The data engineering landscape continues to evolve with specialized tools designed for specific use cases. DeepSeek's recently released smallpond has sparked significant discussion in the developer community as it aims to bridge the gap between local data processing and distributed computing needs.
What is Smallpond?
Smallpond is a lightweight data processing framework built on top of DuckDB and 3FS. It's designed specifically for training pipelines rather than general-purpose data processing. According to community discussions, the framework specializes in providing batches of training data to workers, utilizing Ray for parallelization. Its core strengths lie in supporting random reads (necessary for implementing shuffling across epochs), Arrow support for zero-copy operations with Pandas DataFrames, and efficient checkpointing mechanisms.
Smallpond Key Features
- High-performance data processing powered by DuckDB
- Scalable to handle PB-scale datasets
- Easy operations with no long-running services
- Support for Python 3.8 to 3.12
- Integration with Ray for parallelization
- Specialized for ML training pipelines
GraySort Benchmark Performance
- Data sorted: 110.5TiB
- Time required: 30 minutes and 14 seconds
- Average throughput: 3.66TiB/min
- Infrastructure: 50 compute nodes and 25 storage nodes running 3FS
The 3FS Connection
A crucial component of smallpond's architecture is 3FS, a distributed file system that predates DeepSeek itself. Community members pointed out that 3FS has been around since at least 2019, with references in Chinese tech blogs. This file system appears to be the key to smallpond's ability to handle petabyte-scale datasets. However, some users noted that without 3FS, smallpond's utility might be significantly reduced as network file system performance would become the bottleneck.
I don't think you get any really benefits over duckdb unless your data is 10tb+ or you spin up 3FS (which seem challenging).
The infrastructure requirements for 3FS present another limitation. Community members highlighted that major US cloud providers have limited support for InfiniBand, which appears to be important for 3FS performance. This could restrict smallpond's adoption among companies relying on public cloud infrastructure.
Performance and Benchmarks
Smallpond's performance claims are impressive, with the GraySort benchmark showing the ability to sort 110.5TiB of data in just over 30 minutes using a cluster of 50 compute nodes and 25 storage nodes. This translates to an average throughput of 3.66TiB/min. Interestingly, community analysis of smallpond's code revealed that for the GraySort benchmark, it dispatches to Polars by default to handle the actual sorting rather than using DuckDB directly.
Positioning in the Data Ecosystem
The emergence of smallpond reflects a broader trend in data engineering - the development of specialized query engines for specific workloads. While general-purpose tools like DuckDB, Polars, and managed cloud solutions have existed for years, smallpond appears to target a niche where distributed processing of very large datasets for machine learning is required.
For most users with datasets under 10TB, community sentiment suggests that the benefits of smallpond over existing tools like DuckDB alone may be limited. The real advantages come into play at larger scales where distributed processing becomes necessary.
As data engineering continues to evolve, tools like smallpond represent steps toward more specialized, purpose-built solutions that abstract away some of the complexities of distributed data processing. Whether this represents the beginning of a broader abstraction of backend technologies, as some community members hope, or simply another tool with specific trade-offs, remains to be seen.
Reference: smallpond - A lightweight data processing framework built on DuckDB and 3FS