Github user dongjoon-hyun commented on a diff in the pull request:
https://github.com/apache/spark/pull/16440#discussion_r94286661
--- Diff:
sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
---
@@ -111,9 +115,15 @@ private[hive] class SparkExecuteStatementOperation(
// Reset iter to header when fetching start from first row
if (order.equals(FetchOrientation.FETCH_FIRST)) {
- val (ita, itb) = iterHeader.duplicate
- iter = ita
- iterHeader = itb
+ iter = if (useIncrementalCollect) {
+ resultList = None
+ result.toLocalIterator.asScala
+ } else {
+ if (resultList.isEmpty) {
--- End diff --
Correct. There are two cases and this PR targets `incrementalCollect=true`
mainly.
> you also buffer the whole result into memory locally.
First of all, before SPARK-16563, `FETCH_FIRST` is not supported correctly
because **iterator** can be traversed once.
* Case 1) `incrementalCollect=false`
**Creating** a whole result in a memory **once** by **result.collect** was
the the original Spark way before `SPARK-16563`.
If we can create a whole result once during the query processing, I think
we can keep that for `FETCH_FIRST` with less side effect.
So, I keep them. If this is not allowed, we have to go `Case 2`.
* Case 2) `incrementalCollect=true`
In this case, **by definition**, we cannot create the whole result set in a
memory at any time during the query processing. There is no way to find the
first row with **iterator**. `result.toLocalIterator.asScala` should be used
whenever `FETCH_FIRST` is used.
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