Parser Overview

The DDEX Parser is a high-performance XML parser specifically designed for DDEX (Digital Data Exchange) metadata in the music industry. Built with Rust and offering native bindings for JavaScript, Python, and WebAssembly, it provides up to 15x faster parsing than traditional XML parsers while maintaining complete security and fidelity.

What is DDEX Parser?

DDEX Parser transforms complex DDEX XML messages into clean, structured data that's easy to work with in modern applications. Whether you're building a music distribution platform, analytics dashboard, or content management system, the parser handles all the complexity of DDEX XML while preserving perfect round-trip compatibility.

Key Features

🚀 Blazing Performance

• 15x faster than traditional XML parsers
• Parse 10KB files in <5ms, 100MB files in <5s
• Memory-efficient streaming for large catalogs
• Native Rust core with optimized language bindings

🔒 Security First

• Built-in XXE (XML External Entity) protection
• Entity expansion limits (billion laughs protection)
• Deep nesting protection with configurable limits
• Memory-bounded parsing with timeout controls

🎭 Dual Model Architecture

• Graph Model: Faithful DDEX structure with references (perfect for compliance)
• Flattened Model: Developer-friendly denormalized data (easy to consume)
• Full round-trip fidelity between both representations

🌐 Universal Language Support

• JavaScript/TypeScript: Native Node.js bindings with full type definitions
• Python: PyO3 bindings with pandas DataFrame integration
• WebAssembly: Browser-ready bundle optimized at <500KB
• Rust: Direct access to the core parsing engine

Use Cases

Music Distribution Platforms

Parse incoming DDEX deliveries from record labels to extract release information, track metadata, and commercial terms for your catalog management system.

import { DDEXParser } from 'ddex-parser';

const parser = new DDEXParser();
const delivery = await parser.parseFile('label-delivery.xml');

// Extract key information
const releases = delivery.flat.releases;
const territories = delivery.flat.deals[0].territories;
const useTypes = delivery.flat.deals[0].useTypes;

Data Analytics & Business Intelligence

Convert DDEX data to structured formats for analysis, reporting, and machine learning workflows.

from ddex_parser import DDEXParser
import pandas as pd

parser = DDEXParser()
df = parser.to_dataframe('catalog.xml')

# Analyze genre distribution
genre_analysis = df.groupby('genre')['track_count'].sum()

# Territory coverage analysis  
territory_coverage = df.explode('territories').groupby('territories').size()

Content Management Systems

Build administrative interfaces that allow users to view, edit, and manage DDEX metadata with full validation.

// Parse existing content
const result = await parser.parseFile('release.xml');

// Modify in application
result.flat.releases[0].title = "Updated Album Title";
result.flat.releases[0].displayArtist = "New Artist Name";

// Round-trip with ddex-builder
import { DDEXBuilder } from 'ddex-builder';
const builder = new DDEXBuilder();
const updatedXML = await builder.build(result.toBuildRequest());

Quality Assurance & Validation

Implement automated quality checks and validation workflows for DDEX deliveries.

const parser = new DDEXParser({
  validation: 'strict',
  includeWarnings: true
});

try {
  const result = await parser.parseFile('delivery.xml');
  console.log('✅ Valid DDEX delivery');
  console.log(`Found ${result.flat.releases.length} releases`);
} catch (error) {
  console.log('❌ Validation failed:', error.message);
}

Architecture

Core Components

1. Rust Parser Core: High-performance XML parsing with security features
2. Data Models: Graph and flattened representations of DDEX data
3. Language Bindings: Native interfaces for JavaScript, Python, and WASM
4. Validation Engine: Schema validation and business rule checking

Data Flow

DDEX XML → Security Validation → Core Parser → Data Models → Language Bindings
    ↓              ↓                 ↓            ↓              ↓
XXE Protection  Entity Limits   Graph Model   Flattened    JS/Python/WASM
Deep Nesting    Memory Bounds   References    Denormalized     APIs

Supported DDEX Versions

The parser provides comprehensive support for all major DDEX ERN versions:

Version	Status	Coverage	Notes
ERN 4.3	✅ Full	100%	Latest specification, recommended
ERN 4.2	✅ Full	100%	Stable version, widely adopted
ERN 3.8.2	✅ Full	100%	Legacy support for older systems

Version Detection

The parser automatically detects DDEX versions and applies the appropriate schema:

const parser = new DDEXParser();
const version = await parser.detectVersion(xmlContent);
console.log(`Detected DDEX version: ${version}`);

Performance Characteristics

Parsing Speed Benchmarks

File Size	Traditional Parser	DDEX Parser	Speedup
10KB	12ms	0.8ms	15x
100KB	45ms	3ms	15x
1MB	420ms	28ms	15x
10MB	2.8s	180ms	16x
100MB	28s	1.8s	16x

Memory Efficiency

• 70% less memory than traditional parsers for small files
• Streaming support maintains <100MB memory usage for files of any size
• Configurable limits prevent memory exhaustion attacks

Security Features

XML Security Protections

const parser = new DDEXParser({
  // Entity expansion limits
  maxEntityExpansions: 1000,
  
  // Nesting depth protection  
  maxNestingDepth: 50,
  
  // Memory limits
  maxMemoryMB: 100,
  
  // Processing timeout
  timeoutSeconds: 30
});

Common Attack Mitigations

• XXE (XML External Entity): All external entity processing disabled by default
• Billion Laughs: Entity expansion limits prevent exponential memory growth
• Deep Nesting: Configurable depth limits prevent stack overflow attacks
• Memory Bombs: Memory usage monitoring with automatic termination

Integration Patterns

Microservices Architecture

Deploy as a dedicated parsing service with REST or gRPC APIs:

// Express.js microservice
app.post('/parse', async (req, res) => {
  try {
    const result = await parser.parseString(req.body.xml);
    res.json({
      success: true,
      data: result.flat,
      metadata: {
        version: result.version,
        messageId: result.graph.messageHeader.messageId
      }
    });
  } catch (error) {
    res.status(400).json({ success: false, error: error.message });
  }
});

Event-Driven Processing

Integrate with message queues and event streams:

import asyncio
from ddex_parser import DDEXParser

async def process_ddex_message(message):
    parser = DDEXParser()
    
    try:
        result = await parser.parse_async(message.body)
        
        # Emit events for downstream processing
        await emit_event('ddex.parsed', {
            'message_id': result.message_id,
            'releases': len(result.releases),
            'version': result.version
        })
        
    except Exception as e:
        await emit_event('ddex.parse_failed', {
            'error': str(e),
            'message_id': message.id
        })

Batch Processing

Process large catalogs efficiently with streaming:

import { createReadStream } from 'fs';

const parser = new DDEXParser({ streaming: true });

for await (const batch of parser.streamFile('large-catalog.xml')) {
  // Process releases in batches
  await processBatch(batch.releases);
  
  console.log(`Processed ${batch.releases.length} releases`);
}

Getting Started

Ready to start parsing DDEX files? Choose your path:

• Installation Guide - Set up the parser in your environment
• Quick Start - Parse your first DDEX file in minutes
• API Reference - Complete API documentation
• Advanced Usage - Streaming, optimization, and production tips