Stopping Slow Web Scrapping with Go

In this issue, I'll take you through my journey of building a concurrent web scraper in Go using Test-Driven Development (TDD). No theory dumps – just code.

🚀 What you'll learn:

• How to set up a Go project with modules

• Implementing a basic web scraper

• Adding concurrency to your scraper

• Writing effective tests for concurrent code

• Debugging common Go concurrency issues

The Problem

I needed to scrape data from multiple websites quickly, but my initial implementation was sequential and painfully slow.

Here's what we were dealing with:

💡 Warning Signs:

• Scraping took too long for multiple URLs

• CPU was underutilized

• Code didn't scale well with more URLs

• No error handling or timeouts

Step 1: Setting Up the Project

Before:

No structured project setup.

After:

🎯 Impact:

• Organized project structure

• Dependency management with Go modules

• Ready for TDD

Step 2: Implementing Basic Scraper with TDD

Before:

No tests, no structured code.

After:

The breakthrough came when implementing concurrent scraping.

I realized I could use goroutines to fetch multiple URLs simultaneously, dramatically improving performance.

Real Numbers From This Experience

• Before: 10 seconds to scrape 10 URLs

• After: 2 seconds to scrape 10 URLs

• CPU utilization increased from 20% to 80%

🎉 Improvements:

• Concurrent scraping of multiple URLs

• Proper error handling for each URL

• Scalable solution that utilizes CPU efficiently

• Testable code structure

Monday Morning Action Items

1. Quick Wins (5-Minute Changes)

• Add timeout to http.Client

• Implement basic error logging

• Add a simple rate limiter

2. Next Steps

• Implement more sophisticated rate limiting

• Add context for cancellation

• Improve error handling and retries

Your Turn!

The Concurrent Scraper Challenge

Improve the concurrent scraper by adding a worker pool to limit the number of concurrent requests.

💬 Discussion Prompts:

• How would you implement the worker pool?

• What are the trade-offs between using a fixed worker pool vs unlimited goroutines?

• How would you handle rate limiting in this scenario?

What's Next?

Next week: "Error Handling in Concurrent Go Programs" - I am in a deep dive into error handling strategies for concurrent applications.

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🔧 Useful Resources:

• Go Concurrency Patterns

• Effective Go - Concurrency

• Go by Example: Worker Pools

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Found this useful? Share it with a fellow Gopher! And don't forget to try the challenge and share your solutions.

Happy coding!

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• Note: All code examples are available in the accompanying GitHub repository

• Pro Tip: Always use go test -race to check for race conditions in your concurrent code

• Remember: Concurrency is not always faster. Profile your code to ensure it's actually improving performance