Modern data workflows face increasing challenges due to expanding datasets and the growing complexity of distributed processing. Many organizations find that traditional data processing systems fall short in terms of processing time, memory limitations, and distributed task management. This often leads data scientists and engineers to spend significant time on system maintenance rather than extracting valuable insights from data. Clearly, the market urgently needs a tool that simplifies workflows without sacrificing performance.
Recently, DeepSeek AI released Smallpond, a lightweight data processing framework built on DuckDB and 3FS. Smallpond aims to extend DuckDB's efficient in-process SQL analytics to distributed environments. By combining with 3FS—a high-performance distributed file system optimized for modern SSDs and RDMA networks—Smallpond provides a practical solution for handling large datasets, avoiding the complexity and high infrastructure costs of long-running services.
Smallpond boasts a simple and modular design, compatible with Python 3.8 to 3.12. Users can quickly install it via pip and start processing data immediately. A key feature is its support for manual data partitioning, allowing users to partition based on file count, row count, or the hash value of a specific column. This flexibility enables customized processing based on individual data and infrastructure.
Technically, Smallpond leverages DuckDB's native SQL query performance and integrates with Ray to enable parallel processing across distributed computing nodes. This combination simplifies scaling operations and ensures efficient workload handling across multiple nodes. Furthermore, by avoiding persistent services, Smallpond reduces the operational overhead typically associated with distributed systems.
In performance tests, Smallpond excelled in the GraySort benchmark, sorting 110.5 TiB of data in just over 30 minutes, achieving an average throughput of 3.66 TiB per minute. These performance metrics demonstrate Smallpond's ability to meet the needs of organizations handling data ranging from terabytes to petabytes. As an open-source project, Smallpond welcomes contributions from users and developers to further optimize and adapt it to diverse use cases.
Smallpond represents a significant step forward in distributed data processing. By extending DuckDB's efficiency to distributed environments and combining it with 3FS's high throughput, it provides a practical tool for data scientists and engineers. Whether handling smaller datasets or scaling to petabyte-level operations, Smallpond is an efficient and accessible framework.
Project: https://github.com/deepseek-ai/smallpond?tab=readme-ov-file
Key Highlights:
🌟 Smallpond is a lightweight data processing framework from DeepSeek AI, built on DuckDB and 3FS.
⚙️ Supports Python 3.8 to 3.12, allowing users to quickly install and flexibly customize data processing.
🚀 Demonstrated exceptional performance in the GraySort benchmark, showcasing its ability to handle terabyte-scale data.