ZFDB

A lightweight, pure Python library for file-based database operations with encryption support. ZFDB provides a secure and easy way to store and manage data in a local file system using ZIP archives.

Features

• 🔒 Password-based encryption
• 📁 ZIP-based storage with compression
• 🔍 Record search capabilities
• 🏷️ Metadata support
• ✅ Data integrity validation
• 🔄 Automatic compaction
• 💾 Backup functionality
• 📝 JSON support
• 🐍 Pure Python (no external dependencies)

Installation

Clone the repository or install the package using pip:

pip install zfdb
# or
git clone https://github.com/semolex/zfdb.git
cd zfdb

Quick Start

from zfdb import Database, DatabaseConfig
from pathlib import Path

# Create a database configuration
config = DatabaseConfig(
    name="mydb",
    path=Path("mydb.zip"),
    password="secret123",  # Optional
    compression_level=9
)

# Initialize database
db = Database(config)

# Insert records
db.insert(
    "user1",
    '{"name": "John Doe", "email": "john@example.com"}',
    metadata={"type": "user"}
)

# Read records
record = db.get("user1")
if record:
    user_data = record.json  # Parse as JSON
    print(f"User: {user_data['name']}")
    print(f"Record created: {record.metadata['created_at']}")

# Search records
results = db.search("user")
print(f"Found records: {results}")

# Create backup
db.backup("mydb_backup.zip")

Detailed Usage

Configuration

from zfdb import DatabaseConfig
from pathlib import Path

config = DatabaseConfig(
    name="mydb",                      # Database name
    path=Path("mydb.zip"),            # Database file path
    password="secret123",             # Optional encryption password
    compression_level=6,              # ZIP compression level (0-9)
    max_size=1024 * 1024 * 100,       # Maximum database size (100MB)
    auto_compact=True,                # Enable automatic compaction
    version="1.0.0"                   # Database version
)

Working with Records

Insert Records

# Insert JSON data
db.insert(
    "config1",
    '{"setting": "value"}',
    metadata={"type": "configuration"}
)

# Insert text data
db.insert(
    "note1",
    "This is a text note",
    metadata={"type": "note"}
)

# Insert binary data
db.insert(
    "binary1",
    b"\x00\x01\x02\x03",
    metadata={"type": "binary"}
)

Read Records

# Get record
record = db.get("config1")

# Access data in different formats
raw_data = record.raw      # bytes
text_data = record.text    # str
json_data = record.json    # parsed JSON

# Access metadata
created_at = record.metadata['created_at']
record_size = record.metadata['size']

Update Records

db.update(
    "note1",
    "Updated content",
    metadata={"updated_at": datetime.utcnow().isoformat()}
)

Delete Records

db.delete("note1")

Database Management

List Records

all_records = db.list_records()

Search Records

# Search by name pattern
notes = db.search("note")
configs = db.search("config")

Database Maintenance

# Compact database (remove deleted records)
db.compact()

# Create backup
db.backup("backup.zip")

Data Security

ZFDB provides several security features:

1. Password Protection: Database contents are encrypted using a password-derived key
2. Data Integrity: Each record includes a SHA-256 checksum
3. Size Limits: Configurable database size limits
4. Validation: Automatic data integrity checking

Best Practices

1. Regular Backups: Use the backup() method regularly
2. Error Handling: Always handle potential exceptions:

   try:
       record = db.get("key")
   except DatabaseError as e:
       logger.error(f"Database error: {e}")
   

3. Resource Management: Close database when done:

   try:
       # Use database
   finally:
       db.compact()  # Optional cleanup
   

Limitations

• Not a real database
• Not suitable for concurrent access
• No built-in indexing
• Limited query capabilities
• Not recommended for very large datasets
• Simple encryption (not suitable for highly sensitive data)

Testing and linting

poetry run black zfdb/
poetry run isort zfdb/
poetry run mypy zfdb/
poetry run pytest tests/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/yourfeature)
3. Commit your changes (git commit -m 'Add some yourfeature')
4. Push to the branch (git push origin feature/yourfeature)
5. Open a Pull Request
6. Use isort, mypy and black for code formatting and type checking

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Created to be used in local applications to persists data in case there is no other database available
• Inspired by simple key-value stores
• Built using Python standard library components
• Designed for simplicity and ease of use