Performance Issues

Diagnose and resolve common performance issues in DDEX Suite.

Common Performance Issues

Slow Parsing Performance

Symptoms: XML parsing takes longer than expected

Causes & Solutions:

• Large file size: Use streaming parser for files >10MB
• Complex XML structure: Enable streaming mode
• Memory pressure: Increase available memory or use memory-bounded parsing

// For large files, use streaming
const parser = new DDEXParser({ 
  streaming: true,
  maxMemoryUsage: 512 * 1024 * 1024 // 512MB limit
});

Memory Usage Issues

Symptoms: High memory consumption during processing

Solutions:

• Use streaming parser for large files
• Process files in batches
• Enable garbage collection
• Set memory limits

# Memory-efficient processing
from ddex_parser import DDEXParser

parser = DDEXParser()
parser.set_memory_limit(500 * 1024 * 1024)  # 500MB limit

# Process in chunks
for chunk in parser.parse_streaming(large_xml_file):
    process_chunk(chunk)
    # Memory is released after each chunk

Build Performance Issues

Symptoms: DDEX XML generation is slow

Solutions:

• Use deterministic mode only when needed
• Batch similar operations
• Pre-validate data structure
• Use appropriate buffer sizes

Performance Monitoring

import time
import psutil
from contextlib import contextmanager

@contextmanager
def performance_monitor(operation_name):
    """Monitor performance of DDEX operations"""
    
    start_time = time.time()
    start_memory = psutil.Process().memory_info().rss
    
    print(f"Starting {operation_name}...")
    
    try:
        yield
    finally:
        end_time = time.time()
        end_memory = psutil.Process().memory_info().rss
        
        duration = end_time - start_time
        memory_used = (end_memory - start_memory) / 1024 / 1024  # MB
        
        print(f"{operation_name} completed:")
        print(f"  Duration: {duration:.2f} seconds")
        print(f"  Memory used: {memory_used:.1f} MB")

# Usage
with performance_monitor("DDEX Parsing"):
    result = parser.parse(xml_content)

Optimization Strategies

1. Batch Processing

// Process multiple files efficiently
const results = await parser.parseBatch(xmlFiles, {
  concurrency: 4,
  memoryLimit: 1024 * 1024 * 1024
});

2. Streaming for Large Files

# Stream large files to avoid memory issues
for chunk in parser.parse_streaming(large_file_path):
    process_data_chunk(chunk)

3. Caching

// Cache parsed results
const cache = new Map();

function parseWithCache(xmlContent: string) {
  const hash = calculateHash(xmlContent);
  if (cache.has(hash)) {
    return cache.get(hash);
  }
  
  const result = parser.parse(xmlContent);
  cache.set(hash, result);
  return result;
}

Best Practices

1. Profile First: Use profiling tools to identify bottlenecks
2. Stream Large Files: Use streaming for files >10MB
3. Batch Operations: Process multiple files in batches
4. Monitor Memory: Keep memory usage under control
5. Use Caching: Cache frequently accessed data
6. Optimize Data Structures: Use efficient data representations
7. Parallel Processing: Use worker threads for CPU-intensive tasks