alamb edited a comment on pull request #844:
URL: https://github.com/apache/arrow-datafusion/pull/844#issuecomment-895383017
> If I recall, we will need to perform N x M comparisons where N is the
number of rows in the batch and M the distinct number of items in a group,
around here, roughly represented in for (row, hash) in
batch_hashes.into_iter().enumerate() and the inner
group_values.iter()....all(op).
Yes, I think that is accurate. I would love to figure out how to use a
vectorized approach
In #808 we have a hash table mapping (effectively) `[ScalarValue]` ->
`(aggregate data)` and we have input `[ArrayRef]`. The hash calculation for
each input row is vectorized (so that is good), and then there is a loop that
looks like
```rust
for (hash, index) in ... {
// look up entry in hash table for row `index`
// check if the values at `[ArrayRefs]` @ `index` are the same as in the
entry in the table (what this PR's code, `array_eq`, is used for)
// ...
}
```
In order to vectorize this calculation, I think we would have to somehow
vectorize both the lookup in the table as well as the comparison.
I suppose we could potentially copy the existing keys out of the hash
entries (so they are contiguous) to do a vectorized comparison but my intuition
is that the cost of this copy would outweigh any gains due to vectorization
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