I'm building a large-scale scraper that needs rate limiting and proxy management, what's the easiest approach?

The easiest reliable approach is to separate scraping into three layers:

1. Request scheduler
   Controls when each URL is fetched.
2. Rate limiter
   Controls how fast you hit each target domain / endpoint.
3. Proxy pool manager
   Controls which egress IP each request uses and rotates/fails over when needed.

For a large-scale scraper, the simplest practical implementation is:

Recommended architecture

1) Use a task queue

Put URLs into a queue like:

• Redis + RQ/Celery
• Kafka
• SQS / PubSub
• even a database table for smaller scale

Each worker pulls tasks and asks the rate limiter + proxy manager before sending a request.

2) Rate limit per domain

You usually want limits like:

• example.com: 1 request/second
• api.site.com: 5 requests/minute
• global cap across all workers

A good pattern:

• token bucket per domain
• shared state in Redis so all workers coordinate
• add random jitter to avoid burstiness

3) Proxy pool with health checks

Keep a list of proxies and track:

• success rate
• latency
• ban/403/429 frequency
• cooldown time after failure

Use:

• round-robin or weighted selection
• automatic retry on dead/bad proxies
• per-domain proxy stickiness if needed

4) Retries and backoff

On failures:

• retry a few times
• exponential backoff with jitter
• respect Retry-After headers
• stop retrying on hard blocks if needed

5) Observability

Log:

• request status
• domain
• proxy used
• response time
• error type
• ban rate

Without this, proxy management becomes guesswork.

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Easiest implementation stack

If you want the lowest-friction path, I’d suggest:

• Python
• httpx or aiohttp for async requests
• Redis for shared rate limits / queues
• tenacity for retries
• a simple proxy health tracker in Redis or memory
• Prometheus/Grafana or basic logs for metrics

If you’re using Scrapy, a very easy path is:

• Scrapy + AutoThrottle
• custom middleware for proxy rotation
• Redis-backed dupe filter / queue if scaling out

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Practical rule of thumb

If you have:

• < 1k requests/day: basic per-domain sleep + a few proxies is enough
• 10k–1M requests/day: use Redis-backed token buckets + proxy health scoring
• distributed workers: centralized rate limiting is strongly recommended

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Minimal strategy that works well

If you want the simplest thing that’s still decent:

• one queue of URLs
• one rate limit key per domain in Redis
• one proxy pool with weighted random selection
• retry on 429/5xx with backoff
• mark proxies unhealthy after repeated failures
• rotate user agents and headers

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Important caution

Make sure your scraping follows:

• the target site’s terms
• robots.txt where applicable
• legal/privacy requirements
• rate limits that avoid abusive traffic

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If you want, I can give you either:

1. a simple Python example using httpx + Redis + proxy rotation, or
2. a Scrapy-based setup for larger-scale crawling.