huaxingao commented on a change in pull request #33639:
URL: https://github.com/apache/spark/pull/33639#discussion_r699846813
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetUtils.scala
##########
@@ -127,4 +144,209 @@ 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,
+ isCaseSensitive: Boolean): InternalRow = {
+ val (primitiveType, values) =
+ getPushedDownAggResult(footer, dataSchema, partitionSchema, aggregation,
isCaseSensitive)
+
+ val builder = Types.buildMessage()
+ primitiveType.foreach(t => builder.addField(t))
+ val parquetSchema = builder.named("root")
+
+ val schemaConverter = new ParquetToSparkSchemaConverter
+ val converter = new ParquetRowConverter(schemaConverter, parquetSchema,
aggSchema,
+ None, LegacyBehaviorPolicy.CORRECTED, LegacyBehaviorPolicy.CORRECTED,
NoopUpdater)
+ val primitiveTypeName = primitiveType.map(_.getPrimitiveTypeName)
+ primitiveTypeName.zipWithIndex.foreach {
+ case (PrimitiveType.PrimitiveTypeName.BOOLEAN, i) =>
+ val v = values(i).asInstanceOf[Boolean]
+ converter.getConverter(i).asPrimitiveConverter().addBoolean(v)
+ case (PrimitiveType.PrimitiveTypeName.INT32, i) =>
+ val v = values(i).asInstanceOf[Integer]
+ converter.getConverter(i).asPrimitiveConverter().addInt(v)
+ case (PrimitiveType.PrimitiveTypeName.INT64, i) =>
+ val v = values(i).asInstanceOf[Long]
+ converter.getConverter(i).asPrimitiveConverter().addLong(v)
+ case (PrimitiveType.PrimitiveTypeName.FLOAT, i) =>
+ val v = values(i).asInstanceOf[Float]
+ converter.getConverter(i).asPrimitiveConverter().addFloat(v)
+ case (PrimitiveType.PrimitiveTypeName.DOUBLE, i) =>
+ val v = values(i).asInstanceOf[Double]
+ converter.getConverter(i).asPrimitiveConverter().addDouble(v)
+ case (PrimitiveType.PrimitiveTypeName.BINARY, i) =>
+ val v = values(i).asInstanceOf[Binary]
+ converter.getConverter(i).asPrimitiveConverter().addBinary(v)
+ case (PrimitiveType.PrimitiveTypeName.FIXED_LEN_BYTE_ARRAY, i) =>
+ val v = values(i).asInstanceOf[Binary]
+ converter.getConverter(i).asPrimitiveConverter().addBinary(v)
+ case _ =>
+ throw new SparkException("Unexpected parquet type name: " +
primitiveTypeName)
+ }
+ converter.currentRecord
+ }
+
+ /**
+ * 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,
Review comment:
added datetimeRebaseMode, don't need int96RebaseMode or convertTz
because INT96 is not pushed down
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