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Documentation Index

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Meilisearch uses a bucket sort algorithm to rank search results. Rather than computing a single relevancy score and sorting by it, Meilisearch applies ranking rules one at a time. Each rule sorts documents into groups (“buckets”) of equal relevance, and the next rule only breaks ties within each bucket. This approach produces highly relevant results while remaining fast, even on large datasets.

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How bucket sort works

  1. A search query arrives and Meilisearch identifies all matching documents
  2. The first ranking rule sorts these documents, creating groups of documents that are equally relevant according to that rule
  3. Within each group, the second ranking rule further sorts documents into smaller groups
  4. This process continues through each ranking rule in order
  5. The final ordering is the search result
Because each subsequent rule only operates within the groups created by the previous rule, the order of ranking rules matters significantly. Rules placed higher in the list have a greater overall impact on the final ranking.

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The default ranking pipeline

Meilisearch applies seven built-in ranking rules in this order by default:
StepRuleWhat it does
1wordsSorts by number of matched query terms (more matches = higher rank)
2typoSorts by number of typos in matches (fewer typos = higher rank)
3proximitySorts by distance between matched terms (closer = higher rank)
4attributeRankSorts by which attribute contains the match (higher-priority attribute = higher rank)
5sortApplies user-defined sorting from the sort search parameter
6wordPositionSorts by position of matched terms within attributes (earlier = higher rank)
7exactnessSorts by how closely matches resemble the original query terms
Each rule only breaks ties from the previous one. The order matters: rules placed higher in the list have a greater overall impact. You can reorder these rules and add custom ranking rules (like rating:desc) to inject business logic into the pipeline. See built-in ranking rules for detailed descriptions and visual examples of each rule.

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Visualizing the pipeline

Consider a search for dark knight across a movies index. Here is how documents flow through the pipeline:
  1. Words: 50 documents match both terms, 30 match only one term. The 50 full-match documents form the first bucket.
  2. Typo: Within the 50 full-match documents, 40 have zero typos and 10 have one typo. The 40 zero-typo documents form the top bucket.
  3. Proximity: Within those 40 documents, 15 have “dark” and “knight” adjacent, 25 have them further apart. The 15 adjacent-match documents rank highest.
  4. Attribute rank: Within those 15 documents, 5 have the match in title and 10 have it in overview. The 5 title-match documents rank highest.
  5. Sort: No sort parameter was provided, so this rule has no effect.
  6. Word position: Within the 5 title-match documents, those with “dark knight” appearing earlier in the title rank higher.
  7. Exactness: Final tiebreaker based on exact vs. fuzzy matches.
The final result is a precisely ordered list where the most relevant documents appear first.
For a deeper look at the bucket sort algorithm, see bucket sort internals. For details on each ranking rule, see built-in ranking rules.