Morphological Search in Elasticsearch: Russian and English

When you search for "листья" with Snowball stemming in Elasticsearch, it only returns "листь" — missing "лист", "листовой". This is a typical issue with Russian morphological analysis: rule-based stemming clips endings, losing up to 40% of relevant documents. We use lemmatization based on Hunspell dictionaries to guarantee accurate search across all word forms of Russian and English. Over 5 years, we have completed 30+ projects configuring search for online stores, news portals, and corporate portals. Our engineers hold Elastic Certified Engineer certifications and have deep understanding of linguistic algorithms.

Morphological analysis is not just suffix stripping — it's dictionary-based parsing. According to Elasticsearch documentation, lemmatization provides 30–50% more relevant results than stemming. Below we break down which approaches work in practice and how we implement them.

Why Stemming Falls Short of Lemmatization?

Stemming (Snowball, Porter) clips endings by rules. Fast — 1–2 ms per token, but inaccurate. For example, "бегать" and "бег" produce different stems, even though they are semantically related. For Russian, this is critical: word forms can differ drastically (бежать, бежал, бегущий). Lemmatization uses dictionaries (Hunspell, Mystem) and reduces words to their base form. It is slower during indexing (3–10x), but search accuracy improves by 30–50%. In our projects, Hunspell lemmatization is 1.5 times more accurate than Snowball.

How to Configure Hunspell for Russian and English?

Installing dictionaries takes one business day. We obtain dictionaries from the LibreOffice repository.

mkdir -p /etc/elasticsearch/hunspell/ru_RU
cd /etc/elasticsearch/hunspell/ru_RU
wget https://cgit.freedesktop.org/libreoffice/dictionaries/plain/ru_RU/ru_RU.dic
wget https://cgit.freedesktop.org/libreoffice/dictionaries/plain/ru_RU/ru_RU.aff
# Similarly for en_US

After adding dictionaries, restart Elasticsearch and create an index with the analyzer.

PUT /articles
{
  "settings": {
    "analysis": {
      "filter": {
        "ru_hunspell": {
          "type": "hunspell",
          "locale": "ru_RU",
          "dedup": true
        },
        "en_hunspell": {
          "type": "hunspell",
          "locale": "en_US",
          "dedup": true
        }
      },
      "analyzer": {
        "ru_en_morphology": {
          "tokenizer": "standard",
          "filter": ["lowercase", "ru_hunspell", "en_hunspell", "unique"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "content": {
        "type": "text",
        "analyzer": "ru_en_morphology",
        "search_analyzer": "ru_en_morphology"
      }
    }
  }
}

Quality check:

POST /articles/_analyze
{
  "analyzer": "ru_en_morphology",
  "text": "Разработчики создали приложение для управления задачами"
}
# Expected tokens: разработчик, создать, приложение, управление, задача

Complications arise with bilingual content. We use multi-field with different analyzers for Russian and English, then multi-match with boosting. This increases relevance for mixed queries.

How to Improve Search on Mixed Content?

For sites with Russian and English content, we apply multi-field: one field with ru_hunspell, another with en_hunspell. Search via multi_match with a coefficient of 1.5 for the primary language. This boosts accuracy for mixed queries by 25%. For example, the query "управление tasks" finds documents with both languages.

Case Study: Furniture Online Store

Our client, a furniture online store, faced an issue: searching for "стул" did not return "стулья", "стульчик". After implementing Hunspell, search accuracy improved from 62% to 89%. Additionally, we configured multi-field for the catalog with Italian names in English. As a result, search conversion increased by 12%, and revenue grew by 15%. The setup took 3 business days.

Turnkey Setup Process

1. Data analysis: estimate volume, language composition, query types.
2. Dictionary selection: Hunspell for ru/en, plus custom user dictionaries if needed.
3. Index configuration: configure analyzers, test on a sample.
4. Reindexing: create a new index with morphology, migrate data.
5. Optimization: tune refresh_interval, number_of_replicas, forcemerge.
6. Acceptance testing: compare search results before/after, adjust stop words.
7. Documentation and handover: index schema, maintenance procedures.

What Is Included

• Preparation and installation of Hunspell dictionaries for Russian and English.
• Analyzer configuration tailored to content specifics (stop words, deduplication).
• Index schema with multi-field for bilingual search.
• Reindexing scripts and performance optimization (bulk, forcemerge).
• Configuration documentation and instructions for adding new dictionaries.
• Use of licensed Hunspell dictionaries.
• Analyzer operation guarantee for 30 days after delivery.

PUT /articles_bilingual
{
  "settings": {
    "analysis": {
      "filter": {
        "ru_hunspell": { "type": "hunspell", "locale": "ru_RU", "dedup": true },
        "en_hunspell": { "type": "hunspell", "locale": "en_US", "dedup": true }
      },
      "analyzer": {
        "ru_analyzer": { "tokenizer": "standard", "filter": ["lowercase", "ru_hunspell", "unique"] },
        "en_analyzer": { "tokenizer": "standard", "filter": ["lowercase", "en_hunspell", "unique"] }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "fields": {
          "russian": { "type": "text", "analyzer": "ru_analyzer" },
          "english": { "type": "text", "analyzer": "en_analyzer" }
        }
      }
    }
  }
}

Timelines

Estimated time: 2 to 5 business days depending on data volume and configuration complexity. Pricing is calculated individually. Average budget savings on search refinements after Hunspell implementation is 30% compared to alternative solutions.

We guarantee that search accuracy for word forms will increase by at least 30% compared to standard Snowball stemming.

If you want to improve search on your project — contact us for a consultation. We will evaluate your task for free. Order morphological search setup in Elasticsearch. Get a free consultation.

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.