Improve memory consumption of AggregateNumericRangeEquality
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
Research direction
Locate AggregateNumericRangeEquality and the .fetchall() call first, then inspect how the database results are consumed. The change is done when checks process rows without retaining the full result set and the existing equality behavior remains covered by the relevant tests.
Written by the indexing model from the issue text.
Description
AggregateNumericRangeEquality requires ~ 20 GiB of memory (~ 50 M rows).
.fetchall() returns a list. Could we change this to perform the checks in a streaming fashion that doesn't require all of the data in memory at once?
- Dominant language
- Python
- Stars
- 46
- Forks
- 3
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
More from Quantco/datajudge
-
Difficulty 2/5 1-3 hours Newbie friendliness 62/100
-
enhancement help wanted
Difficulty 5/5 Over a week Newbie friendliness 25/100
-
good first issue refactoring
Difficulty 3/5 1-2 days Newbie friendliness 48/100
-
Difficulty 3/5 1-2 days Newbie friendliness 45/100
-
Difficulty 4/5 3-5 days Newbie friendliness 35/100
All issues in Quantco/datajudge
Similar issues
-
area/auth bug comp/agent P3 platform/discord type/security
Difficulty 2/5 1-3 hours Newbie friendliness 88/100
NousResearch/hermes-agent#117848 ·
-
Difficulty 2/5 1-3 hours Newbie friendliness 74/100
bancolombia/sentinel#23 ·
-
test md OpenCI
Difficulty 2/5 1-3 hours Newbie friendliness 74/100
-
integration:quickjs org:external priority:backlog topic:code-interpreter topic:middleware type:feature
Difficulty 2/5 1-3 hours Newbie friendliness 74/100
langchain-ai/deepagents#6450 ·
-
bug client
Difficulty 2/5 1-3 hours Newbie friendliness 88/100