Headless Browsers for Dynamic Website Scraping

Implementing Scraping with Puppeteer/Playwright (Headless Browser)

When trying to scrape an e-commerce site built on Next.js, we hit a familiar wall: a static HTML parser returned an empty page. Content on React, Vue, or Angular loads dynamically—lazy-loading, infinite scroll, and asynchronous API calls. Without a headless browser, you simply can't get the data. We've tested several tools and settled on Playwright combined with proxy rotation.

Our experience across 50+ projects shows that the right tool choice and process optimization reduce scraper development time by 30–50%. For instance, one client automated competitor price monitoring, cutting manual data collection costs by 40%. On a large e-commerce platform with 10,000 products, we blocked images and fonts—memory usage dropped by 60%, and scraping time per page fell from 8 to 1.2 seconds.

Why a Headless Browser Is Essential for Dynamic Sites

Modern frontend frameworks (React, Vue, Angular) render content on the client. An HTTP request to the page returns only the HTML shell. To get the real data, you need to execute JavaScript. A headless browser does this in the background—you obtain the DOM as in a normal browser, but without a GUI.

Which Sites Require a Headless Browser?

• SPAs (React, Vue, Angular) — content is built on the client.
• Infinite scroll and lazy-loading — data loads on scroll.
• CAPTCHAs and authentication — require JS execution.
• Bot-protected sites (Cloudflare, DataDome) — headless is unavoidable.

Which Headless Browser Is Best for Scraping?

Playwright is preferable for new projects: its auto-wait significantly reduces errors—you don't need to manually wait for each element. According to official documentation, this speeds up script development by 30-40%. In Puppeteer, every waitForSelector must be configured individually, slowing down work.

How to Avoid Headless Browser Blocking?

Protection systems (DataDome, PerimeterX, Cloudflare Bot Management) analyze dozens of automation signals. Key evasion methods:

• playwright-stealth — patches navigator.webdriver and other fields.
• Realistic mouse movements using playwright-mouse-helper.
• Unique fingerprints — different viewport, timezone, locale per session.
• Proxy and user-agent rotation.

Without these measures, up to 80% of requests are blocked. In our projects, the combination yields a pass-through rate of 95%+.

Details on Stealth Library Setup

1. Install playwright-stealth and apply patches before launching the browser.
2. Configure realistic mouse movements with playwright-mouse-helper.
3. Generate a new fingerprint for each session: viewport, timezone, locale, user-agent.
4. Use a proxy pool with rotation every N requests.

Typical Scraping Scenario

// Playwright: catalog scraping with infinite scroll
const browser = await chromium.launch({ headless: true });
const context = await browser.newContext({
  userAgent: 'Mozilla/5.0 ...',
  viewport: { width: 1280, height: 900 }
});
const page = await context.newPage();

await page.goto('https://example.com/catalog');

// Scroll to the bottom
let prevHeight = 0;
while (true) {
  const height = await page.evaluate(() => document.body.scrollHeight);
  if (height === prevHeight) break;
  await page.evaluate(() => window.scrollTo(0, document.body.scrollHeight));
  await page.waitForTimeout(1500 + Math.random() * 1000);
  prevHeight = height;
}

// Extract data
const items = await page.$$eval('.product-card', cards =>
  cards.map(card => ({
    title: card.querySelector('.title')?.textContent?.trim(),
    price: card.querySelector('.price')?.textContent?.trim(),
    url: card.querySelector('a')?.href
  }))
);

Performance Optimization: How to Reduce Load?

Launching a browser is expensive. For production scraping:

• Browser context pool — one Chrome process with multiple isolated contexts.
• Resource blocking — block font, image, and analytics loading via page.route().
• Clustering — playwright-cluster or custom pool with worker_threads.

Blocking unnecessary traffic reduces page load time by 40–70% and memory usage. For example, our scraper for a 10,000-product store required 60% less memory after disabling images.

Scraper Development Process

What's Included

• Complete deployment documentation.
• Code with comments and launch instructions.
• Proxy rotation and user-agent configuration.
• Test run on the target site (up to 1000 pages).
• 30-day performance guarantee after delivery.

Timeline and Pricing

A basic scraper for one site: 2–4 days. A scraper with anti-bot measures, proxy rotation, and monitoring: 7–10 days. Pricing is determined individually after analyzing your project. Contact us for a free consultation—we'll help you choose the optimal solution and estimate the savings for your business.

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.