CaseStudies

Parallel File Operations & Docker Isolation

Status: 📋 Planned
Version: v0.1.15
Target Release: November 21, 2025

Overview

Parallel file operations for improved performance, standard efficiency evaluation and benchmarking methodology, and custom tools running in isolated Docker containers for enhanced security and portability.

Feature Description

Goal

Dramatically improve file operation performance through parallelization, establish systematic efficiency benchmarking, and isolate custom tool execution in Docker containers for security and reproducibility.

Key Components

1. Parallel File Operations
   • Concurrent read operations for multiple files
   • Batch file writes with async I/O
   • Parallel directory scanning and search
   • Thread-safe file access coordination
   • Progress tracking for large operations
2. Efficiency Evaluation Framework
   • Standardized benchmarking suite
   • Performance metrics (throughput, latency, resource usage)
   • Comparison vs. sequential operations
   • Regression testing for performance
   • Automated performance reports
3. Docker Container Isolation
   • Each custom tool runs in isolated container
   • Pre-built images for common tool types (Python, Node.js, etc.)
   • Resource limits (CPU, memory, network)
   • Secure inter-container communication
   • Easy cleanup and reproducibility
4. Security Enhancements
   • Sandboxed tool execution (no host access)
   • Network isolation options
   • Read-only filesystem mounts
   • Secrets management for credentials
   • Audit logging for container operations

Performance Targets

• File Reads: 10x faster for 10+ files
• Directory Scan: 5x faster for large repos
• Write Operations: 3x faster with batching
• Container Startup: <2 seconds per tool

Test Strategy

Performance Tests

• Benchmark parallel vs. sequential file reads (10, 50, 100+ files)
• Measure write operation throughput with batching
• Test large directory scanning (e.g., FastAPI codebase)
• Profile CPU and memory usage under load

Concurrency Tests

• Verify thread safety with concurrent file access
• Test race condition handling
• Validate data consistency across parallel operations
• Stress test with maximum parallelism

Docker Isolation Tests

• Verify tools cannot access host filesystem
• Test network isolation
• Validate resource limits enforcement
• Check container cleanup on failure
• Test with malicious tool code (security)

Benchmark Tests

• Run standard efficiency evaluation suite
• Generate performance reports
• Compare across MassGen versions
• Validate regression detection

Validation Criteria

• ✅ 5x+ speedup for multi-file operations
• ✅ Zero data corruption in concurrent writes
• ✅ 100% container isolation (no host access)
• ✅ <5% performance overhead from Docker
• ✅ Automated benchmarks run on every release

Implementation Notes

Parallel File Operations Architecture:

# Example: Parallel file reads
async def read_files_parallel(file_paths: List[str]) -> List[str]:
    tasks = [read_file_async(path) for path in file_paths]
    return await asyncio.gather(*tasks)

Docker Tool Execution:

custom_tools:
  my_tool:
    type: docker
    image: python:3.11-slim
    script: ./tools/my_tool.py
    resources:
      cpu: 1.0
      memory: 512M
    isolation:
      network: none
      filesystem: read-only

Benchmark Configuration:

benchmarks:
  file_operations:
    - test: read_files_parallel
      file_count: [10, 50, 100]
      repetitions: 10
    - test: write_files_batch
      file_count: [10, 50, 100]
      repetitions: 10
  
  report:
    format: markdown
    compare_to: v0.1.14
    threshold: 0.95  # Fail if <95% of previous performance

Use Cases:

• Codebase Architecture Analysis (read 30+ files in parallel)
• Batch document processing
• Large-scale file transformations
• Secure custom tool execution
• Performance regression testing

Related Work

• Codebase Architecture Analysis (In Testing) - Benefits from parallel reads
• Custom Tools (v0.1.1) - Tool execution foundation
• Universal Code Execution (v0.0.31) - Execution infrastructure