Merged
Conversation
This comment was marked as resolved.
This comment was marked as resolved.
timvisee
approved these changes
Jun 16, 2025
ffuugoo
reviewed
Jun 16, 2025
Contributor
ffuugoo
left a comment
There was a problem hiding this comment.
Took me a little bit to understand why/how bitset would work here. Nice gainz! 💪
generall
pushed a commit
that referenced
this pull request
Jul 17, 2025
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
The mutable inverted index currently uses a
Vecas a container for ids, this means that, while it is expensive to keep adding random values to it, it is still good to binary-search over it.From the history, we can see that it was switched from
BTreeSettoVecin the past. However, indexing withVecmeans that we have to shift elements all over the place every time we insert a new id which is lower than the max in the list. This creates a bottleneck directly on ingestion, not during optimization, which "breaks" the mental model of ingestion being separated from optimization because it's supposed to be quick.Since changing the container is a small (but big impact) change, I've ran the following experiment:
These were the results: