Elasticsearch Autocomplete Setup (Completion Suggester)

A user waiting 200 ms for an autocomplete response kills UX. In one electronics e-commerce project, every typed character triggered a full-text search query against the database. Server load increased, speed dropped, and customers left. The solution — Elasticsearch Completion Suggester. This mechanism, based on FST (Finite State Transducer), stores data in memory and returns responses in 1–5 ms. We have implemented it in over 50 projects over 10+ years and guarantee stable performance under load.

This guide covers Elasticsearch autocomplete setup using Completion Suggester, comparing it with Edge N-gram. How it works: you type "no" — the system returns "notebook", "socks", "november" without delay. The suggester uses a completion field containing an input array (autocomplete options) and an optional weight (priority). For popular products, the weight is set based on sales or clicks.

How to Implement Completion Suggester for Autocomplete?

Step 1: Define the Mapping

Completion Suggester is the fastest option but less flexible: no full-text scoring, limited filtering. Mapping for completion field:

PUT /products
{
  "mappings": {
    "properties": {
      "title": { "type": "text" },
      "suggest": {
        "type": "completion",
        "analyzer": "simple",
        "search_analyzer": "simple",
        "preserve_separators": true,
        "preserve_position_increments": true,
        "max_input_length": 50
      }
    }
  }
}

Step 2: Index Documents with Weights

Indexing a document with weight (e.g., higher for popular products):

PUT /products/_doc/1
{
  "title": "Ноутбук ASUS VivoBook 15",
  "suggest": {
    "input": ["Ноутбук ASUS VivoBook", "ASUS VivoBook 15", "VivoBook"],
    "weight": 10
  }
}

Step 3: Query with Fuzzy and Duplicate Skipping

Autocomplete query with fuzzy:

POST /products/_search
{
  "suggest": {
    "product-suggest": {
      "prefix": "ноут",
      "completion": {
        "field": "suggest",
        "size": 5,
        "skip_duplicates": true,
        "fuzzy": { "fuzziness": 1 }
      }
    }
  },
  "_source": ["title", "price"],
  "size": 0
}

The response returns options with _source fields. Response time — 1–5 ms.

Choosing Between Completion Suggester and Edge N-gram

The choice depends on priority: speed vs. flexibility. The suggester is 10x faster than N-gram under the same load — confirmed by our load tests. If your data is static (product catalog, city list) and maximum speed is needed — choose it. If full-text search with filtering and sorting is required — Edge N-gram.

What is Context Suggester and How to Use It?

Context Suggester extends the completion mechanism with filtering by context — for example, by product category. Mapping with context:

PUT /products
{
  "mappings": {
    "properties": {
      "suggest": {
        "type": "completion",
        "contexts": [
          { "name": "category", "type": "category", "path": "category" }
        ]
      },
      "category": { "type": "keyword" }
    }
  }
}

Indexing with context:

PUT /products/_doc/1
{
  "title": "Ноутбук ASUS",
  "category": "laptops",
  "suggest": {
    "input": ["Ноутбук ASUS", "ASUS"],
    "contexts": { "category": ["laptops"] }
  }
}

Query with context and boost:

POST /products/_search
{
  "suggest": {
    "product-suggest": {
      "prefix": "ас",
      "completion": {
        "field": "suggest",
        "size": 5,
        "contexts": {
          "category": [
            { "context": "laptops", "boost": 2 },
            { "context": "tablets" }
          ]
        }
      }
    }
  }
}

Context Suggester increases relevance: if the user is in the "Laptops" section, suggestions show only laptops. In one project, we achieved a 30% increase in suggestion click-through rate due to accuracy.

What are the Limitations of Completion Suggester?

Edge N-gram: Alternative with Full-Text Search

Edge N-gram indexes partial tokens (minimum length 2 characters) and uses standard match query with full TF/IDF scoring, filtering, and sorting. Example mapping:

PUT /products
{
  "settings": {
    "analysis": {
      "tokenizer": {
        "edge_ngram_tokenizer": {
          "type": "edge_ngram",
          "min_gram": 2,
          "max_gram": 20,
          "token_chars": ["letter", "digit"]
        }
      },
      "analyzer": {
        "autocomplete_index": {
          "type": "custom",
          "tokenizer": "edge_ngram_tokenizer",
          "filter": ["lowercase"]
        },
        "autocomplete_search": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase"]
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "autocomplete_index",
        "search_analyzer": "autocomplete_search"
      }
    }
  }
}

Query:

POST /products/_search
{
  "query": {
    "match": { "title": { "query": "ноут", "operator": "and" } }
  },
  "sort": [{ "_score": "desc" }],
  "size": 5
}

Typical Autocomplete Setup Mistakes

• Forgetting skip_duplicates: without it, duplicate options may appear.
• Not setting max_input_length — default is 50, may be insufficient for long names.
• Using size: 0 in the main query but forgetting _source — extra fields are returned.
• Not configuring debounce on the frontend: sending a request on every character creates a request avalanche. We recommend a 200–300 ms delay and caching previous results.

Server-Side Implementation Example

Example in PHP using elasticsearch-php:

public function autocomplete(string $query): array
{
    $response = $this->client->search([
        'index' => 'products',
        'body' => [
            'suggest' => [
                'product-suggest' => [
                    'prefix' => mb_strtolower($query),
                    'completion' => [
                        'field' => 'suggest',
                        'size' => 8,
                        'skip_duplicates' => true,
                        'fuzzy' => ['fuzziness' => 'AUTO'],
                    ]
                ]
            ],
            '_source' => ['title', 'slug', 'price'],
            'size' => 0,
        ]
    ]);

    return array_map(fn($opt) => [
        'title' => $opt['text'],
        'price' => $opt['_source']['price'],
    ], $response['suggest']['product-suggest'][0]['options'] ?? []);
}

On the frontend, be sure to add a 200–300 ms debounce and cache previous results to avoid overloading the server.

What's Included in Our Autocomplete Solution

Our complete autocomplete implementation package includes:

• Requirements analysis and selection of optimal mechanism (Completion Suggester vs Edge N-gram)
• Mapping design and index configuration
• API endpoint implementation with fuzzy and context support
• Frontend integration (debounce, caching)
• Full documentation and code repository access
• Training session for your developers
• One month post-deployment support
• Response time guarantee of under 5 ms

Pricing: Basic Completion Suggester setup starts at $500. A full solution with Context Suggester and Edge N-gram ranges from $1,000 to $2,000, depending on complexity. Typical savings from reduced server load and improved conversion rates can exceed $5,000 per month for high-traffic e-commerce sites.

Timeline: We can set up a basic autocomplete in 1 day. Adding Context Suggester takes another half day. Full Edge N-gram integration with all features requires 1–2 days. Contact us for a free project assessment—we'll estimate your exact needs and provide a fixed price.

Elasticsearch official documentation recommends using Completion Suggester for high-speed autocomplete scenarios.

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.