pooling | ScrapeRouter

Pooling is the practice of keeping a shared set of reusable resources instead of creating a fresh one for every request. In scraping, that usually means connection pooling or proxy pooling: reusing TCP sessions to cut overhead, or rotating through a pool of IPs so you do not burn a single address and get blocked immediately.

Examples

A couple different things get called pooling in scraping, and they matter for different reasons.

1. Connection pooling

This is about reusing HTTP connections instead of opening a new one every time. It reduces latency and wasted handshakes.

import requests

session = requests.Session()

for url in [
    "https://httpbin.org/get?page=1",
    "https://httpbin.org/get?page=2",
    "https://httpbin.org/get?page=3",
]:
    r = session.get(url, timeout=30)
    print(r.status_code)

2. Proxy pooling

This is about spreading requests across multiple IPs. If you send everything through one proxy, you're not operating a pool, you're just creating a future incident.

import random
import requests

proxies = [
    "http://user:pass@proxy-1.example:8000",
    "http://user:pass@proxy-2.example:8000",
    "http://user:pass@proxy-3.example:8000",
]

url = "https://httpbin.org/ip"
proxy = random.choice(proxies)

r = requests.get(
    url,
    proxies={"http": proxy, "https": proxy},
    timeout=30,
)

print(r.json())

3. Using ScrapeRouter instead of managing proxy pools yourself

If the real problem is keeping a healthy proxy pool, replacing dead exits, and routing around blocks, that's exactly the sort of maintenance work people underestimate.

curl "https://www.scraperouter.com/api/v1/scrape/?url=https://httpbin.org/ip" \
  -H "Authorization: Api-Key $api_key"

Practical tips

Be clear about which pool you mean: connection pool, proxy pool, browser pool, and worker pool are different problems.
Use connection pooling for speed and efficiency: repeated requests to the same host get cheaper when you reuse sessions.
Use proxy pooling for block resistance: one IP taking all your traffic is fine for testing, bad for production.
Don't treat a proxy list as a real pool unless you also handle: health checks, eviction, retry policy, geo selection, concurrency caps.
Watch the operational signals that tell you the pool is degrading: higher connect times, more 403s, more captchas, more timeouts, lower success rate.
Don't overshare traffic across a tiny pool: if 5 IPs are carrying 50,000 requests, the problem is not subtle.
If you need sticky sessions, pooling still applies: you want controlled reuse, not random churn.
If your team is spending time tuning proxy rotation rules instead of collecting data, that is often the point where using a router layer makes more sense.

Use cases

High-volume scraping: distribute requests across a proxy pool so one IP does not get rate-limited immediately.
Multi-step sessions: keep a browser or proxy session pooled and reused for login flows, carts, or paginated navigation.
API-heavy crawling: use connection pooling to reduce repeated TLS and TCP setup overhead when hitting the same origin many times.
Geo-targeted collection: maintain separate pools by country or provider so requests match the market you need.
Reliability under changing defenses: swap traffic away from degraded or blocked proxies without rewriting your scraper every week.