Elasticsearch: Setting Up Search for Web Applications

Imagine your online store grows to 500,000 products, and searching by name via ILIKE % in PostgreSQL stutters with every keystroke. Customers leave, conversion drops—each second of delay reduces it by 7%. We've faced this situation more than once. In one project, searching across 1 million products took 8 seconds; after migrating to Elasticsearch, it was 80 ms. Conversion grew by 15%. Over time, we've configured Elasticsearch for 30+ projects—from catalogs to marketplaces. Our experience shows that proper index and analyzer configuration reduces search time by 10x.

Why Elasticsearch Instead of Full-Text Search in PostgreSQL?

PostgreSQL can do full-text search via tsvector, but it struggles with heavy loads, complex morphology, and facets. Elasticsearch outperforms PostgreSQL by 10-20x in speed under real workloads. Compare:

Plus, Elasticsearch's distributed search handles billions of documents.

How We Configure Elasticsearch for Your Task

We don't set up Elasticsearch "out of the box." We always analyze your data structure and typical queries. Here's a real example: for an electronics catalog with 200,000 items, we created an index with two analyzers: Russian (stemming + stop words) and autocomplete (edge n-gram). The setup took 4 days. In another project, we needed a custom char_filter to clean product names of special characters.

Installing Elasticsearch 8.x

# Add repository
wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | gpg --dearmor -o /usr/share/keyrings/elasticsearch-keyring.gpg
echo "deb [signed-by=/usr/share/keyrings/elasticsearch-keyring.gpg] https://artifacts.elastic.co/packages/8.x/apt stable main" > /etc/apt/sources.list.d/elastic-8.x.list
apt update && apt install -y elasticsearch

# Save superuser password from installation output
systemctl enable elasticsearch && systemctl start elasticsearch

Minimal config for single-node dev:

# /etc/elasticsearch/elasticsearch.yml
cluster.name: myapp-search
node.name: node-1
path.data: /var/lib/elasticsearch
path.logs: /var/log/elasticsearch
network.host: 127.0.0.1
discovery.type: single-node
xpack.security.enabled: true
xpack.security.http.ssl.enabled: false  # for dev; in prod — enable

We allocate heap as: no more than 50% RAM, no more than 32 GB (due to compressed OOPs). 4 GB is enough to start.

Index Mapping

Mapping defines the index schema. An incorrect mapping cannot be fixed without reindexing. Read more at Elasticsearch mapping:

PUT /products
{
  "settings": {
    "number_of_shards": 2,
    "number_of_replicas": 1,
    "analysis": {
      "analyzer": {
        "russian_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "russian_stop", "russian_stemmer"]
        },
        "autocomplete_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "edge_ngram_filter"]
        },
        "autocomplete_search": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase"]
        }
      },
      "filter": {
        "russian_stop": { "type": "stop", "stopwords": "_russian_" },
        "russian_stemmer": { "type": "stemmer", "language": "russian" },
        "edge_ngram_filter": { "type": "edge_ngram", "min_gram": 2, "max_gram": 20 }
      }
    }
  },
  "mappings": {
    "properties": {
      "id": { "type": "keyword" },
      "name": {
        "type": "text",
        "analyzer": "russian_analyzer",
        "fields": {
          "autocomplete": { "type": "text", "analyzer": "autocomplete_analyzer", "search_analyzer": "autocomplete_search" },
          "keyword": { "type": "keyword" }
        }
      },
      "description": { "type": "text", "analyzer": "russian_analyzer" },
      "category": { "type": "keyword" },
      "brand": { "type": "keyword" },
      "price": { "type": "scaled_float", "scaling_factor": 100 },
      "in_stock": { "type": "boolean" },
      "attributes": { "type": "object", "dynamic": true },
      "location": { "type": "geo_point" },
      "created_at": { "type": "date" }
    }
  }
}

Search Query with Facets

POST /products/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "multi_match": {
            "query": "wireless headphones",
            "fields": ["name^3", "description", "name.autocomplete^2"],
            "type": "best_fields",
            "fuzziness": "AUTO"
          }
        }
      ],
      "filter": [
        { "term": { "in_stock": true } },
        { "range": { "price": { "gte": 1000, "lte": 10000 } } },
        { "terms": { "category": ["audio", "headphones"] } }
      ]
    }
  },
  "aggs": {
    "categories": { "terms": { "field": "category", "size": 20 } },
    "brands": { "terms": { "field": "brand", "size": 30 } },
    "price_ranges": {
      "range": {
        "field": "price",
        "ranges": [
          { "to": 1000 },
          { "from": 1000, "to": 5000 },
          { "from": 5000, "to": 15000 },
          { "from": 15000 }
        ]
      }
    }
  },
  "highlight": {
    "fields": { "name": {}, "description": { "fragment_size": 150 } }
  },
  "from": 0,
  "size": 24,
  "sort": [{ "_score": "desc" }, { "created_at": "desc" }]
}

How to Configure Autocomplete?

Autocomplete is implemented via an edge n-gram analyzer, as shown in the mapping. Edge n-gram creates tokens from 2 to 20 characters. Users get suggestions after entering 2-3 characters—improving UX. The name.autocomplete field indexes the start of each word. In search queries, use match_phrase_prefix or multi_match on this field.

Estimated Timelines

Timelines depend on complexity. Basic setup with one index and integration takes 3-5 days. If you need autocomplete, facets, and PostgreSQL synchronization, add another 3-5 days. A 3-node cluster with monitoring takes 1-2 weeks. Costs are calculated individually. Below is an approximate timeline by stage:

Syncing Data from PostgreSQL

For synchronization, we use logical replication via Debezium + Kafka in production scenarios. For a start, periodic reindexing via cron is sufficient. Below is an example in TypeScript:

// sync/product-indexer.ts
import { Client } from '@elastic/elasticsearch'
import { Pool } from 'pg'

const es = new Client({ node: 'http://localhost:9200', auth: { username: 'elastic', password: process.env.ES_PASSWORD! } })
const pg = new Pool({ connectionString: process.env.DATABASE_URL })

export async function indexProduct(id: string) {
  const { rows } = await pg.query(`
    SELECT p.*, c.name AS category_name,
           json_agg(json_build_object('key', a.key, 'value', a.value)) AS attributes
    FROM products p
    LEFT JOIN categories c ON c.id = p.category_id
    LEFT JOIN product_attributes a ON a.product_id = p.id
    WHERE p.id = $1
    GROUP BY p.id, c.name
  `, [id])

  if (!rows.length) {
    await es.delete({ index: 'products', id })
    return
  }

  const p = rows[0]
  await es.index({
    index: 'products',
    id: p.id,
    document: {
      id: p.id,
      name: p.name,
      description: p.description,
      category: p.category_name,
      price: p.price,
      in_stock: p.stock_quantity > 0,
      attributes: Object.fromEntries(p.attributes?.map((a: any) => [a.key, a.value]) ?? []),
      created_at: p.created_at
    }
  })
}

export async function reindexAll() {
  const { rows } = await pg.query('SELECT id FROM products WHERE deleted_at IS NULL')
  const chunks = chunk(rows.map(r => r.id), 100)

  for (const ids of chunks) {
    await Promise.all(ids.map(indexProduct))
    console.log(`Indexed ${ids.length} products`)
  }
}

Step-by-Step Setup Plan

1. Analysis—study data structure, typical queries, speed requirements (1-2 days).
2. Design—develop mapping, analyzers, sync scheme (1-2 days).
3. Implementation—install Elasticsearch, configure index, write integration (3-5 days).
4. Testing—check search relevance, facets, speed (1-2 days).
5. Deployment—deploy to production, configure monitoring (1 day).

What's Included

After completion, you receive:

• Configured Elasticsearch cluster (single node or cluster) with access
• Indices with custom analyzers for Russian language
• Integration with your application via REST API
• Data synchronization scripts (e.g., from PostgreSQL)
• Operation documentation
• Training for your team

We provide a 3-month guarantee: if search doesn't work as expected, we fix it for free.

Cluster Monitoring

We recommend monitoring cluster health via _cluster/health and enabling slowlog for search queries. Use Elastic Metricbeat to collect metrics—this helps detect degradation in time.

Start the Work

If your search is slow or can't handle the load, contact us. We'll evaluate your system and propose a solution. Order a turnkey Elasticsearch setup—get a free consultation. Don't delay: every second of delay costs you customers. Request an audit today.

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.