# ApexBase
🚀 A lightning-fast, feature-rich embedded database designed for modern Python applications.
## Features
✨ **High Performance**
- Built on DuckDB with optimized columnar storage
- Efficient batch operations support
- Automatic performance optimization
- Concurrent query execution
- Optimized for OLAP workloads
🔍 **Powerful Query Capabilities**
- SQL-like query syntax
- Complex queries with multiple conditions
- JSON field support
- Vectorized query execution
📊 **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
```bash
pip install apexbase
```
## Quick Start
```bash
pip install apexbase
```
```python
from apexbase import ApexClient
# Initialize the database
client = ApexClient("my_database")
# 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)
```
### DuckDB Advantages
ApexBase is built on DuckDB, providing significant advantages:
- **Columnar storage** for better analytics performance
- **Efficient compression** for reduced storage footprint
- **Vectorized query execution** for faster results
- **Parallel query processing** for multi-core utilization
- **Native support for complex aggregations**
- **Optimized for both OLAP and OLTP workloads**
- **Efficient memory management** for large datasets
## Advanced Usage
### Multi-table Operations
```python
# Create and switch tables
client.create_table("users")
client.create_table("orders")
client.use_table("users")
# Store user data
user = {"name": "John", "email": "john@example.com"}
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
```python
# 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
```python
# Store large batch of records
records = [{"id": i, "value": i * 2} for i in range(10000)]
ids = client.store(records)
# Optimize the database for analytics performance
client.optimize()
```
## Requirements
- Python >= 3.9
- Dependencies:
- orjson
- pandas
- pyarrow
- polars
- numpy
- psutil
- duckdb
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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