- Database performance testing built almost entirely with AI assistance was completed in roughly two days — here’s what that really looked like.
- The database performance testing tool covers four critical scenarios: N+1 queries, deadlocks, slow query thresholds, and schema regression tracking.
- A slow endpoint isn’t always a slow endpoint — sometimes it’s a poorly written query hiding behind clean-looking API code.
- The project integrates PostgreSQL, pytest, and Grafana into a CI pipeline that fails the build when query latency exceeds a set threshold.
When AI Writes the Code, What Does the Developer Actually Do?
Database performance testing has long been one of those areas where developers know they should be doing more — and rarely do. It’s painstaking, it requires instrumentation at multiple layers, and the payoff isn’t always obvious until something breaks in production. So when a developer on Dev.to decided to build a full database performance testing tool almost entirely with AI assistance, and then wrote honestly about what that experience was actually like, it was worth paying attention.
The project took roughly two days. The stack was Python, PostgreSQL, and pytest, with Grafana bolted on for metrics visualization. The AI — presumably one of the major code-generation models — handled the bulk of the implementation. The developer’s role was closer to architect and reviewer than hands-on coder. That dynamic is still uncomfortable for a lot of engineers, and the author admits as much. But discomfort aside, the result is a genuinely useful piece of tooling that covers four real-world testing scenarios most teams skip entirely.
The Four Pillars of Database Performance Testing
The tool’s value comes from how it frames database performance testing as four distinct problem categories, each requiring a different kind of measurement and analysis.
N+1 Query Detection
The N+1 problem is one of the oldest and most persistent performance bugs in application development. If you’re using an ORM — SQLAlchemy, Entity Framework, Sequelize, take your pick — you’ve almost certainly shipped an N+1 at some point without realizing it. The mechanics are simple: one query fetches a list of records, then one additional query fires for each record to fetch related data. List 20 orders and need each user’s email? That’s 21 database round-trips instead of one well-written JOIN.
At small scale, it’s invisible. At 10,000 concurrent users, it’s a five-alarm incident. The tool simulates this pattern directly and measures the query multiplier, making it easy to catch before it ever reaches production. Incorporating this check into database performance testing early in the development cycle is one of the highest-leverage habits a team can build. The concurrent case is particularly nasty — two threads both triggering their own N+1 cascades simultaneously can multiply database load in ways that are genuinely hard to trace without dedicated instrumentation.
Deadlock Simulation
Deadlocks are the kind of bug that looks intermittent until you understand what’s actually happening. The classic pattern: Transaction A locks row 1, then tries to lock row 2. Transaction B locks row 2, then tries to lock row 1. Neither can proceed. The database detects the cycle and rolls one of them back — but that rollback has a cost in time and system state.
The deadlock test in this project isn’t just confirming that deadlocks can happen. That’s trivial. The interesting measurement is recovery time — how long does the database take to detect and resolve the cycle, and what does the timing impact look like on the calling application? That’s the data teams actually need to tune their transaction ordering and lock acquisition strategies. Good database performance testing surfaces this timing data automatically rather than leaving it to manual investigation.
Query Regression Tracking Across Schema Changes
This scenario is arguably the most practically valuable of the four, and also the most underused in real engineering teams. The tool captures EXPLAIN ANALYZE output before and after a schema migration — index additions, table alterations, column changes — and diffs the query execution plans. It stores timestamped plan snapshots and generates a delta report.
The use cases are obvious once you think about them: validate that a new index actually improved query performance before merging the migration, document performance characteristics across schema versions, and maintain a historical baseline for production investigations. Most teams do none of this. They add an index, cross their fingers, and check the slow query log a week later.
Slow Query Thresholds as a CI Gate
The fourth scenario is the most immediately actionable for teams that already have CI pipelines. Five critical queries are monitored against a configurable latency threshold — defined in config.py as SLOW_QUERY_THRESHOLD_MS. If any query breaches that threshold during a test run, the pipeline fails. Hard stop.
Think of it as a performance budget applied at the database layer rather than the API layer. A slow endpoint might be slow for a dozen different reasons, but this approach lets you isolate exactly whether the database is the bottleneck — and at what point in the query’s lifecycle the time is being spent. The project monitored five critical queries in its benchmark run, and all three measured scenarios passed comfortably. But the author makes a sharp observation: the green light isn’t the point. The baseline is. The next time someone drops an index or rewrites a JOIN, you’ll know immediately if the numbers moved.
Why the Project Structure Matters
One decision in the tool’s architecture deserves specific attention. The analysis/ directory — containing the N+1 detector, deadlock simulator, and EXPLAIN analyzer — is designed to run independently from the full test suite. You don’t have to execute the entire battery to probe one specific concern. That’s a practical engineering choice that makes the tool actually usable in real workflows rather than just impressive in a demo.
The full structure integrates GitHub Actions for CI (a performance-tests.yml workflow handles schema setup, seeding, and pytest execution in sequence), Docker Compose for running PostgreSQL and Grafana locally, and a reports/ directory that exports results both to a benchmark_results database table and to a Grafana dashboard that’s auto-provisioned on startup. For a two-day build, that’s a surprisingly production-adjacent setup.
Database Performance Testing and the AI-Assisted Workflow
The meta-question hovering over all of this is whether the AI assistance was actually a good idea. The developer’s honest answer seems to be: yes, with caveats. The AI handled implementation quickly and competently. The developer handled architecture decisions, scenario selection, and the judgment calls about what was worth testing in the first place — which, it turns out, is where most of the real value lives.
That split is going to feel familiar to anyone who’s spent time with GitHub Copilot, Claude, or GPT-4 in a real codebase. These tools are genuinely fast at producing boilerplate, wiring up libraries, and implementing patterns you describe clearly. They’re less good at knowing what to build in the first place, which is exactly the kind of thing this developer had to supply manually. Picking database performance testing as the domain, identifying the four test scenarios, deciding that query regression tracking was worth the complexity — that’s the work the AI couldn’t do.
There’s also an honest acknowledgment that having AI write most of the code still feels strange. That’s not a minor footnote. A lot of developers are quietly wrestling with the same discomfort, trying to figure out what their role is when a significant portion of the implementation is being generated rather than written. This project is a useful data point: the role shifts toward specification, validation, and architectural judgment. Whether that’s a satisfying trade-off probably depends on the person.
What This Means for QA and Engineering Teams
The broader implication here is that database performance testing is becoming accessible in ways it wasn’t two years ago. Building a tool like this from scratch used to require either deep expertise or significant time investment. The AI assistance compressed that timeline dramatically without obviously compromising the quality of what was produced.
For QA teams specifically, this represents a real opportunity. Performance at the database layer has historically been treated as a backend engineering concern, not a testing concern. But as the developer notes directly: a slow endpoint isn’t always a slow endpoint. Sometimes it’s a slow query behind it. Sometimes it’s an N+1 that nobody noticed. Sometimes it’s a migration that silently invalidated an execution plan. Having automated, CI-integrated database performance testing that catches these issues before they reach production is exactly the kind of shift that makes teams faster in the long run — not slower.
As AI coding tools get better and more teams experiment with this kind of AI-assisted development workflow, expect to see more specialized tooling like this emerge from individuals and small teams who previously wouldn’t have had the bandwidth to build it. The barrier to entry for serious engineering infrastructure is dropping, and database performance testing is one area that’s long overdue for broader adoption.
