musicbrainz-cleaner/README.md

# 🎵 MusicBrainz Data Cleaner v3.0

A powerful command-line tool that cleans and normalizes your song data using the MusicBrainz database. **Now with advanced collaboration detection, artist alias handling, and intelligent fuzzy search for maximum accuracy!**

## 🚀 Quick Start for New Sessions

**If you're starting fresh or after a reboot, follow this exact sequence:**

### 1. Start MusicBrainz Services
```bash
# Quick restart (recommended)
./restart_services.sh

# Or full restart (if you have issues)
./start_services.sh
```

### 2. Wait for Services to Initialize
- **Database**: 5-10 minutes to fully load
- **Web server**: 2-3 minutes to start responding
- **Check status**: `cd ../musicbrainz-docker && docker-compose ps`

### 3. Verify Services Are Ready
```bash
# Test web server
curl -s http://localhost:5001 | head -5

# Test database (should show 2.6M+ artists)
docker-compose exec db psql -U musicbrainz -d musicbrainz_db -c "SELECT COUNT(*) FROM artist;"

# Test cleaner connection
docker-compose run --rm musicbrainz-cleaner python3 -c "from src.api.database import MusicBrainzDatabase; db = MusicBrainzDatabase(); print('Connection result:', db.connect())"
```

### 4. Run Tests
```bash
# Test 100 random songs
docker-compose run --rm musicbrainz-cleaner python3 test_100_random.py

# Or other test scripts
docker-compose run --rm musicbrainz-cleaner python3 [script_name].py
```

**⚠️ Important**: Always run scripts via Docker - the cleaner cannot connect to the database directly from outside the container.

**📋 Troubleshooting**: See `TROUBLESHOOTING.md` for common issues and solutions.

## ✨ What's New in v3.0

- **🚀 Direct Database Access**: Connect directly to PostgreSQL for 10x faster performance
- **🎯 Advanced Fuzzy Search**: Intelligent matching for similar artist names and song titles
- **🔄 Automatic Fallback**: Falls back to API mode if database access fails
- **⚡ No Rate Limiting**: Database queries don't have API rate limits
- **📊 Similarity Scoring**: See how well matches are scored
- **🆕 Collaboration Detection**: Intelligently handle complex collaborations like "Pitbull ft. Ne-Yo, Afrojack & Nayer"
- **🆕 Artist Aliases**: Handle name variations like "98 Degrees" → "98°" and "S Club 7" → "S Club"
- **🆕 Sort Names**: Handle "Last, First" formats like "Corby, Matt" → "Matt Corby"
- **🆕 Edge Case Handling**: Support for artists with hyphens, exclamation marks, numbers, and special characters
- **🆕 Band Name Protection**: Distinguish between band names (Simon & Garfunkel) and collaborations (Lavato, Demi & Joe Jonas)

## ✨ What It Does

**Before:**
```json
{
  "artist": "ACDC",
  "title": "Shot In The Dark",
  "favorite": true
}
```

**After:**
```json
{
  "artist": "AC/DC",
  "title": "Shot in the Dark",
  "favorite": true,
  "mbid": "66c662b6-6e2f-4930-8610-912e24c63ed1",
  "recording_mbid": "cf8b5cd0-d97c-413d-882f-fc422a2e57db"
}
```

## 🚀 Quick Start

### Option 1: Automated Setup (Recommended)

1. **Start MusicBrainz services**:
   ```bash
   ./start_services.sh
   ```
   This script will:
   - Check for Docker and port conflicts
   - Start all MusicBrainz services
   - Wait for database initialization
   - Create environment configuration
   - Test the connection

2. **Run the cleaner**:
   ```bash
   docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --input data/songs.json --output cleaned_songs.json
   ```

### Option 2: Manual Setup

1. **Start MusicBrainz services manually**:
   ```bash
   cd ../musicbrainz-docker
   MUSICBRAINZ_WEB_SERVER_PORT=5001 docker-compose up -d
   ```
   Wait 5-10 minutes for database initialization.

2. **Create environment configuration**:
   ```bash
   # Create .env file in musicbrainz-cleaner directory
   cat > .env << EOF
   DB_HOST=172.18.0.2
   DB_PORT=5432
   DB_NAME=musicbrainz_db
   DB_USER=musicbrainz
   DB_PASSWORD=musicbrainz
   MUSICBRAINZ_WEB_SERVER_PORT=5001
   EOF
   ```

3. **Run the cleaner**:
   ```bash
   docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --input data/songs.json --output cleaned_songs.json
   ```

### For detailed setup instructions, see [SETUP.md](SETUP.md)

## 🔄 After System Reboot

After restarting your Mac, you'll need to restart the MusicBrainz services:

### Quick Restart (Recommended)
```bash
# If Docker Desktop is already running
./restart_services.sh

# Or manually
cd ../musicbrainz-docker && MUSICBRAINZ_WEB_SERVER_PORT=5001 docker-compose up -d
```

### Full Restart (If you have issues)
```bash
# Complete setup including Docker checks
./start_services.sh
```

### Auto-start Setup (Optional)
1. **Enable Docker Desktop auto-start**:
   - Open Docker Desktop
   - Go to Settings → General
   - Check "Start Docker Desktop when you log in"

2. **Then just run**: `./restart_services.sh` after each reboot

**Note**: Your data is preserved in Docker volumes, so you don't need to reconfigure anything after a reboot.

## 🚨 Common Startup Issues & Fixes

### Issue 1: Database Connection Refused
**Problem**: Cleaner can't connect to database with error "Connection refused"
**Root Cause**: Database container not fully initialized or wrong host configuration
**Fix**: 
```bash
# Wait for database to be ready (check logs)
cd ../musicbrainz-docker && docker-compose logs db | tail -10

# Verify database is accepting connections
docker-compose exec db psql -U musicbrainz -d musicbrainz_db -c "SELECT COUNT(*) FROM artist;"
```

### Issue 2: Wrong Database Host Configuration
**Problem**: Cleaner tries to connect to `172.18.0.2` but can't reach it
**Root Cause**: Hardcoded IP address in database connection
**Fix**: Use Docker service name `db` instead of IP address
```python
# In src/api/database.py, change:
host='172.18.0.2'  # ❌ Wrong
host='db'          # ✅ Correct
```

### Issue 3: Test Script Logic Error
**Problem**: Test shows 0% success rate despite finding artists
**Root Cause**: Test script checking `'mbid' in result` where `result` is a tuple `(song_dict, success_boolean)`
**Fix**: Extract song dictionary from tuple
```python
# Wrong:
artist_found = 'mbid' in result

# Correct:
cleaned_song, success = result
artist_found = 'mbid' in cleaned_song
```

### Issue 4: Services Not Fully Initialized
**Problem**: API returns empty results even though database has data
**Root Cause**: MusicBrainz web server still starting up
**Fix**: Wait for services to be fully ready
```bash
# Check if web server is responding
curl -s http://localhost:5001 | head -5

# Wait for database to be ready
docker-compose logs db | grep "database system is ready"
```

### Issue 5: Port Conflicts
**Problem**: Port 5000 already in use
**Root Cause**: Another service using the port
**Fix**: Use alternative port
```bash
MUSICBRAINZ_WEB_SERVER_PORT=5001 docker-compose up -d
```

### Issue 6: Container Name Conflicts
**Problem**: "Container name already in use" error
**Root Cause**: Previous containers not properly cleaned up
**Fix**: Remove conflicting containers
```bash
docker-compose down
docker rm -f <container_name>
```

## 🔧 Startup Checklist

Before running tests, verify:
1. ✅ Docker Desktop is running
2. ✅ All containers are up: `docker-compose ps`
3. ✅ Database is ready: `docker-compose logs db | grep "ready"`
4. ✅ Web server responds: `curl -s http://localhost:5001`
5. ✅ Database has data: `docker-compose exec db psql -U musicbrainz -d musicbrainz_db -c "SELECT COUNT(*) FROM artist;"`
6. ✅ Cleaner can connect: Test database connection in cleaner

## 📋 Requirements

- **Python 3.6+**
- **MusicBrainz Server** running on localhost:8080
- **PostgreSQL Database** accessible on localhost:5432
- **Dependencies**: `requests`, `psycopg2-binary`, `fuzzywuzzy`, `python-Levenshtein`

## 🔧 Server Configuration

### Database Access
- **Host**: localhost (or Docker container IP: 172.18.0.2)
- **Port**: 5432 (PostgreSQL default)
- **Database**: musicbrainz_db (actual database name)
- **User**: musicbrainz
- **Password**: musicbrainz (default, should be changed in production)

### HTTP API (Fallback)
- **URL**: http://localhost:8080
- **Endpoint**: /ws/2/
- **Format**: JSON

### Troubleshooting
- **Database Connection Failed**: Check PostgreSQL is running and credentials are correct
- **API Connection Failed**: Check MusicBrainz server is running on port 8080
- **Slow Performance**: Ensure database indexes are built
- **No Results**: Verify data has been imported to the database
- **NEW**: **Docker Networking**: Use container IP (172.18.0.2) for Docker-to-Docker connections
- **NEW**: **Database Name**: Ensure using `musicbrainz_db` not `musicbrainz`

## 🧪 Testing

### Test File Organization
- **REQUIRED**: All test files must be placed in `src/tests/` directory
- **PROHIBITED**: Test files should not be placed in the root directory
- **Naming Convention**: Test files should follow `test_*.py` or `debug_*.py` patterns
- **Purpose**: Keeps root directory clean and organizes test code properly

### Running Tests
```bash
# Run all tests
python3 src/tests/run_tests.py

# Run specific test categories
python3 src/tests/run_tests.py --unit          # Unit tests only
python3 src/tests/run_tests.py --integration   # Integration tests only

# Run specific test module
python3 src/tests/run_tests.py test_data_loader
python3 src/tests/run_tests.py test_cli

# List all available tests
python3 src/tests/run_tests.py --list
```

#### Test Categories
- **Unit Tests**: Test individual components in isolation
- **Integration Tests**: Test interactions between components and database
- **Debug Tests**: Debug scripts and troubleshooting tools

## 📁 Project Structure

```
musicbrainz-cleaner/
├── src/
│   ├── api/                 # Database and API access
│   ├── cli/                 # Command-line interface
│   ├── config/              # Configuration and constants
│   ├── core/                # Core functionality
│   ├── tests/               # Test files (REQUIRED location)
│   └── utils/               # Utility functions
├── data/                    # Data files and output
│   ├── known_artists.json   # Name variations (ACDC → AC/DC)
│   ├── known_recordings.json # Known recording MBIDs
│   └── songs.json           # Source songs file
└── docker-compose.yml       # Docker configuration
```

### Data Files

The tool uses external JSON files for name variations:

- **`data/known_artists.json`**: Contains name variations (ACDC → AC/DC, ft. → feat.)
- **`data/known_recordings.json`**: Contains known recording MBIDs for common songs

These files can be easily updated without touching the code, making it simple to add new name variations.

## 🎯 Features

### ✅ Artist Name Fixes
- `ACDC` → `AC/DC`
- `Bruno Mars ft. Cardi B` → `Bruno Mars feat. Cardi B`
- `featuring` → `feat.`
- `98 Degrees` → `98°` (artist aliases)
- `S Club 7` → `S Club` (numerical suffixes)
- `Corby, Matt` → `Matt Corby` (sort names)

### ✅ Collaboration Detection
- **Primary Patterns**: "ft.", "feat.", "featuring" (always collaborations)
- **Secondary Patterns**: "&", "and", "," (intelligent detection)
- **Band Name Protection**: 200+ known band names from `data/known_artists.json`
- **Complex Collaborations**: "Pitbull ft. Ne-Yo, Afrojack & Nayer"
- **Case Insensitive**: "Featuring" → "featuring"

### ✅ Song Title Fixes
- `Shot In The Dark` → `Shot in the Dark`
- Removes `(Karaoke Version)`, `(Instrumental)` suffixes
- Normalizes capitalization and formatting

### ✅ Added Data
- **`mbid`**: Official MusicBrainz Artist ID
- **`recording_mbid`**: Official MusicBrainz Recording ID

### ✅ Preserves Your Data
- Keeps all your existing fields (guid, path, disabled, favorite, etc.)
- Only adds new fields, never removes existing ones

### 🆕 Advanced Fuzzy Search
- **Intelligent Matching**: Finds similar names even with typos or variations
- **Similarity Scoring**: Shows how well each match scores (0.0 to 1.0)
- **Configurable Thresholds**: Adjust matching sensitivity
- **Multiple Algorithms**: Uses ratio, partial ratio, and token sort matching
- **Enhanced Search Fields**: artist.name, artist_alias.name, artist.sort_name
- **Dash Handling**: Regular dash (-) vs Unicode dash (‐)
- **Substring Protection**: Avoids false matches like "Sleazy-E" vs "Eazy-E"

### 🆕 Edge Case Support
- **Hyphenated Artists**: "Blink-182", "Ne-Yo", "G-Eazy"
- **Exclamation Marks**: "P!nk", "Panic! At The Disco", "3OH!3"
- **Numbers**: "98 Degrees", "S Club 7", "3 Doors Down"
- **Special Characters**: "a-ha", "The B-52s", "Salt-N-Pepa"

### 🆕 Simplified Processing
- **Default Behavior**: Process all songs by default (no special flags needed)
- **Separate Output Files**: Successful and failed songs saved to different files
- **Progress Tracking**: Real-time progress with song counter and status
- **Smart Defaults**: Sensible defaults for all file paths and options
- **Detailed Reporting**: Comprehensive statistics and processing report
- **Batch Processing**: Efficient handling of large song collections

## 📖 Usage Examples

### Basic Usage (Default)
```bash
# Process all songs with default settings (data/songs.json)
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main
# Output: data/songs-success.json and data/songs-failure.json
```

### Custom Source File
```bash
# Process specific file
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --source data/my_songs.json
# Output: data/my_songs-success.json and data/my_songs-failure.json
```

### Custom Output Files
```bash
# Specify custom output files
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --source data/songs.json --output-success cleaned.json --output-failure failed.json
```

### Limit Processing
```bash
# Process only first 1000 songs
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --limit 1000
```

### Force API Mode
```bash
# Use HTTP API instead of database (slower but works without PostgreSQL)
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --use-api
```

### Test Connections
```bash
# Test database connection
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --test-connection

# Test with API mode
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --test-connection --use-api
```

### Help
```bash
# Show usage information
docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --help
```

## 📁 Data Files

### Input Format
Your JSON file should contain an array of song objects:

```json
[
  {
    "artist": "ACDC",
    "title": "Shot In The Dark",
    "disabled": false,
    "favorite": true,
    "guid": "8946008c-7acc-d187-60e6-5286e55ad502",
    "path": "z://MP4\\ACDC - Shot In The Dark (Karaoke Version).mp4"
  },
  {
    "artist": "Bruno Mars ft. Cardi B",
    "title": "Finesse Remix",
    "disabled": false,
    "favorite": false,
    "guid": "946a1077-ab9e-300c-3a72-b1e141e9706f",
    "path": "z://MP4\\Bruno Mars ft. Cardi B - Finesse Remix (Karaoke Version).mp4"
  }
]
```

## 📤 Output Format

The tool creates **three output files**:

### 1. Successful Songs (`source-success.json`)
Array of successfully processed songs with MBIDs added:

```json
[
  {
    "artist": "AC/DC",
    "title": "Shot in the Dark",
    "disabled": false,
    "favorite": true,
    "guid": "8946008c-7acc-d187-60e6-5286e55ad502",
    "path": "z://MP4\\ACDC - Shot In The Dark (Karaoke Version).mp4",
    "mbid": "66c662b6-6e2f-4930-8610-912e24c63ed1",
    "recording_mbid": "cf8b5cd0-d97c-413d-882f-fc422a2e57db"
  },
  {
    "artist": "Bruno Mars feat. Cardi B",
    "title": "Finesse (remix)",
    "disabled": false,
    "favorite": false,
    "guid": "946a1077-ab9e-300c-3a72-b1e141e9706f",
    "path": "z://MP4\\Bruno Mars ft. Cardi B - Finesse Remix (Karaoke Version).mp4",
    "mbid": "afb680f2-b6eb-4cd7-a70b-a63b25c763d5",
    "recording_mbid": "8ed14014-547a-4128-ab81-c2dca7ae198e"
  }
]
```

### 2. Failed Songs (`source-failure.json`)
Array of songs that couldn't be processed (same format as source):

```json
[
  {
    "artist": "Unknown Artist",
    "title": "Unknown Song",
    "disabled": false,
    "favorite": false,
    "guid": "12345678-1234-1234-1234-123456789012",
    "path": "z://MP4\\Unknown Artist - Unknown Song.mp4"
  }
]
```

### 3. Processing Report (`processing_report_YYYYMMDD_HHMMSS.txt`)
Human-readable text report with statistics and failed song list:

```
MusicBrainz Data Cleaner - Processing Report
==================================================

Source File: data/songs.json
Processing Date: 2024-12-19 14:30:22
Processing Time: 15263.3 seconds

SUMMARY
--------------------
Total Songs Processed: 49,170
Successful Songs: 40,692
Failed Songs: 8,478
Success Rate: 82.8%

DETAILED STATISTICS
--------------------
Artists Found: 44,526/49,170 (90.6%)
Recordings Found: 40,998/49,170 (83.4%)
Processing Speed: 3.2 songs/second

OUTPUT FILES
--------------------
Successful Songs: data/songs-success.json
Failed Songs: data/songs-failure.json
Report File: data/processing_report_20241219_143022.txt

FAILED SONGS (First 50)
--------------------
  1. Unknown Artist - Unknown Song
  2. Invalid Artist - Invalid Title
  3. Test Artist - Test Song
...
```

## 🎬 Example Run

### Basic Processing
```bash
$ docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main

🚀 Starting song processing...
📊 Total songs to process: 49,170
Using database connection
==================================================

[1 of 49,170] ✅ PASS: ACDC - Shot In The Dark
[2 of 49,170] ❌ FAIL: Unknown Artist - Unknown Song
[3 of 49,170] ✅ PASS: Bruno Mars feat. Cardi B - Finesse (remix)
[4 of 49,170] ✅ PASS: Taylor Swift - Love Story
...

  📈 Progress: 100/49,170 (0.2%) - Success: 85.0% - Rate: 3.2 songs/sec
  📈 Progress: 200/49,170 (0.4%) - Success: 87.5% - Rate: 3.1 songs/sec
  ...

==================================================
🎉 Processing completed!
📊 Final Results:
  ⏱️  Total processing time: 15263.3 seconds
  🚀 Average speed: 3.2 songs/second
  ✅ Artists found: 44,526/49,170 (90.6%)
  ✅ Recordings found: 40,998/49,170 (83.4%)
  ❌ Failed songs: 8,478 (17.2%)
📄 Files saved:
  ✅ Successful songs: data/songs-success.json
  ❌ Failed songs: data/songs-failure.json
  📋 Text report: data/processing_report_20241219_143022.txt
  📊 JSON report: data/processing_report_20241219_143022.json
```

### Limited Processing
```bash
$ docker-compose run --rm musicbrainz-cleaner python3 -m src.cli.main --limit 1000

⚠️  Limiting processing to first 1000 songs
🚀 Starting song processing...
📊 Total songs to process: 1,000
Using database connection
==================================================

[1 of 1,000] ✅ PASS: ACDC - Shot In The Dark
[2 of 1,000] ❌ FAIL: Unknown Artist - Unknown Song
...

==================================================
🎉 Processing completed!
📊 Final Results:
  ⏱️  Total processing time: 312.5 seconds
  🚀 Average speed: 3.2 songs/second
  ✅ Artists found: 856/1,000 (85.6%)
  ✅ Recordings found: 789/1,000 (78.9%)
  ❌ Failed songs: 211 (21.1%)
📄 Files saved:
  ✅ Successful songs: data/songs-success.json
  ❌ Failed songs: data/songs-failure.json
  📋 Text report: data/processing_report_20241219_143022.txt
  📊 JSON report: data/processing_report_20241219_143022.json
```

## 🔧 Troubleshooting

### "Could not find artist"
- The artist might not be in the MusicBrainz database
- Try checking the spelling or using a different variation
- The search index might still be building (wait a few minutes)
- Check fuzzy search similarity score - lower threshold if needed
- **NEW**: Check for artist aliases (e.g., "98 Degrees" → "98°")
- **NEW**: Check for sort names (e.g., "Corby, Matt" → "Matt Corby")

### "Could not find recording"
- The song might not be in the database
- The title might not match exactly
- Try a simpler title (remove extra words)
- Check fuzzy search similarity score - lower threshold if needed
- **NEW**: For collaborations, check if it's stored under the main artist

### Connection errors
- **Database**: Make sure PostgreSQL is running and accessible
- **API**: Make sure your MusicBrainz server is running on `http://localhost:8080`
- Check that Docker containers are up and running
- Verify the server is accessible in your browser
- **NEW**: For Docker, use container IP (172.18.0.2) instead of localhost

### JSON errors
- Make sure your input file is valid JSON
- Check that it contains an array of objects
- Verify all required fields are present

### Performance issues
- Use database mode instead of API mode for better performance
- Ensure database indexes are built for faster queries
- Check fuzzy search thresholds - higher thresholds mean fewer but more accurate matches

### Collaboration detection issues
- **NEW**: Check if it's a band name vs collaboration (e.g., "Simon & Garfunkel" vs "Lavato, Demi & Joe Jonas")
- **NEW**: Verify the collaboration pattern is supported (ft., feat., featuring, &, and, ,)
- **NEW**: Check case sensitivity - patterns are case-insensitive

## 🎯 Use Cases

- **Karaoke Systems**: Clean up song metadata for better search and organization
- **Music Libraries**: Standardize artist names and add official IDs
- **Music Apps**: Ensure consistent data across your application
- **Data Migration**: Clean up legacy music data when moving to new systems
- **Fuzzy Matching**: Handle typos and variations in artist/song names
- **NEW**: **Collaboration Handling**: Process complex artist collaborations
- **NEW**: **Edge Cases**: Handle artists with special characters and unusual names

## 📚 What are MBIDs?

**MBID** stands for **MusicBrainz Identifier**. These are unique, permanent IDs assigned to artists, recordings, and other music entities in the MusicBrainz database.

**Benefits:**
- **Permanent**: Never change, even if names change
- **Universal**: Used across many music applications
- **Reliable**: Official identifiers from the MusicBrainz database
- **Linked Data**: Connect to other music databases and services

## 🆕 Performance Comparison

| Method | Speed | Rate Limiting | Fuzzy Search | Setup Complexity |
|--------|-------|---------------|--------------|------------------|
| **Database** | ⚡ 10x faster | ❌ None | ✅ Yes | 🔧 Medium |
| **API** | 🐌 Slower | ⏱️ Yes (0.1s delay) | ❌ No | ✅ Easy |

## 🆕 Collaboration Detection Examples

| Input | Type | Detection | Output |
|-------|------|-----------|---------|
| `Bruno Mars ft. Cardi B` | Collaboration | ✅ Primary pattern | `Bruno Mars feat. Cardi B` |
| `Pitbull ft. Ne-Yo, Afrojack & Nayer` | Complex Collaboration | ✅ Multiple patterns | `Pitbull feat. Ne-Yo, Afrojack & Nayer` |
| `Simon & Garfunkel` | Band Name | ❌ Protected | `Simon & Garfunkel` |
| `Lavato, Demi & Joe Jonas` | Collaboration | ✅ Comma detection | `Lavato, Demi & Joe Jonas` |
| `Hall & Oates` | Band Name | ❌ Protected | `Hall & Oates` |

## 🆕 Edge Case Examples

| Input | Type | Handling | Output |
|-------|------|----------|---------|
| `ACDC` | Name Variation | ✅ Alias lookup | `AC/DC` |
| `98 Degrees` | Artist Alias | ✅ Alias search | `98°` |
| `S Club 7` | Numerical Suffix | ✅ Suffix removal | `S Club` |
| `Corby, Matt` | Sort Name | ✅ Sort name search | `Matt Corby` |
| `Blink-182` | Dash Variation | ✅ Unicode dash handling | `blink‐182` |
| `P!nk` | Special Characters | ✅ Direct search | `P!nk` |
| `3OH!3` | Numbers + Special | ✅ Direct search | `3OH!3` |

## 🤝 Contributing

Found a bug or have a feature request? 

1. Check the existing issues
2. Create a new issue with details
3. Include sample data if possible

## 📄 License

This tool is provided as-is for educational and personal use.

## 🔗 Related Links

- [MusicBrainz](https://musicbrainz.org/) - The open music encyclopedia
- [MusicBrainz API](https://musicbrainz.org/doc/Development) - API documentation
- [MusicBrainz Docker](https://github.com/metabrainz/musicbrainz-docker) - Docker setup
- [FuzzyWuzzy](https://github.com/seatgeek/fuzzywuzzy) - Fuzzy string matching library

## 📝 Lessons Learned

### Database Integration
- **Direct PostgreSQL access is 10x faster** than API calls
- **Docker networking** requires container IPs, not localhost
- **Database name matters**: `musicbrainz_db` not `musicbrainz`
- **Static caches cause problems**: Wrong MBIDs override correct database lookups

### Collaboration Handling
- **Primary patterns** (ft., feat.) are always collaborations
- **Secondary patterns** (&, and) require intelligence to distinguish from band names
- **Comma detection** helps identify collaborations
- **Artist credit lookup** is essential for preserving all collaborators

### Edge Cases
- **Dash variations** (regular vs Unicode) cause exact match failures
- **Artist aliases** are common and important (98 Degrees → 98°)
- **Sort names** handle "Last, First" formats
- **Numerical suffixes** in names need special handling (S Club 7 → S Club)

### Performance Optimization
- **Remove static caches** for better accuracy
- **Database-first approach** ensures live data
- **Fuzzy search thresholds** need tuning for different datasets
- **Connection pooling** would improve performance for large datasets

### CLI Design
- **Simplified interface** with smart defaults reduces complexity
- **Array format consistency** makes output files easier to work with
- **Human-readable reports** improve user experience
- **Test file organization** keeps project structure clean

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**Happy cleaning! 🎵✨**