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499 Status Code – What It Is and How to Avoid It

The infamous HTTP status code 499 is a rare but concerning error that can bring your web scraping project grinding to a halt. This post will explain exactly what the 499 status code means, why it occurs, and most importantly – provide actionable tips on how to prevent it.

What is the 499 Status Code?

The 499 status code is defined as "Client Closed Request" and is specified in RFC 6585. It is an error response that indicates the connection was closed unexpectedly while the server was still processing the request and preparing a response.

In the context of web scraping, a 499 error essentially means that the server has identified the client as a scraper or bot and has proactively decided to terminate the connection. This preemptive connection closure is done to block automated scraping attempts.

Why Does the 499 Error Happen?

There are a variety of technical methods servers can use to detect web scraping activity and bot traffic:

  • Analyzing request patterns – Scrapers tend to follow much more systematic flows compared to human visitors browsing a site. Unusual or repetitive sequences of pages visited can trigger suspicion.

  • Checking for scraper user agents – The standard user agent strings from common scraping libraries are recognizable. Not changing the default user agent makes discovery easy.

  • Monitoring request frequency – Scrapers generate requests much faster than humans could manually. A spike in traffic rate is a giveaway something automated is at work.

  • Tracking session length – Bots plow through pages ignoring content. Short average session durations are a red flag.

  • Detecting non-human mouse movements – The cursor movements and scrolling of scrapers fail to mimic realistic human behavior.

  • Using CAPTCHAs and JS challenges – When these appear more frequently for some visitors, it‘s clear scrapers are hitting the site.

  • IP address blacklists – Blocking specific IP ranges known to be associated with scrapers. Residential proxies help avoid this.

Once a scraper is identified by one or more of these methods, the site can start issuing 499 errors to disrupt further data extraction. This forces an immediate disconnect before the bot can receive any response content.

Why the 499 Error Matters for Web Scraping

Getting sporadic 499 status codes is usually not a catastrophic issue alone. However, receiving high volumes of 499s indicates your scraper is being aggressively targeted.

If this pattern continues, the site will often escalate to imposing a complete block against your server IP, user agent, or entire proxy pool. This makes it impossible to extract any meaningful data.

In essence, the 499 status serves as an early warning signal your scraper is being noticed and you‘re at high risk of being fully barred from the site. This means it‘s crucial to take immediate steps to better disguise your scraper traffic when running into these errors.

How to Prevent Getting the 499 Status Code

Here are powerful tips to avoid triggering 499 errors and having your web scraper blocked:

Use proxies – Proxies enable sending requests from thousands of different IP addresses, making it vastly harder to pinpoint and block your scraper. Proxy rotation also gives the appearance of many distinct users.

Customize user agents – Changing the default scraper user agent to mimic real browsers helps avoid instant identification of your requests as belonging to an automation tool.

Employ random delays – Introducing variable pauses between scraper requests introduces randomness that gets much closer to human patterns. This defeats tracking based on unusually high frequency.

Rotate proxies and user agents frequently – Regularly changing these attributes improves evasion by preventing the same patterns from being tracked over extended sessions.

Solve captcha and JS challenges when presented – Completing these can convince servers your traffic comes from real humans willing to perform additional verification steps before accessing content.

Parse pages intelligently – Scrape sites in a logical order at a human pace rather than dumping all data as quickly as possible. This helps bypass monitoring based on abnormal session durations.

Limit request frequency – Keep request rates modest and be sure to stay well below a site‘s published scrape rate limits. Too much visibility attracts negative attention.

Monitor for early warning signs – Watch for 429 (too many requests) and 503 (service unavailable) errors in addition to 499s as signs scraping activity has been noticed.

Use residential proxies when possible – These originate from real devices of common users, making them far stealthier than data center proxies that are easier to categorize as scrapers.

Leveraging Proxy Services for Web Scraping

Working with established proxy providers gives access to large, diverse, and high-quality proxy pools that are vital for reliably preventing blocks.

Top services like BrightData, SmartProxy, and Soax offer tens of millions of proxies spanning residential IPs, data center proxies, and ISPs from around the globe. This makes proxy rotation seamless and limits the chance of detection.

For example, BrightData provides over 72 million proxies specifically designed to enable stable web scraping at scale. The pool includes 40 million residential proxies from major ISPs, ensuring requests perfectly mimic real user traffic.

The sheer size and variety of enterprise proxy pools create enough noise inactivity and patterns to hide scraper requests within normal human traffic. Advanced proxy services also handlecaptcha solving, real-time blacklisting to avoid bans, and API or browser extensions to incorporate proxies directly into your scraping workflow.

Scraping Code Examples Using Proxies

Here is a Python example using the Requests module along with a proxy from BrightData‘s API:

import requests
from brightdata.client import BrightData

brightdata = BrightData(API_KEY)
proxy = brightdata.get_proxy()

proxies = {
  ‘http‘: ‘http://‘ + +‘:‘ + proxy.port, 
  ‘https‘: ‘https://‘ + + ‘:‘ + proxy.port

response = requests.get(‘‘, proxies=proxies)

And here is an example using Selenium in Java to extract data from a site, with the proxy details loaded from a txt file:

Proxy proxy = getProxyFromFile("proxies.txt");

ChromeOptions options = new ChromeOptions();

WebDriver driver = new ChromeDriver(options);

// Extract data from site using Selenium


These patterns work across all major languages and frameworks to integrate proxies. The key is having a robust pool of proxies available to maximize uptime.

A Web Scraping Checklist

Follow this comprehensive checklist covering the range of tools and techniques needed to build a highly robust web scraper:

  • Use proxy rotation with a pool of millions of residential and data center proxies
  • Customize scraper user agents to appear like real browsers
  • Implement random headers, delays, mouse movements and other human-like patterns
  • Solve captcha and complete JS challenges when encountered
  • Parse pages intelligently like a real user would
  • Avoid repeating static request sequences
  • Monitor for error codes like 429 and 503 to catch blocks early
  • Limit request rates and obey robots.txt guidelines
  • Test sites in moderation when initially building scrapers
  • Leverage experienced proxy providers to handle blacklisting and banning


The 499 status code serves as a critical warning that your web scraper has been detected by a remote server and that your requests are at risk of being permanently blocked. Carefully implementing evasion techniques like proxy rotation and intentional randomization of traffic patterns can help avoid triggering these errors. Partnering with a reliable proxy service gives access to the diverse IPs needed to hide scraper traffic at scale. Stay vigilant in monitoring for 499s and other early warning signals, and be ready to refine your scraper‘s behavior when necessary to stay under the radar.

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