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.

Backend Development Services: Laravel, Node.js, Go, Django, PostgreSQL

On a production server at 3:14 AM, the Laravel Jobs queue stopped processing. 40,000 unprocessed jobs in Redis. Cause: worker crashed due to a memory leak in one of the Jobs (leak via a static variable in an Eloquent observer), supervisor didn't restart it because of misconfigured stopwaitsecs. This is not a hypothetical scenario — it's Tuesday. We analyzed such an incident on a project with 500 RPS load: diagnosis took 4 hours, fix — 20 minutes. So you don't lose money on downtime, we offer backend development services with a focus on production-grade reliability. We'll assess your project in 2 days.

Backend is what works when no one is watching. Or doesn't work. We guarantee you'll have the first option.

How do we ensure production-grade reliability from day one?

What we do correctly from day one

Service Layer over Fat Controllers. Controller receives HTTP request, validates it via Form Request, passes data to Service, returns response. Business logic in Service, not Controller. This sounds trivial, but most legacy projects have controllers with 500 lines and SQL queries inside.

Repository Pattern we use cautiously. If you just wrap Model::where(...) in a repository method — that's boilerplate without benefit. Repository is justified when: you need to abstract from the data source (DB + cache + external API) or when query logic is complex enough to isolate.

Jobs, Events, Listeners. Everything that can be async — make async. Sending email, PDF generation, external API sync, aggregate recalculation — into Queue. Laravel Horizon for queue monitoring in Redis: see throughput, failed jobs, processing time per queue.

How Octane handles high load

Laravel Octane with RoadRunner or Swoole keeps the app in memory between requests — removes bootstrap overhead (config loading, class autoloading) on each HTTP request. Gain: 3–8x on synthetic benchmarks, 2–4x on real applications. Important: no state between requests in static variables — that leads to exactly the incidents from the beginning. We use this in projects with >1000 RPS.

What to do about N+1 queries

N+1 is the most common cause of slow pages in Laravel apps. Standard story: page worked fine on dev with 10 records, on production with 10,000 — 8-second load.

Laravel Debugbar in dev environment shows the number of queries per page. More than 20 queries per page — signal for audit.

Model::preventLazyLoading(! app()->isProduction());

Telescope for profiling in staging: logs all queries, jobs, mail, notifications with time detail. Numbers: after implementing eager loading, page load time drops from 8s to 0.3s — 27 times faster.

PostgreSQL: indexes that are actually needed

PostgreSQL 14+ is the primary DB on all projects. We use PgBouncer + PostgreSQL combination. 10+ years experience, more than 50 backend projects, 5 years on the market.

How PostgreSQL helps avoid slow queries

Composite indexes for frequent WHERE + ORDER BY. If you have WHERE user_id = ? AND status = ? ORDER BY created_at DESC — you need (user_id, status, created_at DESC). A separate index on (user_id) doesn't help much with sorting.

Partial indexes. If 95% of queries go with WHERE status = 'active':

CREATE INDEX idx_orders_active ON orders (created_at DESC)
WHERE status = 'active';

The index is small, fast, covers the main load.

GIN indexes for JSONB and arrays. @> operator without GIN index — seq scan. With index — fast even on millions of rows.

GIN for full-text search. to_tsvector + GIN instead of LIKE '%query%'. LIKE without index is always seq scan. With pg_trgm extension and gin_trgm_ops — supports LIKE with index, useful for CRM search by partial match.

Connection pooling: why it's more important than it seems

Rails, Laravel, Django open a new connection to PostgreSQL for each PHP/Python process. With 100 workers — 100 connections. PostgreSQL starts degrading from 200–300 active connections — overhead on connection management becomes significant.

PgBouncer — connection pooler in front of PostgreSQL. Transaction pooling mode: connection to PostgreSQL is occupied only during a transaction, returned to pool between requests. 1000 application workers → 20–50 actual connections to PostgreSQL. This reduces latency by 40% and hosting costs by 30%.

Node.js with Fastify: when it's better than Laravel

Node.js is justified for:

• Realtime: WebSocket servers, Server-Sent Events, chat, live updates
• Streaming: large files, video, streaming data
• High I/O concurrency: many parallel requests to external APIs without heavy business logic
• Serverless: Lambda/Cloud Functions — Node.js starts faster than PHP

Fastify over Express: 2–3 times faster on benchmarks, built-in JSON Schema validation, better TypeScript support, plugin architecture.

Typical realtime architecture: Laravel — core business logic and REST API. Node.js + Socket.io or ws — WebSocket server. Laravel publishes events to Redis Pub/Sub, Node.js subscribes and broadcasts to clients. This separation allows scaling the WebSocket server independently of the main app.

Go: microservices and high load

Go we use for:

• High-load microservices (>10,000 RPS)
• Background workers with strict latency requirements
• DevOps tools and CLI
• gRPC services in microservice architecture

Goroutines — thousands of times cheaper than OS threads. 10,000 concurrent connections on Go is normal on one server.

But Go is not a silver bullet. Development is slower than Laravel: more boilerplate, no ORM at Eloquent level, error handling with if err != nil everywhere. Justified only when performance is a real requirement, not an assumption.

Django and Python backend

Django with DRF (Django REST Framework) — for tasks where Python is needed: ML pipelines, data processing, integrations with AI tools.

Celery for background tasks — similar to Laravel Queue but more complex to configure. Celery Beat for cron tasks.

Django ORM vs raw SQL: ORM is convenient for CRUD. For analytical queries with multiple JOINs, window functions, and CTEs — connection.execute() with raw SQL is more readable and predictable.

Redis: not just cache

Redis in our projects plays multiple roles:

Redis Cluster for horizontal scaling. Sentinel for automatic failover on standalone setups.

Deployment and infrastructure

Docker + docker-compose — standard for local development and production. Each service in a container: PHP-FPM/Octane, Nginx, PostgreSQL, Redis, Queue Worker, Scheduler.

CI/CD via GitHub Actions:

1. Run tests (PHPUnit / Pest, Vitest, Playwright)
2. Build Docker image
3. Push to Container Registry
4. Deploy: docker pull → docker-compose up -d on server, or Kubernetes rolling update

Zero-downtime deploy for Laravel: php artisan down --secret=TOKEN is not needed with proper configuration. Strategy: new container starts next to the old one, Nginx switches traffic after health check, old container stops.

Monitoring: Sentry for exception tracking with alerting in Slack/Telegram. Grafana + Prometheus (or Grafana Cloud) for metrics: CPU, memory, request rate, queue depth, database connection count. Alerts on: error rate > 1%, p99 latency > 2s, queue depth > 1000 jobs.

What's included in turnkey work

• Architecture design (API documentation, DB schema, service diagram)
• Implementation according to agreed specification with code review
• CI/CD, monitoring, alerting setup
• Load testing (k6, wrk) with report
• Handover of source code, access, deployment instructions
• Training of customer's team (2-3 sessions)
• Warranty support for 1 month after delivery

Timeline benchmarks

Pricing is calculated individually after analyzing load, integrations, and business logic. Contact us for a free audit of your current backend — get an optimization plan in 2 days. Request a consultation.