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Overview

Refactron includes several performance optimization features designed to handle large codebases efficiently:
  • AST Caching - Avoid re-parsing unchanged files
  • Incremental Analysis - Only analyze changed files
  • Parallel Processing - Analyze multiple files concurrently

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Quick Start

Enable all optimizations in .refactron.yaml:
.refactron.yaml
# Performance optimizations
enable_ast_cache: true
max_ast_cache_size_mb: 100

enable_incremental_analysis: true

enable_parallel_processing: true
max_parallel_workers: 4

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AST Caching

Cache parsed Abstract Syntax Trees to avoid re-parsing.
  • 5-10x faster on repeated analysis
  • Reduces CPU usage
  • Especially effective for large files
from refactron import Refactron
from refactron.core.config import RefactronConfig

config = RefactronConfig(
    enable_ast_cache=True,
    max_ast_cache_size_mb=100
)
refactron = Refactron(config)

stats = refactron.get_performance_stats()
print(f"Hit rate: {stats['ast_cache']['hit_rate']}%")
print(f"Cache size: {stats['ast_cache']['cache_size_mb']} MB")

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Incremental Analysis

Only analyze files that changed since the last run. Benefits:
  • Up to 90% reduction in analysis time
  • Ideal for CI/CD pipelines
  • Perfect for iterative development
Example: 
from refactron import Refactron

refactron = Refactron()

# First run - analyzes all files
result1 = refactron.analyze("project/")
print(f"Analyzed {result1.summary['files_analyzed']} files")

# Second run (no changes) - skips unchanged files
result2 = refactron.analyze("project/")
print(f"Analyzed {result2.summary['files_analyzed']} files")  # Much less!

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Parallel Processing

Analyze multiple files concurrently using multiprocessing. Configuration:
.refactron.yaml
enable_parallel_processing: true
max_parallel_workers: 4  # Number of parallel workers
When to Use:
  • ✅ Large codebases (1000+ files)
  • ✅ Multi-core systems
  • ❌ Small codebases (<10 files)

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Best Practices by Project Size

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Small Projects (<1000 files)

enable_ast_cache: true
enable_incremental_analysis: true
enable_parallel_processing: false  # Overhead not worth it

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Medium Projects (1000-10000 files)

enable_ast_cache: true
enable_incremental_analysis: true
enable_parallel_processing: true
max_parallel_workers: 4

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Large Projects (10000+ files)

enable_ast_cache: true
max_ast_cache_size_mb: 200  # Larger cache
enable_incremental_analysis: true
enable_parallel_processing: true
max_parallel_workers: 8  # More workers

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Performance Statistics

Get detailed performance stats: 
stats = refactron.get_performance_stats()

# AST Cache
print(f"Cache hits: {stats['ast_cache']['hits']}")
print(f"Hit rate: {stats['ast_cache']['hit_rate']}%")

# Parallel Processing
print(f"Workers: {stats['parallel']['max_workers']}")

# Clear caches when needed
refactron.clear_caches()

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Troubleshooting

Check if optimizations are enabled:
stats = refactron.get_performance_stats()
print(f"Cache enabled: {stats['ast_cache']['enabled']}")
print(f"Hit rate: {stats['ast_cache']['hit_rate']}%")
  • Reduce cache size: max_ast_cache_size_mb: 50
  • Lower parallel workers: max_parallel_workers: 2
  • Clear caches periodically: refactron.clear_caches()
Disable for small codebases:
enable_parallel_processing: false

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Next Steps

Monitoring Guide

Learn how to monitor Refactron in production