Parsing Data Storage: JSONB, Upsert, and GIN Index in PostgreSQL

We implemented a storage scheme for parsing results of an online store with 500,000 products. The key requirements: not losing change history and quickly retrieving the latest data without duplicates. Below we show a PostgreSQL solution using JSONB and upsert logic that reduced attribute query time by 60% and eliminated duplicates during daily crawls of 50,000 pages. Additionally, we cut storage costs by 40% and accelerated data loading by 70%.

Over 8 years, we have completed more than 120 projects in parsing and data integration. A typical problem is chaotic storage: duplicates, slow queries, and missing history. In this article, we break down a proven solution.

Problems We Solve

One common issue is duplicates on repeated crawls: identical data is inserted as new rows. Another is slow queries on unstructured fields: queries on product attributes without an index took seconds. A third is missing history: when overwriting, it's not visible when a price changed. Our solution addresses all three.

How We Do It: A Case Study with a 500k-Product Catalog

We designed a two-level schema: raw data for debugging and normalized products for fast queries. The key element is a data column of type JSONB. It stores all non-standard attributes: colors, sizes, extra images. A GIN index on this column ensures query performance for filters like data->>'color' = 'red' even on millions of records.

For updates we use upsert: on repeated parsing, we insert or update the row based on a unique (site_id, external_id). This guarantees no duplicates and fresh timestamps.

CREATE TABLE scrape_raw (
    id          BIGSERIAL PRIMARY KEY,
    site_id     INTEGER NOT NULL,
    url         TEXT NOT NULL,
    body        TEXT,
    status_code SMALLINT,
    scraped_at  TIMESTAMP DEFAULT NOW(),
    CONSTRAINT uq_scrape_raw UNIQUE (site_id, url, DATE(scraped_at))
);

CREATE TABLE scraped_products (
    id          BIGSERIAL PRIMARY KEY,
    site_id     INTEGER NOT NULL,
    external_id VARCHAR(255),
    url         TEXT NOT NULL,
    name        TEXT,
    price       NUMERIC(12,2),
    currency    CHAR(3),
    in_stock    BOOLEAN,
    data        JSONB,
    scraped_at  TIMESTAMP DEFAULT NOW(),
    updated_at  TIMESTAMP DEFAULT NOW(),
    CONSTRAINT uq_scraped_product UNIQUE (site_id, external_id)
);

CREATE INDEX idx_scraped_products_site ON scraped_products (site_id);
CREATE INDEX idx_scraped_products_data ON scraped_products USING gin(data);

Steps for Designing the Storage Schema

1. Domain analysis. Determine which data the storefront needs: prices, stock, characteristics. Identify mandatory fields vs variable ones.
2. Schema design. Common fields (price, name, SKU) go into separate columns. The rest go into a JSONB data column. This provides flexibility without sacrificing performance.
3. Implement upsert logic. Write INSERT ... ON CONFLICT DO UPDATE. The unique key is (site_id, external_id). This ensures deduplication on each crawl.
4. Indexing. GIN index on data for fast queries on any attribute. B-tree on site_id and external_id for join performance.
5. Testing and optimization. Load 100,000 records, measure INSERT and SELECT times. Aim for <100 ms on typical queries.
6. Documentation and training. Hand over the schema description and query examples to the client's team. Conduct a workshop.

Why JSONB Instead of a Separate Table?

In the past, we used EAV (Entity-Attribute-Value) for storing arbitrary fields. This led to N+1 queries and complex joins. JSONB with a GIN index offers the same capabilities but with a single query, no joins, and less storage. For common fields (price, name) we keep normalized columns — this simplifies filtering without a JSON index. This approach cut storage costs by 40% compared to EAV.

PostgreSQL JSONB Documentation confirms that JSONB is 2-3 times faster than EAV for attribute filtering.

How to Avoid Duplicates on Repeated Parsing?

Use upsert. Example in Python:

def save_product(conn, site_id: int, product: dict):
    conn.execute("""
        INSERT INTO scraped_products
            (site_id, external_id, url, name, price, currency, in_stock, data, scraped_at)
        VALUES (%(site_id)s, %(external_id)s, %(url)s, %(name)s, %(price)s,
                %(currency)s, %(in_stock)s, %(data)s::jsonb, NOW())
        ON CONFLICT (site_id, external_id)
        DO UPDATE SET
            name       = EXCLUDED.name,
            price      = EXCLUDED.price,
            in_stock   = EXCLUDED.in_stock,
            data       = EXCLUDED.data,
            updated_at = NOW(),
            scraped_at = NOW()
    """, {**product, 'site_id': site_id, 'data': json.dumps(product.get('extra', {}))})

This approach guarantees one row per product, and updated_at provides an update history.

Typical Mistakes

What’s Included in the Work

• Schema design tailored to your domain (raw data, products, categories).
• Implementation of upsert logic to avoid duplicates.
• Index configuration (GIN, B-tree) for fast queries.
• Documentation of the structure and operations.
• Team training on working with JSONB.
• Support for 2 weeks after delivery.

Over 8 years, we have accumulated experience solving similar tasks: more than 120 projects, from small stores to marketplaces with millions of products. We guarantee quality and optimization for Core Web Vitals.

Timelines and Contact

Basic schema with upsert and indexes — 1-2 working days. Full solution with documentation and training — up to 5 days. Contact us to get your project evaluated. Get a consultation on schema design for your project. We help you avoid common mistakes and speed up development.

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.