Rachelint commented on code in PR #12996:
URL: https://github.com/apache/datafusion/pull/12996#discussion_r1807827680
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datafusion/physical-plan/src/aggregates/group_values/column.rs:
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@@ -160,11 +162,112 @@ macro_rules! instantiate_primitive {
};
}
-fn append_col_value<C>(mut core: C, array: &ArrayRef, row: usize)
-where
- C: FnMut(&ArrayRef, usize),
-{
- core(array, row);
+struct AggregationHashTable<T: AggregationHashTableEntry> {
+ /// Raw table storing values in a `Vec`
+ raw_table: Vec<T>,
Review Comment:
> Based on some experiments in changing hash join algorithm, I think it's
likely `hashbrown` performs much better than implementing a hashtable ourselves
although I would like to be surprised 🙂
🤔 Even if we can perform something like `vectorized compare` or `vectorized
append` in our hashtable?
I found in `multi group by` case, we will perform the `compare` for each row
leading to the array downcasting again and again... And actually the `downcast`
operation will be compiled to many asm codes....
And I foudn we can't eliminate it and perform the `vectorized compare` with
`hashbrown`...
```
fn equal_to_inner(&self, lhs_row: usize, array: &ArrayRef, rhs_row:
usize) -> bool {
let array = array.as_byte_view::<B>();
```
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