Github user WeichenXu123 commented on the issue:
https://github.com/apache/spark/pull/17419
As the dataframe version is much slower than RDD version (currently test
against vector of size 1)
I also guess there is some performance issue in
`ObjectAggregationIterator.processInput()`
in the following code block:
```
private def processInputs(): Unit = {
// ...
if (groupingExpressions.isEmpty) {
// If there is no grouping expressions, we can just reuse the same
buffer over and over again.
val groupingKey = groupingProjection.apply(null)
val buffer: InternalRow = getAggregationBufferByKey(hashMap,
groupingKey)
while (inputRows.hasNext) {
val newInput = safeProjection(inputRows.next())
processRow(buffer, newInput)
}
}
...
```
This statement `val newInput = safeProjection(inputRows.next())` maybe do
some redundant data copy (for walk-around some bugs?)
cc @cloud-fan @liancheng
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