rapidsai / cudf

cuDF - GPU DataFrame Library
https://docs.rapids.ai/api/cudf/stable/
Apache License 2.0
8.45k stars 903 forks source link

[FEA] Support strong index types in other `cudf::experimental::row::` comparators and hashers #11091

Open ttnghia opened 2 years ago

ttnghia commented 2 years ago

When we have multiple tables to compare, we need strong index types. On the other hand, when we just do one table operations like comparing or hashing rows of the same table, we typically don't need these strong types.

However, sometimes we actually need to support strong types in these self- operations. For example, we create a hash map with strong index types. In such cases, row comparator and hasher need to operate on strong index types.

ttnghia commented 2 years ago

CC @bdice for awareness.

github-actions[bot] commented 2 years ago

This issue has been labeled inactive-30d due to no recent activity in the past 30 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed. This issue will be labeled inactive-90d if there is no activity in the next 60 days.

github-actions[bot] commented 2 years ago

This issue has been labeled inactive-90d due to no recent activity in the past 90 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.

GregoryKimball commented 2 years ago

sometimes we actually need to support strong types in these self- operations

@ttnghia would you please elaborate on this use case? What code changes would you propose?

This issue references competed work in #10730

ttnghia commented 2 years ago

The use cases already existed, and implementation of this FEA was added here:

I proposed this issue and already made a draft PR but that was not approved so I had to implement it locally only in that file.