Scalable Web Scraping: 7 Patterns + Apache Kafka for Distributed Crawls at 1M+ Pages/Day

Scalable web scraping at 1M+ pages/day needs 7 architectural patterns working together: (1) Kafka job queue for URL distribution across worker pools, (2) Redis for hot deduplication (bloom filter + exact-match hash), (3) headless-browser pool (Playwright/Puppeteer in containers) for JS-rendered pages, (4) residential proxy rotation with health-check eviction, (5) S3 for raw response storage (parquet + compression), (6) Postgres for structured output, and (7) Elasticsearch for full-text search across scraped content. The 4 Kafka topic designs are per-domain (one topic per host for stricter ordering), per-stage (one topic per pipeline stage for cleaner isolation), hybrid (per-stage for raw, per-domain for parsed), and priority-queue (separate topics for high/low priority URLs) — hybrid wins for most production. The 3 scaling bottlenecks that always appear at 1M+ pages/day are: (a) proxy exhaustion (need 10-50x the proxy pool size of your daily page count for rotation), (b) headless browser memory pressure (each Playwright instance uses ~200MB; 100 instances = 20GB), and (c) Kafka consumer lag when parsing is slower than fetching (need 3-5x more parser workers than fetcher workers). Below is the 7-pattern architecture diagram, the 4 topic design decision matrix, the 3 bottleneck mitigations with capacity planning numbers, and the 2 monitoring metrics (consumer_lag + proxy_health_ratio) that predict when the system will saturate.

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• Building a Distributed Web Scraping System with Apache Kafka
• Building a Robust Web Scraping Pipeline with Apache Nifi

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