How I Monitor 77 Web Scrapers Without Going Crazy (My Exact Setup)

When you have 1 scraper, monitoring is easy. Check if it runs. Done. When you have 77 scrapers running on different schedules, extracting data from sites that change their layout every Tuesday at 3am, monitoring becomes a full-time job. Here is the system I built to stay sane. The Problem Web scrapers fail silently. They do not crash with an error. They just return empty data, or stale data, or wrong data. And you only notice when a client says: "Hey, the data looks off." I needed a monitoring system that catches: Scrapers that return 0 results (site changed layout) Scrapers that return the same data twice (caching issue) Scrapers that take 10x longer than usual (being rate-limited) Scrapers that return data in the wrong format (schema changed) Layer 1: Health Checks (5 minutes to set up) Every scraper writes a heartbeat file after a successful run: import json from datetime import datetime from pathlib import Path def write_heartbeat ( scraper_name , result_count , duration_seconds ):