PostgreSQL storage for scraped data with versioning and search

Engineering approach to scraped data storage: PostgreSQL, versioning, JSONB

You've scraped 10,000 listings from Avito, and a week later half have changed. CSV quickly turns into mush — changes are untraceable. MongoDB without a strict schema is just a delayed disaster: data gets dirty over time. You need a system that remembers every change and lets you find what you need in milliseconds. We build such solutions — on PostgreSQL with versioning, full-text search, and a REST API. Our schema has been running in production for decades. PostgreSQL documentation recommends GIN indexes for JSONB, providing speed and flexibility. Let's dive into a concrete implementation. The main table scraped_items holds current data, and scraped_items_history stores the change archive. This approach ensures a full audit trail without sacrificing performance.

Storage schema in PostgreSQL

-- Main table with change history
CREATE TABLE scraped_items (
    id          BIGSERIAL PRIMARY KEY,
    source_id   INTEGER REFERENCES sources(id),
    external_id TEXT NOT NULL,           -- ID at the source
    url         TEXT NOT NULL,
    data        JSONB NOT NULL,          -- flexible schema for different sources
    data_hash   CHAR(64) NOT NULL,       -- SHA-256 of data for change detection
    first_seen  TIMESTAMPTZ DEFAULT NOW(),
    last_seen   TIMESTAMPTZ DEFAULT NOW(),
    changed_at  TIMESTAMPTZ,
    UNIQUE (source_id, external_id)
);

-- Change history
CREATE TABLE scraped_items_history (
    id          BIGSERIAL PRIMARY KEY,
    item_id     BIGINT REFERENCES scraped_items(id),
    data        JSONB NOT NULL,
    recorded_at TIMESTAMPTZ DEFAULT NOW()
);

-- Indexes
CREATE INDEX ON scraped_items USING GIN (data);           -- search in JSONB
CREATE INDEX ON scraped_items (source_id, last_seen);
CREATE INDEX ON scraped_items USING GIN (
    to_tsvector('russian', data->>'title' || ' ' || COALESCE(data->>'description', ''))
);

Key decisions: using JSONB for variable schemas, separate history table, data hash for fast change detection. This schema ensures ACID transactions and integrity under parallel load.

Update logic

def upsert_item(source_id, external_id, url, data):
    data_hash = hashlib.sha256(
        json.dumps(data, sort_keys=True).encode()
    ).hexdigest()

    existing = db.query(
        'SELECT id, data_hash FROM scraped_items WHERE source_id=%s AND external_id=%s',
        (source_id, external_id)
    ).fetchone()

    if existing is None:
        # new item
        db.execute(
            'INSERT INTO scraped_items (source_id, external_id, url, data, data_hash) '
            'VALUES (%s, %s, %s, %s, %s)',
            (source_id, external_id, url, json.dumps(data), data_hash)
        )
    elif existing['data_hash'] != data_hash:
        # data changed — save history
        db.execute(
            'INSERT INTO scraped_items_history (item_id, data) '
            'SELECT id, data FROM scraped_items WHERE id=%s',
            (existing['id'],)
        )
        db.execute(
            'UPDATE scraped_items SET data=%s, data_hash=%s, last_seen=NOW(), changed_at=NOW() '
            'WHERE id=%s',
            (json.dumps(data), data_hash, existing['id'])
        )
    else:
        # data unchanged — update only last_seen
        db.execute(
            'UPDATE scraped_items SET last_seen=NOW() WHERE id=%s',
            (existing['id'],)
        )

The upsert_item function handles three scenarios: insert new object, update with history preservation (if hash changed), and just update last_seen timestamp with no changes. This minimizes I/O and speeds up processing. Under a load of 100,000 records per day, the entire pipeline completes in under 15 minutes.

Why JSONB instead of separate columns?

Scraped data often has an unstable structure: today a product has weight, tomorrow a color. JSONB eliminates migration issues and allows indexing any field via a GIN index. We use a hybrid approach: key fields are extracted to columns for fast filters, the rest stays in JSONB. This gives the speed of a relational model and the flexibility of a document-oriented one. In practice, JSONB in PostgreSQL is 3x faster than MongoDB for queries on structured fields.

How do we detect changes without losing performance?

We use SHA-256 of serialized JSON. The hash is compared to the stored one on each upsert. This check runs in O(1) and does not require reading the entire row. For large volumes (millions of records), we apply partitioning by source_id.

Comparison of storage approaches

Processing pipeline: stages and tools

Implementation process

1. Source analysis — determine data structure and update frequency.
2. Schema design — choose indexes, configure partitioning for large volumes.
3. Develop upsert logic — write function with change detection via hash.
4. Processing pipeline — normalization, enrichment, aggregation.
5. API and export — REST endpoints with pagination, plus CSV/XLSX download.
6. Monitoring and archiving — TTL policy, failure notifications.

What's included

• Designed database schema with migrations
• GitHub repository with code (upsert, pipeline, API)
• API documentation (OpenAPI/Swagger)
• Deployment instructions (Docker Compose)
• Guarantee of correctness during acceptance

Archiving and TTL

Old data (not seen for more than 90 days) is moved to archive or deleted, depending on requirements. Change history is retained longer than main data — by default 365 days. Everything is configurable for your business case.

Export

• CSV/XLSX — via pandas.to_excel() or csv.DictWriter
• REST API — FastAPI/Laravel with filtering, pagination, sorting
• Webhook — real-time push of new/changed records to an external system

Implementation time for the storage system with change history and API is 4–6 days. We guarantee correct operation under load up to 100k records per day. If you need reliable scraped data storage — contact us to discuss the schema. Our experience includes dozens of deployed systems. Get a consultation — we'll help you choose the optimal schema.

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.