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Pre-commit Performance Profiling Scripts

This directory contains several scripts for analyzing pre-commit hook performance and identifying bottlenecks.

Scripts Overview

🚀 quick-profile.sh

Purpose: Fast comparison between different execution methods
Best for: Quick checks and basic performance monitoring

What it does:

  • Compares global lint function vs local pre-commit
  • Tests pre-commit in commit context vs direct execution
  • Measures shell hook performance directly
  • Provides simple timing comparisons

Usage:

./quick-profile.sh

Sample output:

⚡ Quick Pre-commit Performance Comparison
Lint function (global config): ✅ Success: 0.837s
Local pre-commit (repo config): ✅ Success: 0.555s
Pre-commit in commit context: ✅ Success: 0.712s
Shell hook directly: ✅ Success: 0.371s

📊 detailed-profile.sh

Purpose: Comprehensive performance breakdown with multiple test runs
Best for: Detailed analysis and consistent measurements

What it does:

  • Runs each test 3 times and calculates averages
  • Measures individual tool performance (shellcheck, shfmt)
  • Analyzes environment setup overhead
  • Compares global vs local config loading
  • Tests commit simulation scenarios

Usage:

./detailed-profile.sh

Key metrics provided:

  • Pre-commit environment initialization time
  • Individual tool execution times
  • Config loading differences
  • Statistical averages across multiple runs

🏁 compare-approaches.sh

Purpose: Direct side-by-side comparison of different approaches
Best for: Understanding the performance impact of each layer

What it does:

  • Tests direct tool usage (shellcheck + shfmt)
  • Compares shell hook script execution
  • Measures pre-commit framework overhead
  • Simulates actual commit process
  • Shows cumulative overhead at each layer

Usage:

./compare-approaches.sh

Demonstrates:

  • Raw tool performance vs framework overhead
  • Step-by-step performance degradation
  • Real-world commit simulation timing

📈 performance-analysis.md

Purpose: Detailed analysis report of profiling findings
Best for: Understanding results and optimization recommendations

Contains:

  • Performance metrics summary table
  • Bottleneck analysis breakdown
  • Root cause identification
  • Optimization recommendations
  • Visual performance comparison charts

Usage Recommendations

For Regular Monitoring

Use quick-profile.sh to check if performance has changed after configuration updates or system changes.

For Investigation

Use detailed-profile.sh when you need precise measurements or want to track performance over time.

For Optimization

Use compare-approaches.sh to understand exactly where time is being spent and evaluate alternative approaches.

For Documentation

Reference performance-analysis.md for the complete analysis and recommendations.

Understanding the Results

Expected Performance Ranges

  • Direct tools: 0.04-0.10s (shellcheck + shfmt combined)
  • Shell hook script: 0.08-0.16s (tools + file management)
  • Pre-commit runs: 0.5-0.6s (tools + framework overhead)
  • Commit context: 0.6-0.8s (tools + framework + git integration)

Performance Baseline

The pre-commit framework has an inherent ~400-500ms initialization cost. This is normal and provides:

  • Consistent tool versions across environments
  • Dependency management
  • Output formatting and collection
  • Git integration features
  • Hook orchestration and error handling

When to Investigate Further

Consider investigating if you see:

  • Sudden increases in timing (>50% slower than baseline)
  • Individual tools taking >0.1s consistently
  • Framework overhead >1s regularly
  • Significant variance between runs (>100ms difference)

Troubleshooting Performance Issues

  1. Run all scripts to establish current baseline
  2. Compare with expected ranges above
  3. Check system resources (CPU, memory, disk I/O)
  4. Review recent changes to pre-commit config or system
  5. Test with minimal config to isolate problematic hooks

Script Dependencies

All scripts require:

  • bash (GNU Bash 5.x+)
  • python3 (for timing calculations)
  • git (for repository operations)
  • pre-commit (installed and configured)
  • Access to the configured shell linting tools (shellcheck, shfmt)

Notes

  • Scripts create temporary test files that are automatically cleaned up
  • All timing uses Python's time.time() for consistent measurements
  • Scripts are safe to run repeatedly and won't modify your actual code files
  • Performance will vary based on system load and hardware specifications