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title: Fuzzy String Matching in Django and PostgreSQL | daily.dev
description: A practical walkthrough of implementing fuzzy name search using Django ORM and PostgreSQL, covering four building blocks: Soundex, Daitch-Mokotoff, Levenshtein...
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# Fuzzy String Matching in Django and PostgreSQL

**[Planet Python](https://daily.dev/sources/planetpython)** · 6 min read · 1 upvotes · 0 comments

## Summary

A practical walkthrough of implementing fuzzy name search using Django ORM and PostgreSQL, covering four building blocks: Soundex, Daitch-Mokotoff, Levenshtein edit distance, and trigrams. Includes custom Func wrapper classes for functions without Django built-ins, code examples for each matching technique, and a table of which index type (B-tree, GIN, GiST) to add for each search strategy. Notes that functional indexes only apply when queries use the identical expression, and that Levenshtein is expensive and best used as a precision filter atop broader phonetic matches. Companion post to a DjangoCon US 2026 talk on search-as-you-type at scale.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.caktusgroup.com/blog/2026/08/21/fuzzy-string-matching-django-postgresql>

## Questions this post answers

### How do I do fuzzy string matching for names in Django with PostgreSQL?

Combine four PostgreSQL functions through Django: Soundex and Daitch-Mokotoff for phonetic matching, Levenshtein edit distance as a precision filter, and Django's built-in TrigramSimilarity/TrigramDistance for closeness ranking. Wrap Soundex, Daitch-Mokotoff, and Levenshtein in custom Func expressions since Django has no built-ins for them, enabled via the fuzzystrmatch and pg_trgm PostgreSQL extensions.

_Developers building name-search features can find implementation patterns like this through daily.dev._

### What index should I use for trigram similarity search versus Levenshtein distance in PostgreSQL?

Trigram similarity or KNN searches should use a GiST index with gist_trgm_ops on the column, while Soundex equality searches use a B-tree index and Daitch-Mokotoff overlap searches use a GIN index. Levenshtein distance has no dedicated index since it can't be pre-computed; instead apply it as a filter on top of results already narrowed by an indexed phonetic search.

_Anyone tuning search performance can compare indexing tradeoffs like these on daily.dev._

### Why is levenshtein_less_equal case-sensitive in PostgreSQL and how do I handle it?

The levenshtein_less_equal function from the fuzzystrmatch extension is case-sensitive, unlike soundex(), daitch_mokotoff(), and the trigram operators, which normalize case automatically. To match case-insensitively, wrap both the column and the comparison value in Upper() before computing the edit distance, otherwise case differences get counted as edits and skew the distance threshold.

_Developers debugging inconsistent fuzzy-match results can track gotchas like this via daily.dev._

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Tags: [#python](https://daily.dev/tags/python), [#database](https://daily.dev/tags/database), [#postgresql](https://daily.dev/tags/postgresql), [#django](https://daily.dev/tags/django)

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