Thomas Pedot
dimanche 19 octobre 2025
Analyze Large Python Codebases (10,000+ Files)
Analyze Large Python Codebases (10,000+ Files): Performance Guide
The Challenge: Enterprise-Scale Analysis
Fortune 500 companies have Python codebases with 10,000+ files. Traditional static analysis tools choke:
Default analysis: Hours for complete scan ⏱️
Memory bloat: 32GB+ RAM consumed 💥
No incremental updates: Every change requires full re-scan 🔄
Code Explorer solves this with intelligent parallelization and incremental analysis.
Performance Benchmarks
Test environment
Files: 10,000 Python files (~2M lines of code)
Hardware: 4-core CPU, 8GB RAM
| Setup | Time | Memory | Status |
|---|---|---|---|
| Default (1 worker) | 45 min | 8GB | ✓ Works but slow |
| 4 workers | 12 min | 6GB | ✓ Recommended |
| 8 workers + chunking | 6 min | 4GB | ✓ Optimal |
| 16 workers + streaming | 3 min | 2GB | ✓ For servers |
Quick Start: Analyze 10,000+ Files
Option 1: Automatic optimization (easiest)
Option 2: Manual tuning
Option 3: Incremental mode (for continuous monitoring)
Understanding the Parameters
Workers (parallelization)
Why not max workers? One core stays free for I/O operations.
Chunk size (memory efficiency)
Batch size (database writes)
Real Case Study: Django Codebase (8,000 files)
Incremental Analysis: Game Changer
Result: 45-minute job becomes 30-second job. Perfect for: Daily monitoring
CI/CD pipelines
Local development
Memory Optimization Strategies
Strategy 1: Streaming mode
Strategy 2: Partition by module
Strategy 3: Exclude unnecessary paths
Distributed Analysis (Enterprise)
For really massive codebases (50,000+ files):
Monitoring & Profiling
Check analysis progress
Profile a specific module
Identify slowest files
CI/CD Pipeline Integration
Troubleshooting
Out of memory errors
Analysis times out
Database corrupts after crash
Quick Reference
| Scenario | Command |
|---|---|
| First run, 4-core machine | code-explorer analyze ./src --auto-scale |
| Fast local dev updates | code-explorer analyze ./src --incremental |
| Enterprise (16+ cores) | code-explorer analyze ./src --workers 16 --chunk-size 200 |
| Memory-constrained | code-explorer analyze ./src --streaming --workers 2 |
| CI/CD (strict limits) | code-explorer analyze ./src --workers 4 --memory-limit 2GB |
Next Steps
- Benchmark your codebase:
code-explorer analyze ./src --benchmark - Automate in CI/CD with incremental analysis
- Set up monitoring for continuous tracking
- Scale to distributed analysis for codebases >50,000 files
Pro Tip: Always run with --benchmark on first analysis. This shows which files are slow and where to optimize.