Dandandan commented on pull request #786:
URL: https://github.com/apache/arrow-datafusion/pull/786#issuecomment-888229063
> > we can keep the keys in a contiguous (mutable) array instead and keep
offsets/pointers to the values in this array (and null values can be stored in
a bitmap, so only 1 bit per value).
>
> @Dandandan this is a great idea - I will write up in more detail what I
think you are proposing to make sure we are on the same page
Some more details to get you started, this is the rough idea that is a
similar to how hash join currently works (there it's a bit easier as we can
concatenate the data upfront):
* We can store both group by keys and aggregate values in typed / mutable
Arrays. This means the memory overhead is kept to a minimum and is much more
vectorization / cache friendly.
E.g. for a count grouped on u64 values, the state we keep for the group by
values is `MutableArray<U64Type>` and for the count state another
`MutableArray<U64Type>` (no bitmap needed here). The group by values can have
null values which are stored in a bitmap.
* A hashmap that only contains hashes / pointers: `Hashmap<u64,
SmallVec<u64>`
- first `u64` is the hash of the group by values -> this can be
calculated in a vectorized manner for the entire batch
- second `u64` is the offset in both the group by value(s) and aggregate
state arrays -> inserted to the mutable arrays when there is no match
* hash collisions (group by values mapping to same `u64` should/can be
handled by comparing values in the arrays at the group by offset and scanning
the `SmallVec`
Above structure will reduce the memory needed for the state, and will also
reduce the time to create / (re)hash the keys and will reduce the amount of
intermediate vec / small allocations that are needed in the hash aggregate code.
There are maybe slightly different ways to encode the data in the hashmap /
check collisions, and above structure makes some further optimizations /
vectorization possible.
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