Hey everyone, Sarah here from Sarah's Forge.

Today, I want to share a very real, very raw story about a recent project: building a dark web search engine. It was an ambitious undertaking, a deep dive into distributed systems, web crawling, and the ethical quagmire of the internet's hidden corners. While the project ultimately had to be shut down, the technical journey and the lessons learned were invaluable.

The Genesis: A Distributed Challenge

The initial spark was a desire to tackle a complex, distributed system from scratch. I wanted to build something that could crawl, index, and search a vast, unpredictable network like Tor. The architecture was designed with a clear separation of concerns from the outset:

The Crawler (Go): The workhorse, responsible for discovering and fetching content.

The Search Backend (Go + MySQL): Where the indexed data lived and queries were processed.

The Admin Panel (Go + HTML): For managing sites, approving submissions, and monitoring the system.

The Frontend (Originally Flutter, then Go HTML): The user-facing search interface.

The idea was to have robust, independent services that could scale and fail gracefully. Go was the natural choice for its concurrency primitives and performance, making it ideal for both the high-throughput crawler and the responsive backend.

The Crawler: A Redis-Powered Hydra

Our crawler was a fascinating beast, designed for resilience and efficiency. We used Redis as a central message queue, turning our system into a classic Producer-Consumer model.

Producer: A "scheduler" component would periodically check known good sites and push "crawl jobs" (specific URLs to visit) onto the Redis queue.

Consumers (Workers): Multiple Go routines would constantly pull jobs from the queue. Each worker was equipped to:

    Fetch the content of a given URL via Tor's SOCKS5 proxy.

    Parse the HTML, extracting text, title, description, and crucially, new links.

    Save the cleaned, indexed data into our MySQL database.

    The Hydra Effect: If a worker found new, unvisited links, it would push those back onto the Redis queue, potentially for new sites, effectively feeding the beast.

We implemented critical features like:

Backpressure: To prevent Redis from being overwhelmed, workers would pause scheduling new jobs if the queue grew too large.

Site-Specific Limits: Capping the number of pages indexed per site to prevent runaway crawls.

Health Checks: Regularly pinging sites to mark dead ones and prevent wasted crawl attempts.

Forbidden Word Filtering: Early attempts to pre-filter titles and content based on a blacklist.

"Garbage" Title Detection: Discarding pages where the title was just the onion domain itself (a common sign of low-quality or default pages).

Site-Approval Check: Most importantly, ensuring the crawler only deep-crawled sites that had been manually approved in the admin panel. This was a critical safety measure, added after early experiences.

The Frontend Pivot: From Flutter to Go-Generated HTML

Initially, I envisioned a sleek, modern frontend using Flutter for a desktop or web client. However, as the project evolved, a more pragmatic approach emerged. For a project focused on the dark web, simplicity, speed, and minimal dependencies became paramount.

The decision was made to pivot to a Go-generated HTML frontend. This meant:

No JavaScript frameworks.

No complex client-side rendering.

The Go backend would dynamically generate standard HTML directly from templates.

This drastically simplified the deployment and reduced the attack surface, aligning perfectly with the ethos of a dark web service. It also made the entire system a self-contained Go application, which was a satisfying architectural outcome.

The Inevitable: When the Wild West Went Too Wild

Everything was working. The crawler was efficient, the index was growing, the search results were fast. And that was precisely the problem.

Initially, I had a simple "pending queue" for new sites, with manual human approval. My assumption was that most discovered sites would be interesting, quirky, or insightful. The reality was a brutal awakening.

Even with initial keyword filtering and the ability to block sites, the sheer volume of undesirable content was overwhelming. The dark web, when crawled indiscriminately, is a torrent of:

Scam sites: Phishing, fake markets, fraudulent services.

Spam and link farms: Designed to boost visibility for the scams.

Deeply objectionable and illegal material: Content that no responsible operator should host or index.

The crawler, in its efficiency, was rapidly filling the database with precisely the kind of content I wanted to avoid. Manual review became impossible, and the ethical, legal, and psychological burden of encountering such material was immense.

The realization hit hard: maintaining a general, "wild" dark web search engine requires either a massive team, extremely sophisticated AI filtering beyond the scope of a solo project, or a complete disregard for the content indexed. None of those options were viable or desirable.

The Decision to Shut Down

Faced with this overwhelming reality, the choice became clear. While the technical challenge was met, the operational and ethical costs were too high. The crawler had to be switched off, and the project, in its live search engine form, eventually shut down.

This wasn't a failure of code, but a harsh lesson in the realities of operating on the dark web. It underscored why there are so few public Tor search engines, and why those that exist often struggle immensely.

Lessons Learned

Despite the shutdown, the project was an incredible learning experience:

Distributed Systems are Hard (and Fun): Building the Redis-backed Go workers was a masterclass in concurrency.

The Power of Go: Its performance, tooling, and simplicity for networking and web services are truly impressive.

Pragmatism Wins: The pivot from Flutter to Go HTML for the frontend was a valuable lesson in choosing the right tool for the specific project context.

Content is King (and sometimes a nightmare): The critical importance of robust content moderation and ethical considerations when dealing with user-generated or publicly crawled data, especially in unmoderated environments.

The "Abyss Problem": The dark web's content landscape is overwhelmingly hostile to general indexing.

While you won't be searching the dark web with Sarah's Forge, the code and the lessons will fuel future projects. Sometimes, knowing when to stop is the most important part of the journey.

Thanks for reading, and happy coding!

— Sarah