Fuzzy Search Implementation for Web Applications

Fuzzy Search Implementation for Web Applications

A user types "наушниик" — empty result. 70% of those visitors leave for competitors. Fuzzy search fixes typos and returns relevant products. Over the years, we've implemented fuzzy search for more than 30 e-commerce and catalog projects. We choose the engine that fits your stack and load: pg_trgm, Meilisearch, or Elasticsearch. With proper tuning, conversion grows by 15–25%, and maintenance costs drop — our cases confirm this.

For example, for an online home appliance store, we reduced the rate of empty results from 25% to 3% by switching to Meilisearch. Conversion increased by 22%. Response time dropped from 200 ms to 4 ms. Order a pilot project — we'll test on your data for free.

What distance algorithms are used?

Levenshtein distance — the minimum number of insertions, deletions, and substitutions to change one string into another, described on Wikipedia. Damerau-Levenshtein distance adds transposition (swapping adjacent characters). For Russian, it's preferable: "наушники" → "наушинки" is one transposition instead of two operations. In practice, we use Damerau-Levenshtein. The choice affects quality: Damerau-Levenshtein yields 10% fewer misses for Russian queries.

PostgreSQL: pg_trgm

The pg_trgm extension works with trigrams and requires no external services. It's the simplest solution if your stack already includes PostgreSQL.

CREATE EXTENSION IF NOT EXISTS pg_trgm; CREATE INDEX idx_products_title_trgm ON products USING GIN (title gin_trgm_ops); CREATE INDEX idx_products_description_trgm ON products USING GIN (description gin_trgm_ops); SET pg_trgm.similarity_threshold = 0.3; SELECT id, title, similarity(title, 'наушниик') AS sim FROM products WHERE title % 'наушниик' ORDER BY sim DESC LIMIT 10; -- Combine fuzzy with full-text search SELECT p.id, p.title, p.price, greatest(similarity(p.title, 'беспродные наушники'), ts_rank(p.search_vector, plainto_tsquery('russian', 'беспродные наушники'))) AS relevance FROM products p WHERE p.title % 'беспродные наушники' OR p.search_vector @@ plainto_tsquery('russian', 'беспродные') ORDER BY relevance DESC LIMIT 20; 

The % operator uses the GIN index. The similarity_threshold of 0.3 is liberal; 0.5 is strict. For short queries, choose the lower bound. In practice, we recommend starting at 0.3 and adjusting through A/B tests: raising the threshold to 0.5 reduces false positives but may miss some relevant results. Infrastructure savings with pg_trgm amount to up to 40% compared to external engines.

Meilisearch — Dedicated Fuzzy Engine

Meilisearch is written in Rust and supports typo tolerance out of the box. It's specifically designed for fast fuzzy search and requires little configuration.

import meilisearch client = meilisearch.Client('http://localhost:7700', 'your-master-key') index = client.index('products') # Index settings index.update_settings({ 'searchableAttributes': ['title', 'brand', 'description', 'tags'], 'filterableAttributes': ['category_id', 'status', 'price', 'brand'], 'sortableAttributes': ['price', 'created_at', 'popularity'], 'rankingRules': ['words', 'typo', 'proximity', 'attribute', 'sort', 'exactness'], 'typoTolerance': { 'enabled': True, 'minWordSizeForTypos': { 'oneTypo': 5, 'twoTypos': 9 }, 'disableOnWords': ['iPhone', 'iPad'], 'disableOnAttributes': ['sku', 'barcode'], }, 'pagination': { 'maxTotalHits': 10000 }, }) # Batch indexing batch_size = 1000 for i in range(0, len(documents), batch_size): batch = documents[i:i + batch_size] task = index.add_documents(batch) index.wait_for_task(task.task_uid) 

{ "hits": [ { "id": 1234, "title": "Sony WH-1000XM5 wireless headphones", "_formatted": { "title": "Sony WH-1000XM5 wireless <mark>headphones</mark>" } } ], "query": "headphon es sony", "processingTimeMs": 4, "totalHits": 38, "page": 1, "hitsPerPage": 20 } 

{ "query": { "bool": { "should": [ { "multi_match": { "query": "наушниик", "fields": ["title^3", "brand^2", "description"], "fuzziness": "AUTO", "prefix_length": 2, "max_expansions": 50 } }, { "match_phrase": { "title": { "query": "наушниик", "slop": 2 } } } ] } } }