smallpond
S
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.
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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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