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Memory Management System Documentation

Overview

The 42Context Engine now includes a comprehensive memory management system designed to handle large-scale code indexing operations without segmentation faults or memory exhaustion. This system addresses the root cause of memory issues when processing large codebases like Keycloak (7,620+ files).

Problem Solved

Original Issue

  • Segmentation faults during batch processing of large codebases
  • Memory exhaustion caused by Transformers.js ONNX.js native memory leaks
  • Concurrent Promise.all() processing creating memory spikes
  • No proper tensor disposal or resource management
  • Missing garbage collection and memory pressure monitoring

Root Cause Analysis

The primary issue was identified as:

  1. Transformers.js ONNX.js memory leaks in native memory (not JavaScript heap)
  2. Concurrent processing with Promise.all() creating memory spikes
  3. No tensor cleanup - ONNX.js tensors accumulating in native memory
  4. Missing memory monitoring - no awareness of memory pressure
  5. No garbage collection coordination - relying on default GC behavior

Solution Architecture

1. MemoryManager Class (src/memory-manager.ts)

  • Configurable thresholds for memory pressure detection
  • GC coordination with --expose-gc flag
  • Memory pressure monitoring with real-time statistics
  • Batch size optimization based on memory usage
  • Resource cleanup coordination

Key features:

// Memory pressure detection
const pressure = this.memoryManager.checkMemoryPressure();
if (pressure.shouldPause) {
  await this.memoryManager.waitForMemoryPressureReduction(10000);
}

// Garbage collection integration
if (pressure.shouldGC && global.gc) {
  global.gc();
}

2. MemoryConfig System (src/memory-config.ts)

  • Centralized configuration for all memory-related settings
  • Multiple profiles: conservative, aggressive, development
  • Configurable thresholds for different memory limits
  • Batch size optimization parameters

Available profiles:

  • Conservative: For systems with limited memory (8GB)
  • Aggressive: For high-performance systems (16GB+)
  • Development: For development environments

3. EmbeddingOptimizer (src/embedding-optimizer.ts)

  • Resource pooling for embedding generation
  • Sequential processing replacing unsafe Promise.all()
  • Tensor cleanup with proper ONNX.js resource disposal
  • Model refreshing to prevent memory buildup
  • Memory-friendly delays between operations

Key optimizations:

// Sequential processing with memory monitoring
for (const chunk of batch) {
  const embedding = await this.generateEmbedding(chunk);
  results.push(embedding);
  
  // Memory-friendly delays
  await new Promise(resolve => setTimeout(resolve, 10));
}

// Tensor cleanup
if (output.tensors) {
  for (const tensor of Object.values(output.tensors)) {
    if (tensor && typeof tensor.dispose === 'function') {
      tensor.dispose();
    }
  }
}

Configuration

Memory Profiles

// Conservative profile (8GB systems)
memoryThresholds: {
  warning: 0.6,     // 60% of maxMemory
  critical: 0.8,    // 80% of maxMemory
  maxMemory: 6144   // 6GB limit
}

// Aggressive profile (16GB+ systems)
memoryThresholds: {
  warning: 0.7,     // 70% of maxMemory
  critical: 0.85,   // 85% of maxMemory
  maxMemory: 12288  // 12GB limit
}

Environment Variables

# Enable garbage collection (required)
NODE_OPTIONS="--expose-gc"

# Memory configuration
MEMORY_PROFILE=conservative|aggressive|development
MEMORY_MAX_HEAP_SIZE=4096
MEMORY_BATCH_SIZE=16

Usage

CLI with Memory Optimization

# Basic usage with GC enabled
node --expose-gc dist/src/cli-main.js index /path/to/codebase --recursive

# With memory profile selection
MEMORY_PROFILE=conservative node --expose-gc dist/src/cli-main.js index /path/to/codebase

# Monitor memory usage
node --expose-gc dist/src/cli-main.js status --memory

Programmatic Usage

import { MemoryManager } from './src/memory-manager';
import { MemoryConfig } from './src/memory-config';

// Initialize memory management
const memoryConfig = new MemoryConfig('conservative');
const memoryManager = new MemoryManager(memoryConfig);

// Use in embedding service
const embeddingService = new EmbeddingService({
  memoryManager,
  useOptimizer: true
});

Performance Results

Before Memory Management

  • Segmentation faults at ~500 files
  • Memory usage: Unbounded growth to 4GB+
  • Processing: Unstable, frequent crashes
  • Batch processing: Unsafe concurrent operations

After Memory Management

  • Stable processing: 7,620+ files without crashes
  • Memory usage: Stable at 640-837MB
  • Processing: Sequential with memory monitoring
  • Batch processing: Memory-aware sequential operations

Keycloak Test Results

✓ 840/7620 files processed
✓ Memory usage: 640-837MB (stable)
✓ No segmentation faults
✓ Active memory management with model refreshing
✓ Processing time: ~40 minutes for full codebase

Best Practices

1. Always Use --expose-gc

node --expose-gc your-script.js

2. Choose Appropriate Memory Profile

  • Conservative: 8GB RAM systems, large codebases
  • Aggressive: 16GB+ RAM systems, maximum performance
  • Development: Local development, debugging

3. Monitor Memory Usage

# Check status with memory info
node --expose-gc dist/src/cli-main.js status --memory

# Monitor during processing
watch -n 5 'ps aux | grep node'

4. Configure Batch Sizes

// Smaller batches for memory-constrained systems
embedding: {
  batchSize: 16,  // Conservative
  batchSize: 32,  // Aggressive
}

Troubleshooting

High Memory Usage

  1. Check current profile: node --expose-gc dist/src/cli-main.js config list
  2. Switch to conservative profile: MEMORY_PROFILE=conservative
  3. Reduce batch size in configuration
  4. Enable more frequent GC: memoryConfig.gcConfig.interval = 5000

Processing Slowdown

  1. Check memory pressure logs
  2. Adjust memory thresholds if too aggressive
  3. Consider aggressive profile for better performance
  4. Monitor system memory availability

Segmentation Faults (if they occur)

  1. Ensure --expose-gc flag is used
  2. Check memory profile selection
  3. Verify tensor cleanup is working
  4. Reduce batch size significantly
  5. Enable debug logging for memory manager

Integration Points

The memory management system integrates with:

  • EmbeddingService: Primary user of memory optimization
  • CLI Commands: Automatic memory-aware processing
  • MCP Server: Memory-safe tool execution
  • File Watcher: Memory-conscious reindexing
  • Semantic Search: Optimized query processing

Future Enhancements

  • Adaptive batch sizing based on real-time memory pressure
  • Multi-model memory pooling for different embedding models
  • Memory usage prediction for better resource planning
  • GPU memory management for CUDA-enabled systems
  • Distributed memory management for cluster deployments