Smallpond - A lightweight open-source data processing framework from DeepSeek

What is Smallpond?

Smallpond is a lightweight data processing framework developed by DeepSeek, built on DuckDB and 3FS, and designed for high-performance and large-scale data processing. Smallpond supports petabyte-scale datasets, leveraging the high-performance analytical capabilities of DuckDB and the distributed storage advantages of 3FS to achieve efficient data loading, querying, and transformation. Smallpond is easy to get started with, providing sample data and detailed documentation, making it suitable for users and developers who need to efficiently process large-scale data.

Smallpond's main functions

• Lightweight and easy to useIt provides a concise API and an easy-to-understand workflow, allowing users to get started quickly.
• High-performance data processingPowered by DuckDB, it supports efficient processing of large-scale datasets.
• PB-level data scalabilityBuilt on 3FS, it supports processing petabyte-level data.
• Easy to useData processing tasks can be completed without requiring services to run for extended periods.
• Get started quicklyIt provides a quick start guide and sample data, and supports data processing using DuckDB SQL.

Smallpond's technical principles

• Data loadingIt loads data based on 3FS and supports multiple data formats (such as Parquet and CSV).
• Data processingUse DuckDB's SQL engine to process data, supporting complex query and analysis operations.
• Data storageThe processed data is saved back to 3FS, supporting partitioned storage and efficient read and write operations.
• Parallel processingSmallpond supports data partitioning and parallel processing, making full use of cluster resources and improving processing efficiency.

Smallpond's project address

• GitHub repository:https://github.com/deepseek-ai/smallpond

Smallpond's performance

Smallpond was evaluated using the GraySort benchmark on a 3FS cluster consisting of 50 compute nodes and 25 storage nodes. The benchmark sorted 110.5 TiB of data in 30 minutes and 14 seconds, achieving an average throughput of 3.66 TiB/min.

Smallpond application scenarios

• Large-scale data preprocessingSmallpond can efficiently process and transform large-scale datasets, supporting operations such as data cleaning, format conversion, and feature extraction, providing high-quality input data for machine learning and deep learning tasks.
• Data analysis and real-time queryIt can quickly execute complex data analysis and real-time query tasks, and is suitable for scenarios that require rapid generation of analysis results, such as data dashboards and real-time monitoring systems.
• Distributed machine learning trainingIt provides powerful data support for distributed machine learning training tasks, improves training efficiency, and is suitable for handling petabyte-scale training data.
• Embedded data analytics applicationsEasily embedded into various applications, providing efficient data analysis capabilities for embedded devices or resource-constrained environments.
• Data warehouse and lake storage integrationIt integrates seamlessly with existing data warehouse and data lake storage systems (such as 3FS), supports efficient data reading, writing and management, and is suitable for building modern data processing and analysis architectures.