Day 5: From Basic WAL to Production Patterns in One Day

👨‍💻 Human Perspective • June 1, 2025

Day 5 • 🤖 See Claude's perspective →

#ferrisdb #rust #learning #wal #performance

The Setup

Day 5 started innocently enough. We had a working Write-Ahead Log from Day 4 - it could write entries, read them back, handle checksums. Basic stuff. But as I reviewed the code, something nagged at me.

Me: Our WAL works, but real databases have file headers with magic numbers and version info. Shouldn’t we add that?

Claude: You’re absolutely right! File headers are critical for:

Version compatibility checks
File type identification
Storing metadata like creation time
Preventing accidental corruption

Let me implement a proper 64-byte header format…

And that kicked off the most intense development day yet.

The Performance Rabbit Hole

After Claude implemented the file headers (with a clean FileFormat/FileHeader trait system), I noticed the WAL reader was doing a lot of unnecessary allocations.

Me: The reader allocates a new buffer for every entry. Can we optimize this?

Claude: Great observation! We could implement a zero-copy approach using uninitialized memory. Let me create a BytesMutExt trait…

What followed was a masterclass in unsafe Rust. Claude implemented read_exact_from that reads directly into uninitialized memory, complete with 16 tests covering every edge case I could think of.

The benchmarks showed 23-33% performance improvement. Not bad for a morning’s work!

The Metrics Revelation

Me: Now that we have BytesMutExt, should we integrate it into the WAL?

Claude: Yes, but while we’re at it, let me add comprehensive metrics. We should track read/write success rates, sync durations, buffer statistics…

I watched as Claude built an entire metrics system with atomic counters, timed operations, and thread-safe tracking. The WAL went from “it works” to “we can monitor everything about it.”

The Windows Wake-Up Call

Then CI failed. Windows, specifically.

Me: The Windows tests are failing on permission checks. What’s going on?

Claude: Ah, I used Unix-specific APIs for the permission tests. Let me fix that with cross-platform alternatives…

It was a good reminder that “production-ready” means more than just Linux support in our educational project.

The Test Explosion

By the end of the day, we had:

108 unit tests for WAL functionality
5 concurrent operation tests
22 format validation tests
6 integration tests
12 property-based tests
Performance benchmarks

That’s 153 tests for one component. When Claude says “comprehensive testing,” they mean it.

The CI Bonus Round

Me: These tests are making PRs take forever. Can we optimize CI?

Claude: Let me analyze the workflow… I see several redundancies:

Duplicate security audits
Building docs when only Rust changed
Running all tests for README changes

After Claude’s optimization, PR times dropped from 40-60 minutes to 15-20 minutes. Sometimes the side quests are worth it.

The Reality Check

After all this incredible work - file headers, performance optimization, metrics, cross-platform support, 153 tests - I reviewed our README and guidelines.

Me: Wait. We keep calling this “production-ready.” But this is a learning project.

That’s when it hit me. We’d gotten so caught up in building quality code that we’d lost sight of our purpose. FerrisDB isn’t trying to compete with PostgreSQL. It’s teaching people how databases work.

Me: We MUST not claim production-ready. This is educational.

Claude: You’re absolutely right. Let me update everything to clarify this is for learning, not production use…

The Guidelines Marathon

What followed was a comprehensive update:

Added “Never Claim Production-Ready” as a core principle
Updated 7 guideline files to enforce educational focus
Rewrote the README with strong disclaimers
Added implementation status markers everywhere

It felt like pumping the brakes after a day of acceleration, but it was necessary.

Reflection

Day 5 taught me something important: it’s easy to get caught up in building “real” software and forget your actual goals. We built genuinely good code today - the kind of WAL implementation you’d see in a real database. The file headers, the metrics, the performance optimizations - it’s all solid engineering.

But solid engineering isn’t our only goal. We’re here to teach, to learn, to document the journey. The moment I saw “production-ready” creeping into our language, I knew we needed to course-correct.

The irony? By building production-quality code for educational purposes, we’re actually teaching better. Students can see what real database code looks like, complete with:

Proper file formats with versioning
Performance optimizations using unsafe code safely
Comprehensive metrics for observability
Cross-platform compatibility
Exhaustive testing

They just need to understand it’s for learning how these systems work, not for storing their company’s data.

What’s Next

We now have a solid WAL implementation that shows students how real databases handle durability. Next up: integrating all these pieces into a working storage engine. But this time, we’ll be clear about what we’re building and why.

Remember: the best teaching examples are real enough to learn from but honest about their limitations. That’s what Day 5 ultimately taught me.