Web Scraping Without Getting Blocked

Tutorial on how to scrape websites without getting blocked. Learn about 9 different solutions to overcoming website scraping blocks.
13 min read
Web scraping without getting blocked

The act of web scraping can often feel like a treasure hunt where you’re exploring the web for hidden information that’s not provided by APIs. And as with any good treasure hunt, there are challenges to overcome.

One notable obstacle is encountering access blocks imposed by the target website. These blocks can arise for various reasons, such as stringent scraping policies, concerns related to resource abuse, source IP reputation issues, or the detection of fake user agents.

But fear not, this tutorial will teach you how to web scrape without getting blocked by your target website by fully avoiding detection so that you can easily find your treasure on the internet.

Strategies to Help You Avoid Access Blocks

Because web scraping is a complex endeavor, avoiding access blocks often requires you to utilize multiple techniques. Following are nine strategies you can employ to sidestep these pesky blocks.

1. Understand Your Target’s Policies and Terms of Service

As you begin to scrape a new site, you need to familiarize yourself with it beyond just learning the HTML structure of the page. Familiarization should also include understanding the policies and terms of service of the site you intend to scrape. This often involves what the site’s stance is toward web scraping, whether they allow scraping, and which specific pages you’re allowed to scrape. Failure to respect these terms causes you to get blocked and potentially expose you to legal risk.

One crucial document to be aware of is the robots.txt file. This file is located in the website’s root directory and provides instructions to web robots about which parts of the website cannot be scanned or processed.

Following is a sample of a robots.txt file:

User-agent: *
Disallow: /private/
Disallow: /temp/

Here, the robots.txt file instructs all web robots (denoted by the * after User-agent) to avoid scraping the website’s private and temp directories.

Respectful web scraping involves adhering to your particular website’s guidelines.

2. Adhere to Ethical Scraping Standards

In the same way that you should adhere to a website’s policies, it’s also best if you adhere to a code of conduct. Ethical scraping standards not only help you avoid getting blocked but also respect the rights and resources of your target website.

Following these guidelines is crucial:

  • Don’t bombard the servers with incessant requests: Allow sufficient time gaps between requests. Some websites may detect and block web scrapers that extract large amounts of data quickly because it doesn’t seem like human behavior. To appear more natural and decrease the chances of getting blocked, adding a time delay to requests is advisable. However, rather than having a fixed time delay, it’s better to use irregular intervals to mimic human behavior more closely.
  • Don’t scrape personal data without consent: This isn’t just an ethical issue but often a legal one. Always ensure you have the necessary permissions before scraping personal data.
  • Respect the data you obtain: Use the data you scrape responsibly and legally. Ensure that your use of data is following all applicable laws and regulations, such as copyright laws and General Data Protection Regulation (GDPR).

Following is how you can implement irregular intervals between requests in Python:

import time
import random

urls = ['https://www.targetwebsite.com/page1', 'https://www.targetwebsite.com/page2', 'https://www.targetwebsite.com/page3']

for url in urls:
    response = requests.get(url)
    # Process response
    sleep_time = random.uniform(1, 10)  # Generate a random sleep time between 1 and 10 seconds
    time.sleep(sleep_time)  # Sleep for a random time between requests

This code cycles through a list of URLs in the urls array. For each URL, it makes a request to fetch it and then pauses, using the time.sleep() function before proceeding with the next request. These random intervals help mimic human browsing behavior, reducing the chances of detection.

3. Use (Rotating) Proxies

A useful tool in your web scraping toolkit is proxies, particularly rotating proxies. A proxy serves as a gateway between you and the website you’re scraping. It masks your IP address, making your requests appear to be coming from different locations.

Rotating proxies take this a step further. Instead of using a single proxy IP, they give you a pool of IP addresses. Your requests rotate through these IPs, constantly changing your digital appearance. This greatly reduces the chances of your scraper being detected and blocked since it’s much harder for a website to identify patterns in the requests.

Additionally, rotating proxies help distribute your requests over several IPs, reducing the risk of any single IP address getting banned for too many requests.

Following is a code snippet that can be used to help you implement a rotating proxy in Python:

import requests
from itertools import cycle

# List of proxies
proxy_list = ['ip1:port1', 'ip2:port2', ...] 
proxy_pool = cycle(proxy_list) # create a cycle of proxies

url = 'https://www.targetwebsite.com'

for i in range(1,3):
    # Get a proxy from the pool
    proxy = next(proxy_pool)
    print(f"Request #{i}:")
        response = requests.get(url, proxies={"http": proxy, "https": proxy})
        # Most free proxies will often get connection errors, so we catch them here
        print("Connection error with proxy:", proxy)

This code snippet uses a list of proxies (ie proxy_list) that is cycled through so that each request carried out has a different IP address. This makes it harder for sites to detect your web scraping operations.

Rotating proxies are a powerful tool, but they should be part of a larger strategy. To navigate the choppy seas of web scraping without getting blocked, you must combine them with the other techniques mentioned in this article.

4. Use the Right Headers and User Agents

Websites often use headers and user agents to detect bots. A User-Agent is a header your browser sends to the server, providing details about the software and system initiating the request. It usually includes the application type, operating system, software vendor, and software version. This information helps the server deliver content suitable for your specific browser and system.

When web scraping, it’s crucial to employ legitimate user agent strings. By mimicking a real user, you can effectively sidestep detection mechanisms and reduce the likelihood of getting blocked.

In addition to the User-Agent, another important header to consider is the Referer header. The Referer header reveals the URL of the web page that’s linked to the resource being requested. Including this in your scraper’s requests makes it seem more like a human user navigating from one page to another.

Other helpful headers your scraper can include are Accept-LanguageAccept-Encoding, and Connection. These headers are usually sent by web browsers and are rarely included by scrapes. Scrapers normally neglect these headers because they don’t have a direct impact on the retrieval of web content. Including them, however, helps makes the scraper’s requests look more genuine, reducing the chances of detection.

Following is a Python snippet that sets the User-Agent and Referer in the request header to mimic a genuine browsing session:

url = 'https://www.targetwebsite.com'
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/ Safari/537.36',
    'Referer': 'https://www.google.com/'

response = requests.get(url, headers=headers)

5. Handle Honeypot Traps and Errors

Navigating the terrain of a website can be challenging thanks to obstacles such as honeypots. Honeypots are hidden links intentionally designed to go unnoticed by regular users but can be detected by scrapers and bots. These links are often concealed using HTML elements set to hidden or none, or disguised as buttons with colors matching the page background. The primary aim of incorporating honeypots is to identify and blacklist bots.

Following is a simple code snippet you can use to try and avoid honeypots in Python:

from bs4 import BeautifulSoup
import requests

url = 'https://www.targetwebsite.com'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

for link in soup.select('a'):
    if 'display' in link.get('style', '') and 'none' in link['style']:
        continue  # Skip this link
    # Process link

This code skips any link with display: none in its style attribute, a common characteristic of honeypot links.

When scraping data, another important thing to watch out for is errors, as it’s not uncommon to encounter error responses. These errors are often indicated by HTTP status codes in the 4xx range (client errors) or 5xx range (server errors). Handling these errors gracefully is crucial to avoid overwhelming the server with excessive requests, which could potentially lead to getting blocked.

One effective strategy for managing such errors is to implement an exponential backoff algorithm. This approach involves progressively increasing the time interval between subsequent retry attempts, allowing for more efficient handling of errors.

6. Use a CAPTCHA Solving Service

Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a security measure implemented by many websites to prevent automated bot activities, including web scraping. They’re designed to be easy for humans to solve but challenging for machines, hence the name.

If you run into CAPTCHAs, you should consider using Bright Data’s Web Unlocker. This service employ various methods, including machine learning algorithms and even human solvers, to decipher CAPTCHA challenges on your behalf. It’s role is to automate the CAPTCHA-solving process, enabling your scraper to continue with the data extraction process unimpeded.

7. Monitor Rate Limits and Access Denials

Most websites enforce rate limits and access denials to protect their resources from being exploited by automated bots. Since every request you send to a server consumes resources, thousands of bots sending requests every second could easily bring down a server or degrade a website’s performance. To prevent this, websites enforce rate limits, and some even provide an X-RateLimit-Limit header in their responses, detailing their rate limits. You must respect these limits to avoid getting blocked.

The server usually communicates these restrictions through HTTP status codes. A 200 status code means everything went smoothly, but a 429 code means that you’ve sent too many requests in a given amount of time. Similarly, 403 means access is forbidden, while 503 indicates the server is unavailable, possibly due to overload. Knowing these codes is vital to navigating data extraction.

Following is a Python snippet that uses the requests library to respect rate limits:

import time
import requests

def respectful_requester(url, delay_interval=1):
    response = requests.get(url)
    # If the status code indicates rate limiting, sleep then retry
    if response.status_code == 429:
        print('Rate limit reached. Sleeping...')
        return respectful_requester(url, delay_interval)
    elif response.status_code != 200:
        print(f'Error: {response.status_code}. Try a different proxy or user-agent')
    return response

This function sends a GET request to a URL and checks the response. If it encounters a 429 status code, it pauses for a specified delay interval and then tries the request again. You could also add more sophisticated handling for other status codes as necessary.

8. Scrape from Google’s Cache

For hard-to-scrape websites or non-time-sensitive data, an alternative approach is to scrape data from Google’s cached copy of the website rather than the website itself. This technique can be particularly useful when dealing with extremely challenging websites that actively block web scrapers. These cached pages can be scraped instead of the original web pages to avoid triggering any anti-scraping mechanisms. Keep in mind that this method may not be foolproof, as some websites instruct Google not to cache their content (rare). Additionally, the data from Google’s cache may not be up-to-date.

To scrape a website from Google’s cache, simply add the site’s URL to the end of http://webcache.googleusercontent.com/search?q=cache:. For example, if you want to scrape the Bright Data website, you can use the following URL: http://webcache.googleusercontent.com/search?q=cache:https://https://brightdata.com/.

Although scraping from Google’s cache can be more reliable than scraping a site actively blocking your requests, remember to consider the limitations and verify the relevancy of the cached data. In general, this is a great way to avoid detection while web scraping.

9. Change The Request Pattern

Avoiding detection while web scraping is all about blending in. Think of each server as a watchful guard – if your scraping pattern is too predictable, like sending requests at exact intervals, you’ll get caught. To avoid detection while scraping, mix it up! Use Python to add random pauses and switch up the order of your visits, just like a real person browsing by utilizing the random and time libraries. With tools like Selenium or even Puppeteer, you can even make your script act like a normal user, clicking around and entering different information. Keep it unpredictable, and you’ll scrape without setting off any alarms.

10. Use Third-Party Proxies and Scraping Services

As the game of cat and mouse between web scrapers and website administrators intensifies, the complexities of maintaining an effective and stealthy web scraping setup grow. Websites are always coming up with new ways to detect, slow down, or block web scrapers, necessitating a dynamic approach to overcome these defenses.

Sometimes, the best approach is to let the experts handle the hard parts. This is where third-party proxies and scraping services such as Bright Data excel. Bright Data is constantly at the cutting edge of anti-scraping technologies, quickly adapting their strategies to outmaneuver new roadblocks.

Bright Data offers solutions that help you convincingly mimic human behavior, such as rotating residential proxies and automated CAPTCHA solving, allowing your scraping efforts to operate under the radar. Their services are also built to scale, helping you effortlessly accommodate the increasing needs of your web scraping projects.

Utilizing these solutions helps you save time and resources, freeing you up to focus on other parts of your project, such as analyzing the data obtained and deriving insights from it.


At this point, you’ve made it through the treacherous terrain of web scraping roadblocks. By understanding your target’s policies; using ethical scraping standards; employing tactics such as rotating proxies, appropriate headers, and user agents; and handling honeypot traps and errors, you’re now well-equipped to set up your web scraping projects without getting blocked.

However, remember that even the most proficient explorers need a reliable toolkit. That’s where Bright Data comes in. Their comprehensive solutions offer a broad array of services tailored to streamline your web scraping journey. Make use of their Web Unlocker for accessing data hidden behind CAPTCHAs. Or select from diverse proxy services, including robust proxy serversdatacenter proxies, and residential proxies, to maintain anonymity.

Happy scraping!