tl;dr:

A Developer's Dilemma

You've been hammering away at a new project, and the core engine is finally humming. It works. The initial burst of creativity has produced something real, and the excitement is palpable. You can see the future of the project stretching out before you, a landscape of possibilities.

And right there, at that exciting moment, a three-headed beast of a question rears its head: What's next?

I'm standing at that very crossroads right now. I'm neck-deep in a new side project in Rust: Heave, an EAV (Entity-Attribute-Value) data model library. The goal is to persist strongly-typed Rust structs into a flexible, schemaless SQLite database. Think of it as a bridge between the wild, anything-goes world of EAV and the wonderful, compiler-checked safety of Rust.

The initial version is done. I can save a basic struct and read it back. So, what's my next move? The beast presents its three heads, each whispering a tempting path:

• (A) The Path of Features: "Add support for nested structs! Handle Vec<T>! Build a query engine!"
• (B) The Path of Robustness: "Slow down. Cover what you have with tests. Refactor the messy bits. Make it bulletproof."
• (C) The Path of Performance: "EAV is famously slow! Benchmark it now! Shave off every millisecond you can!"

While the siren song of new features and the engineering thrill of optimization are strong, I've chosen a third path—one that I believe is the only sustainable way forward: building a bedrock of correctness and reliability. Let me walk you through my reasoning.

The Siren's Call of New Features

Let's be real: this is the fun part. This is the path of visible progress, of adding shiny new things to the README file. The pull is incredibly strong, and for some good reasons.

Sometimes, you only learn if your foundation is right by trying to build on it. It’s like designing a perfect kitchen before you’ve ever tried to cook a meal in it. You won't know if your core abstractions are any good until you try to add a genuinely complex feature. What if adding vector support reveals a fundamental flaw in how I serialize data? Better to find that out now, right?

Plus, a project with only one feature isn't very useful. To get to a "Minimum Viable Product" that someone (even just me) can actually use, I need more capabilities. A library that can only save a single integer isn't much of a library at all.

But the biggest danger is that building feature after feature on an untested foundation is like adding beautiful new floors to a skyscraper with a cracked foundation. From the outside, it looks like incredible progress. On the inside, you're multiplying your risk with every line of code, praying that the whole thing doesn't come crashing down later.

The Need for Speed: Chasing Nanoseconds

If new features are the sugar rush of development, performance is the espresso shot. As engineers, we love to make things go fast. And for my project, this isn't just a vanity metric. EAV models have a reputation for being, shall we say, "leisurely" when it comes to reading data. Proving that my library can be performant is key to its viability.

Focusing on performance early can also guide the API design. If I discover that fetching entities one by one is a performance nightmare (a classic N+1 query problem), it would force me to design a better batch-loading API from the very beginning. That's a huge win for future users.

But then, the wise words of Donald Knuth echo in my mind: "Premature optimization is the root of all evil." Optimizing code you can't prove is correct is a recipe for disaster. The real danger isn't just wasting time; it's creating a fast bug—a bug that executes flawlessly and efficiently gives you the wrong answer. Without a way to verify correctness, optimization is just a shot in the dark.

My Choice: Building on Bedrock with Tests and Refactoring

So, if I'm not chasing features and I'm not chasing nanoseconds, what am I doing?

I'm pouring concrete.

I've chosen to stop all forward progress on new capabilities and instead turn my attention inward. My entire focus for the next phase is on strengthening the current implementation by covering it with a comprehensive test suite and refactoring for clarity and correctness.

Here’s why.

1. For a Data Library, Correctness is the Only Feature That Matters.

This is the big one. My library's job is to handle someone's data. In this context, there is zero tolerance for error. Data loss or corruption is the cardinal sin. What good is a blazing-fast, feature-rich database library that occasionally, silently, corrupts your data? It's worse than useless; it's a liability. My library's core promise is its "type-safety contract"—a guarantee that the data coming out is exactly what went in, with the correct types. A test suite is the only way to formally uphold that contract.

2. A Strong Test Suite is a License to Go Fast Later.

This is the most counter-intuitive but powerful reason. A comprehensive test suite isn't a ball and chain that slows you down. It's a jetpack. It's the safety net that gives you the confidence to make bold changes later.

• Want to add that complex feature? With a full test suite, you can refactor the core logic to accommodate it, and a green test run tells you that you haven't broken anything.
• Want to optimize that slow query? You can rewrite an entire function with a more complex, high-performance algorithm (or even unsafe Rust!), and as long as the tests pass, you know you haven't sacrificed correctness for speed.

Tests don't prevent you from adding features or optimizing; they are the prerequisite that enables you to do both safely and quickly in the future.

3. It Forces Me to Be My Own First User.

Writing tests forces you to use your own API—over and over and over. You quickly discover the rough edges, the awkward function names, and the confusing error messages. It's the ultimate dogfooding. By fixing these ergonomic issues now, before anyone else ever sees them, I'm building a better, more intuitive library for its future users.

The Plan in Action: A Roadmap to Reliability

So, theory is great, but what does this actually look like day-to-day? Here's my simple, four-step plan:

• Build the Harness. I'm writing tests for everything that currently exists. Not just the "happy path," but the "sad path" too. What happens if the database contains a string but the struct expects a number? What about null values? Empty strings? I'm actively trying to break my own code.
• Refactor with Confidence. As the test coverage grows, I'm using that safety net to go back and clean up the implementation. I'm improving error handling, simplifying logic, and adding comments where things are tricky.
• Establish a Baseline. Once I'm confident the code is correct, I'll write a few simple benchmarks for the most common operations. This isn't about optimization yet; it's about knowing my starting point.
• Iterate Intelligently. Now, the magic happens. With a solid, tested, and benchmarked foundation, I can finally turn back to the other two paths, but this time with superpowers. I can pick a new feature, write the tests for it first, and then implement it with confidence. Or, I can identify a performance bottleneck, try to fix it, and use my tests to prove it's still correct and my benchmarks to prove it's actually faster.

Foundation First, Then the Sky's the Limit

Choosing robustness over immediate features or speed can feel like you're not making progress. But it's not about choosing robustness instead of those things; it's about choosing robustness first.

It's an investment in future velocity. By pouring a solid concrete foundation now, I'm ensuring that I can build a taller, more complex, and more reliable structure later on, and do it far more quickly than if I had started on shaky ground.

First, you make it right. Then, you can easily make it better.

tags: #rust, #database, #project:heave