this. ########## spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/source/SparkScanBuilder.java: ########## @@ -145,6 +164,78 @@ public Filter[] pushedFilters() { return pushedFilters; } + @Override + public boolean pushAggregation(Aggregation aggregation) { + if (!(table instanceof BaseTable)) { + return false; + } + boolean aggregatePushdown = + Boolean.parseBoolean( + table + .properties() + .getOrDefault(AGGREGATE_PUSHDOWN_ENABLED, AGGREGATE_PUSHDOWN_ENABLED_DEFAULT)); + if (!aggregatePushdown) { + return false; + } + + String deleteMode = table.properties().getOrDefault(DELETE_MODE, DELETE_MODE_DEFAULT); + String updateMode = table.properties().getOrDefault(UPDATE_MODE, UPDATE_MODE_DEFAULT); + String mergeMode = table.properties().getOrDefault(MERGE_MODE, MERGE_MODE_DEFAULT); + // the statistics might be changed for merge on read and can't be used to calculate + // min/max/count, so disable aggregate push down + // Todo: enable aggregate push down if there are not deletes files for merge-on-read + if (deleteMode.equals("merge-on-read") + || updateMode.equals("merge-on-read") + || mergeMode.equals("merge-on-read")) { + return false; + } + + // If the group by expression is not the same as the partition, the statistics information + // in manifest files cannot be used to calculate min/max/count. However, if the + // group by expression is not the same as the partition, the statistics information can still + // be used to calculate min/max/count. + // Todo: enable aggregate push down for partition col group by expression + if (aggregation.groupByExpressions().length > 0) { + return false; + } + + List<Expression> expressions = + Lists.newArrayListWithExpectedSize(aggregation.aggregateExpressions().length); + List<AggregateFunc> pushed = + Lists.newArrayListWithExpectedSize(aggregation.aggregateExpressions().length); + for (AggregateFunc aggregate : aggregation.aggregateExpressions()) { + Expression expr = SparkAggregates.convert(aggregate); + if (expr != null) { + try { + Binder.bind(schema.asStruct(), expr, caseSensitive); + expressions.add(expr); + pushed.add(aggregate); + } catch (ValidationException e) { + // binding to the table schema failed, so this expression cannot be pushed down Review Comment: Fixed. 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