From Idea to MVP in One Day: AI Built a PostgreSQL Profiler

Migrating from MS SQL to PostgreSQL brought an unexpected surprise: there's no built-in profiler. One of the key features familiar to anyone who has worked with SQL Server is simply missing. Previously, this would have meant: assigning the task to developers, agreeing on specifications, waiting weeks for results. One engineer chose a different path and built a working MVP in a single day—without writing a single line of code manually.

Why PostgreSQL Has No Built-in Profiler

PostgreSQL surpasses MS SQL in functionality in many areas, but diagnostics and profiling are its known weak point. In SQL Server, the profiler is built directly into the database kernel. It allows you to track every query executed: execution time, consumed resources, optimization plans, locks. In PostgreSQL, it's different. There is no full-fledged built-in profiler. Yes, there are extensions—pg_stat_statements for statistics, auto_explain for plan logging—but they require separate installation and configuration. And most importantly, they don't provide the completeness of information that SQL Server's built-in profiler does.

How AI Created Code in One Day

Instead of the traditional cycle (task → specification → development → weeks of waiting), the engineer chose a different approach. He opened a dialog with an AI assistant and described the requirement: an instrument is needed that intercepts queries to PostgreSQL and outputs useful metrics. Not "write a Python class," not "create a function to parse logs." Just a description of the problem. Then iterative development began:

• Initial prompt with task description
• Receiving ready-made code from AI
• Testing on a real production database
• Describing bugs found in a new prompt
• Corrected version of the code
• Repeating the cycle until a working MVP

In 24 hours of intensive work (not weeks of waiting in the developer queue), a tool appeared that actually works. No meetings, approvals, or sprint planning.

What Came Out

The profiler MVP intercepts SQL queries going into the database and collects metrics: execution time for each query, used indexes, number of rows. There is a basic interface for viewing collected statistics. This is not a final commercial product with all the bells and whistles of optimization and integration with monitoring systems. But it's a working solution that provides what's needed: visibility into what's happening inside the database.

What This Means

The story illustrates a paradigm shift in development. Previously, a developer wrote code themselves (time-consuming). Now: describe the task, AI generates the foundation, the developer refines the details (quickly). This is not a replacement for programmers—an acceleration of their work by many times over. For companies, this means less technical debt, shorter development cycles, and developers focused on logic rather than syntax.