A well-structured bulk proxy list is the backbone of reliable scraping, SEO monitoring, and automation at scale. If you’re new to ProxiesThatWork, start by reviewing our guide on getting started with your proxies and the common types of proxies. This article focuses on the practical steps to access, normalize, organize, and operate a bulk datacenter proxy list—so your pipelines stay fast, stable, and compliant.
A bulk proxy list is a collection of proxy endpoints—typically in the hundreds or thousands—used to distribute requests across many IPs. Lists often include multiple regions, subnets, and authentication credentials. Your goal is to transform a raw list into a curated, labeled, and health-checked pool your applications can trust.
Organizing your proxies improves:
Follow this workflow to move from raw list to production-ready pool.
Store the list in a secure, versioned location (e.g., private Git repo with secrets removed, object storage, or a secrets manager).
Example .env (do not commit):
PROXY_FILE=./secrets/proxies.txt
PROXY_USERNAME=ptw_user
PROXY_PASSWORD=${PTW_PASSWORD}
Python example to read, normalize, and dedupe:
import re
from urllib.parse import urlparse
PROTOCOL_DEFAULT = "http"
def normalize(line, default_protocol=PROTOCOL_DEFAULT, user=None, pwd=None):
line = line.strip()
if not line or line.startswith('#'):
return None
# If protocol missing, prepend
if '://' not in line:
line = f"{default_protocol}://{line}"
# If missing credentials and provided externally
parsed = urlparse(line)
netloc = parsed.netloc
if '@' not in netloc and user and pwd:
netloc = f"{user}:{pwd}@{netloc}"
normalized = f"{parsed.scheme}://{netloc}"
return normalized
with open('secrets/proxies.txt') as f:
raw = f.readlines()
seen = set()
proxies = []
for line in raw:
p = normalize(line, user='${PTW_USER}', pwd='${PTW_PASSWORD}')
if p and p not in seen:
seen.add(p)
proxies.append(p)
print(f"Loaded {len(proxies)} unique proxies")
Tag proxies by attributes like region, ASN, subnet, and last-seen health status. This enables domain-specific pools and smarter rotation.
Example YAML (stored in config/proxies.yaml):
pools:
search_monitoring:
tags: ["us", "low-latency"]
rules:
max_reuse_per_minute: 2
sticky_sessions: true
ecommerce_scrape:
tags: ["eu", "resilient"]
rules:
max_reuse_per_minute: 1
sticky_sessions: false
Node.js rotating selection example:
class Rotator {
constructor(list) {
this.list = list;
this.i = 0;
}
next() {
const p = this.list[this.i % this.list.length];
this.i += 1;
return p;
}
}
const rotator = new Rotator(proxies);
function getProxy() { return rotator.next(); }
Run ongoing health checks for connectivity, latency, TLS handshake, HTTP codes, and block signals. See our guide on testing and validating your proxies.
Simple Python validator:
import requests, time
def check(proxy, url='https://httpbin.org/ip', timeout=10):
try:
r = requests.get(url, proxies={
'http': proxy,
'https': proxy
}, timeout=timeout)
return r.status_code, r.elapsed.total_seconds()
except Exception as e:
return None, None
results = []
start = time.time()
for p in proxies[:100]:
code, latency = check(p)
results.append((p, code, latency))
healthy = [r for r in results if r[1] == 200]
print(f"Healthy: {len(healthy)}/{len(results)} in {time.time()-start:.1f}s")
Use a single, normalized format across your stack for simpler integration.
curl -x http://user:pass@host:port https://httpbin.org/ip
import requests
session = requests.Session()
session.proxies = {
'http': 'http://user:pass@host:port',
'https': 'http://user:pass@host:port'
}
# Reuse the same session to maintain stickiness
r = session.get('https://example.com')
const { chromium } = require('playwright');
(async () => {
const browser = await chromium.launch({
proxy: { server: 'http://host:port', username: 'user', password: 'pass' }
});
const page = await browser.newPage();
await page.goto('https://example.com');
await browser.close();
})();
Consider storing a compact record per proxy:
Keep this in a lightweight DB (SQLite/Postgres) or a JSON store for fast lookups by pool.
Python example: domain-aware selection
def choose_proxy(domain, pools):
if 'search' in domain:
return pools['search_monitoring'].next()
return pools['ecommerce_scrape'].next()
Track and alert on:
Emit JSON logs for later analysis:
{"ts":"2025-01-01T12:00:00Z","proxy":"http://x.y.z.w:1234","domain":"example.com","code":200,"latency_ms":420,"pool":"search_monitoring"}
Automate:
With a clean, tagged, and validated proxy list, your teams can scale scraping, monitoring, and automation confidently. If you need more IPs, geos, or throughput, explore our options and compare proxy plans.
How many proxies should I have per concurrent thread?
How do I keep sticky sessions stable?
Should I mix countries in one pool?
What if I use IP allowlisting?
How often should I validate proxies?
Can I combine HTTP and SOCKS proxies?
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