ApexBase

🚀 A lightning-fast, feature-rich embedded database designed for modern Python applications.

Features

✨ High Performance

• Built on SQLite/DuckDB with optimized configurations
• Efficient batch operations support
• Automatic performance optimization
• Concurrent access support
• Columnar storage support with DuckDB backend

🔍 Powerful Query Capabilities

• SQL-like query syntax
• Complex queries with multiple conditions
• JSON field support

📊 Data Framework Integration

• Seamless integration with Pandas
• Native support for PyArrow
• Built-in Polars compatibility

🎯 Multi-table Support

• Multiple table management
• Easy table switching
• Automatic table creation and deletion

🛡️ Data Integrity

• ACID compliance
• Transaction support
• Automatic error handling
• Data consistency guarantees

🔧 Developer Friendly

• Simple and intuitive API
• Minimal configuration required
• Comprehensive documentation
• Extensive test coverage

Installation

pip install apexbase

Quick Start

pip install apexbase

from apexbase import ApexClient

# Initialize the database with SQLite backend (default)
client = ApexClient("my_database")

# Or use DuckDB backend for columnar storage and analytics
client = ApexClient("my_database", backend="duckdb")

# Store single record
record = {"name": "John", "age": 30, "tags": ["python", "rust"]}
id_ = client.store(record)

# Store multiple records
records = [
    {"name": "Jane", "age": 25},
    {"name": "Bob", "age": 35}
]
ids = client.store(records)

# Query records
results = client.query("age > 25")
for record in results:
    print(record)

# Import from Pandas
import pandas as pd
df = pd.DataFrame({"name": ["Alice", "Bob"], "age": [28, 32]})
client.from_pandas(df)

Storage Backend Selection

# Use SQLite backend (default) - optimized for OLTP workloads
client = ApexClient("my_database", backend="sqlite")

# Use DuckDB backend - optimized for OLAP and analytics workloads
client = ApexClient("my_database", backend="duckdb")

# DuckDB backend advantages:
# - Columnar storage for better analytics performance
# - Efficient compression
# - Vectorized query execution
# - Better performance for analytical queries
# - Native support for complex aggregations

Advanced Usage

Multi-table Operations

# Create and switch tables
client.create_table("users")
client.create_table("orders")
client.use_table("users")

# Store user data
user = {"name": "John", "email": "[email protected]"}
user_id = client.store(user)

# Switch to orders table
client.use_table("orders")
order = {"user_id": user_id, "product": "Laptop"}
client.store(order)

Complex Queries

# Multiple conditions
results = client.query("age > 25 AND city = 'New York'")

# Range queries
results = client.query("score >= 85.0 AND score <= 90.0")

# LIKE queries
results = client.query("name LIKE 'J%'")

Performance Optimization

# Store large batch of records
records = [{"id": i, "value": i * 2} for i in range(10000)]
ids = client.store(records)

Requirements

• Python >= 3.9
• Dependencies:
  • orjson
  • pandas
  • pyarrow
  • polars
  • numpy
  • psutil
  • duckdb (optional, for columnar storage backend)

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Contributing

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