kazuyukitanimura commented on code in PR #1055:
URL: https://github.com/apache/datafusion-comet/pull/1055#discussion_r1842078002
##########
common/src/main/scala/org/apache/comet/vector/NativeUtil.scala:
##########
@@ -107,16 +109,16 @@ class NativeUtil {
null
}
- // The array and schema structures are allocated by native side.
- // Don't need to deallocate them here.
- val arrowSchema = ArrowSchema.wrap(schemaAddrs(index))
- val arrowArray = ArrowArray.wrap(arrayAddrs(index))
Review Comment:
updated
##########
common/src/main/scala/org/apache/comet/vector/NativeUtil.scala:
##########
@@ -133,34 +135,35 @@ class NativeUtil {
// the Arrow arrays. For example, Iceberg column reader will skip deleted
rows internally in
// its `CometVector` implementation. The `ColumnarBatch` returned by the
reader will report
// logical number of rows which is less than actual number of rows due to
row deletion.
- numRows.headOption.getOrElse(batch.numRows())
+
+ builder.result()
}
/**
* Gets the next batch from native execution.
*
- * @param numOutputCols
- * The number of output columns
* @param func
* The function to call to get the next batch
* @return
* The number of row of the next batch, or None if there are no more
batches
*/
- def getNextBatch(
- numOutputCols: Int,
- func: (Array[Long], Array[Long]) => Long): Option[ColumnarBatch] = {
- val (arrays, schemas) = allocateArrowStructs(numOutputCols)
-
- val arrayAddrs = arrays.map(_.memoryAddress())
- val schemaAddrs = schemas.map(_.memoryAddress())
-
- val result = func(arrayAddrs, schemaAddrs)
Review Comment:
updated
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