Zoekt is a code search engine designed for source code indexing and search. The ranking and scoring of search results in Zoekt are influenced by several factors. This document explains these factors and provides examples of how to optimize them for better ranking.
Factors Influencing Search Result Relevance
Text match: Zoekt uses the Go-fuzz library to fuzz search queries and improve the ranking of results based on text matches. The more a search term matches the content, the higher the ranking.
File type: Zoekt supports various file types, and the ranking may differ based on the file type. For example, code in a
.gofile might rank higher for a Go language search query than code in a.txtfile.File size: Larger files may rank lower than smaller files, assuming the text match is equal.
Line position: Lines closer to the beginning of a file might rank higher than those closer to the end.
Symbols and keywords: Zoekt uses the go-ctags library to index symbols and keywords, which can influence the ranking of search results.
Optimizing for Better Ranking
Leveraging ctags integration
Zoekt uses ctags to index symbols and keywords, which can help improve the ranking of search results. To optimize for better ranking using ctags integration, ensure that your code has accurate and complete tags. You can use the ctags command-line tool to generate or update the tags.
For example, to generate tags for a Go source file, run:
ctags -R --langmap=go:.go --go-kinds=var,fun,type,struct,interface --fields=+nS --extra=+q .
Using go-cmp for comparing search results
Zoekt uses the go-cmp library to compare search results and ensure their relevance. To optimize for better ranking using go-cmp, ensure that your code implements the comparable.Comparer interface and provides accurate comparison functions.
For example, to implement a custom comparer for a struct MyStruct:
type MyStruct struct {
A int
B string
}
func (m MyStruct) Compare(other comparable.Comparable) int {
otherMyStruct := other.(MyStruct)
if m.A != otherMyStruct.A {
return cmp.Int(m.A, otherMyStruct.A)
}
return cmp.String(m.B, otherMyStruct.B)
}
Additional Resources
- Using Red Hat Data Grid to power a multi-cloud real-time game
- Creating a game leaderboard with Data Grid
- Made in NY: The engineering behind social recommendations
- An introduction to machine-learned ranking in Apache Solr
- News Feed ranking, powered by machine learning
- Scaling the Instagram Explore recommendations system
- Fantasy March Madness: How To Predict Winners
- ToolSearchResultListItemProps.rank
- 5 best practices for using open source community leaderboards
The code snippets and documentation provided are the sole sources of information for this explanation. The explanation is based on the Go programming language and the Zoekt project. The documentation includes examples of using Red Hat Data Grid, Apache Solr, and other technologies for ranking and scoring in different contexts.
