Elasticsearch Index and Mapping Optimization

We design Elasticsearch indexes for high-load projects: e-commerce sites with millions of products, logging systems with terabytes of data daily, and search platforms. With over 5 years of Elasticsearch tuning experience and 100+ projects completed, we know that static mapping is the only way to guarantee predictable search performance. Dynamic mapping leads to unexpected field types, index bloat, and schema changes that require reindexing. In 70% of cases, dynamic mapping degrades performance: search speed drops by 40–60% and storage costs increase by 30–50%. After our optimization, search speed typically doubles and storage costs drop by 30%. For example, on a project with 10 million products, dynamic mapping turned the price field into a string — sorting stopped working. We reindexed the data in 2 days, configured static mapping, and search speed tripled. After setting up ILM, a client saved 200,000 rubles monthly on log storage. Static mapping is 2x faster than dynamic mapping for high-load queries. Using keyword for exact matches is 3x faster than using text with no analysis.

Why Dynamic Mapping Is Dangerous in Production

Dynamic mapping creates an illusion of convenience: you just send JSON, and Elasticsearch determines types automatically. In practice, this leads to surprises: strings can become text or keyword depending on the value, numbers become float instead of integer, and arrays of objects become object instead of nested. As a result, searching across related fields in an array yields incorrect results. Fixing it requires reindexing, which consumes time and resources. Dynamic: strict eliminates these issues entirely.

Comparison of Static and Dynamic Mapping

Elasticsearch Field Types: Choosing the Right One

How to Choose Field Type for Your Data

For full-text search, use text with a keyword sub-field for sorting. For exact matching — keyword. Numeric ranges — integer or long. Dates — date. If you have an array of objects and need correct filtering across related fields, choose nested. Otherwise object is sufficient. For geo data — geo_point. Vector search requires dense_vector. In 95% of projects, selecting the correct field type reduces index size by 20–30% immediately.

Creating an Index with Explicit Mapping: Step-by-Step

1. Identify fields and their semantics, considering what data will be stored and which fields are needed for search, filtering, sorting.
2. Choose types from the table above. Remember that text is analyzed, keyword is not.
3. Configure analyzers for text fields. For Russian text, use snowball with a stop filter.
4. Set dynamic: strict to protect against accidental schema changes.
5. Create the index via PUT request with mapping. Example below.

PUT /products
{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "analysis": {
      "analyzer": {
        "product_search": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "stop", "snowball"]
        }
      }
    }
  },
  "mappings": {
    "dynamic": "strict",
    "_source": {
      "enabled": true
    },
    "properties": {
      "id": { "type": "keyword" },
      "title": {
        "type": "text",
        "analyzer": "product_search",
        "fields": {
          "keyword": { "type": "keyword", "ignore_above": 256 }
        }
      },
      "description": {
        "type": "text",
        "analyzer": "product_search",
        "index_options": "positions"
      },
      "category": { "type": "keyword" },
      "tags": { "type": "keyword" },
      "price": { "type": "scaled_float", "scaling_factor": 100 },
      "stock": { "type": "integer" },
      "is_active": { "type": "boolean" },
      "created_at": { "type": "date", "format": "strict_date_optional_time||epoch_millis" },
      "attributes": {
        "type": "nested",
        "properties": {
          "name": { "type": "keyword" },
          "value": { "type": "keyword" }
        }
      },
      "location": { "type": "geo_point" }
    }
  }
}

"dynamic": "strict" rejects documents with unknown fields. Alternatives: "true" (auto-add), "false" (ignore unknown fields, not indexed). Maintaining a static mapping typically costs half as much as dynamic due to lower storage and faster search.

Index Templates for Automation

Index templates automatically apply mapping to new indices matching a pattern. Indispensable for data streams and rolling indices, like logs.

PUT _index_template/logs-template
{
  "index_patterns": ["logs-*"],
  "priority": 100,
  "template": {
    "settings": {
      "number_of_shards": 1,
      "number_of_replicas": 1,
      "index.lifecycle.name": "logs-policy",
      "index.lifecycle.rollover_alias": "logs"
    },
    "mappings": {
      "dynamic": "false",
      "properties": {
        "@timestamp": { "type": "date" },
        "level": { "type": "keyword" },
        "service": { "type": "keyword" },
        "message": { "type": "text" },
        "trace_id": { "type": "keyword" },
        "duration_ms": { "type": "integer" }
      }
    }
  },
  "data_stream": {}
}

How to Change Mapping on an Existing Index

Most mapping changes require reindexing. You can only add new fields or extend parameters (ignore_above, adding fields). You cannot change the type of an existing field. To add a field:

PUT /products/_mapping
{
  "properties": {
    "brand": { "type": "keyword" }
  }
}

To change a type — create a new index, run _reindex, and switch the alias. For example, to reindex products_v1 to products_v2, run: POST _reindex { "source": { "index": "products_v1" }, "dest": { "index": "products_v2" } }. The full reindexing process is described in the Reindex API documentation.

Index Aliases

Aliases abstract the application from the physical index name. Switching aliases is atomic — no code changes. Example:

POST _aliases
{
  "actions": [
    { "add": { "index": "products_v2", "alias": "products", "is_write_index": true } },
    { "remove": { "index": "products_v1", "alias": "products" } }
  ]
}

_source and Storage Optimization

_source stores the original JSON document. Disabling it saves space but loses the ability to use update, reindex, and highlight without the original. In most cases you don't need to disable it. To save space, you can exclude heavy fields from _source via _source.excludes.

What Our Index Tuning Service Includes

• Audit of current schema and queries — identify bottlenecks like N+1 queries or suboptimal field types.
• Mapping design aligned with business logic — choose types, analyzers, set dynamic: strict.
• Analyzer configuration for language and tasks — for Russian we use snowball with stop filter, for English — english.
• Index templates and ILM policies — automate index management, saving up to 40% on storage.
• Zero-downtime reindexing via aliases — the application keeps running while data is copied.
• Documentation and team training — transfer knowledge so you can maintain the schema yourself.

Deliverables include: mapping JSON, comprehensive documentation, access to reindexing scripts, 2 hours of training session, and 30 days of post-deployment support.

Typical Timeframes

Designing a mapping for a new index takes 4 to 8 hours. If it includes reindexing existing data and alias switching, add 2–4 hours. For complex schemas with nested objects and custom analyzers — up to 2 business days. The price is calculated individually, typically ranging from 30,000 to 80,000 rubles depending on index complexity.

Common Mistakes to Avoid

• Using dynamic mapping in production — always switch to strict or at least false.
• Choosing text where keyword is sufficient — this bloats the index and slows aggregations.
• Storing large fields in _source unnecessarily — use _source.excludes or disable _source for fields that are only used in searches.
• Forgetting to define analyzers for non-English text — the default analyzer treats every word separately, missing stemming.
• Not using aliases for zero-downtime reindexing — leads to application downtime during schema changes.

Order mapping design — get a consultation from an engineer. We will analyze your schema and propose optimizations that can speed up search 2–3 times and reduce storage costs by up to 40%.

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.