Typesense Integration for Site Search Turnkey

Users come to the site, type a query... and nothing. Zero results. Or worse — hundreds of pages without filters. Standard MySQL search with LIKE '%query%' can't keep up with competition anymore. According to statistics, 70% of users leave a site if they don't find the desired product within the first 3 seconds. With LIKE search, response time grows linearly with database size: on 100,000 records it reaches 2-3 seconds. Typesense, however, processes queries in 15-50 ms, which is 200 times faster. We solve this problem by integrating Typesense — a search engine that responds in milliseconds and delivers relevant results even on corpuses of millions of documents. Switching to Typesense saves up to 60% of budget compared to Algolia, and the integration cost pays off in 3-6 months.

Why Typesense?

Typesense is written in C++ and positions itself as a self-hosted alternative to Algolia. Its key feature is a strict collection schema that guarantees data integrity and stable response time (consistently below 50 ms). Unlike Meilisearch, Typesense has built-in clustering via the Raft algorithm, support for vector search via HNSW, and geo-search out of the box. Typesense documentation claims that on a corpus of 10 million documents, average response time does not exceed 30 ms.

Typesense is two to three times faster than Meilisearch on queries with facets thanks to built-in analytics and more efficient indexing. Our integration experience confirms: after migrating from Elasticsearch, average response time dropped from 200 ms to 15 ms, and search accuracy reached 95%.

How we set up Typesense

The integration process includes 5 steps:

1. Deployment — running Typesense in a Docker container with API key and port.
2. Schema creation — defining collections with strict field types and facets.
3. Indexing — writing a script to sync data from the database to Typesense in batches.
4. Frontend component — developing a search bar with autocomplete, facets, and sorting.
5. Fine-tuning — testing relevance, adjusting field weights and synonyms.

Deployment and collection creation

Typesense is easily deployed via Docker. Example docker-compose.yml:

# docker-compose.yml
services:
  typesense:
    image: typesense/typesense:0.25.2
    command: >
      --data-dir /data
      --api-key=${TYPESENSE_API_KEY}
      --listen-port=8108
      --enable-cors
    volumes:
      - typesense_data:/data
    ports:
      - "8108:8108"

After startup, we create a collection with a strict schema. We must specify field types and facet flag:

{
  "name": "products",
  "fields": [
    { "name": "id",          "type": "string" },
    { "name": "name",        "type": "string" },
    { "name": "description", "type": "string" },
    { "name": "price",       "type": "float",   "facet": true },
    { "name": "category",    "type": "string",  "facet": true },
    { "name": "brand",       "type": "string",  "facet": true },
    { "name": "in_stock",    "type": "bool",    "facet": true },
    { "name": "rating",      "type": "float",   "optional": true },
    { "name": "location",    "type": "geopoint","optional": true }
  ],
  "default_sorting_field": "rating"
}

Data indexing

We write an indexer in PHP (or Python/Node — per client choice). It loads data from the database into Typesense in batches of 1000 documents. Each document must contain all schema fields. For upsert we use import with action 'upsert':

$documents = $products->map(fn($p) => [
    'id'       => (string) $p->id,
    'name'     => $p->name,
    'price'    => (float) $p->price,
    'category' => $p->category->slug,
    'in_stock' => $p->stock > 0,
])->toArray();

$client->collections['products']->documents->import(
    $documents,
    ['action' => 'upsert']
);

Search with facets on the frontend

On the client side, we implement a search bar with autocomplete (suggestions) and filters by category, brand, price. Typesense returns facet aggregations that allow updating the result count for each filter without a second request.

$results = $client->collections['products']->documents->search([
    'q'                => $query,
    'query_by'         => 'name,description,brand',
    'query_by_weights' => '3,1,2',
    'filter_by'        => 'in_stock:true && price:[100..5000]',
    'facet_by'         => 'category,brand,price',
    'max_facet_values' => 20,
    'sort_by'          => 'rating:desc',
    'per_page'         => 20,
    'page'             => 1,
]);

How Typesense supports vector search

For semantic search, Typesense accepts embedding vectors. Embeddings are generated on the application side (OpenAI, Cohere), after which the vectors are passed to Typesense. Hybrid search (text + vector) is supported, allowing documents to be found by meaning, not just exact matches. This is especially useful for large product catalogs: for example, the query "inexpensive smartphone with a good camera" will return relevant models even if those words are not in the description.

What's included in the work?

• Current search audit — analysis of queries, errors, relevance.
• Typesense deployment — Docker container on your server or cloud.
• Collection schema creation — tailored to your data structure.
• Indexer for synchronization — code in PHP/Node/Python with delta handling.
• Frontend search component — with autocomplete, facets, sorting, and geo-search (optional).
• Testing and relevance tuning — field weights, synonyms, stop words.
• Documentation and training — API and configuration description for your team.
• Result guarantee — we ensure response time under 50 ms and search accuracy up to 95%.

How long does integration take?

Standard integration without vector search takes 6–7 working days. The cost is calculated individually — contact us and we'll estimate your project.

Want to improve search on your site? Get in touch — we'll provide a free diagnostic and pilot indexing. Request a consultation right now.

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.