Can this skill identify specific functions causing slow builds?

It identifies the types of operations causing delays (like type checking or codegen); for specific function names, you may need to supplement this with -Z print-mono-items.

How does DuckDB help with Rust compilation analysis?

DuckDB allows you to run high-performance SQL queries directly against large JSON profiling files, making it easy to aggregate time spent on specific compiler phases or query events.

What is a sign of monomorphization issues in the results?

If the analysis shows 'items_of_instance' with over 30,000 invocations, you likely have a trait or generic explosion that is slowing down your build.

What prerequisites are needed for this skill?

You must have DuckDB installed and a chrome_profiler.json file generated by the rustc-self-profile tool (often processed via crox).

Rust Compile Time Analyzer with DuckDB

Name: Rust Compile Time Analyzer with DuckDB
Author: bearcove

bybearcove

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Analytics & Monitoring

Analyzes Rust compilation profiling data using DuckDB to identify performance bottlenecks and monomorphization issues.

About

This skill empowers developers to deep-dive into Rust compilation performance by leveraging the analytical power of DuckDB on chrome_profiler.json data. It provides a suite of pre-defined SQL queries to pinpoint slow trait resolutions, excessive generic instantiations (monomorphization), and LLVM backend delays. By transforming raw profiling JSON into actionable insights, it enables developers to implement targeted optimizations that significantly reduce build times for complex, generic-heavy Rust projects.

Key Features

Breakdown of LLVM backend versus frontend compilation time
Detailed reporting on expensive query events like items_of_instance
CSV export capabilities for long-term performance tracking
94 GitHub stars
SQL-based analysis of chrome_profiler.json files
Detection of trait explosion and monomorphization bottlenecks

Use Cases

Comparing compilation profiles across different Rust compiler versions
Debugging build performance regressions after adding complex generics
Optimizing long CI/CD pipelines for enterprise Rust codebases

About

Key Features

Breakdown of LLVM backend versus frontend compilation time
Detailed reporting on expensive query events like items_of_instance
CSV export capabilities for long-term performance tracking
94 GitHub stars
SQL-based analysis of chrome_profiler.json files
Detection of trait explosion and monomorphization bottlenecks

Use Cases

Comparing compilation profiles across different Rust compiler versions
Debugging build performance regressions after adding complex generics
Optimizing long CI/CD pipelines for enterprise Rust codebases