Building a Web Crawler in Go

I recently built a web crawler using Go, and it was an exciting project. A web crawler is a program that visits websites, reads their pages, and follows links to other pages. Mine starts with a single URL, fetches the page, pulls out links, and keeps going—storing everything in a database and letting me control it through a simple API. It was a great way to learn Go, and I want to share how I did it, what went wrong, and how I fixed it.

How I Built It

The crawler works by starting with one URL, like a seed. It fetches the page’s content using an HTTP request, then parses the HTML to find more links. Those links get added to a queue, and the crawler keeps pulling from that queue to visit new pages. To make it faster, I used Go’s goroutines, which let it crawl multiple pages at the same time. I also stored the page data in MongoDB and added API endpoints—like /api/crawl to start it and /api/stats to check its progress—using Go’s built-in web server tools.

I broke the project into steps. First, I got it to fetch and parse a single page. Then I added concurrency with goroutines to handle multiple pages. After that, I hooked it up to MongoDB to save the data. Finally, I built the API so I could interact with it. I kept the code organized with structs like Queue for managing URLs and CrawledSet to track what I’d already visited. Go’s standard libraries helped a lot, and I leaned on online examples to figure out the best way to structure everything.

Challenges I Faced

Building this wasn’t easy—there were plenty of bumps along the way. Here’s what I ran into:

• Concurrency Issues: Using goroutines to crawl pages at once was cool but tricky. Sometimes, multiple goroutines tried to update the queue or visited list at the same time, causing errors or crashes.

• Parsing HTML: Websites have messy HTML. Figuring out how to grab links from <a> tags without getting confused by scripts or broken code took some work.

• Tracking Pages: I had to make sure the crawler didn’t visit the same page twice or get stuck in a loop. Doing that with lots of goroutines running was hard.

• Handling Errors: If a URL was bad or a server failed, the crawler would stop. I needed it to keep going no matter what.

• Database Setup: I’d never used MongoDB with Go before. Connecting it and saving data without slowing everything down was new to me.

• API Building: Setting up endpoints to start the crawler or get data out meant learning how Go handles web requests, which was unfamiliar at first.

Each problem felt big at the time, but solving them taught me a ton.

How I Solved Them

Here’s how I tackled those challenges:

• Concurrency Issues: I dug into Go’s concurrency tools—goroutines, channels, and mutexes. Channels let me send page data between goroutines safely, and mutexes locked the queue and visited list so only one goroutine could touch them at a time. It took some trial and error, but I got it stable.

• Parsing HTML: I used Go’s html package instead of trying to write my own parser. It handled the messy stuff and let me focus on grabbing <a> tags with links. I added checks to skip junk like stylesheets and turn relative URLs into full ones.

• Tracking Pages: I made a CrawledSet with a map and a mutex to safely mark URLs as visited. I also capped the queue so it wouldn’t grow forever, keeping the crawler under control.

• Handling Errors: I updated the fetching code to check for bad responses—like 404s—and log them instead of crashing. If something failed, it just moved on to the next URL.

• Database Setup: I followed MongoDB’s Go driver docs to connect and save pages. I made sure database writes didn’t block the crawler by running them in the background.

• API Building: Go’s net/http package made this doable. I set up endpoints to start the crawler, list pages, and show stats. It was fun figuring out how to handle JSON and make it all work together.

Online tutorials, Go’s docs, and forums like Stack Overflow were lifesavers. Whenever I got stuck, like when the crawler kept revisiting pages, I’d find an example or explanation that pointed me in the right direction.

What I Learned

This project taught me a lot about Go—how it handles concurrency, its standard libraries, and how it talks to the web. It also showed me how websites work behind the scenes, like how links connect everything. I’m proud of what I built, even if it’s not perfect. The crawler works, it’s fast, and I can control it through the API. More than that, I feel more confident taking on bigger coding projects now.

Looking back, the toughest parts—like debugging concurrency or parsing weird HTML—were also the most rewarding. Every fix made the crawler better and me a better programmer. I’d love to keep improving it, maybe adding features like rate limiting or better stats. For now, though, I’m happy with what I’ve got—and excited to see where I can take these skills next.