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

# text.jaroWinkler

> Computes the Jaro-Winkler similarity between two strings, returning a value between 0 (no similarity) and 1 (exact match).

## Description

Computes the Jaro-Winkler similarity between two strings. This metric is particularly effective for short strings like names and addresses. It gives more favorable ratings to strings that match from the beginning. Returns a value between 0 (no similarity) and 1 (exact match).

## Syntax

```cypher theme={null}
flex.text.jaroWinkler(string1, string2)
```

## Parameters

| Parameter | Type   | Required | Description                  |
| --------- | ------ | -------- | ---------------------------- |
| `string1` | string | Yes      | The first string to compare  |
| `string2` | string | Yes      | The second string to compare |

## Returns

**Type:** number (float)

A similarity score between 0 and 1:

* `1.0` indicates an exact match
* `0.0` indicates no similarity
* Higher values indicate greater similarity

## Examples

### Example 1: Name Matching

```cypher theme={null}
// Compare similar names
RETURN flex.text.jaroWinkler('Martha', 'Marhta') AS similarity
```

**Output:**

```text theme={null}
similarity
----------
0.961
```

### Example 2: Fuzzy Name Search

```cypher theme={null}
// Find people with names similar to "William"
MATCH (p:Person)
WHERE flex.text.jaroWinkler(p.firstName, 'William') > 0.85
RETURN p.firstName, p.lastName, flex.text.jaroWinkler(p.firstName, 'William') AS score
ORDER BY score DESC
```

### Example 3: Deduplication by Company Name

```cypher theme={null}
// Find potential duplicate company records
MATCH (c1:Company)
MATCH (c2:Company)
WHERE id(c1) < id(c2)
WITH c1, c2, flex.text.jaroWinkler(c1.name, c2.name) AS similarity
WHERE similarity > 0.9
RETURN c1.name, c2.name, similarity
ORDER BY similarity DESC
```

## Notes

* Particularly effective for short strings (names, addresses)
* Gives higher weight to strings that match from the beginning
* Handles `null` values by returning appropriate default values
* Case-sensitive comparison
* More forgiving than exact match but stricter than pure Jaro similarity
* Commonly used in record linkage and deduplication tasks

## See Also

* [text.levenshtein](/udfs/flex/text/levenshtein) - Edit distance metric for string comparison
* [sim.jaccard](/udfs/flex/similarity/jaccard) - Set-based similarity

## Frequently Asked Questions

<AccordionGroup>
  <Accordion title="When should I use Jaro-Winkler vs Levenshtein?">
    Use Jaro-Winkler for comparing **short strings** like names and addresses where prefix similarity matters. Use Levenshtein when you need an exact edit distance count.
  </Accordion>

  <Accordion title="Is the comparison case-sensitive?">
    Yes. `flex.text.jaroWinkler` performs a case-sensitive comparison. Apply `toLower()` to both strings for case-insensitive matching.
  </Accordion>
</AccordionGroup>