> ## Documentation Index
> Fetch the complete documentation index at: https://www.meilisearch.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Bucket sort

> How Meilisearch uses bucket sort to rank search results through sequential ranking rules.

Meilisearch uses **bucket sort** to rank search results. This algorithm distributes documents into buckets based on ranking rules, then recursively sorts within each bucket using subsequent rules.

## How bucket sort works in Meilisearch

When you search, Meilisearch doesn't score documents with a single number. Instead, it applies [ranking rules](/capabilities/full_text_search/relevancy/ranking_rules) sequentially, sorting documents into buckets at each step.

### Example: Searching for "Badman dark knight returns"

**Step 1: Apply the `words` rule**

The first ranking rule (`words`) sorts documents by how many query words they contain:

| Bucket | Matches     | Documents                         |
| ------ | ----------- | --------------------------------- |
| 1      | All 4 words | "Batman: The Dark Knight Returns" |
| 2      | 3 words     | "Batman: The Dark Knight"         |
| 3      | 2 words     | ...                               |
| 4      | 1 word      | "Angel and the Badman"            |

**Step 2: Apply the `typo` rule within buckets**

If a bucket contains multiple documents, the next rule (`typo`) breaks ties. For example, in the 1-word bucket where "Badman" appears:

| Bucket | Typos   | Documents                               |
| ------ | ------- | --------------------------------------- |
| 4.1    | 0 typos | Documents containing "Badman" exactly   |
| 4.2    | 1 typo  | Documents corrected "Badman" → "Batman" |

This continues recursively until all buckets contain single documents or all ranking rules are exhausted.

## Why bucket sort?

Bucket sort offers several advantages for search ranking:

1. **Flexibility**: Different sorting algorithms can be applied within individual buckets
2. **Configurable priority**: You control which criteria matter most by reordering rules
3. **Efficient tie-breaking**: Only documents that tie on one rule need evaluation by the next

## Best and worst cases

| Case      | Condition                              | Complexity                            |
| --------- | -------------------------------------- | ------------------------------------- |
| **Best**  | Documents spread evenly across buckets | O(n+k) where n=documents, k=buckets   |
| **Worst** | All documents in one bucket            | Depends on the sorting algorithm used |

## Default ranking rules

Meilisearch applies these rules in order:

1. **words**: Documents containing more query words rank higher
2. **typo**: Documents with fewer typos rank higher
3. **proximity**: Documents where query words appear closer together rank higher
4. **attributeRank**: Documents matching in more important attributes rank higher
5. **sort**: User-defined sort order (if specified)
6. **wordPosition**: Documents with matches closer to the beginning of an attribute rank higher
7. **exactness**: Documents with exact matches rank higher

## Customizing ranking rules

You can reorder, add, or remove ranking rules:

```bash theme={null}
curl -X PUT "${MEILISEARCH_URL}/indexes/movies/settings/ranking-rules" \
  -H "Authorization: Bearer ${MEILISEARCH_KEY}" \
  -H "Content-Type: application/json" \
  --data-binary '[
    "words",
    "typo",
    "proximity",
    "attributeRank",
    "sort",
    "wordPosition",
    "exactness",
    "release_date:desc"
  ]'
```

Adding `release_date:desc` as a custom rule means newer movies rank higher when all other factors are equal.

## Related concepts

* [Ranking rules](/capabilities/full_text_search/relevancy/ranking_rules): Configure ranking behavior
* [Ranking score](/capabilities/full_text_search/relevancy/ranking_score): Understanding search relevance scores
* [Custom ranking rules](/capabilities/full_text_search/relevancy/custom_ranking_rules): Add business logic to ranking