Add new benchmark config for query approximation #16323
Merged
Conversation
This file contains 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
We have guarded the experimental query approximation framework behind a feature flag. In order to easily measure the impact of approximation on big5 benchmarks, it would be nice to have a benchmark config. Signed-off-by: Michael Froh <froh@amazon.com>
rishabh6788
approved these changes
Oct 14, 2024
mch2
approved these changes
Oct 14, 2024
dk2k
pushed a commit
to dk2k/OpenSearch
that referenced
this pull request
Oct 16, 2024
…16323) We have guarded the experimental query approximation framework behind a feature flag. In order to easily measure the impact of approximation on big5 benchmarks, it would be nice to have a benchmark config. Signed-off-by: Michael Froh <froh@amazon.com>
dk2k
pushed a commit
to dk2k/OpenSearch
that referenced
this pull request
Oct 17, 2024
…16323) We have guarded the experimental query approximation framework behind a feature flag. In order to easily measure the impact of approximation on big5 benchmarks, it would be nice to have a benchmark config. Signed-off-by: Michael Froh <froh@amazon.com>
dk2k
pushed a commit
to dk2k/OpenSearch
that referenced
this pull request
Oct 21, 2024
…16323) We have guarded the experimental query approximation framework behind a feature flag. In order to easily measure the impact of approximation on big5 benchmarks, it would be nice to have a benchmark config. Signed-off-by: Michael Froh <froh@amazon.com>
akolarkunnu
pushed a commit
to akolarkunnu/OpenSearch
that referenced
this pull request
Jan 21, 2025
…16323) We have guarded the experimental query approximation framework behind a feature flag. In order to easily measure the impact of approximation on big5 benchmarks, it would be nice to have a benchmark config. Signed-off-by: Michael Froh <froh@amazon.com>
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.
Description
We have guarded the experimental query approximation framework behind a feature flag. In order to easily measure the impact of approximation on big5 benchmarks, it would be nice to have a benchmark config.
This config just copies
id_5, but adds the feature flag to enable range query approximation.Related Issues
N/A
Check List
Functionality includes testing.API changes companion pull request created, if applicable.Public documentation issue/PR created, if applicable.By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.