The ultimate guide to datacenter proxies for web scraping

Datacenter proxies are the scrapers secret weapon – they provide speed, scale, and cost savings. But using them effectively takes know-how. This comprehensive 4500+ word guide will cover everything you need to successfully scrape at scale with datacenter proxies.

What are datacenter proxies?

A proxy acts as an intermediary between your scraper and the target website:

Instead of the site seeing your IP address, it sees the proxy server‘s IP address. This allows you to:

• Rotate IPs to avoid blocks
• Bypass geographic restrictions
• Scrape anonymously
• Overcome rate limits by spreading load

Datacenter proxies specifically run on servers hosted in large data centers (hence the name). The machines are owned by companies like BrightData, Oxylabs, and Apify who sell proxy access.

Datacenter proxies are also known as backconnect proxies because multiple users connect through a single IP address. The proxy maintains a pool of connections, assigns you a random open connection per request, and reconnects once done. This allows thousands of users to share IPs.

BrightData, for example, has over 72 million IPs according to similarweb data. Oxylabs touts 40+ million IPs. This scale is crucial for spreading scraping load and avoiding blocks.

Residential vs datacenter proxies

The alternative proxy type is residential proxies. These run on real devices like smartphones, laptops, and smart TVs.

Here‘s how datacenter and residential proxies compare:

As you can see, datacenter proxies are significantly cheaper and faster. But residential IPs are less suspicious and better for solving CAPTCHAs.

We recommend using datacenter proxies for most scraping jobs. Only use residential proxies if you absolutely must or are targeting challenging sites.

Getting started with datacenter proxies

To start using datacenter proxies, you‘ll need to purchase access from a provider like:

• BrightData (recommended)
• Apify
• Oxylabs
• Smartproxy

These providers offer datacenter proxies at tiered monthly prices:

BrightData is among the cheapest at only $1 per GB.

Once signed up, you‘ll get proxy URLs or ports to use in your code:

# Python example

import requests

proxy_url = ‘http://user:[email protected]:8000‘ 

response = requests.get(‘https://example.com‘, proxies={
  ‘http‘: proxy_url,
  ‘https‘: proxy_url  
})

Many providers also offer REST APIs and SDKs in Node, Python, Java, etc to programatically manage proxies.

Proxy banning techniques

Before we dive into optimizing proxies, let‘s first understand how sites detect and block them:

1. Blacklisting specific IPs

The simplest method is blacklisting by IP address. Sites maintain lists of known bad IPs and block any matching requests.

Shared datacenter IPs often get blacklisted because previous users abused them. Dedicated static IPs you own exclusively avoid this issue.

According to Apify, over 92% of sites block by blacklists. Quickly rotating shared IPs is key to avoiding issues.

2. Blocking entire IP ranges

Sites also blacklist by IP range using the unique ASN identifier assigned to each IP block. Common datacenter ranges are easy to identify and ban.

For example, all Azure datacenter IPs start with 52.160.0.0 through 52.191.255.255. So sites may block any request from those ~1 million IPs.

Using proxies from multiple providers with varying ranges helps avoid widescale ASN blocks.

3. Analyzing traffic patterns

Some protection services like Cloudflare build statistical models to identify suspicious traffic patterns.

For example, if all traffic comes exactly 5 minutes apart, or follows similar user-agent patterns, it may get flagged as bot-like.

Mimicking human patterns is key, as we‘ll discuss later.

4. Banning entire countries

Sites commonly blacklist traffic from certain regions to reduce attacks or simply improve performance.

Rotating proxy location helps avoid location-based blocking. Most datacenter providers let you set country in the proxy URLs.

5. Analyzing HTTP headers

Another common tactic is looking for suspicious HTTP headers like:

• No browser user-agent
• Missing headers like Accept-Language
• Odd user-agents like Python/3.6 aiohttp/3.6.2

Fixing headers to mimic browsers is crucial. Tools like BrightData and Apify do this automatically.

6. Frequency and rate limiting

One of the most aggressive protections is rate limiting – allowing only X requests per minute/hour from a single IP.

Rotating frequently among a large pool of datacenter IPs allows you to bypass rate limits.

Optimizing proxies for success

Simply avoiding basic blocks is not enough. You need to carefully optimize proxy usage for success, performance, and longevity when scraping at scale.

Use proxy sessions

Tool like BrightData and Oxylabs offer the crucial concept of proxy sessions. This allows "locking" an IP to your session for multiple requests before rotating.

This prevents rotating too frequently among IPs. Reuse sessions instead of IPs themselves.

Example session architecture:

Session 1 > IP 1 
             IP 2
             IP 3

Session 2 > IP 4
             IP 5 
             IP 6

Rotate sessions on the scale of minutes or hours rather than requests.

Persist cookies and headers

Don‘t swap cookies between sessions/IPs. Use the same session-specific cookies consistently across requests.

Same for headers – each session should mimic a unique browser with custom header values.

Add randomness

Don‘t overload a small set of IPs or sessions. Rotate randomly to distribute load across large proxy pools for optimal performance.

Limit concurrent requests

Too many parallel requests can overload proxies and get them banned. Limit concurrency to ~10 requests per IP as a safe benchmark.

Monitor health proactively

Watch for 5xx errors, timeouts, blocks, etc. Disable unhealthy sessions allowing them time to reset before reuse.

Enable retry logic

Retry individual failed requests 2-3 times before disabling the underlying proxy session. This minimizes false positives.

Take timeouts slowly

Start with higher 60-90 second timeouts. Quick failures increase load across new proxies.

Avoid loops

Don‘t rapid retry failed requests in a tight loop – this amplifies load. Use backoff delays or queues.

Incorporate delays

Add small randomized delays between requests to mimic human patterns. Starting at 1-3s/request is good.

Advanced anti-blocking techniques

Let‘s discuss some more advanced tactics sites may use – and how to counter them:

Browser fingerprinting

Browser fingerprints involve techniques like canvas rendering, font detection, and WebGL fingerprinting to identify real browsers.

Solutions:

• Use tools like BrightData and Browserless that offer full browser emulation
• Enable headless browser scrapping using Puppeteer or Playwright
• Proxy services can provide real browser fingerprints

CAPTCHA challenges

Sites may force difficult CAPTCHAs, especially after seeing signs of bot traffic.

Solutions:

• Switch to residential proxies which more easily solve CAPTCHAs
• Use CAPTCHA solving services like Anti-Captcha
• Avoid getting flagged in the first place by mimicking human behavior

Sophisticated machine learning

Large sites may train complex ML models on traffic patterns or user behavior. Very difficult to bypass.

Solution:

• Use residential rotating proxies which provide high anonymity by frequently rotating real IPs

Legal blocks

In some cases sites may threaten or enact legal action if scraping continues after warnings.

Solutions:

• Consult an attorney to understand risks
• Check website Terms of Service for allowed usage
• Consider alternatives like scraping data from a upstream aggregator instead

Using proxies with popular libraries

All the major scraping and automation libraries make it easy to use proxies:

Python

import requests

proxies = {
  ‘http‘: ‘http://user:[email protected]:5678‘,
  ‘https‘: ‘http://user:[email protected]:5678‘
}

response = requests.get(‘https://example.com‘, proxies=proxies)

Node.js

const axios = require(‘axios‘);

const proxyUrl = ‘http://user:[email protected]:5678‘;  

const response = await axios.get(‘https://example.com‘, {
  proxy: {
    protocol: ‘http‘,
    host: ‘1.2.3.4‘, 
    port: 5678,
    auth: {
      username: ‘user‘,
      password: ‘pass‘  
    }
  }
});

Java

import java.net.Proxy;

Proxy proxy = new Proxy(Proxy.Type.HTTP, new InetSocketAddress("1.2.3.4", 5678));

HttpClient httpClient = HttpClientBuilder.create()
        .setProxy(proxy)
        .build();

HttpResponse response = httpClient.execute(request);  

See the documentation for each library for specifics on how to integrate proxies.

Managing proxies programmatically

Most proxy providers also offer APIs and SDKs to manage proxies programmatically:

// Rotate proxy IP using BrightData SDK

const { BrightDataClient } = require(‘brightdata‘);

const client = new BrightDataClient({
  authToken: ‘AUTH_TOKEN‘  
});

const proxyUrl = await client.getProxyUrl(); // Returns fresh proxy URL

This allows dynamically rotating IPs based on health, solving CAPTCHAs, selecting location, and more.

See the documentation for:

• BrightData
• Oxylabs
• Apify
• Smartproxy

for details on programmatic access.

Conclusion

As this comprehensive guide demonstrated, datacenter proxies provide a fast and cost-effective solution for large-scale web scraping when used properly.

The key is carefully managing proxy use to maximize performance while mimicking organic human behavior. Techniques like proxy sessions, custom headers, controlled rotation, and traffic analysis are crucial.

Advanced anti-bot services can still pose challenges. In these cases, residential proxies may be required. Be sure to consult legal counsel if continuing to scrape after blocking and warnings.

Powerful tools like BrightData, Oxylabs, Apify and Smartproxy make it easy to incorporate datacenter proxies into your scraping projects. With proper setup, you can scrape data successfully and at scale.

Have something to add about datacenter proxies? Feel free to reach out! I‘m always happy to discuss the latest proxy scraping techniques.