How to Check URLs in Bulk: Complete Guide for Developers

As a developer maintaining documentation sites, knowledge bases, or large web applications, you know the pain of broken links. Whether you're managing API docs, internal wikis, or a content-heavy website, checking thousands of URLs manually is impractical. This comprehensive guide shows you how to check URLs in bulk efficiently, focusing on developer workflows and automation.

Why Bulk URL Checking Matters for Developers

Broken links in documentation can frustrate users, damage SEO, and waste engineering time. When you're maintaining:

• API Documentation with hundreds of endpoint references
• Knowledge bases (Confluence, Notion, GitBook) with internal and external links
• Content management systems with thousands of pages
• Third-party integrations that rely on external URLs

...manual link checking becomes impossible. You need bulk URL validation that integrates with your development workflow.

The Problem: Why Traditional URL Checkers Fail Developers

Most bulk URL checkers are built for SEO professionals, not developers. Here's what makes them frustrating:

• Small batch limits: Tools that check only 100-500 URLs are useless when you have 10,000+ links
• Desktop software: Tools like Screaming Frog require installation and local resources
• Rate limit issues: Simple checkers hit 429 errors and quit midway through large batches
• No automation: Manual uploads don't fit into CI/CD pipelines
• Poor data format: Difficult to parse results programmatically

Solution 1: Build Your Own Bulk URL Checker (Script)

For developers who want full control, here's a Python script to check URLs in bulk:

1import requests
2import csv
3import time
4from concurrent.futures import ThreadPoolExecutor
5from requests.adapters import HTTPAdapter
6from urllib3.util.retry import Retry
7
8def create_session():
9    """Create session with retry logic"""
10    session = requests.Session()
11    retries = Retry(
12        total=3,
13        backoff_factor=1,
14        status_forcelist=[429, 500, 502, 503, 504]
15    )
16    adapter = HTTPAdapter(max_retries=retries)
17    session.mount('http://', adapter)
18    session.mount('https://', adapter)
19    return session
20
21def check_url(url, session):
22    """Check single URL and return status"""
23    try:
24        response = session.head(url, timeout=10, allow_redirects=True)
25        return {
26            'url': url,
27            'status_code': response.status_code,
28            'final_url': response.url,
29            'response_time': response.elapsed.total_seconds()
30        }
31    except requests.exceptions.RequestException as e:
32        return {
33            'url': url,
34            'status_code': 'ERROR',
35            'final_url': url,
36            'error': str(e)
37        }
38
39def check_urls_bulk(urls, max_workers=10):
40    """Check multiple URLs concurrently"""
41    session = create_session()
42    results = []
43    
44    with ThreadPoolExecutor(max_workers=max_workers) as executor:
45        futures = [executor.submit(check_url, url, session) for url in urls]
46        for future in futures:
47            results.append(future.result())
48            time.sleep(0.1)  # Rate limiting
49    
50    return results
51
52# Usage
53urls = [
54    'https://example.com/api/v1',
55    'https://example.com/docs',
56    # ... add your URLs
57]
58
59results = check_urls_bulk(urls)
60
61# Save to CSV
62with open('url_check_results.csv', 'w', newline='') as f:
63    writer = csv.DictWriter(f, fieldnames=['url', 'status_code', 'final_url', 'response_time'])
64    writer.writeheader()
65    writer.writerows(results)

Limitations of the DIY Approach:

• Still hits rate limits on 10,000+ URLs
• No proxy rotation for bypassing blocks
• Doesn't handle JavaScript-rendered content
• Requires server resources for large batches
• No scheduling or monitoring

Solution 2: Cloud-Based Bulk URL Checker

For production use cases, a cloud-based bulk URL checker solves the scaling problems:

Key Features Developers Need:

1. Handle large batches (10,000-50,000 URLs): Upload a CSV and let the cloud infrastructure process it. No local resources consumed.
2. Automatic proxy rotation: Bypass rate limits and 429 errors automatically. Your batch always completes.
3. CSV/JSON export: Get results in developer-friendly formats for parsing and analysis.
4. API access: Integrate bulk URL checking into CI/CD pipelines and automated workflows.
5. Scheduled checks: Automate weekly or monthly link validation without manual intervention.

Use Case: Checking Documentation Site Links

Let's walk through a real example of checking URLs in a documentation site with 5,000 pages:

Step 1: Extract URLs from your docs

For static site generators (Gatsby, Next.js, Hugo):

1# Extract all links from your built site
2grep -r -o 'https://[^"]*' ./build > urls.txt
3
4# Clean and deduplicate
5sort -u urls.txt > unique_urls.txt

Step 2: Convert to CSV

1# Simple one-column CSV
2echo "url" > urls.csv
3cat unique_urls.txt >> urls.csv

Step 3: Bulk URL Check

Upload the CSV to a bulk URL checker. For 5,000 URLs, expect processing to take 1-3 hours depending on server response times.

Step 4: Filter for broken links

Once you get the results CSV, filter for problematic status codes:

1import pandas as pd
2
3# Load results
4df = pd.read_csv('url_check_results.csv')
5
6# Find broken links (4xx and 5xx errors)
7broken = df[df['status_code'] >= 400]
8
9# Group by status code
10broken.groupby('status_code')['url'].count()
11
12# Export broken links for fixing
13broken.to_csv('broken_links.csv', index=False)

Integrating Bulk URL Checking into CI/CD

For continuous validation, integrate bulk URL checking into your CI/CD pipeline:

1# .github/workflows/check-links.yml
2name: Check Links Weekly
3
4on:
5  schedule:
6    - cron: '0 0 * * 1'  # Every Monday
7  workflow_dispatch:
8
9jobs:
10  check-links:
11    runs-on: ubuntu-latest
12    steps:
13      - uses: actions/checkout@v2
14      
15      - name: Extract URLs from docs
16        run: |
17          # Your extraction logic
18          ./scripts/extract-urls.sh > urls.csv
19      
20      - name: Check URLs via API
21        run: |
22          curl -X POST https://bulkurlchecker.com/api/check \
23            -H "Authorization: Bearer ${{ secrets.API_KEY }}" \
24            -F "file=@urls.csv" \
25            -F "notify_email=${{ secrets.NOTIFY_EMAIL }}"
26      
27      - name: Wait for results
28        # Poll API for completion or use webhooks

Best Practices for Bulk URL Checking

1. Check regularly: Set up automated weekly or monthly checks to catch broken links before users do.
2. Prioritize internal links: Broken internal links hurt SEO more than external ones.
3. Monitor redirect chains: Long redirect chains slow down your site. Aim for direct links.
4. Track response times: Slow external resources can impact your page load speed.
5. Use proper headers: Include User-Agent to avoid being blocked as a bot.
6. Respect rate limits: Use proxy rotation and delays to avoid getting blocked.

Comparing Bulk URL Checking Options

Conclusion: Choose the Right Tool for Your Scale

For developers checking URLs in bulk, the right solution depends on your scale:

• Under 1,000 URLs: A simple Python script works fine
• 1,000-5,000 URLs: Desktop tools like Screaming Frog can handle it
• 5,000-50,000 URLs: Cloud-based bulk URL checkers are essential

If you're maintaining documentation with thousands of links, automating bulk URL checking saves hours of manual work and prevents broken links from affecting your users. Cloud-based solutions handle the complexity of rate limits, proxy rotation, and large-scale processing, so you can focus on building your product.