

Smallpond
Overview :
Smallpond is a high-performance data processing framework designed for large-scale data processing. Built on DuckDB and 3FS, it can efficiently handle petabyte-scale datasets without requiring long-running services. Smallpond provides a simple and easy-to-use API, supporting Python 3.8 to 3.12, making it ideal for data scientists and engineers to quickly develop and deploy data processing tasks. Its open-source nature allows developers to freely customize and extend its functionality.
Target Users :
Smallpond is suitable for data scientists, data engineers, and development teams that need to efficiently process large-scale data. It helps users quickly build data processing workflows and improve data processing efficiency, especially in scenarios requiring high performance and scalability.
Use Cases
Use Smallpond to analyze stock price data and calculate the daily high and low prices
Run GraySort benchmark tests on large-scale datasets to verify data processing performance
Combine with the 3FS storage system to achieve distributed data processing and storage
Features
High-performance data processing: Provides fast data query and processing capabilities based on DuckDB
Scalability: Capable of handling petabyte-scale datasets, suitable for large-scale data processing scenarios
Ease of use: No need for long-running services, simple operation
Support for multiple data formats: Supports reading and writing of common data formats such as Parquet
Powerful SQL support: Implement complex data processing logic through SQL statements
Integration with 3FS: Supports distributed storage to improve data processing efficiency
Comprehensive documentation support: Provides quick start guides and API reference documentation
How to Use
1. Install Smallpond: Install via `pip install smallpond`
2. Initialize session: Initialize the session using `smallpond.init()`
3. Load data: Load data files using `smallpond.read_parquet()`
4. Data processing: Use `smallpond.partial_sql()` to execute SQL queries for data processing
5. Save results: Save the processed data in Parquet format
6. View results: View the processed data using `df.to_pandas()`
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