Yaohua628 commented on a change in pull request #34575:
URL: https://github.com/apache/spark/pull/34575#discussion_r756512790
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FileScanRDD.scala
##########
@@ -103,6 +116,135 @@ class FileScanRDD(
context.killTaskIfInterrupted()
(currentIterator != null && currentIterator.hasNext) || nextIterator()
}
+
+ ///////////////////////////
+ // FILE METADATA METHODS //
+ ///////////////////////////
+
+ // whether a metadata column exists and it is a `MetadataAttribute`
+ private lazy val hasMetadataAttribute: Boolean = {
+ metadataStruct.exists {
+ case MetadataAttribute(_) => true
+ case _ => false
+ }
+ }
+
+ // metadata struct unsafe row, will only be updated when the current
file is changed
+ @volatile private var metadataStructUnsafeRow: UnsafeRow = _
+ // metadata generic row, will only be updated when the current file is
changed
+ @volatile private var metadataStructGenericRow: Row = _
+ // an unsafe joiner to join an unsafe row with the metadata unsafe row
+ lazy private val unsafeRowJoiner =
+ if (hasMetadataAttribute)
+ GenerateUnsafeRowJoiner.create(requiredSchema,
Seq(metadataStruct.get).toStructType)
+
+ // Create a off/on heap WritableColumnVector
+ private def createColumnVector(numRows: Int, dataType: DataType):
WritableColumnVector = {
+ if (offHeapColumnVectorEnabled) {
+ new OffHeapColumnVector(numRows, dataType)
+ } else {
+ new OnHeapColumnVector(numRows, dataType)
+ }
+ }
+
+ /**
+ * For each partitioned file, metadata columns for each record in the
file are exactly same.
+ * Only update metadata columns when `currentFile` is changed.
+ */
+ private def updateMetadataStruct(): Unit = {
+ if (hasMetadataAttribute) {
+ val meta = metadataStruct.get
+ if (currentFile == null) {
+ metadataStructUnsafeRow = new UnsafeRow(1)
+ metadataStructGenericRow = new GenericRow(1)
+ } else {
+ // make an generic row
+ assert(meta.dataType.isInstanceOf[StructType])
+ metadataStructGenericRow = Row.fromSeq(
+ meta.dataType.asInstanceOf[StructType].names.map {
+ case FILE_PATH => UTF8String.fromString(new
File(currentFile.filePath).toString)
+ case FILE_NAME => UTF8String.fromString(
+ currentFile.filePath.split("/").last)
+ case FILE_SIZE => currentFile.fileSize
+ case FILE_MODIFICATION_TIME => currentFile.modificationTime
+ case _ => None // be exhaustive, won't happen
+ }
+ )
+
+ // convert the generic row to an unsafe row
+ val unsafeRowConverter = {
+ val converter = UnsafeProjection.create(
+ Array(METADATA_STRUCT))
+ (row: Row) => {
+ converter(CatalystTypeConverters.convertToCatalyst(row)
+ .asInstanceOf[InternalRow])
+ }
+ }
+ metadataStructUnsafeRow =
+ unsafeRowConverter(Row.fromSeq(Seq(metadataStructGenericRow)))
+ }
+ }
+ }
+
+ /**
+ * Create a writable column vector containing all required metadata
fields
+ */
+ private def createMetadataStructColumnVector(
+ c: ColumnarBatch, meta: AttributeReference): WritableColumnVector = {
+ val columnVector = createColumnVector(c.numRows(), METADATA_STRUCT)
+ val filePathBytes = new File(currentFile.filePath).toString.getBytes
+ val fileNameBytes = currentFile.filePath.split("/").last.getBytes
+ var rowId = 0
+
+ assert(meta.dataType.isInstanceOf[StructType])
+ meta.dataType.asInstanceOf[StructType].names.zipWithIndex.foreach {
case (name, ind) =>
+ name match {
+ case FILE_PATH =>
+ rowId = 0
+ // use a tight-loop for better performance
+ while (rowId < c.numRows()) {
+ columnVector.getChild(ind).putByteArray(rowId, filePathBytes)
Review comment:
Thanks, Bart! Maybe in a follow-up PR (?) (I had a benchmark for
parquet: 100000 rows, 100 columns, ~20% slower when selecting metadata).
But yeah, good idea to reference it in every row!
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