heatherleaf / heatherleaf/korpsearch
Regular expressions can be very slow
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- Dominant language
- Python
- Stars
- 2
- Forks
- 6
- PR merge metrics
- No merged PRs in 30d
Description
Currently regular expressions within tokens ([word="a.*e"]) are handled by finding all words in the lexicon that matches the query, and then create a disjunction of all of those, i.e., [word="abalone"|word="abate"|...|word="azure"]. For general regexes this becomes a very large query, and slow.
Sometimes it's simply better to filter the results afterwards.
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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.
Research direction
Start by tracing how regular expressions inside token queries are converted into lexicon matches and then expanded into a disjunction. Compare the generated query size and performance for a general regex such as [word="a.*e"]. Done means regex queries avoid unnecessarily large expansions while preserving their matching behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- search
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100