huaxingao commented on a change in pull request #32049:
URL: https://github.com/apache/spark/pull/32049#discussion_r611267565
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +147,360 @@ object ParquetUtils {
file.getName == ParquetFileWriter.PARQUET_COMMON_METADATA_FILE ||
file.getName == ParquetFileWriter.PARQUET_METADATA_FILE
}
+
+ /**
+ * When the 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 calculate the 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 aggResultToSparkInternalRows(
+ footer: ParquetMetadata,
+ parquetTypes: Seq[PrimitiveType.PrimitiveTypeName],
+ values: Seq[Any],
+ dataSchema: StructType,
+ datetimeRebaseModeInRead: String,
+ int96RebaseModeInRead: String,
+ convertTz: Option[ZoneId]): InternalRow = {
+ val mutableRow = new SpecificInternalRow(dataSchema.fields.map(x =>
x.dataType))
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get,
+ datetimeRebaseModeInRead)
+ val int96RebaseMode = DataSourceUtils.int96RebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get,
+ int96RebaseModeInRead)
+ parquetTypes.zipWithIndex.map {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ dataSchema.fields(i).dataType match {
+ case b: ByteType =>
+ mutableRow.setByte(i, values(i).asInstanceOf[Integer].toByte)
+ case s: ShortType =>
+ mutableRow.setShort(i, values(i).asInstanceOf[Integer].toShort)
+ case int: IntegerType =>
+ mutableRow.setInt(i, values(i).asInstanceOf[Integer])
+ case d: 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 IllegalArgumentException("Unexpected type for
INT32")
+ }
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ dataSchema.fields(i).dataType match {
+ case long: 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 IllegalArgumentException("Unexpected type for
INT64")
+ }
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT96, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ dataSchema.fields(i).dataType match {
+ case l: LongType =>
+ mutableRow.setLong(i, values(i).asInstanceOf[Long])
+ case d: TimestampType =>
+ val int96RebaseFunc =
DataSourceUtils.creteTimestampRebaseFuncInRead(
+ int96RebaseMode, "Parquet INT96")
+ val julianMicros =
+
ParquetRowConverter.binaryToSQLTimestamp(values(i).asInstanceOf[Binary])
+ val gregorianMicros = int96RebaseFunc(julianMicros)
+ val adjTime =
+ convertTz.map(DateTimeUtils.convertTz(gregorianMicros, _,
ZoneOffset.UTC))
+ .getOrElse(gregorianMicros)
+ mutableRow.setLong(i, adjTime)
+ case _ => throw new IllegalArgumentException("Unexpected type for
INT96")
+ }
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ mutableRow.setFloat(i, values(i).asInstanceOf[Float])
+ }
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ mutableRow.setDouble(i, values(i).asInstanceOf[Double])
+ }
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ mutableRow.setBoolean(i, values(i).asInstanceOf[Boolean])
+ }
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ dataSchema.fields(i).dataType match {
+ case s: StringType =>
+ mutableRow.update(i, UTF8String.fromBytes(bytes))
+ case b: 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 IllegalArgumentException("Unexpected type for
Binary")
+ }
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ if (values(i) == null) {
+ mutableRow.setNullAt(i)
+ } else {
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ dataSchema.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 IllegalArgumentException("Unexpected type for
FIXED_LEN_BYTE_ARRAY")
+ }
+ }
+ case _ =>
+ throw new IllegalArgumentException("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 calculate 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 aggResultToSparkColumnarBatch(
+ footer: ParquetMetadata,
+ parquetTypes: Seq[PrimitiveType.PrimitiveTypeName],
+ values: Seq[Any],
+ dataSchema: StructType,
+ offHeap: Boolean,
+ datetimeRebaseModeInRead: String,
+ int96RebaseModeInRead: String,
+ convertTz: Option[ZoneId]): ColumnarBatch = {
+ val capacity = 4 * 1024
+ val footerFileMetaData = footer.getFileMetaData
+ val datetimeRebaseMode = DataSourceUtils.datetimeRebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get,
+ datetimeRebaseModeInRead)
+ val int96RebaseMode = DataSourceUtils.int96RebaseMode(
+ footerFileMetaData.getKeyValueMetaData.get,
+ int96RebaseModeInRead)
+ val columnVectors = if (offHeap) {
+ OffHeapColumnVector.allocateColumns(capacity, dataSchema)
+ } else {
+ OnHeapColumnVector.allocateColumns(capacity, dataSchema)
+ }
+
+ parquetTypes.zipWithIndex.map {
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ dataSchema.fields(i).dataType match {
+ case b: ByteType =>
+
columnVectors(i).appendByte(values(i).asInstanceOf[Integer].toByte)
+ case s: ShortType =>
+
columnVectors(i).appendShort(values(i).asInstanceOf[Integer].toShort)
+ case int: IntegerType =>
+ columnVectors(i).appendInt(values(i).asInstanceOf[Integer])
+ case d: DateType =>
+ val dateRebaseFunc = DataSourceUtils.creteDateRebaseFuncInRead(
+ datetimeRebaseMode, "Parquet")
+
columnVectors(i).appendInt(dateRebaseFunc(values(i).asInstanceOf[Integer]))
+ case _ => throw new IllegalArgumentException("Unexpected type for
INT32")
+ }
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ }
+ case (PrimitiveType.PrimitiveTypeName.INT96, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ dataSchema.fields(i).dataType match {
+ case l: LongType =>
+ columnVectors(i).appendLong(values(i).asInstanceOf[Long])
+ case d: TimestampType =>
+ val int96RebaseFunc =
DataSourceUtils.creteTimestampRebaseFuncInRead(
+ int96RebaseMode, "Parquet INT96")
+ val julianMicros =
+
ParquetRowConverter.binaryToSQLTimestamp(values(i).asInstanceOf[Binary])
+ val gregorianMicros = int96RebaseFunc(julianMicros)
+ val adjTime =
+ convertTz.map(DateTimeUtils.convertTz(gregorianMicros, _,
ZoneOffset.UTC))
+ .getOrElse(gregorianMicros)
+ columnVectors(i).appendLong(adjTime)
+ case _ => throw new IllegalArgumentException("Unexpected type for
INT96")
+ }
+ }
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ columnVectors(i).appendFloat(values(i).asInstanceOf[Float])
+ }
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ columnVectors(i).appendDouble(values(i).asInstanceOf[Double])
+ }
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ }
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ val bytes = values(i).asInstanceOf[Binary].getBytes
+ columnVectors(i).putByteArray(0, bytes, 0, bytes.length)
+ }
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ if (values(i) == null) {
+ columnVectors(i).appendNull()
+ } else {
+ columnVectors(i).appendBoolean(values(i).asInstanceOf[Boolean])
+ }
+ case _ =>
+ throw new IllegalArgumentException("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,
+ aggregation: Aggregation)
+ : (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]
+
+ for (i <- 0 until aggregation.aggregateExpressions.size) {
+ var value: Any = None
+ var rowCount = 0L
+ var isCount = false
+ var index = 0
+ blocks.forEach { block =>
+ val blockMetaData = block.getColumns()
+ aggregation.aggregateExpressions(i) match {
+ case Max(col, _) =>
+ index = dataSchema.fieldNames.toList.indexOf(col)
+ val currentMax = getCurrentBlockMaxOrMin(footer, blockMetaData,
index, true)
+ if (currentMax != None &&
+ (value == None ||
currentMax.asInstanceOf[Comparable[Any]].compareTo(value) > 0)) {
+ value = currentMax
+ }
+
+ case Min(col, _) =>
+ index = dataSchema.fieldNames.toList.indexOf(col)
+ val currentMin = getCurrentBlockMaxOrMin(footer, blockMetaData,
index, false)
+ if (currentMin != None &&
+ (value == None ||
currentMin.asInstanceOf[Comparable[Any]].compareTo(value) < 0)) {
+ value = currentMin
+ }
+
+ case Count(col, _, _) =>
+ index = dataSchema.fieldNames.toList.indexOf(col)
+ rowCount += getRowCountFromParquetMetadata(footer)
+ if (!col.equals("1")) { // "1" is for count(*)
+ rowCount -= getNumNulls(footer, blockMetaData, index)
+ }
+ isCount = true
+
+ case _ =>
+ }
+ }
+ if (isCount) {
+ valuesBuilder += rowCount
+ typesBuilder += PrimitiveType.PrimitiveTypeName.INT96
+ } else {
+ valuesBuilder += value
+ typesBuilder += fields.get(index).asPrimitiveType.getPrimitiveTypeName
+ }
+ }
+ (typesBuilder.result(), valuesBuilder.result())
+ }
+
+ /**
+ * get the Max or Min value for ith column in the current block
+ *
+ * @return the Max or Min value
+ */
+ private def getCurrentBlockMaxOrMin(
+ footer: ParquetMetadata,
+ columnChunkMetaData: util.List[ColumnChunkMetaData],
+ i: Int,
+ isMax: Boolean): Any = {
+ val parquetType = footer.getFileMetaData.getSchema.getType(i)
+ if (!parquetType.isPrimitive) {
+ throw new IllegalArgumentException("Unsupported type : " +
parquetType.toString)
+ }
+ val statistics = columnChunkMetaData.get(i).getStatistics()
+ if (isMax) statistics.genericGetMax() else statistics.genericGetMin()
Review comment:
I can't find a good way to fall back. We won't be able to read footer
until `FilePartitionReaderFactory.createReader`, that's when we can get a
partition of the file to read. Seems to me it's too late to fall back at that
time. I looked at Presto parquet partial aggregation implementation, and it
throws Exception. I will throw Exception for now. If anybody has a better idea,
please let know.
--
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.
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
