sunchao commented on a change in pull request #33639:
URL: https://github.com/apache/spark/pull/33639#discussion_r686389729
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +145,260 @@ object ParquetUtils {
file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+
+ /**
+ * When the partial Aggregates (Max/Min/Count) are pushed down to parquet,
we don't need to
+ * createRowBaseReader to read data from parquet and aggregate at spark
layer. Instead we want
+ * to get the partial Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct an InternalRow from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ private[sql] def createAggInternalRowFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): InternalRow = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val mutableRow = new SpecificInternalRow(aggSchema.fields.map(x =>
x.dataType))
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ mutableRow.setByte(i, values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+ mutableRow.setShort(i, values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ mutableRow.setInt(i, values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+ mutableRow.update(i,
dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Integer].toLong,
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ aggSchema.fields(i).dataType match {
+ case LongType =>
+ mutableRow.setLong(i, values(i).asInstanceOf[Long])
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Long], d.precision,
d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT64")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ mutableRow.setFloat(i, values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ mutableRow.setDouble(i, values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ mutableRow.setBoolean(i, values(i).asInstanceOf[Boolean])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case StringType =>
+ mutableRow.update(i, UTF8String.fromBytes(bytes))
+ case BinaryType =>
+ mutableRow.update(i, bytes)
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for Binary")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for FIXED_LEN_BYTE_ARRAY")
+ }
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ mutableRow
+ }
+
+ /**
+ * When the Aggregates (Max/Min/Count) are pushed down to parquet, in the
case of
+ * PARQUET_VECTORIZED_READER_ENABLED sets to true, we don't need
buildColumnarReader
+ * to read data from parquet and aggregate at spark layer. Instead we want
+ * to get the Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct a ColumnarBatch from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of ColumnarBatch
+ */
+ private[sql] def createAggColumnarBatchFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ offHeap: Boolean,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): ColumnarBatch = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val capacity = 4 * 1024
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+ val columnVectors = if (offHeap) {
+ OffHeapColumnVector.allocateColumns(capacity, aggSchema)
+ } else {
+ OnHeapColumnVector.allocateColumns(capacity, aggSchema)
+ }
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ columnVectors(i).appendByte(values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+
columnVectors(i).appendShort(values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ columnVectors(i).appendInt(values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+
columnVectors(i).appendInt(dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ s" for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ columnVectors(i).appendFloat(values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ columnVectors(i).appendDouble(values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ columnVectors(i).appendBoolean(values(i).asInstanceOf[Boolean])
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ new ColumnarBatch(columnVectors.asInstanceOf[Array[ColumnVector]], 1)
+ }
+
+ /**
+ * Calculate the pushed down Aggregates (Max/Min/Count) result using the
statistics
+ * information from parquet footer file.
+ *
+ * @return A tuple of `Array[PrimitiveType.PrimitiveTypeName]` and
Array[Any].
+ * The first element is the PrimitiveTypeName of the Aggregate
column,
+ * and the second element is the aggregated value.
+ */
+ private[sql] def getPushedDownAggResult(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ isCaseSensitive: Boolean)
+ : (Array[PrimitiveType.PrimitiveTypeName], Array[Any]) = {
+ val footerFileMetaData = footer.getFileMetaData
+ val fields = footerFileMetaData.getSchema.getFields
+ val blocks = footer.getBlocks()
+ val typesBuilder = ArrayBuilder.make[PrimitiveType.PrimitiveTypeName]
+ val valuesBuilder = ArrayBuilder.make[Any]
+
+ aggregation.aggregateExpressions().foreach { agg =>
+ var value: Any = None
+ var rowCount = 0L
+ var isCount = false
+ var index = 0
+ blocks.forEach { block =>
+ val blockMetaData = block.getColumns()
+ agg match {
+ case max: Max =>
+ index =
dataSchema.fieldNames.toList.indexOf(max.column.fieldNames.head)
+ val currentMax = getCurrentBlockMaxOrMin(blockMetaData, index,
true)
+ if (currentMax != None &&
+ (value == None ||
currentMax.asInstanceOf[Comparable[Any]].compareTo(value) > 0)) {
+ value = currentMax
+ }
+ case min: Min =>
+ index =
dataSchema.fieldNames.toList.indexOf(min.column.fieldNames.head)
+ val currentMin = getCurrentBlockMaxOrMin(blockMetaData, index,
false)
+ if (currentMin != None &&
+ (value == None ||
currentMin.asInstanceOf[Comparable[Any]].compareTo(value) < 0)) {
+ value = currentMin
+ }
+ case count: Count =>
+ rowCount += block.getRowCount
Review comment:
nit: `rowCount = block.getRowCount`? it's easier to understand.
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +145,260 @@ object ParquetUtils {
file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+
+ /**
+ * When the partial Aggregates (Max/Min/Count) are pushed down to parquet,
we don't need to
+ * createRowBaseReader to read data from parquet and aggregate at spark
layer. Instead we want
+ * to get the partial Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct an InternalRow from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ private[sql] def createAggInternalRowFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): InternalRow = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val mutableRow = new SpecificInternalRow(aggSchema.fields.map(x =>
x.dataType))
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ mutableRow.setByte(i, values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+ mutableRow.setShort(i, values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ mutableRow.setInt(i, values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+ mutableRow.update(i,
dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Integer].toLong,
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ aggSchema.fields(i).dataType match {
+ case LongType =>
+ mutableRow.setLong(i, values(i).asInstanceOf[Long])
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Long], d.precision,
d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT64")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ mutableRow.setFloat(i, values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ mutableRow.setDouble(i, values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ mutableRow.setBoolean(i, values(i).asInstanceOf[Boolean])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case StringType =>
+ mutableRow.update(i, UTF8String.fromBytes(bytes))
+ case BinaryType =>
+ mutableRow.update(i, bytes)
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for Binary")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for FIXED_LEN_BYTE_ARRAY")
+ }
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ mutableRow
+ }
+
+ /**
+ * When the Aggregates (Max/Min/Count) are pushed down to parquet, in the
case of
+ * PARQUET_VECTORIZED_READER_ENABLED sets to true, we don't need
buildColumnarReader
+ * to read data from parquet and aggregate at spark layer. Instead we want
+ * to get the Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct a ColumnarBatch from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of ColumnarBatch
+ */
+ private[sql] def createAggColumnarBatchFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ offHeap: Boolean,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): ColumnarBatch = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val capacity = 4 * 1024
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+ val columnVectors = if (offHeap) {
+ OffHeapColumnVector.allocateColumns(capacity, aggSchema)
+ } else {
+ OnHeapColumnVector.allocateColumns(capacity, aggSchema)
+ }
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ columnVectors(i).appendByte(values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+
columnVectors(i).appendShort(values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ columnVectors(i).appendInt(values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+
columnVectors(i).appendInt(dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ s" for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ columnVectors(i).appendFloat(values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ columnVectors(i).appendDouble(values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ columnVectors(i).appendBoolean(values(i).asInstanceOf[Boolean])
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ new ColumnarBatch(columnVectors.asInstanceOf[Array[ColumnVector]], 1)
+ }
+
+ /**
+ * Calculate the pushed down Aggregates (Max/Min/Count) result using the
statistics
+ * information from parquet footer file.
+ *
+ * @return A tuple of `Array[PrimitiveType.PrimitiveTypeName]` and
Array[Any].
+ * The first element is the PrimitiveTypeName of the Aggregate
column,
+ * and the second element is the aggregated value.
+ */
+ private[sql] def getPushedDownAggResult(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ isCaseSensitive: Boolean)
+ : (Array[PrimitiveType.PrimitiveTypeName], Array[Any]) = {
+ val footerFileMetaData = footer.getFileMetaData
+ val fields = footerFileMetaData.getSchema.getFields
+ val blocks = footer.getBlocks()
+ val typesBuilder = ArrayBuilder.make[PrimitiveType.PrimitiveTypeName]
+ val valuesBuilder = ArrayBuilder.make[Any]
+
+ aggregation.aggregateExpressions().foreach { agg =>
+ var value: Any = None
+ var rowCount = 0L
+ var isCount = false
+ var index = 0
+ blocks.forEach { block =>
+ val blockMetaData = block.getColumns()
+ agg match {
+ case max: Max =>
+ index =
dataSchema.fieldNames.toList.indexOf(max.column.fieldNames.head)
+ val currentMax = getCurrentBlockMaxOrMin(blockMetaData, index,
true)
+ if (currentMax != None &&
+ (value == None ||
currentMax.asInstanceOf[Comparable[Any]].compareTo(value) > 0)) {
+ value = currentMax
+ }
+ case min: Min =>
+ index =
dataSchema.fieldNames.toList.indexOf(min.column.fieldNames.head)
+ val currentMin = getCurrentBlockMaxOrMin(blockMetaData, index,
false)
+ if (currentMin != None &&
+ (value == None ||
currentMin.asInstanceOf[Comparable[Any]].compareTo(value) < 0)) {
+ value = currentMin
+ }
+ case count: Count =>
+ rowCount += block.getRowCount
+ var isPartitionCol = false;
+ if (partitionSchema.fields.map(PartitioningUtils.getColName(_,
isCaseSensitive))
+ .toSet.contains(count.column().fieldNames.head)) {
+ isPartitionCol = true
+ }
+ isCount = true
+ if(!isPartitionCol) {
+ index =
dataSchema.fieldNames.toList.indexOf(count.column.fieldNames.head)
+ // Count(*) includes the null values, but Count (colName)
doesn't.
+ rowCount -= getNumNulls(blockMetaData, index)
Review comment:
what about `COUNT(DISTINCT)`?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +145,260 @@ object ParquetUtils {
file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+
+ /**
+ * When the partial Aggregates (Max/Min/Count) are pushed down to parquet,
we don't need to
+ * createRowBaseReader to read data from parquet and aggregate at spark
layer. Instead we want
+ * to get the partial Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct an InternalRow from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ private[sql] def createAggInternalRowFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): InternalRow = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val mutableRow = new SpecificInternalRow(aggSchema.fields.map(x =>
x.dataType))
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ mutableRow.setByte(i, values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+ mutableRow.setShort(i, values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ mutableRow.setInt(i, values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+ mutableRow.update(i,
dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Integer].toLong,
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ aggSchema.fields(i).dataType match {
+ case LongType =>
+ mutableRow.setLong(i, values(i).asInstanceOf[Long])
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Long], d.precision,
d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT64")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ mutableRow.setFloat(i, values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ mutableRow.setDouble(i, values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ mutableRow.setBoolean(i, values(i).asInstanceOf[Boolean])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case StringType =>
+ mutableRow.update(i, UTF8String.fromBytes(bytes))
+ case BinaryType =>
+ mutableRow.update(i, bytes)
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for Binary")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for FIXED_LEN_BYTE_ARRAY")
+ }
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ mutableRow
+ }
+
+ /**
+ * When the Aggregates (Max/Min/Count) are pushed down to parquet, in the
case of
+ * PARQUET_VECTORIZED_READER_ENABLED sets to true, we don't need
buildColumnarReader
+ * to read data from parquet and aggregate at spark layer. Instead we want
+ * to get the Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct a ColumnarBatch from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of ColumnarBatch
+ */
+ private[sql] def createAggColumnarBatchFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ offHeap: Boolean,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): ColumnarBatch = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val capacity = 4 * 1024
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+ val columnVectors = if (offHeap) {
+ OffHeapColumnVector.allocateColumns(capacity, aggSchema)
+ } else {
+ OnHeapColumnVector.allocateColumns(capacity, aggSchema)
+ }
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ columnVectors(i).appendByte(values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+
columnVectors(i).appendShort(values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ columnVectors(i).appendInt(values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+
columnVectors(i).appendInt(dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ s" for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ columnVectors(i).appendFloat(values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ columnVectors(i).appendDouble(values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ columnVectors(i).appendBoolean(values(i).asInstanceOf[Boolean])
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ new ColumnarBatch(columnVectors.asInstanceOf[Array[ColumnVector]], 1)
+ }
+
+ /**
+ * Calculate the pushed down Aggregates (Max/Min/Count) result using the
statistics
+ * information from parquet footer file.
+ *
+ * @return A tuple of `Array[PrimitiveType.PrimitiveTypeName]` and
Array[Any].
+ * The first element is the PrimitiveTypeName of the Aggregate
column,
+ * and the second element is the aggregated value.
+ */
+ private[sql] def getPushedDownAggResult(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ isCaseSensitive: Boolean)
+ : (Array[PrimitiveType.PrimitiveTypeName], Array[Any]) = {
+ val footerFileMetaData = footer.getFileMetaData
+ val fields = footerFileMetaData.getSchema.getFields
+ val blocks = footer.getBlocks()
+ val typesBuilder = ArrayBuilder.make[PrimitiveType.PrimitiveTypeName]
+ val valuesBuilder = ArrayBuilder.make[Any]
+
+ aggregation.aggregateExpressions().foreach { agg =>
+ var value: Any = None
+ var rowCount = 0L
+ var isCount = false
+ var index = 0
+ blocks.forEach { block =>
+ val blockMetaData = block.getColumns()
+ agg match {
+ case max: Max =>
+ index =
dataSchema.fieldNames.toList.indexOf(max.column.fieldNames.head)
+ val currentMax = getCurrentBlockMaxOrMin(blockMetaData, index,
true)
+ if (currentMax != None &&
+ (value == None ||
currentMax.asInstanceOf[Comparable[Any]].compareTo(value) > 0)) {
+ value = currentMax
+ }
+ case min: Min =>
+ index =
dataSchema.fieldNames.toList.indexOf(min.column.fieldNames.head)
+ val currentMin = getCurrentBlockMaxOrMin(blockMetaData, index,
false)
+ if (currentMin != None &&
+ (value == None ||
currentMin.asInstanceOf[Comparable[Any]].compareTo(value) < 0)) {
+ value = currentMin
+ }
+ case count: Count =>
+ rowCount += block.getRowCount
+ var isPartitionCol = false;
+ if (partitionSchema.fields.map(PartitioningUtils.getColName(_,
isCaseSensitive))
+ .toSet.contains(count.column().fieldNames.head)) {
+ isPartitionCol = true
+ }
+ isCount = true
+ if(!isPartitionCol) {
+ index =
dataSchema.fieldNames.toList.indexOf(count.column.fieldNames.head)
+ // Count(*) includes the null values, but Count (colName)
doesn't.
+ rowCount -= getNumNulls(blockMetaData, index)
Review comment:
oh NVM it is not supported
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +145,260 @@ object ParquetUtils {
file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+
+ /**
+ * When the partial Aggregates (Max/Min/Count) are pushed down to parquet,
we don't need to
+ * createRowBaseReader to read data from parquet and aggregate at spark
layer. Instead we want
+ * to get the partial Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct an InternalRow from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ private[sql] def createAggInternalRowFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): InternalRow = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val mutableRow = new SpecificInternalRow(aggSchema.fields.map(x =>
x.dataType))
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ mutableRow.setByte(i, values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+ mutableRow.setShort(i, values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ mutableRow.setInt(i, values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+ mutableRow.update(i,
dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Integer].toLong,
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ aggSchema.fields(i).dataType match {
+ case LongType =>
+ mutableRow.setLong(i, values(i).asInstanceOf[Long])
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Long], d.precision,
d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT64")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ mutableRow.setFloat(i, values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ mutableRow.setDouble(i, values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ mutableRow.setBoolean(i, values(i).asInstanceOf[Boolean])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case StringType =>
+ mutableRow.update(i, UTF8String.fromBytes(bytes))
+ case BinaryType =>
+ mutableRow.update(i, bytes)
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for Binary")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for FIXED_LEN_BYTE_ARRAY")
+ }
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ mutableRow
+ }
+
+ /**
+ * When the Aggregates (Max/Min/Count) are pushed down to parquet, in the
case of
+ * PARQUET_VECTORIZED_READER_ENABLED sets to true, we don't need
buildColumnarReader
+ * to read data from parquet and aggregate at spark layer. Instead we want
+ * to get the Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct a ColumnarBatch from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of ColumnarBatch
+ */
+ private[sql] def createAggColumnarBatchFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ offHeap: Boolean,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): ColumnarBatch = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val capacity = 4 * 1024
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+ val columnVectors = if (offHeap) {
+ OffHeapColumnVector.allocateColumns(capacity, aggSchema)
+ } else {
+ OnHeapColumnVector.allocateColumns(capacity, aggSchema)
+ }
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ columnVectors(i).appendByte(values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+
columnVectors(i).appendShort(values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ columnVectors(i).appendInt(values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+
columnVectors(i).appendInt(dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ s" for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ columnVectors(i).appendFloat(values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ columnVectors(i).appendDouble(values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ columnVectors(i).appendBoolean(values(i).asInstanceOf[Boolean])
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ new ColumnarBatch(columnVectors.asInstanceOf[Array[ColumnVector]], 1)
+ }
+
+ /**
+ * Calculate the pushed down Aggregates (Max/Min/Count) result using the
statistics
+ * information from parquet footer file.
+ *
+ * @return A tuple of `Array[PrimitiveType.PrimitiveTypeName]` and
Array[Any].
+ * The first element is the PrimitiveTypeName of the Aggregate
column,
+ * and the second element is the aggregated value.
+ */
+ private[sql] def getPushedDownAggResult(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ isCaseSensitive: Boolean)
+ : (Array[PrimitiveType.PrimitiveTypeName], Array[Any]) = {
+ val footerFileMetaData = footer.getFileMetaData
+ val fields = footerFileMetaData.getSchema.getFields
+ val blocks = footer.getBlocks()
+ val typesBuilder = ArrayBuilder.make[PrimitiveType.PrimitiveTypeName]
+ val valuesBuilder = ArrayBuilder.make[Any]
+
+ aggregation.aggregateExpressions().foreach { agg =>
+ var value: Any = None
+ var rowCount = 0L
+ var isCount = false
+ var index = 0
+ blocks.forEach { block =>
+ val blockMetaData = block.getColumns()
+ agg match {
+ case max: Max =>
+ index =
dataSchema.fieldNames.toList.indexOf(max.column.fieldNames.head)
+ val currentMax = getCurrentBlockMaxOrMin(blockMetaData, index,
true)
+ if (currentMax != None &&
+ (value == None ||
currentMax.asInstanceOf[Comparable[Any]].compareTo(value) > 0)) {
+ value = currentMax
+ }
+ case min: Min =>
+ index =
dataSchema.fieldNames.toList.indexOf(min.column.fieldNames.head)
+ val currentMin = getCurrentBlockMaxOrMin(blockMetaData, index,
false)
+ if (currentMin != None &&
+ (value == None ||
currentMin.asInstanceOf[Comparable[Any]].compareTo(value) < 0)) {
+ value = currentMin
+ }
+ case count: Count =>
+ rowCount += block.getRowCount
+ var isPartitionCol = false;
+ if (partitionSchema.fields.map(PartitioningUtils.getColName(_,
isCaseSensitive))
+ .toSet.contains(count.column().fieldNames.head)) {
+ isPartitionCol = true
+ }
+ isCount = true
+ if(!isPartitionCol) {
+ index =
dataSchema.fieldNames.toList.indexOf(count.column.fieldNames.head)
+ // Count(*) includes the null values, but Count (colName)
doesn't.
+ rowCount -= getNumNulls(blockMetaData, index)
+ }
+ case _: CountStar =>
+ rowCount += block.getRowCount
Review comment:
ditto
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +145,260 @@ object ParquetUtils {
file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+
+ /**
+ * When the partial Aggregates (Max/Min/Count) are pushed down to parquet,
we don't need to
+ * createRowBaseReader to read data from parquet and aggregate at spark
layer. Instead we want
+ * to get the partial Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct an InternalRow from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ private[sql] def createAggInternalRowFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): InternalRow = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val mutableRow = new SpecificInternalRow(aggSchema.fields.map(x =>
x.dataType))
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ mutableRow.setByte(i, values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+ mutableRow.setShort(i, values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ mutableRow.setInt(i, values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+ mutableRow.update(i,
dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Integer].toLong,
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ aggSchema.fields(i).dataType match {
+ case LongType =>
+ mutableRow.setLong(i, values(i).asInstanceOf[Long])
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Long], d.precision,
d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT64")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ mutableRow.setFloat(i, values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ mutableRow.setDouble(i, values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ mutableRow.setBoolean(i, values(i).asInstanceOf[Boolean])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case StringType =>
+ mutableRow.update(i, UTF8String.fromBytes(bytes))
+ case BinaryType =>
+ mutableRow.update(i, bytes)
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for Binary")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for FIXED_LEN_BYTE_ARRAY")
+ }
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ mutableRow
+ }
+
+ /**
+ * When the Aggregates (Max/Min/Count) are pushed down to parquet, in the
case of
+ * PARQUET_VECTORIZED_READER_ENABLED sets to true, we don't need
buildColumnarReader
+ * to read data from parquet and aggregate at spark layer. Instead we want
+ * to get the Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct a ColumnarBatch from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of ColumnarBatch
+ */
+ private[sql] def createAggColumnarBatchFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ offHeap: Boolean,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): ColumnarBatch = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val capacity = 4 * 1024
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+ val columnVectors = if (offHeap) {
+ OffHeapColumnVector.allocateColumns(capacity, aggSchema)
+ } else {
+ OnHeapColumnVector.allocateColumns(capacity, aggSchema)
+ }
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ columnVectors(i).appendByte(values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+
columnVectors(i).appendShort(values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ columnVectors(i).appendInt(values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+
columnVectors(i).appendInt(dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ s" for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ columnVectors(i).appendFloat(values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ columnVectors(i).appendDouble(values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ columnVectors(i).appendBoolean(values(i).asInstanceOf[Boolean])
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ new ColumnarBatch(columnVectors.asInstanceOf[Array[ColumnVector]], 1)
+ }
+
+ /**
+ * Calculate the pushed down Aggregates (Max/Min/Count) result using the
statistics
+ * information from parquet footer file.
+ *
+ * @return A tuple of `Array[PrimitiveType.PrimitiveTypeName]` and
Array[Any].
+ * The first element is the PrimitiveTypeName of the Aggregate
column,
+ * and the second element is the aggregated value.
+ */
+ private[sql] def getPushedDownAggResult(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ isCaseSensitive: Boolean)
+ : (Array[PrimitiveType.PrimitiveTypeName], Array[Any]) = {
+ val footerFileMetaData = footer.getFileMetaData
+ val fields = footerFileMetaData.getSchema.getFields
+ val blocks = footer.getBlocks()
+ val typesBuilder = ArrayBuilder.make[PrimitiveType.PrimitiveTypeName]
+ val valuesBuilder = ArrayBuilder.make[Any]
+
+ aggregation.aggregateExpressions().foreach { agg =>
+ var value: Any = None
+ var rowCount = 0L
+ var isCount = false
+ var index = 0
+ blocks.forEach { block =>
+ val blockMetaData = block.getColumns()
+ agg match {
+ case max: Max =>
+ index =
dataSchema.fieldNames.toList.indexOf(max.column.fieldNames.head)
+ val currentMax = getCurrentBlockMaxOrMin(blockMetaData, index,
true)
+ if (currentMax != None &&
+ (value == None ||
currentMax.asInstanceOf[Comparable[Any]].compareTo(value) > 0)) {
+ value = currentMax
+ }
+ case min: Min =>
+ index =
dataSchema.fieldNames.toList.indexOf(min.column.fieldNames.head)
+ val currentMin = getCurrentBlockMaxOrMin(blockMetaData, index,
false)
+ if (currentMin != None &&
+ (value == None ||
currentMin.asInstanceOf[Comparable[Any]].compareTo(value) < 0)) {
+ value = currentMin
+ }
+ case count: Count =>
+ rowCount += block.getRowCount
+ var isPartitionCol = false;
+ if (partitionSchema.fields.map(PartitioningUtils.getColName(_,
isCaseSensitive))
+ .toSet.contains(count.column().fieldNames.head)) {
+ isPartitionCol = true
+ }
+ isCount = true
+ if(!isPartitionCol) {
+ index =
dataSchema.fieldNames.toList.indexOf(count.column.fieldNames.head)
+ // Count(*) includes the null values, but Count (colName)
doesn't.
+ rowCount -= getNumNulls(blockMetaData, index)
+ }
+ case _: CountStar =>
+ rowCount += block.getRowCount
+ isCount = true
+ case _ =>
Review comment:
what about `Sum` - will it ever get here?
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +145,260 @@ object ParquetUtils {
file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+
+ /**
+ * When the partial Aggregates (Max/Min/Count) are pushed down to parquet,
we don't need to
+ * createRowBaseReader to read data from parquet and aggregate at spark
layer. Instead we want
+ * to get the partial Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct an InternalRow from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of InternalRow
+ */
+ private[sql] def createAggInternalRowFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): InternalRow = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val mutableRow = new SpecificInternalRow(aggSchema.fields.map(x =>
x.dataType))
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ mutableRow.setByte(i, values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+ mutableRow.setShort(i, values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ mutableRow.setInt(i, values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+ mutableRow.update(i,
dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Integer].toLong,
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ aggSchema.fields(i).dataType match {
+ case LongType =>
+ mutableRow.setLong(i, values(i).asInstanceOf[Long])
+ case d: DecimalType =>
+ val decimal = Decimal(values(i).asInstanceOf[Long], d.precision,
d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for INT64")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ mutableRow.setFloat(i, values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ mutableRow.setDouble(i, values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ mutableRow.setBoolean(i, values(i).asInstanceOf[Boolean])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case StringType =>
+ mutableRow.update(i, UTF8String.fromBytes(bytes))
+ case BinaryType =>
+ mutableRow.update(i, bytes)
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for Binary")
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ aggSchema.fields(i).dataType match {
+ case d: DecimalType =>
+ val decimal =
+ Decimal(new BigDecimal(new BigInteger(bytes), d.scale),
d.precision, d.scale)
+ mutableRow.setDecimal(i, decimal, d.precision)
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ " for FIXED_LEN_BYTE_ARRAY")
+ }
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ mutableRow
+ }
+
+ /**
+ * When the Aggregates (Max/Min/Count) are pushed down to parquet, in the
case of
+ * PARQUET_VECTORIZED_READER_ENABLED sets to true, we don't need
buildColumnarReader
+ * to read data from parquet and aggregate at spark layer. Instead we want
+ * to get the Aggregates (Max/Min/Count) result using the statistics
information
+ * from parquet footer file, and then construct a ColumnarBatch from these
Aggregate results.
+ *
+ * @return Aggregate results in the format of ColumnarBatch
+ */
+ private[sql] def createAggColumnarBatchFromFooter(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ aggSchema: StructType,
+ offHeap: Boolean,
+ datetimeRebaseModeInRead: String,
+ isCaseSensitive: Boolean): ColumnarBatch = {
+ val (parquetTypes, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+ val capacity = 4 * 1024
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get, datetimeRebaseModeInRead)
+ val columnVectors = if (offHeap) {
+ OffHeapColumnVector.allocateColumns(capacity, aggSchema)
+ } else {
+ OnHeapColumnVector.allocateColumns(capacity, aggSchema)
+ }
+
+ parquetTypes.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ aggSchema.fields(i).dataType match {
+ case ByteType =>
+ columnVectors(i).appendByte(values(i).asInstanceOf[Integer].toByte)
+ case ShortType =>
+
columnVectors(i).appendShort(values(i).asInstanceOf[Integer].toShort)
+ case IntegerType =>
+ columnVectors(i).appendInt(values(i).asInstanceOf[Integer])
+ case DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+
columnVectors(i).appendInt(dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case _ => throw new SparkException(s"Unexpected type
${aggSchema.fields(i).dataType}" +
+ s" for INT32")
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ columnVectors(i).appendFloat(values(i).asInstanceOf[Float])
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ columnVectors(i).appendDouble(values(i).asInstanceOf[Double])
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ columnVectors(i).appendBoolean(values(i).asInstanceOf[Boolean])
+ case _ =>
+ throw new SparkException("Unexpected parquet type name")
+ }
+ new ColumnarBatch(columnVectors.asInstanceOf[Array[ColumnVector]], 1)
+ }
+
+ /**
+ * Calculate the pushed down Aggregates (Max/Min/Count) result using the
statistics
+ * information from parquet footer file.
+ *
+ * @return A tuple of `Array[PrimitiveType.PrimitiveTypeName]` and
Array[Any].
+ * The first element is the PrimitiveTypeName of the Aggregate
column,
+ * and the second element is the aggregated value.
+ */
+ private[sql] def getPushedDownAggResult(
+ footer: ParquetMetadata,
+ dataSchema: StructType,
+ partitionSchema: StructType,
+ aggregation: Aggregation,
+ isCaseSensitive: Boolean)
+ : (Array[PrimitiveType.PrimitiveTypeName], Array[Any]) = {
+ val footerFileMetaData = footer.getFileMetaData
+ val fields = footerFileMetaData.getSchema.getFields
+ val blocks = footer.getBlocks()
+ val typesBuilder = ArrayBuilder.make[PrimitiveType.PrimitiveTypeName]
+ val valuesBuilder = ArrayBuilder.make[Any]
+
+ aggregation.aggregateExpressions().foreach { agg =>
+ var value: Any = None
+ var rowCount = 0L
+ var isCount = false
+ var index = 0
+ blocks.forEach { block =>
+ val blockMetaData = block.getColumns()
+ agg match {
+ case max: Max =>
+ index =
dataSchema.fieldNames.toList.indexOf(max.column.fieldNames.head)
+ val currentMax = getCurrentBlockMaxOrMin(blockMetaData, index,
true)
+ if (currentMax != None &&
+ (value == None ||
currentMax.asInstanceOf[Comparable[Any]].compareTo(value) > 0)) {
+ value = currentMax
+ }
+ case min: Min =>
+ index =
dataSchema.fieldNames.toList.indexOf(min.column.fieldNames.head)
+ val currentMin = getCurrentBlockMaxOrMin(blockMetaData, index,
false)
+ if (currentMin != None &&
+ (value == None ||
currentMin.asInstanceOf[Comparable[Any]].compareTo(value) < 0)) {
+ value = currentMin
+ }
+ case count: Count =>
+ rowCount += block.getRowCount
+ var isPartitionCol = false;
+ if (partitionSchema.fields.map(PartitioningUtils.getColName(_,
isCaseSensitive))
+ .toSet.contains(count.column().fieldNames.head)) {
+ isPartitionCol = true
+ }
+ isCount = true
+ if(!isPartitionCol) {
+ index =
dataSchema.fieldNames.toList.indexOf(count.column.fieldNames.head)
+ // Count(*) includes the null values, but Count (colName)
doesn't.
+ rowCount -= getNumNulls(blockMetaData, index)
+ }
+ case _: CountStar =>
+ rowCount += block.getRowCount
+ isCount = true
+ case _ =>
Review comment:
NVM saw that it is not supported
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