Custom Elasticsearch Analyzers: Language, Synonyms

Search on your site doesn't find "ноутбук" when a user types "лэптоп"? Or "смартфон" returns no results for "телефон"? This is a typical problem of the standard Elasticsearch analyzer: it doesn't understand synonyms and language morphology. To make search work properly, we develop custom analyzers turnkey. Over 5 years, we've tuned search for 50+ projects, including e-commerce stores and news portals. A custom analyzer consists of a tokenizer and token filters. They transform text into a set of tokens for the index. In this article, we'll break down how to assemble such an analyzer for Russian, configure synonyms and autocomplete, and look at typical pitfalls and how to avoid them.

Up to 70% of users don't find the right product due to incorrect analyzer configuration. This reduces conversion by 20–30%. Configuring a custom analyzer solves the problem by selecting filters tailored to your domain.

Why a custom analyzer beats the built-in?

The built-in russian analyzer uses the Snowball algorithm for stemming. For most tasks, that's enough, but if you need precise morphology or a custom set of stop words, we create a custom analyzer. It gives you full control over the filter chain: you decide which stop words to remove, how to handle synonyms, and whether stemming is needed. Additionally, a custom configuration allows adding transliteration for searching by translit (e.g., "noutbuk" → "ноутбук").

How to set up a language analyzer for Russian?

Basic configuration

Example with custom stop words and a field for exact match:

{
  "settings": {
    "analysis": {
      "filter": {
        "russian_stop": { "type": "stop", "stopwords": "_russian_" },
        "russian_stemmer": { "type": "stemmer", "language": "russian" },
        "custom_stopwords": { "type": "stop", "stopwords": ["это", "также", "при", "для", "что", "как"] }
      },
      "analyzer": {
        "russian_custom": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "russian_stop", "custom_stopwords", "russian_stemmer"]
        },
        "russian_exact": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase"]
        }
      }
    }
  }
}

Adding transliteration

Users often type translit: "noutbuk" instead of "ноутбук". Transliteration is implemented via a char_filter of type mapping, where each Cyrillic letter is mapped to a Latin equivalent. The full mapping list is in the documentation — we include it in the analyzer. For advanced transliteration, we use the analysis-icu plugin with ICU transformers, which correctly handles Unicode normalization.

How to set up synonyms without reindexing?

Synonyms allow finding documents with different wording. There are two approaches:

The recommended option is search-time synonyms. They allow changing rules without reindexing, which is critical for e-commerce stores with rapidly changing assortments. For example, a synonym file config/synonyms/synonyms.txt:

ноутбук, лэптоп, laptop, notebook
смартфон, телефон, мобильный
телевизор, тв, tv

Analyzer configuration with synonyms (only for search):

{
  "settings": {
    "analysis": {
      "filter": {
        "synonym_filter": {
          "type": "synonym",
          "synonyms_path": "synonyms/synonyms.txt",
          "updateable": true
        }
      },
      "analyzer": {
        "search_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "russian_stop", "synonym_filter"]
        },
        "index_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "russian_stop", "russian_stemmer"]
        }
      }
    }
  }
}

The flag "updateable": true is mandatory for search-only synonyms. It allows reloading synonyms via _reload_search_analyzers without reindexing. Budget saving: you don't need to rebuild the index every time the dictionary changes.

Setting up autocomplete with Edge N-gram

For autocomplete during input, you need an analyzer with edge_ngram:

"filter": {
  "edge_ngram_filter": {
    "type": "edge_ngram",
    "min_gram": 2,
    "max_gram": 15
  }
}

At search time, use an analyzer without N-gram (e.g., just lowercase), otherwise a query for "ноут" will match all N-gram tokens, causing false positives.

Multilingual indexes

Two approaches: one index with fields for each language or a separate index per language. For multisite setups, one index is more convenient:

"properties": {
  "title_ru": { "type": "text", "analyzer": "russian_custom" },
  "title_en": { "type": "text", "analyzer": "english" },
  "title_de": { "type": "text", "analyzer": "german" }
}

Query across multiple languages:

{
  "query": {
    "multi_match": {
      "query": "search term",
      "fields": ["title_ru^2", "title_en", "title_de"]
    }
  }
}

Boost ^2 for the Russian field if your primary audience is Russian-speaking.

What's included in development and timelines

We provide a fully turnkey solution. Development includes:

• Analysis of your data and search requirements
• Designing a filter chain for your tasks
• Configuring synonyms with online update capability
• Integration with your existing index
• Testing on real queries (at least 100 test scenarios)
• Documentation on settings and maintenance
• Training for your team (up to 2 hours online)

Development and testing of a custom analyzer for one language — from 1 business day. Configuring synonyms with a file and update mechanism — half a day more. Multilingual configuration with 3–5 languages — 2–3 days, including search relevance tests. Cost is calculated individually.

Order analyzer configuration for your project — we'll find the optimal setup.

How we approach the setup

1. Current search audit — collect query logs, identify problematic scenarios.
2. Analyzer design — define filter set, synonyms, stop words.
3. Prototyping — deploy a test index and check on real data.
4. Testing — use the _analyze API to verify each filter. For example:

POST /products/_analyze
{
  "analyzer": "russian_custom",
  "text": "Ноутбуки и лэптопы для работы"
}

The response contains a list of tokens — their count and type should match expectations (stemming trims endings, synonyms expand tokens). 5. Optimization — adjust settings based on test results. 6. Deployment — update the production index without downtime. 7. Monitoring — after launch, track search metrics (CTR, empty results).

Why entrust configuration to professionals?

Incorrect analyzer configuration can reduce search relevance by 2–3 times. Our certified engineers guarantee that after setup, search will be accurate and fast. Get a consultation on analyzer development — we'll evaluate your project and suggest the best solution. Contact us to discuss details.

Learn more about Elasticsearch analyzers.

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.