SQL vs. NoSQL: Why Superthread Chose Document-Based Speed

The Relational Trap: Why SQL Joins Fail at Scale

I've been working with databases for over 25 years, and there is a recurring theme in software engineering: the 'Relational Trap.'

About 15 years ago, I was the tech lead for a major news outlet. I inherited a SQL database where queries were crawling. When I investigated, I found only about 100,000 records, hardly 'big data.' The culprit? Joins. The nature of news data is incredibly relational. I found queries with over 100 joins. In a SQL environment, every join forces the system to iterate through record sets to find matches. If you haven't indexed perfectly, it becomes an exercise in 'archaeology' to fix. Most developers hate archaeological work; it’s slow, brittle, and unstable.

Gaming, Cassandra, and the Need for Speed

After that corporate role, I built a browser-based game engine. In gaming, speed is everything. Users expect instant responses. I knew SQL couldn't handle the sheer volume of assets and records with the latency requirements we had.

At the time, an open-source NoSQL database called CassandraDB was just emerging. I started contributing to it and ran my entire backend on it. What I learned was a hard truth: Writing for NoSQL is actually much harder.

In SQL, you have the 'crutch' of relationships and joins. In NoSQL, you have to collate everything into a single document. Instead of pulling data from ten different tables, you request one document that contains everything you need to render the page.

Document-Based Architecture vs. Tabular Relational

Think about the news site example:

• The SQL Way: You have separate tables for Articles, Authors, Comments, and Metadata. You run a complex JOIN query to stitch them together at runtime.

• The NoSQL Way: You store the entire article and its related data as one JSON-like document. One request, one result.

Why Superthread is Built on NoSQL

When we built Superthread, we decided to go all-in on NoSQL.

Most project management tools use SQL. As a result, when you start adding records, tasks, docs, comments, the system starts to slow down because the data is inherently relational.

In Superthread, whether you have one record or 10 million, the load speed is exactly the same. When you call a board, a page, or a task, you are calling exactly one document. We invested massive effort into this architecture because we believe speed is a feature, not an afterthought.

I’ve built systems with 20,000 concurrent users using MongoDB and Redis, and I’ve never looked back. Once you experience the performance of a well-architected NoSQL system, the 'relational goodness' of SQL starts to look like a legacy bottleneck.

If you have questions about how we handle data consistency or specific NoSQL implementations, let me know in the comments.

Further Reading & Resources

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Further Reading & Resources

• The Anti-Bloat Philosophy: Why we prioritize performance over everything else.

• Superthread vs. Jira: See the speed difference for yourself.