I'm building a crawler for competitor product data and need something reliable

For a reliable competitor product crawler, the biggest win is to design it like a small data pipeline, not a one-off scraper.

What “reliable” usually means

You want the crawler to:

• handle page structure changes gracefully
• retry transient failures
• detect and skip duplicates
• normalize messy product data
• log what failed and why
• be easy to update when a site changes

Recommended approach

1. Use a modular crawler Split it into:

   • fetcher: downloads pages
   • parser: extracts product fields
   • normalizer: cleans prices, currencies, sizes, availability
   • storage: saves results
   • monitoring/logging: tracks failures and schema changes

2. Prefer structured data when available Many ecommerce sites expose product info in:

   • JSON-LD
   • embedded scripts
   • API endpoints used by the frontend

   These are often more stable than scraping visible HTML.

3. Build fallback selectors For each field, support multiple extraction paths, e.g.:

   • JSON-LD price → meta tag → visible text selector That reduces breakage when the site changes.

4. Add anti-breakage checks

   • validate product name, price, and URL format
   • flag pages where key fields are missing
   • alert when extraction rate drops suddenly

5. Use sensible crawling hygiene

   • obey robots.txt and site terms
   • rate limit requests
   • randomize request timing slightly
   • use caching and deduplication

6. Instrument everything Track:

   • HTTP status codes
   • retry counts
   • parse success rate
   • missing-field rate
   • new page patterns

Good tech choices

• Python + Scrapy for robust crawling
• Playwright if content is heavily JavaScript-rendered
• BeautifulSoup/lxml for parsing
• PostgreSQL or S3/JSONL for storage
• Redis/queue if you need scale

Minimal reliability pattern

• fetch page
• if fetch fails, retry with backoff
• parse with primary selector
• if missing required fields, try fallback parser
• validate schema
• save raw HTML snapshot on failure
• store structured output plus crawl metadata

Practical tip

Keep a raw HTML archive of every page you crawl. When a parser breaks, you can re-run extraction without re-fetching the site and quickly compare old vs new structure.

If you want, I can help you with one of these:

1. a crawler architecture
2. a Python/Scrapy starter template
3. a data schema for product crawling
4. a reliability checklist for production use