c21 commented on a change in pull request #34298:
URL: https://github.com/apache/spark/pull/34298#discussion_r736874359
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
##########
@@ -960,6 +960,13 @@ object SQLConf {
.booleanConf
.createWithDefault(true)
+ val ORC_AGGREGATE_PUSHDOWN_ENABLED =
buildConf("spark.sql.orc.aggregatePushdown")
+ .doc("If true, MAX/MIN/COUNT without filter and group by will be pushed" +
+ " down to ORC for optimization. MAX/MIN for complex types can't be
pushed down")
Review comment:
@sunchao - yes, updated the doc.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/orc/OrcScan.scala
##########
@@ -37,35 +38,65 @@ case class OrcScan(
readDataSchema: StructType,
readPartitionSchema: StructType,
options: CaseInsensitiveStringMap,
+ pushedAggregate: Option[Aggregation] = None,
pushedFilters: Array[Filter],
partitionFilters: Seq[Expression] = Seq.empty,
dataFilters: Seq[Expression] = Seq.empty) extends FileScan {
- override def isSplitable(path: Path): Boolean = true
+ override def isSplitable(path: Path): Boolean = {
+ // If aggregate is pushed down, only the file footer will be read once,
+ // so file should be not split across multiple tasks.
+ pushedAggregate.isEmpty
Review comment:
@huaxingao - cool then I can address for Parquet in a followup PR, no
urgent anyway.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -377,4 +381,106 @@ object OrcUtils extends Logging {
case _ => false
}
}
+
+ /**
+ * When the partial aggregates (Max/Min/Count) are pushed down to ORC, we
don't need to read data
+ * from ORC and aggregate at Spark layer. Instead we want to get the partial
aggregates
+ * (Max/Min/Count) result using the statistics information from ORC file
footer, and then
+ * construct an InternalRow from these aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ def createAggInternalRowFromFooter(
+ reader: Reader,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ isCaseSensitive: Boolean): InternalRow = {
+ require(aggregation.groupByColumns.length == 0,
+ s"aggregate $aggregation with group-by column shouldn't be pushed down")
+ val columnsStatistics = OrcFooterReader.readStatistics(reader)
+
+ // Get column statistics with column name.
+ def getColumnStatistics(columnName: String): ColumnStatistics = {
+ val columnIndex = dataSchema.fieldNames.indexOf(columnName)
+ columnsStatistics.get(columnIndex).getStatistics
+ }
+
+ // Get Min/Max statistics and store as ORC `WritableComparable` format.
+ def getMinMaxFromColumnStatistics(
+ statistics: ColumnStatistics,
+ dataType: DataType,
+ isMax: Boolean): WritableComparable[_] = {
+ statistics match {
+ case s: BooleanColumnStatistics =>
+ val value = if (isMax) s.getTrueCount > 0 else !(s.getFalseCount > 0)
+ new BooleanWritable(value)
+ case s: IntegerColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case ByteType => new ByteWritable(value.toByte)
+ case ShortType => new ShortWritable(value.toShort)
+ case IntegerType => new IntWritable(value.toInt)
+ case LongType => new LongWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType " +
+ "for IntegerColumnStatistics")
+ }
+ case s: DoubleColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case FloatType => new FloatWritable(value.toFloat)
+ case DoubleType => new DoubleWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType" +
+ "for DoubleColumnStatistics")
+ }
+ case s: DateColumnStatistics =>
+ new DateWritable(
+ if (isMax) s.getMaximumDayOfEpoch.toInt else
s.getMinimumDayOfEpoch.toInt)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take
${statistics.getClass.getName}: " +
+ s"$statistics as the ORC column statistics")
+ }
+ }
+
+ val aggORCValues: Seq[WritableComparable[_]] =
+ aggregation.aggregateExpressions.zipWithIndex.map {
+ case (max: Max, index) =>
+ val columnName = max.column.fieldNames.head
+ val statistics = getColumnStatistics(columnName)
+ val dataType = aggSchema(index).dataType
+ getMinMaxFromColumnStatistics(statistics, dataType, isMax = true)
+ case (min: Min, index) =>
+ val columnName = min.column.fieldNames.head
+ val statistics = getColumnStatistics(columnName)
+ val dataType = aggSchema.apply(index).dataType
+ getMinMaxFromColumnStatistics(statistics, dataType, isMax = false)
+ case (count: Count, _) =>
+ val columnName = count.column.fieldNames.head
+ val isPartitionColumn = partitionSchema.fields
+ .map(PartitioningUtils.getColName(_, isCaseSensitive))
Review comment:
@huaxingao - thanks for checking. Removed for ORC. I can do another PR
for Parquet to help this PR review faster, but if you are already on it for
Parquet code path, feel free to go ahead.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/AggregatePushDownUtils.scala
##########
@@ -0,0 +1,142 @@
+/*
+ * 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
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.connector.expressions.NamedReference
+import org.apache.spark.sql.connector.expressions.aggregate.{AggregateFunc,
Aggregation, Count, CountStar, Max, Min}
+import org.apache.spark.sql.execution.RowToColumnConverter
+import org.apache.spark.sql.execution.vectorized.{OffHeapColumnVector,
OnHeapColumnVector}
+import org.apache.spark.sql.types.{BooleanType, ByteType, DateType,
DoubleType, FloatType, IntegerType, LongType, ShortType, StructField,
StructType}
+import org.apache.spark.sql.vectorized.{ColumnarBatch, ColumnVector}
+
+/**
+ * Utility class for aggregate push down to Parquet and ORC.
+ */
+object AggregatePushDownUtils {
+
+ /**
+ * Get the data schema for aggregate to be pushed down.
+ */
+ def getSchemaForPushedAggregation(
+ aggregation: Aggregation,
+ schema: StructType,
+ partitionNames: Set[String],
+ dataFilters: Seq[Expression]): Option[StructType] = {
+
+ var finalSchema = new StructType()
+
+ def getStructFieldForCol(col: NamedReference): StructField = {
+ schema.apply(col.fieldNames.head)
+ }
+
+ def isPartitionCol(col: NamedReference) = {
+ partitionNames.contains(col.fieldNames.head)
+ }
+
+ def processMinOrMax(agg: AggregateFunc): Boolean = {
+ val (column, aggType) = agg match {
+ case max: Max => (max.column, "max")
+ case min: Min => (min.column, "min")
+ case _ =>
+ throw new IllegalArgumentException(s"Unexpected type of
AggregateFunc ${agg.describe}")
+ }
+
+ if (isPartitionCol(column)) {
+ // don't push down partition column, footer doesn't have max/min for
partition column
+ return false
+ }
+ val structField = getStructFieldForCol(column)
+
+ structField.dataType match {
+ // not push down complex type
+ // not push down Timestamp because INT96 sort order is undefined,
+ // Parquet doesn't return statistics for INT96
+ // not push down Parquet Binary because min/max could be truncated
+ // (https://issues.apache.org/jira/browse/PARQUET-1685), Parquet Binary
+ // could be Spark StringType, BinaryType or DecimalType.
+ // not push down for ORC with same reason.
+ case BooleanType | ByteType | ShortType | IntegerType
+ | LongType | FloatType | DoubleType | DateType =>
+ finalSchema = finalSchema.add(structField.copy(s"$aggType(" +
structField.name + ")"))
+ true
+ case _ =>
+ false
+ }
+ }
+
+ if (aggregation.groupByColumns.nonEmpty || dataFilters.nonEmpty) {
+ // Parquet/ORC footer has max/min/count for columns
+ // e.g. SELECT COUNT(col1) FROM t
+ // but footer doesn't have max/min/count for a column if max/min/count
+ // are combined with filter or group by
+ // e.g. SELECT COUNT(col1) FROM t WHERE col2 = 8
+ // SELECT COUNT(col1) FROM t GROUP BY col2
+ // Todo: 1. add support if groupby column is partition col
+ // (https://issues.apache.org/jira/browse/SPARK-36646)
+ // 2. add support if filter col is partition col
+ // (https://issues.apache.org/jira/browse/SPARK-36647)
+ return None
+ }
+
+ aggregation.aggregateExpressions.foreach {
+ case max: Max =>
+ if (!processMinOrMax(max)) return None
+ case min: Min =>
+ if (!processMinOrMax(min)) return None
+ case count: Count =>
+ if (count.column.fieldNames.length != 1 || count.isDistinct) return
None
+ finalSchema =
+ finalSchema.add(StructField(s"count(" + count.column.fieldNames.head
+ ")", LongType))
+ case _: CountStar =>
+ finalSchema = finalSchema.add(StructField("count(*)", LongType))
+ case _ =>
+ return None
+ }
+
+ Some(finalSchema)
+ }
+
+ /**
+ * Check if two Aggregation `a` and `b` is equal or not.
+ */
+ def equivalentAggregations(a: Aggregation, b: Aggregation): Boolean = {
Review comment:
@viirya - I think so, `Aggregation` is not a `QueryPlan` here, btw this
was introduced in https://github.com/apache/spark/pull/33639, and I am
refactoring here.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -377,4 +381,106 @@ object OrcUtils extends Logging {
case _ => false
}
}
+
+ /**
+ * When the partial aggregates (Max/Min/Count) are pushed down to ORC, we
don't need to read data
+ * from ORC and aggregate at Spark layer. Instead we want to get the partial
aggregates
+ * (Max/Min/Count) result using the statistics information from ORC file
footer, and then
+ * construct an InternalRow from these aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ def createAggInternalRowFromFooter(
+ reader: Reader,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ isCaseSensitive: Boolean): InternalRow = {
+ require(aggregation.groupByColumns.length == 0,
+ s"aggregate $aggregation with group-by column shouldn't be pushed down")
+ val columnsStatistics = OrcFooterReader.readStatistics(reader)
+
+ // Get column statistics with column name.
+ def getColumnStatistics(columnName: String): ColumnStatistics = {
+ val columnIndex = dataSchema.fieldNames.indexOf(columnName)
+ columnsStatistics.get(columnIndex).getStatistics
+ }
+
+ // Get Min/Max statistics and store as ORC `WritableComparable` format.
+ def getMinMaxFromColumnStatistics(
+ statistics: ColumnStatistics,
+ dataType: DataType,
+ isMax: Boolean): WritableComparable[_] = {
+ statistics match {
+ case s: BooleanColumnStatistics =>
+ val value = if (isMax) s.getTrueCount > 0 else !(s.getFalseCount > 0)
+ new BooleanWritable(value)
+ case s: IntegerColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case ByteType => new ByteWritable(value.toByte)
+ case ShortType => new ShortWritable(value.toShort)
+ case IntegerType => new IntWritable(value.toInt)
+ case LongType => new LongWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType " +
+ "for IntegerColumnStatistics")
+ }
+ case s: DoubleColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case FloatType => new FloatWritable(value.toFloat)
+ case DoubleType => new DoubleWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType" +
+ "for DoubleColumnStatistics")
+ }
+ case s: DateColumnStatistics =>
+ new DateWritable(
+ if (isMax) s.getMaximumDayOfEpoch.toInt else
s.getMinimumDayOfEpoch.toInt)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take
${statistics.getClass.getName}: " +
Review comment:
> Why not use DateColumnStatistics instead of
${statistics.getClass.getName}?
Sorry if it's not clear, but this is code path for `case _`, not `case s:
DateColumnStatistics`. I want to print out the class name for the statistics we
do not handle.
##########
File path:
sql/core/src/main/java/org/apache/spark/sql/execution/datasources/orc/OrcColumnStatistics.java
##########
@@ -0,0 +1,57 @@
+/*
+ * 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.orc;
+
+import org.apache.orc.ColumnStatistics;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * Columns statistics interface wrapping ORC {@link ColumnStatistics}s.
+ *
+ * Because ORC {@link ColumnStatistics}s are stored as an flatten array in ORC
file footer,
+ * this class is used to covert ORC {@link ColumnStatistics}s from array to
nested tree structure,
+ * according to data types. The flatten array stores all data types (including
nested types) in
+ * tree pre-ordering. This is used for aggregate push down in ORC.
+ */
+public class OrcColumnStatistics {
+ private final ColumnStatistics statistics;
+ private final List<OrcColumnStatistics> children;
Review comment:
@viirya - sure, added some comments and an example.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -377,4 +381,106 @@ object OrcUtils extends Logging {
case _ => false
}
}
+
+ /**
+ * When the partial aggregates (Max/Min/Count) are pushed down to ORC, we
don't need to read data
+ * from ORC and aggregate at Spark layer. Instead we want to get the partial
aggregates
+ * (Max/Min/Count) result using the statistics information from ORC file
footer, and then
+ * construct an InternalRow from these aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ def createAggInternalRowFromFooter(
+ reader: Reader,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ isCaseSensitive: Boolean): InternalRow = {
+ require(aggregation.groupByColumns.length == 0,
+ s"aggregate $aggregation with group-by column shouldn't be pushed down")
+ val columnsStatistics = OrcFooterReader.readStatistics(reader)
+
+ // Get column statistics with column name.
+ def getColumnStatistics(columnName: String): ColumnStatistics = {
+ val columnIndex = dataSchema.fieldNames.indexOf(columnName)
+ columnsStatistics.get(columnIndex).getStatistics
+ }
+
+ // Get Min/Max statistics and store as ORC `WritableComparable` format.
+ def getMinMaxFromColumnStatistics(
+ statistics: ColumnStatistics,
+ dataType: DataType,
+ isMax: Boolean): WritableComparable[_] = {
+ statistics match {
+ case s: BooleanColumnStatistics =>
+ val value = if (isMax) s.getTrueCount > 0 else !(s.getFalseCount > 0)
+ new BooleanWritable(value)
+ case s: IntegerColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
Review comment:
@sunchao - great catch! Added handling for empty file (0 value/row), we
should return null instead. Also added the unit test for empty file in
`FileSourceAggregatePushDownSuite/"aggregate push down - different data
types"`, thanks.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -377,4 +381,106 @@ object OrcUtils extends Logging {
case _ => false
}
}
+
+ /**
+ * When the partial aggregates (Max/Min/Count) are pushed down to ORC, we
don't need to read data
+ * from ORC and aggregate at Spark layer. Instead we want to get the partial
aggregates
+ * (Max/Min/Count) result using the statistics information from ORC file
footer, and then
+ * construct an InternalRow from these aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ def createAggInternalRowFromFooter(
+ reader: Reader,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ isCaseSensitive: Boolean): InternalRow = {
+ require(aggregation.groupByColumns.length == 0,
+ s"aggregate $aggregation with group-by column shouldn't be pushed down")
+ val columnsStatistics = OrcFooterReader.readStatistics(reader)
+
+ // Get column statistics with column name.
+ def getColumnStatistics(columnName: String): ColumnStatistics = {
+ val columnIndex = dataSchema.fieldNames.indexOf(columnName)
+ columnsStatistics.get(columnIndex).getStatistics
+ }
+
+ // Get Min/Max statistics and store as ORC `WritableComparable` format.
+ def getMinMaxFromColumnStatistics(
+ statistics: ColumnStatistics,
+ dataType: DataType,
+ isMax: Boolean): WritableComparable[_] = {
+ statistics match {
+ case s: BooleanColumnStatistics =>
+ val value = if (isMax) s.getTrueCount > 0 else !(s.getFalseCount > 0)
+ new BooleanWritable(value)
+ case s: IntegerColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case ByteType => new ByteWritable(value.toByte)
+ case ShortType => new ShortWritable(value.toShort)
+ case IntegerType => new IntWritable(value.toInt)
+ case LongType => new LongWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType " +
+ "for IntegerColumnStatistics")
+ }
+ case s: DoubleColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case FloatType => new FloatWritable(value.toFloat)
+ case DoubleType => new DoubleWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType" +
+ "for DoubleColumnStatistics")
+ }
+ case s: DateColumnStatistics =>
+ new DateWritable(
+ if (isMax) s.getMaximumDayOfEpoch.toInt else
s.getMinimumDayOfEpoch.toInt)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take
${statistics.getClass.getName}: " +
+ s"$statistics as the ORC column statistics")
+ }
+ }
+
+ val aggORCValues: Seq[WritableComparable[_]] =
+ aggregation.aggregateExpressions.zipWithIndex.map {
+ case (max: Max, index) =>
+ val columnName = max.column.fieldNames.head
+ val statistics = getColumnStatistics(columnName)
+ val dataType = aggSchema(index).dataType
+ getMinMaxFromColumnStatistics(statistics, dataType, isMax = true)
+ case (min: Min, index) =>
+ val columnName = min.column.fieldNames.head
+ val statistics = getColumnStatistics(columnName)
+ val dataType = aggSchema.apply(index).dataType
+ getMinMaxFromColumnStatistics(statistics, dataType, isMax = false)
+ case (count: Count, _) =>
+ val columnName = count.column.fieldNames.head
+ val isPartitionColumn = partitionSchema.fields
+ .map(PartitioningUtils.getColName(_, isCaseSensitive))
+ .contains(columnName)
+ // NOTE: Count(columnName) doesn't include null values.
+ // org.apache.orc.ColumnStatistics.getNumberOfValues() returns
number of non-null values
+ // for ColumnStatistics of individual column. In addition to this,
ORC also stores number
+ // of all values (null and non-null) separately.
+ val nonNullRowsCount = if (isPartitionColumn) {
+ columnsStatistics.getStatistics.getNumberOfValues
Review comment:
@sunchao - because for every row, the partition column should not be
NULL (similar reason for Parquet in
https://github.com/apache/spark/pull/33639#discussion_r725682376). So for
partition column, every row should be counted. Also updated the unit test
`FileSourceAggregatePushDownSuite."Count(partition column): push down"` to test
for null values.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetPartitionReaderFactory.scala
##########
@@ -175,24 +175,26 @@ case class ParquetPartitionReaderFactory(
} else {
new PartitionReader[ColumnarBatch] {
private var hasNext = true
- private val row: ColumnarBatch = {
+ private val batch: ColumnarBatch = {
val footer = getFooter(file)
if (footer != null && footer.getBlocks.size > 0) {
- ParquetUtils.createAggColumnarBatchFromFooter(footer,
file.filePath, dataSchema,
- partitionSchema, aggregation.get, readDataSchema,
enableOffHeapColumnVector,
+ val row = ParquetUtils.createAggInternalRowFromFooter(footer,
file.filePath,
+ dataSchema, partitionSchema, aggregation.get, readDataSchema,
getDatetimeRebaseMode(footer.getFileMetaData), isCaseSensitive)
+ AggregatePushDownUtils.convertAggregatesRowToBatch(
+ row, readDataSchema, enableOffHeapColumnVector)
Review comment:
@sunchao - makes sense to me, this is also existing behavior of
`ParquetPartitionReaderFactory.createParquetVectorizedReader()`. Updated.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -377,4 +381,106 @@ object OrcUtils extends Logging {
case _ => false
}
}
+
+ /**
+ * When the partial aggregates (Max/Min/Count) are pushed down to ORC, we
don't need to read data
+ * from ORC and aggregate at Spark layer. Instead we want to get the partial
aggregates
+ * (Max/Min/Count) result using the statistics information from ORC file
footer, and then
+ * construct an InternalRow from these aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ def createAggInternalRowFromFooter(
+ reader: Reader,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ isCaseSensitive: Boolean): InternalRow = {
+ require(aggregation.groupByColumns.length == 0,
+ s"aggregate $aggregation with group-by column shouldn't be pushed down")
+ val columnsStatistics = OrcFooterReader.readStatistics(reader)
+
+ // Get column statistics with column name.
+ def getColumnStatistics(columnName: String): ColumnStatistics = {
+ val columnIndex = dataSchema.fieldNames.indexOf(columnName)
+ columnsStatistics.get(columnIndex).getStatistics
+ }
+
+ // Get Min/Max statistics and store as ORC `WritableComparable` format.
+ def getMinMaxFromColumnStatistics(
+ statistics: ColumnStatistics,
+ dataType: DataType,
+ isMax: Boolean): WritableComparable[_] = {
+ statistics match {
+ case s: BooleanColumnStatistics =>
+ val value = if (isMax) s.getTrueCount > 0 else !(s.getFalseCount > 0)
+ new BooleanWritable(value)
+ case s: IntegerColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case ByteType => new ByteWritable(value.toByte)
+ case ShortType => new ShortWritable(value.toShort)
+ case IntegerType => new IntWritable(value.toInt)
+ case LongType => new LongWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType " +
Review comment:
@viirya - sorry, fixed.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -377,4 +381,106 @@ object OrcUtils extends Logging {
case _ => false
}
}
+
+ /**
+ * When the partial aggregates (Max/Min/Count) are pushed down to ORC, we
don't need to read data
+ * from ORC and aggregate at Spark layer. Instead we want to get the partial
aggregates
+ * (Max/Min/Count) result using the statistics information from ORC file
footer, and then
+ * construct an InternalRow from these aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ def createAggInternalRowFromFooter(
+ reader: Reader,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ isCaseSensitive: Boolean): InternalRow = {
+ require(aggregation.groupByColumns.length == 0,
+ s"aggregate $aggregation with group-by column shouldn't be pushed down")
+ val columnsStatistics = OrcFooterReader.readStatistics(reader)
Review comment:
@sunchao - normally it should have. Added code here to throw an
actionable exception here in case the file's statistics are not valid.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/orc/OrcUtils.scala
##########
@@ -377,4 +381,106 @@ object OrcUtils extends Logging {
case _ => false
}
}
+
+ /**
+ * When the partial aggregates (Max/Min/Count) are pushed down to ORC, we
don't need to read data
+ * from ORC and aggregate at Spark layer. Instead we want to get the partial
aggregates
+ * (Max/Min/Count) result using the statistics information from ORC file
footer, and then
+ * construct an InternalRow from these aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ def createAggInternalRowFromFooter(
+ reader: Reader,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ isCaseSensitive: Boolean): InternalRow = {
+ require(aggregation.groupByColumns.length == 0,
+ s"aggregate $aggregation with group-by column shouldn't be pushed down")
+ val columnsStatistics = OrcFooterReader.readStatistics(reader)
+
+ // Get column statistics with column name.
+ def getColumnStatistics(columnName: String): ColumnStatistics = {
+ val columnIndex = dataSchema.fieldNames.indexOf(columnName)
+ columnsStatistics.get(columnIndex).getStatistics
+ }
+
+ // Get Min/Max statistics and store as ORC `WritableComparable` format.
+ def getMinMaxFromColumnStatistics(
+ statistics: ColumnStatistics,
+ dataType: DataType,
+ isMax: Boolean): WritableComparable[_] = {
+ statistics match {
+ case s: BooleanColumnStatistics =>
+ val value = if (isMax) s.getTrueCount > 0 else !(s.getFalseCount > 0)
+ new BooleanWritable(value)
+ case s: IntegerColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case ByteType => new ByteWritable(value.toByte)
+ case ShortType => new ShortWritable(value.toShort)
+ case IntegerType => new IntWritable(value.toInt)
+ case LongType => new LongWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType " +
+ "for IntegerColumnStatistics")
+ }
+ case s: DoubleColumnStatistics =>
+ val value = if (isMax) s.getMaximum else s.getMinimum
+ dataType match {
+ case FloatType => new FloatWritable(value.toFloat)
+ case DoubleType => new DoubleWritable(value)
+ case _ => throw new IllegalArgumentException(
+ s"getMaxFromColumnStatistics should not take type $dataType" +
Review comment:
@sunchao - added.
--
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]
