Scrapy Python: Scalable Parsing with Pipelines and Middlewares

Implementing Parsing with Scrapy (Python)

You need to collect 100,000 product pages in a day. Requests + BeautifulSoup take a week, and that's with interruptions. Scrapy solves it in a day — it's an industrial web scraping framework for Python. Unlike custom solutions, Scrapy provides a built-in request queue, middleware system, pipeline for data processing, robots.txt support, automatic User-Agent rotation, and caching. Our team has used it in production for over 5 years and implemented more than 30 parsing projects for online stores, aggregators, and marketplaces. We guarantee stable data collection even under complex protection — experience shows that 95% of sessions run error-free.

Why Scrapy is Better Than Ready-Made Parser Aggregators?

Ready-made services like Octoparse or Parsehub are fine for one-off tasks, but at industrial volumes they hit limitations: page count caps, closed code, and inability to fine-tune. Scrapy gives full control: you decide how to handle captchas, how often to change proxies, and how to store data. In one project, we increased collection speed by 4 times by replacing a custom script on requests+bs4 with Scrapy using parallel requests. The average engineer configures a spider in 2 days, not a week — reducing costs by 60%.

Scrapy Architecture

Spider (crawl logic)
    ↓
Scrapy Engine
    ↓
Scheduler (URL queue)
    ↓
Downloader (HTTP requests)
    ↓ (via Downloader Middlewares)
Response → Spider
    ↓
Items → Item Pipeline
    ↓
Storage (DB, CSV, JSON, S3)

Each component is replaceable: you can add your own queue (Redis via scrapy-redis), your own downloader (Playwright via scrapy-playwright), or your own pipeline. This makes the framework suitable for tasks of any complexity.

How to Scale Scrapy with Redis?

For distributed collection across multiple servers:

# settings.py
SCHEDULER = 'scrapy_redis.scheduler.Scheduler'
DUPEFILTER_CLASS = 'scrapy_redis.dupefilter.RFPDupeFilter'
REDIS_URL = 'redis://redis:6379'
SCHEDULER_PERSIST = True  # queue persists across restarts

With scrapy-redis, multiple workers read from a shared Redis queue — horizontal scaling without changing spider code. This allows processing millions of URLs per day.

Why Configure Middleware to Bypass Protection?

class RotateUserAgentMiddleware:
    agents = [
        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 ...',
        'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) ...',
    ]

    def process_request(self, request, spider):
        request.headers['User-Agent'] = random.choice(self.agents)

Additionally, we connect scrapy-rotating-proxies for automatic proxy rotation with status tracking for each address. In complex scenarios, we use scrapy-playwright with a headless browser — this achieves 95% successful requests even under Cloudflare. One client after implementing such a scheme reduced collection time by 70%.

Pipeline for PostgreSQL

class PostgreSQLPipeline:
    def open_spider(self, spider):
        self.conn = psycopg2.connect(DATABASE_URL)
        self.cur = self.conn.cursor()

    def process_item(self, item, spider):
        self.cur.execute(
            'INSERT INTO products (title, price, url) VALUES (%s, %s, %s) '
            'ON CONFLICT (url) DO UPDATE SET price = EXCLUDED.price',
            (item['title'], item['price'], item['url'])
        )
        self.conn.commit()
        return item

ON CONFLICT DO UPDATE handles deduplication at the database level without additional checks in code. In one project, this reduced stored data volume by 30%.

Monitoring and Statistics

Scrapy writes detailed statistics for each run: request count, processed items, errors, average response time. Through scrapy-prometheus, these metrics are exported to Prometheus and visualized in Grafana. We add alerts for drops in collection speed or rising error counts — so you always know about issues.

What's Included in Scrapy Parser Development?

• Designing spider architecture for your data sources
• Configuring middleware: proxy rotation, User-Agent, cookies
• Implementing pipelines for cleaning, validation, and data storage
• Integration with your database or cloud storage
• Preparing monitoring (Grafana, alerts)
• Documentation for launch and support
• Training your developer to work with the system

Scrapy vs. Other Approaches

Timelines

Contact Us

Get a consultation for your parsing project. We'll evaluate the task in 1 business day and propose the optimal solution. Order development — let's discuss the details.

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.