Implement queryDSL range support for exponential histograms#143379
Merged
JonasKunz merged 3 commits intoelastic:mainfrom Mar 6, 2026
Merged
Implement queryDSL range support for exponential histograms#143379JonasKunz merged 3 commits intoelastic:mainfrom
JonasKunz merged 3 commits intoelastic:mainfrom
Conversation
elasticsearchmachine
added
v9.4.0
external-contributor
elasticsearchmachine
added
the
Team:Analytics
Collaborator
|
Pinging @elastic/es-analytical-engine (Team:Analytics) |
12 tasks
Member
|
@martijnvg, can you help with this one? |
spinscale
pushed a commit
to spinscale/elasticsearch
that referenced
this pull request
Mar 6, 2026
sidosera
pushed a commit
to sidosera/elasticsearch
that referenced
this pull request
Mar 6, 2026
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
4 participants
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Implements the queryDSL range aggregation for exponential histograms. This is implemented very similar to how it is done for T-Digests: We assume that all points per bucket lie in the center of the bucket and are assigned a range based on that.
The code is also mostly copy pasted, with one core difference: When iterating the T-Digest centroids, we go through them sorted by the mean in ascending order. When we iterate through exponential histogram buckets, we iterate first from
-0to-Inf, and then from0toInf. Because the algorithm does some binary search optimizations, it needed to be adapted for this.