Static HTML Parsing with Cheerio and BeautifulSoup

Imagine you need to scrape a competitor's product catalog, but Selenium loads each page in 30 seconds and consumes 200 MB of memory. We solve this in a fraction of a second—static HTML parsing without a browser. Just an HTTP request and parsing the returned HTML. No extra resources, no waiting. One HTTP request and the HTML document is ready to parse.

We use Cheerio (Node.js) and BeautifulSoup (Python)—proven tools on which we've built over 50 projects. We guarantee the parser works after delivery and provide one month of support. We evaluate your project for free in 1-2 days.

When It Works

Static parsing works on WordPress, 1C-Bitrix, and classic PHP/Ruby applications where content is present in the server's HTML response without JavaScript rendering. To check: open DevTools → Network → find the main HTML document → look in Preview for the needed data. If present, static parsing works. For dynamic sites with JS rendering, a browser parser is needed—we combine approaches.

Problems We Solve

Typical scraping challenges:

• N+1 queries when extracting data from detail pages. We use pagination and parallel requests with concurrency limits.
• Anti-bot protection (Cloudflare, reCAPTCHA). We combine static parsing with browser-based bypass when needed.
• Large data volumes—scraping tens of thousands of pages. We use queues (Bull, Celery) and distributed workers. We process up to 1000 pages in 10 minutes without blocking.

How We Do It

Case study: scraping a 1C-Bitrix e-commerce catalog.

We needed to extract 15,000 products with SKUs, prices, stock, and images. We used Cheerio + axios with User-Agent rotation and a 500ms delay between requests. Each product was in a div.product-item with a data-id attribute. Data was written to PostgreSQL via batch inserts of 100 records. The entire scraping took 2 days, including CI/CD setup for daily updates. Using the Cheerio API, we extract data by selectors.

const axios = require('axios');
const cheerio = require('cheerio');

async function parseCatalog(url) {
  const { data } = await axios.get(url, {
    headers: { 'User-Agent': 'Mozilla/5.0' }
  });
  const $ = cheerio.load(data);
  const items = [];
  $('.product-item').each((i, el) => {
    items.push({
      id: $(el).attr('data-id'),
      name: $(el).find('.name').text(),
      price: $(el).find('.price').text()
    });
  });
  return items;
}

How to Choose Between Cheerio and BeautifulSoup?

For simple data collection from one site, choose the language your team prefers. For high-load systems, Cheerio is preferred due to async handling.

Why Static Parsing Is Faster Than Browser Parsing

A browser parser (Puppeteer, Playwright) launches a full browser, renders JavaScript, loads styles and scripts. This increases page load time by 5-10x and memory consumption by 200-500 MB per instance. Static parsing does only an HTTP request and parses HTML—speed measured in milliseconds. On 1000 pages, the difference can be hours vs days.

Our Process

1. Analysis — study website structure, identify data sources, check for anti-bot protection.
2. Design — select tool (Cheerio/BeautifulSoup), design data schema, plan pagination.
3. Implementation — write parser with error handling, retries, logging.
4. Testing — run on test sample, verify completeness and correctness.
5. Deployment — deploy on server (Docker, cron jobs), set up monitoring.

What's Included

• Parser code with comments and error handling.
• Documentation for running and supported selectors.
• Setup for automatic data updates (scheduled).
• 1 month of support after delivery (bug fixes, adaptation to site changes).

Estimated Timelines

• Simple parser (one site, few fields) — from 1 business day, costing approximately $200–$500.
• Medium (pagination, multiple pages, authentication) — 2-4 days, costing $500–$1000.
• Complex (anti-bot, bypass, database integration) — up to 5 days, costing $1000–$2000.

Cost is calculated individually after analyzing the target site. Order a turnkey parser and get a consultation within an hour. Contact us via Telegram or through our contact form. Saving time on scraping can reach 90% compared to manual collection.

Over 5 years of experience in web scraping, more than 50 projects delivered. We guarantee the parser works after delivery.

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.