Elasticsearch Faceted Search with Aggregations: Accurate Counters

Elasticsearch Faceted Search with Aggregations: Accurate Counters

Imagine a catalog with a million products. Without faceted search, the user pages through 200 pages. With it, they find the desired brand and price range in seconds. But counters lie if you don't configure post_filter and global aggregations. This article covers how to build correct faceted counters on Elasticsearch, avoiding typical mistakes.

We integrate Elasticsearch with faceted search in catalogs and e-commerce stores. These are filter counters: "Brand: Samsung (143), Apple (89)", "Price: up to 30,000 (234), 30–60,000 (145)". When a filter is selected, the product list narrows, and the counters of other values recalculate. In relational databases such operations are expensive — GROUP BY with COUNT(*) on a filtered set. In Elasticsearch, the task is solved with aggregations, often in a single request. We will assess your project and implement it turnkey in 2–4 working days.

How to Implement Correct Facet Counters

A typical request for a catalog: search query + active filters + aggregations for counting facets. Example of a basic request:

POST /products/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "laptop" } }
      ],
      "filter": [
        { "term": { "is_active": true } },
        { "range": { "price": { "gte": 20000, "lte": 80000 } } }
      ]
    }
  },
  "aggs": {
    "by_brand": {
      "terms": {
        "field": "brand",
        "size": 20,
        "order": { "_count": "desc" }
      }
    },
    "price_ranges": {
      "range": {
        "field": "price",
        "ranges": [
          { "key": "up to 30,000", "to": 30000 },
          { "key": "30–60,000", "from": 30000, "to": 60000 },
          { "key": "60–100,000", "from": 60000, "to": 100000 },
          { "key": "from 100,000", "from": 100000 }
        ]
      }
    },
    "price_stats": {
      "stats": { "field": "price" }
    },
    "by_category": {
      "terms": { "field": "category", "size": 10 }
    },
    "by_rating": {
      "histogram": { "field": "rating", "interval": 1, "min_doc_count": 1 }
    }
  },
  "size": 20,
  "from": 0
}

The Problem of Disappearing Counters When Selecting a Facet

If the user selects brand "Samsung" and adds it to filter, the by_brand aggregation counts only within the filtered set. Counters of other brands become zero — the user cannot see how many products are from other brands. The solution is Post Filter combined with Global Aggregation:

POST /products/_search
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "laptop" } }
      ],
      "filter": [
        { "term": { "is_active": true } }
      ]
    }
  },
  "post_filter": {
    "bool": {
      "filter": [
        { "term": { "brand": "Samsung" } }
      ]
    }
  },
  "aggs": {
    "all_brands": {
      "global": {},
      "aggs": {
        "filtered_brands": {
          "filter": {
            "bool": {
              "must": [
                { "match": { "title": "laptop" } }
              ],
              "filter": [
                { "term": { "is_active": true } }
              ]
            }
          },
          "aggs": {
            "brands": {
              "terms": { "field": "brand", "size": 20 }
            }
          }
        }
      }
    },
    "price_ranges": {
      "range": {
        "field": "price",
        "ranges": [
          { "key": "up to 30,000", "to": 30000 },
          { "key": "30–60,000", "from": 30000, "to": 60000 },
          { "key": "from 60,000", "from": 60000 }
        ]
      }
    }
  },
  "size": 20
}

According to Elasticsearch documentation, post_filter is applied to results after aggregation. The global aggregation goes beyond the query context — it allows counting aggregates across all documents with additional filters.

How to Work with Nested Attributes?

If attributes (size, color, material) are stored as nested objects:

"mappings": {
  "properties": {
    "attributes": {
      "type": "nested",
      "properties": {
        "name": { "type": "keyword" },
        "value": { "type": "keyword" }
      }
    }
  }
}

Aggregation over nested attributes:

"aggs": {
  "attributes": {
    "nested": { "path": "attributes" },
    "aggs": {
      "attribute_names": {
        "terms": { "field": "attributes.name", "size": 50 },
        "aggs": {
          "attribute_values": {
            "terms": { "field": "attributes.value", "size": 20 }
          }
        }
      }
    }
  }
}

This is two-level nested aggregation: first group by attribute name ("Color", "Size"), within each group — values ("Black", "White").

Cardinality — Counting Unique Values

To display "Found 1,247 products from 89 brands", use cardinality aggregation with field brand and precision_threshold 100. This guarantees accuracy up to 100 unique values; above that, error margin is 1–6%.

Setting Up Faceted Search in 5 Steps

1. Identify fields for filtering: brand, price, category, rating, attributes. Typical set is 5–10 fields.
2. Configure mapping: use type keyword for key fields, nested for embedded. Ensure fields for aggregations are not analyzed.
3. Build a query with aggregations: include all necessary aggregations (terms, range, histogram, nested). For correct counters, use post_filter and global aggregations.
4. Apply post_filter: separate filters from the main query. This preserves aggregation counters.
5. Optimize performance: use execution_hint: map for low-cardinality fields, caching for aggregations without search query, limit size to 20.

Aggregation Optimization

terms aggregation with a large size is an expensive operation: each shard returns top-N values, the coordinating node merges results. For high-cardinality fields (thousands of unique values), performance degrades. Optimization methods:

Comparison of Aggregation Types

Implementation on the Application Side

PHP class for building facet queries:

class FacetedSearchService
{
    public function search(array $params): array
    {
        $query = $this->buildQuery($params);
        $response = $this->client->search([
            'index' => 'products',
            'body' => $query,
        ]);

        return [
            'hits' => $response['hits']['hits'],
            'total' => $response['hits']['total']['value'],
            'facets' => $this->extractFacets($response['aggregations']),
        ];
    }

    private function extractFacets(array $aggs): array
    {
        $facets = [];

        if (isset($aggs['by_brand'])) {
            $facets['brand'] = array_map(fn($b) => [
                'value' => $b['key'],
                'count' => $b['doc_count'],
            ], $aggs['by_brand']['buckets']);
        }

        if (isset($aggs['price_ranges'])) {
            $facets['price'] = array_map(fn($r) => [
                'label' => $r['key'],
                'count' => $r['doc_count'],
            ], $aggs['price_ranges']['buckets']);
        }

        return $facets;
    }
}

What's Included in Turnkey Faceted Search Setup

• Designing mapping schemas and indices.
• Configuring all types of aggregations (terms, range, nested, cardinality).
• Implementing post_filter + global aggregations logic for correct counters.
• Performance optimization (execution_hint, caching).
• Writing server-side code for building queries and processing responses.
• Documentation, team training, post-implementation support.

Timelines

• Basic implementation (3–5 aggregation types) — 2 working days.
• Complex scenario (post_filter, global, nested) — 3–4 days.
• Optimization on real volumes (>5 million documents) — additional day.

Our team has 6+ years of experience with Elasticsearch and certified engineers. We have completed over 50 faceted search projects, guaranteeing counter accuracy and performance. Contact us for a consultation — we'll help you avoid expensive mistakes. The cost is calculated individually, and the savings from implementation can be significant. Get a complete solution with full documentation and support.

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.