Fast Full-Text Search with Meilisearch Integration

Meilisearch Integration for Website Search

Note: when the number of records in a catalog exceeds 50,000, the standard LIKE '%query%' in PostgreSQL starts to lag. Query time grows, database load increases. We have run into this problem on multiple projects. The solution — Meilisearch: a full-text search engine built in Rust that responds faster than 50 ms even on millions of records. It supports typos, fuzzy search, faceted filtering, and autocomplete out of the box. Configuring Meilisearch involves index setup, ranking rules, and synchronization with the database. We offer turnkey Meilisearch integration: from infrastructure configuration to the UI component. Based on our estimates, this integration pays for itself in 2–3 months, with infrastructure cost savings of up to $5,000 per year. Typical project cost starts at $3,500. Get a consultation on integrating your project.

Meilisearch official documentation: "Meilisearch is an open-source, RESTful search engine built in Rust. It is designed to be fast, relevant, and easy to use."

Why Choose Meilisearch?

PostgreSQL built-in search becomes inefficient beyond 50,000 records. Meilisearch builds an inverted index separately, so queries bypass the main database. This reduces load and speeds up search by 10–20 times. Compare: a 200,000 product table queried via Meilisearch returns in 30–50 ms, while a SQL LIKE query can take 500 ms or more. Furthermore, Meilisearch fixes typos and supports faceted filtering out of the box — features that require additional configuration in Elasticsearch. Our Meilisearch integration service delivers average ROI of 300%. With over 5 years of experience and 50+ search integration projects, we guarantee stable performance under peak loads. Contact us to evaluate your project.

Integration Architecture

Browser → Backend API → Meilisearch HTTP API
                    ↓
              PostgreSQL (data source)
              Indexer (Queued Job / Cron)

Meilisearch does not replace the main database. Data lives in PostgreSQL; only search-relevant information flows into Meilisearch. Synchronization is done via queues on record changes or periodic index rebuilds.

How to Set Up Synchronization?

1. Install Meilisearch via Docker: docker run -p 7700:7700 getmeili/meilisearch
2. Create an index with the required fields and set ranking rules.
3. Connect Laravel Scout with the official Meilisearch driver. Example model:

use Laravel\Scout\Searchable;

class Product extends Model
{
    use Searchable;

    public function toSearchableArray(): array
    {
        return [
            'id'          => $this->id,
            'name'        => $this->name,
            'description' => strip_tags($this->description),
            'brand'       => $this->brand->name,
            'category_id' => $this->category_id,
            'price'       => $this->price,
            'in_stock'    => $this->stock > 0,
        ];
    }
}

4. Run the initial indexing: php artisan scout:import "App\Models\Product".
5. For search with filters, use:

$results = Product::search($query)
    ->where('in_stock', true)
    ->where('category_id', $categoryId)
    ->orderBy('price')
    ->paginate(20);

Faceted Filtering

Meilisearch returns aggregations for facets in a single request. For example, to filter by category, brand, and availability, send a query with filter and facets parameters. The response contains a distribution for each facet, simplifying the construction of UI filters.

Direct Search from the Browser

Direct requests from JavaScript to Meilisearch are possible using a Search-only API Key – a key with limited permissions (only search against specific indexes). This reduces latency by eliminating an extra hop through the backend. Example: client.index('products').search(query, { filter: 'in_stock = true', limit: 10 }).

Case Study: E-commerce Store with 200,000 Products

In our practice, we worked with an electronics store client with a catalog of 200,000 SKUs. Initially, search ran via PostgreSQL LIKE, response time reached 800 ms, and database CPU load hit 70%. We set up Meilisearch: Docker on a separate server, synchronization via Laravel Scout with queues. After integration, search time dropped to 40 ms, database load decreased by 75%. Infrastructure cost savings amounted to $5,000 per year. The client reported improved UX and a 12% conversion rate increase. The project took 8 business days. Our Meilisearch integration for this client delivered fast full-text search with typo tolerance and faceted filtering.

What's Included in the Work

Estimated Timeline

Total: 7–9 business days for a typical catalog. Pricing is individual — contact us for a project evaluation.

Monitoring

We guarantee that search will remain fast even under concurrent requests from thousands of users. Experience shows that properly configured indexing reduces database load by 60–80%. Order Meilisearch integration for your site and get fast, relevant search. Contact us for a consultation and project evaluation.

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.