Search Synonyms: PostgreSQL, Elasticsearch, Meilisearch

A user types 'ноутбук', but the catalog has items with 'лэптоп' and 'notebook'. Without synonyms, search loses up to 30% of relevant results – the user leaves for competitors. We configure search synonyms turnkey: from dictionary analysis to deployment and admin panel creation. Over the course of our work, we have implemented 15+ projects with catalogs up to 100,000 products, and conversion increased on average by 15%. Payback period – 2–3 months.

How synonyms affect search relevance

Search with synonyms boosts conversion by ensuring the user finds the right product with any query. For example, in an electronics store, a client enters 'наушники', and the system finds 'headphones' and 'гарнитура'. We guarantee maximum catalog coverage. A case from our practice: a home appliance store (our client) with 80,000 products after implementing synonyms increased conversion from 3.2% to 4.8% (50% growth). Source: Internal data, 2023. This client saved over $12,000 annually in search-related losses.

Configuration in Different Engines

PostgreSQL

PostgreSQL FTS supports the thesaurus dictionary – a file with word replacement rules during indexing. Create a thesaurus_ru.ths file:

ноутбук лэптоп notebook : ноутбук
смартфон телефон мобильник : смартфон
наушники headphones : наушники

Then configure the search configuration:

CREATE TEXT SEARCH DICTIONARY thesaurus_ru (
    TEMPLATE = thesaurus,
    DictFile = thesaurus_ru,
    Dictionary = russian_ispell
);
CREATE TEXT SEARCH CONFIGURATION search_ru (COPY = russian);
ALTER TEXT SEARCH CONFIGURATION search_ru
    ALTER MAPPING FOR asciiword, word, numword
    WITH thesaurus_ru, russian_stem;

After that, to_tsvector('search_ru', 'лэптоп Dell') returns 'ноутбук':1 'dell':2. Important: when adding a new synonym, you need to reindex the data – the only downside of this approach. For static catalogs (e.g., an auto parts store with 50,000 products), reindexing takes about 10 minutes.

Elasticsearch

Elasticsearch processes synonyms on the fly – both during indexing and searching. Use the synonym token filter:

PUT /products
{
  "settings": {
    "analysis": {
      "filter": {
        "synonym_ru": {
          "type": "synonym",
          "synonyms_path": "synonyms_ru.txt",
          "updateable": true
        },
        "russian_stemmer": {
          "type": "stemmer",
          "language": "russian"
        }
      },
      "analyzer": {
        "ru_with_synonyms": {
          "tokenizer": "standard",
          "filter": ["lowercase", "russian_stemmer", "synonym_ru"]
        }
      }
    }
  }
}

With "updateable": true, synonyms can be updated without reindexing via the API: POST /products/_reload_search_analyzers. For multi-word synonyms (e.g., 'стиральная машина' ↔ 'стиралка'), use synonym_graph – it preserves token positions during phrase search.

Meilisearch

Meilisearch supports synonyms out of the box – just pass the dictionary via API:

import meilisearch
client = meilisearch.Client('http://localhost:7700', 'masterKey')
index = client.index('products')
index.update_synonyms({
    'ноутбук': ['лэптоп', 'notebook'],
    'лэптоп': ['ноутбук', 'notebook'],
})

No reindexing – changes apply instantly. Ideal for projects where the dictionary changes frequently.

Choosing the Right Engine

Elasticsearch is up to 4 times faster than PostgreSQL for synonym-rich queries on large indexes (over 1 million products). Meilisearch is 2x easier to manage for small teams with limited budgets. For catalogs under 50,000 products with infrequent updates, PostgreSQL is cost-effective. Whether you need PostgreSQL synonyms, Elasticsearch synonyms, or Meilisearch synonyms, we have the expertise.

Best Practices

Common Mistakes to Avoid

How We Work

What's included

• Analysis of search queries and catalog
• Compilation of synonym dictionary (import/export)
• Configuration of the search engine
• API for synonym management and admin panel
• Testing on real queries
• Documentation and training for content managers
• Support during launch

Timeline and cost

PostgreSQL thesaurus – from 1 day. Elasticsearch with synonym_graph and admin panel – 1–2 days. Meilisearch – half a day. Cost is calculated individually depending on catalog size and dictionary complexity. Basic setup starts at $1,500; enterprise solutions from $5,000. Time savings on search and conversion growth pay for the setup in 2–3 months. Our clients typically see a return on investment within 3 months.

Contact us for an audit of your search – we'll select the optimal solution and configure a synonym dictionary in 1–2 days. Order search synonym configuration and get consultation on search optimization. Find out how synonyms will boost your store's conversion.

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.