huaxingao commented on code in PR #5872:
URL: https://github.com/apache/iceberg/pull/5872#discussion_r988618124
##########
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;
+ }
Review Comment:
I changed this to
```
Map<String, String> map = table.currentSnapshot().summary();
if (Integer.parseInt(map.get("total-position-deletes")) > 0
|| Integer.parseInt(map.get("total-equality-deletes")) > 0) {
return false;
}
```
I am not sure if this is correct.
I added a test for this, but somehow the test failed at delete. I ignore the
test for now. Will figure out why the delete failed.
##########
spark/v3.3/spark/src/main/java/org/apache/iceberg/spark/source/SparkScanBuilder.java:
##########
@@ -233,6 +324,16 @@ public Scan build() {
}
}
+ // if aggregates are pushed down, instead of constructing a
SparkBatchQueryScan, creating file
+ // read tasks and sending over the tasks to Spark executors, a
SparkLocalScan will be created
+ // and the scan is done locally on the Spark driver instead of the
executors. The statistics
+ // info will be retrieved from manifest file and used to build a Spark
internal row, which
+ // contains the pushed down aggregate values.
+ if (pushedAggregateRows != null) {
+ return new SparkLocalScan(
+ table, aggregateExpressions, pushedAggregateSchema,
pushedAggregateRows);
+ }
+
Review Comment:
Fixed. Thanks!
##########
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(
Review Comment:
Added. Thanks
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