How to Use Proxies with Scrapy for Web Scraping

Scrapy is one of the most popular frameworks for large-scale web scraping in Python. However, many sites these days implement advanced bot detection systems that can block out scrapers. Using proxy services is the most reliable technique to avoid blocks while scraping target sites at scale over sustained periods.

In this comprehensive guide, we’ll cover:

  • Proxy concepts and how they mask scrapers
  • Comparing free vs premium proxies
  • Steps to set up Bright Data proxies
  • Integrating with Scrapy using meta parameters or middleware
  • Rotating proxies properly to avoid blocks
  • Complete walkthrough of a Bright Data Scrapy spider

Let’s get started!

The Importance of Proxies for Web Scraping

First, how do websites detect web scraping bots in the first place?

Common signs they look for:

Rate Limits – Too many requests from the same IP trigger blocks

Traffic Volume – High bandwidth usage indicates automation

No Cookies or Javascript – Scrapers don't run JS which is required to render sites

Unusual Access Patterns – Random crawling reveals its not a real user

When sites detect these signals, they can terminate scrapers by:

  • Blocking the IP address causing excessive requests
  • Requiring additional verification like CAPTCHAs
  • Failing requests from suspicious user agents

This results in extraction failures which can jeopardize entire data projects.

How Proxies Help Mask Web Scrapers

Proxies provide an intermediate server that forwards requests from your scraper to the target site:

Benefits of using proxies:

✅ Masks real IP address visible to the site

✅ Allows distribution of requests across multiple IPs to prevent overuse

✅ Residential proxies mimic real devices so requests appear organic

This hides signs of automation, making the site think it's receiving legitimate user traffic instead of bots.

Free vs Premium Proxies

You can find many free public proxies online, however, they have several downsides:

Limitations of free proxies:

❌ Often slow, unstable, and get blocked on many sites

❌ No API access for automation

❌ Must configure credentials manually in code

❌ No control over rotating IPs leading to reuse

In comparison, premium proxies offer major advantages:

<table> <tr> <td> <strong>Free Proxies</strong> </td> <td><strong>Premium Proxies</strong> </td> </tr> <tr> <td> ❌ Slow speeds </td> <td> ✅ Very fast connections </td> </tr> <tr> <td>Unstable uptime </td> <td>✅ 99%+ uptime </td> </tr> <tr> <td>Limited IP pools </td> <td>✅ Millions of IPs </td> </tr> <tr> <td>❌ Frequent blocks </td> <td>✅ High success rates </td> </tr> <tr> <td>❌ No automation support </td> <td>✅ Easy API integration </td> </tr> <tr> <td>Manual proxy configs </td> <td>✅ Tools for automation </td> </tr> </table>

According to benchmarks, premium proxies have 99%+ success rates for scraping compared to just 63% with free proxies due to better evasion capabilities.

Services like Bright Data offer enterprise-grade proxies optimized specifically for web scraping stacks.

Introducing Bright Data Proxies

Bright Data provides high-quality proxies with advanced tools perfect for web automation.

Why Bright Data stands out:

  • 195+ geographic locations – Target sites by proximity
  • 40+ million IPs – Massive pools to prevent blocks
  • 99.9% uptime – No more instability frustration
  • High-speed residential proxies mimic real users for effective scraping
  • Monitor proxy health and auto-rotate to distribute requests
  • Easy integration with Python, Scrapy, Selenium

Bright Data also handles tricky proxy ops like:

✅ Managing IP pools and excluding banned IPs

✅ Multi-threaded scraping with traffic splitting

✅ Failover handling if proxies go down

This allows you to focus on building scrapers while Bright Data manages proxies efficiently in the background.

Setting Up Bright Data With Scrapy

Let's go through the steps to configure Bright Data proxies within Scrapy:

Step 1: Create a Bright Data Account

First, sign up for a Bright Data account here to access their proxy API.

Step 2: Choose Data Center or Residential Proxies

Based on your scraping needs, pick a plan to access either:

Data Center proxies – Extremely fast with 1 GBPS+ connection speeds
Residential proxies – More limited bandwidth but highest success rates mimicking real devices

You can select proxies closest to your geographic targets based on their infrastructure.

Step 3: Generate Your Proxy Credentials

Next, create a Proxy Zone to manage access and credentials:

Configure your zone settings:

☑️ Give it a memorable name
☑️ Select proxy type and max connections
☑️ Choose username and password

This allocates ports for a block of proxies you can use in API requests later.

Save these proxy credential details for your scripts.

Two Ways to Integrate Bright Data Proxies in Scrapy

There are two main methods to add proxies to your Scrapy spiders:

1. Passing Credentials in Meta Parameters

This involves directly passing your authentication info into Request() calls:

import scrapy

class ExampleSpider(scrapy.Spider):

  name = 'example' 
  def start_requests(self):
    urls = [...]
    for url in urls:
      yield scrapy.Request(url=url, callback=self.parse, 
        meta={'proxy': 'http://customer-id:[email protected]:22222')


  • customer-id – Your Bright Data account number
  • password – The proxy zone password
  • 22222 – Zone port number


✅ Quick and easy to add proxies


❌ Need to modify each spider manually

❌ No way to dynamically rotate proxies

2. Creating a Custom Proxy Middleware

For more control, best practice is creating a middleware class to handle proxies.

This acts as a layer that processes all requests between Scrapy and sites.

How to set up middleware:

Define new middleware class:

import logging

class BrightDataMiddleware():

  def process_request(self, request, spider):
    request.meta['proxy'] = "http://proxy:port"

Enable this middleware in Scrapy settings:

   'myproject.middleware.BrightDataMiddleware': 700,  

Import middleware into each spider file:

from myproject.middleware import BrightDataMiddleware
custom_settings = {
     'myproject.middleware.BrightDataMiddleware': 700  

Now all spiders will use the proxy automatically! 🎉

Comparing the Two Proxy Methods

Meta Parameter Pros:

✅ Faster setup

Meta Parameter Cons:

❌ Need to modify each spider manually

❌ No rotating proxies automatically

Middleware Pros:

✅ Configure once, available across all spiders

✅ Easy to add other functionality like rotating IPs

Middleware Cons:

❌ More complex initial setup

In most cases, creating a custom middleware class is the best approach for easier proxy management.

Rotating Proxies with Bright Data

While proxies hide your scraper IP address, websites can still block individual proxies if used excessively.

The main solution – rotating proxies automatically.

This spreads requests across multiple proxy IPs in your credential “zone” to prevent overuse.

Fortunately, Bright Data natively supports automatic rotation without any coding!

But you can also force rotation manually in middleware:

import itertools
import random

class ProxyMiddleware():

  def __init__(self):
      self.proxies = ['proxy1','proxy2', 'proxy3']
      self.proxy_iter = itertools.cycle(self.proxies)
  def process_request(self, request, spider): 
      proxy = next(self.proxy_iter)  
      request.meta['proxy'] = random.choice(self.proxies)

This cycles through your list randomly with each new request.

Here are some best practices when rotating proxies:

Distribute traffic evenly instead of Round-robin

Implement random intervals between IP switches

Create request patterns that mimic human behavior

Following general volume guidelines and mixing up your scraping patterns makes the activity blend into normal traffic. This minimizes the chances of bot detection systems flagging your scrapers.

Bright Data also provides advanced load balancing features to optimize proxy distribution.

Scraping With Bright Data Residential Proxies: Full Example

Now let's walk through an end-to-end example leveraging Bright Data for web scraping with Scrapy.

We'll extract product data from an online store protected by Cloudflare browser checks.

Instead of getting blocked by the bot mitigation, we'll use Bright Data residential proxies to mimic real users and bypass the protections.

Here's the full spider code:

import scrapy, json
from myproject.middleware import BrightDataMiddleware 

class BrightDataSpider(scrapy.Spider):

  # Spider name  
  name = 'brightdataspiders'

  # Start URLs
  start_urls = ['']

  # Enable BrightData Middleware 
  custom_settings = {
        BrightDataMiddleware': 700    

  # Parse start pages
  def start_requests(self):

    for url in self.start_urls:
      yield scrapy.Request(url, 
         meta={'proxy': 'http://CUSTOMER-ID:[email protected]:22222'},  

  # Parse product listings
  def parse(self, response):
    products = response.xpath('//div[contains(@class,"product")]')

    for product in products:
      name = product.xpath('.//a/text()[1]').get()
      price = product.xpath('.//span[@class="price"]/text()[1]').get() 
      yield {
         'name': name,
         'price': price 

    # Crawl to next page  
    next_page = response.xpath('//a[@title="Next Page"]/@href').get()

    if next_page is not None:
      yield scrapy.Request(
         meta={'proxy': 'http://CUSTOMER-ID:[email protected]:22222'},

# Export scraped data to JSON  
class JsonExportPipeline(object):

  def open_spider(self, spider):
    self.file = open('products.json', 'w')
  def close_spider(self, spider):
  def process_item(self, item, spider):
    line = json.dumps(dict(item)) + "\n"
    return item

Walkthrough of what's happening:

  1. Enable BrightData middleware so all requests hit the proxy URL
  2. Pass Bright Data credentials in meta parameter to authenticate
  3. Crawl start URLs and parse product listings via XPath
  4. Recursively scrape through pagination
  5. Export extracted listings into a JSON file

By leveraging Bright Data proxies within this standard Scrapy crawler, we can bypass protections and extract large amounts of data without blocks.

The residential proxies mimic real users by providing accurate:

  • User agent strings
  • Browser fingerprints
  • Geolocation info

This fools the site into allowing automated scraper requests without flagging bot activity.

Why Use Bright Data Proxies?

Compared to alternatives, Bright Data provides:

Highest Success Rates

Bright Data has a 99%+ success rate for scraping protected sites due to robust residential proxies.

Top Speed Performance

Optimized proxy routing provides fast, stable connection speeds perfect for automation.

Reliable Customer Support

Get solutions fast with 24/7 customer service via live chat or Discord.

Affordable Pricing

Flexible subscription plans to meet any budget, starting at $300/month.

Optimized Proxy Management

Tools like automatic rotation prevent blocks without headaches.

Easy Integration

Works seamlessly with all major languages and web scraping stacks.

By handling proxies, Bright Data allows you to focus dev time on building crawlers vs. infrastructure.


As you've learned throughout this guide, web scraping proxies are crucial for bypassing bot mitigation when harvesting data at scale.

Manually configuring public proxies has too many issues to be a reliable solution. Advanced premium services like Bright Data solve these problems through robust residential proxies and easy API integrations.

Setting up Bright Data within Scrapy takes minutes via custom middleware or request metadata parameters. Both options provide smooth automation, but middleware gives more flexibility to manage proxies cleanly in one place.

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