Manticore Search Integration for Full-Text Site Search

When a site exceeds 100,000 pages, standard SQL LIKE queries start to fail. We have repeatedly encountered projects where search times reached 5–10 seconds, driving users away and hurting conversion. For example, in one project with a catalog of 3 million products, after implementing Manticore, average search time dropped from 7 seconds to 35 ms, and database server load decreased 10-fold. A full-text search engine like Manticore Search solves the issue — response times drop to 20–50 ms. We use Manticore Search 6.x in Docker with PHP 8.x and PDO. With over 5 years of experience and 30+ successful integrations, we connect and configure Manticore for your project.

What problems do we solve?

• Slow search with large data volumes. Manticore processes queries on indexes of hundreds of gigabytes in milliseconds. Typical SQL LIKE queries cause full table scans and the N+1 problem — Manticore avoids this thanks to inverted indexes.
• Complex Russian morphology. The built-in stemmer stem_ru and lemmatizer lemmatize_ru_all correctly handle cases and word forms. Configuration is mandatory for Russian-language content.
• Need for result ranking. We configure field weights, BM25, weighting by rating and date — the search returns the most relevant results first. For example, article title weight 10, body weight 1, author weight 2.
• Synchronization with the main database. We implement event-driven index updates via Observer or batch indexing. Without synchronization, search returns stale data.

Why Manticore over Sphinx for new projects?

Manticore is actively developed: it supports JSON documents, HTTP API, columnar storage. Search speed is 20–30% higher due to optimizations in 6.x. Elasticsearch is 2–3 times slower on simple full-text queries, and licensing costs are lower. According to official Manticore documentation, search speed on RT indexes reaches 10,000 queries per second. If you have legacy Sphinx — we help migrate to Manticore without service downtime.

What does morphology offer for Russian?

Note: as stated in Manticore morphology documentation, the stemmer stem_ru and lemmatizer lemmatize_ru_all ensure correct recognition of cases and word forms. Without this configuration, a search for "автомобиль" won't find "автомобиля" or "автомобилей". This is critical for Russian-language content.

How we set up search: stack and example

We use Manticore Search 6.x, Docker, PHP 8.x with PDO. In a typical project, we deploy a container, configure an RT index with Russian morphology, and connect via MySQL protocol. Below are the configuration and code example.

Installing Manticore Search

# docker-compose.yml services: manticore: image: manticoresearch/manticore:6.2.12 environment: - EXTRA=1 ports: - "9306:9306" # MySQL-совместимый порт - "9308:9308" # HTTP API volumes: - manticore_data:/var/lib/manticore - ./manticore.conf:/etc/manticoresearch/manticore.conf 

Index configuration

# manticore.conf index articles { type = rt path = /var/lib/manticore/articles rt_field = title rt_field = body rt_field = author rt_attr_uint = category_id rt_attr_bigint = created_at rt_attr_float = rating rt_attr_string = slug morphology = stem_ru, stem_en min_word_len = 2 expand_keywords = 1 min_infix_len = 3 stopwords = /etc/manticoresearch/stopwords_ru.txt } searchd { listen = 0.0.0.0:9306:mysql41 listen = 0.0.0.0:9308:http log = /var/log/manticore/searchd.log query_log = /var/log/manticore/query.log max_matches = 10000 } 

Connecting via MySQL protocol (PHP)

$pdo = new PDO('mysql:host=localhost;port=9306;charset=utf8', '', ''); $pdo->setAttribute(PDO::ATTR_ERRMODE, PDO::ERRMODE_EXCEPTION); // Вставка документа $stmt = $pdo->prepare(" INSERT INTO articles (id, title, body, author, category_id, created_at, rating) VALUES (:id, :title, :body, :author, :category_id, :created_at, :rating) "); $stmt->execute([ 'id' => $article->id, 'title' => $article->title, 'body' => strip_tags($article->content), 'author' => $article->user->name, 'category_id' => $article->category_id, 'created_at' => $article->created_at->timestamp, 'rating' => $article->rating, ]); // Полнотекстовый поиск с весами полей $stmt = $pdo->prepare(" SELECT id, title, author, rating, WEIGHT() AS relevance FROM articles WHERE MATCH(:query) ORDER BY relevance DESC, rating DESC LIMIT :offset, :limit OPTION ranker=bm25, field_weights=(title=10, body=1, author=2) "); 

Synchronization with the database

class ArticleObserver { public function saved(Article $article): void { ManticoreIndexJob::dispatch($article->id); } public function deleted(Article $article): void { ManticoreDeleteJob::dispatch($article->id); } } 

SELECT id, title, SNIPPET(body, :query, 'limit=200, around=5, html_strip_mode=strip') AS excerpt FROM articles WHERE MATCH(:query) LIMIT 20 

Work process

1. Analyze search requirements: data volume, content types, need for morphology.
2. Design index schema: fields, attributes, weights.
3. Deploy Manticore in Docker, configure settings.
4. Write indexing code: initial load, incremental updates.
5. Implement search API in the application with ranking and snippets.
6. UI: search box, results, pagination, highlighting.
7. Testing: load testing, relevance checks.
8. Deployment and monitoring.

What is included in the work

• Installation and configuration of Manticore Search in Docker.
• Creation of an RT index with morphology, stop words, infixes.
• Development of a data synchronization module (Observer or batch).
• REST API for search (SQL or JSON).
• Frontend adaptation: search bar, snippets, pagination.
• Load testing and optimization.
• Operation documentation and access details.

Timelines

Typical errors and their solutions

Real-time index synchronization

We use the Observer pattern: when a record is saved or deleted in Eloquent (Laravel), we dispatch a Job to update the index. For batch loading of large data volumes, we use a background process that indexes batches of 1000 records. This keeps the index up-to-date without delays.

Order search integration

Contact us for a consultation and project assessment. We will analyze your data structure, load, and search requirements, and propose an optimal solution. We deliver turnkey in 6–7 days. Guaranteed stability and relevant results. Get a consultation on integrating Manticore into your project today.