Web Scraping at Scale: Adaptive Pacing Without Getting Blocked

Web Scraping at Scale: Adaptive Pacing Without Getting Blocked

Fixed-rate scraping fails in production because server tolerance changes over time. Adaptive pacing uses feedback signals to stay stable and respectful.

Step 1: Track per-domain response quality

stats = {
  "ok": 0,
  "timeouts": 0,
  "status_429": 0
}

Step 2: Adjust delay window based on recent errors

def next_delay(base, stats):
    penalty = stats["timeouts"] * 0.3 + stats["status_429"] * 0.6
    return min(8.0, base + penalty)

Step 3: Persist crawl cursor for resumability

state["last_url"] = current_url
save_state(state)

Pitfalls

• Constant request interval regardless of server signals.
• No checkpointing, forcing full restart after errors.
• Ignoring Retry-After on throttled responses.

Validation

• Throttle events decrease after adaptive pacing.
• Interrupted jobs resume from saved cursor.
• Error budget per domain stays within target.