DeepSeek's smallpond extends DuckDB, the popular in-process analytical database, with distributed computing capabilities. It leverages a shared-nothing architecture where each node holds a portion of the data, allowing for parallel processing of queries across a cluster. Smallpond introduces a distributed query planner that optimizes query execution by distributing tasks and aggregating results efficiently. This empowers DuckDB to handle larger-than-memory datasets and significantly improves performance for complex analytical workloads. The project aims to make distributed computing accessible within the familiar DuckDB environment, retaining its ease of use and performance characteristics for larger-scale data analysis.
Smallpond is a lightweight Python framework designed for efficient data processing using DuckDB and the Apache Arrow-based filesystem 3FS. It simplifies common data tasks like loading, transforming, and analyzing datasets by leveraging the performance of DuckDB for querying and the flexibility of 3FS for storage. Smallpond aims to provide a convenient and scalable solution for working with various data formats, including Parquet, CSV, and JSON, while abstracting away the complexities of data management and enabling users to focus on their analysis. It offers a Pandas-like API for familiarity and ease of use, promoting a more streamlined workflow for data scientists and engineers.
Hacker News commenters generally expressed interest in Smallpond, praising its simplicity and the potential combination of DuckDB and fsspec. Several noted the clever use of these existing tools to create a lightweight yet powerful framework. Some questioned the long-term viability of relying solely on DuckDB for complex ETL pipelines, citing performance limitations for very large datasets or specific transformation tasks. Others discussed the benefits of using Polars or DataFusion as alternative processing engines. A few commenters also suggested potential improvements, like adding support for streaming data ingestion and more sophisticated data validation features. Overall, the sentiment was positive, with many seeing Smallpond as a useful tool for certain data processing scenarios.
The blog post details how Definite integrated concurrent read/write functionality into DuckDB using Apache Arrow Flight. Previously, DuckDB only supported single-writer, multi-reader access. By leveraging Flight's DoPut and DoGet streams, they enabled multiple clients to simultaneously read and write to a DuckDB database. This involved creating a custom Flight server within DuckDB, utilizing transactions to manage concurrency and ensure data consistency. The post highlights performance improvements achieved through this integration, particularly for analytical workloads involving large datasets, and positions it as a key advancement for interactive data analysis and real-time applications. They open-sourced this integration, making concurrent DuckDB access available to a wider audience.
Hacker News users discussed DuckDB's new concurrent read/write feature via Arrow Flight. Several praised the project's rapid progress and innovative approach. Some questioned the performance implications of using Flight for this purpose, particularly regarding overhead. Others expressed interest in specific use cases, such as combining DuckDB with other data tools and querying across distributed datasets. The potential for improved performance with columnar data compared to row-based systems was also highlighted. A few users sought clarification on technical aspects, like the level of concurrency achieved and how it compares to other databases.
Summary of Comments ( 11 )
https://news.ycombinator.com/item?id=43248947
Hacker News commenters generally expressed excitement about the potential of combining DeepSeek's distributed computing capabilities with DuckDB's analytical power. Some questioned the performance implications and overhead of such a distributed setup, particularly concerning query planning and data transfer. Others raised concerns about the choice of Raft consensus, suggesting alternative distributed consensus algorithms might be more performant. Several users highlighted the value proposition for data lakes, allowing direct querying without complex ETL pipelines. The discussion also touched on the competitive landscape, comparing the approach to existing solutions like Presto and Spark, with some speculating on potential acquisition scenarios. A few commenters shared their positive experiences with DuckDB's speed and ease of use, further reinforcing the appeal of this integration. Finally, there was curiosity around the specifics of DeepSeek's technology and its impact on DuckDB's licensing.
The Hacker News post "DeepSeek's smallpond: Bringing Distributed Computing to DuckDB" (linking to an article about Deepseek's distributed implementation of DuckDB called smallpond) generated several interesting comments.
Several commenters discussed the performance implications and trade-offs of smallpond compared to existing distributed query engines like Spark and ClickHouse. One commenter pointed out that while smallpond might offer advantages in specific use cases, Spark's maturity and broader ecosystem make it a compelling choice for many users. Another commenter questioned whether smallpond's performance claims held up under rigorous benchmarking, highlighting the importance of independent evaluations. This skepticism around performance was echoed by others who suggested real-world testing was needed to validate the claims made in the original article.
The discussion also touched upon the architectural choices made by smallpond. One user asked about the choice of using Raft for consensus, wondering about its performance implications and how it compared to alternatives. This led to further discussion about fault tolerance and data consistency in a distributed setting. Another user inquired about the use of Apache Arrow, expressing interest in how it facilitated data transfer and interoperability within the system. This prompted a response mentioning its role in zero-copy data sharing and its potential benefits for performance.
Some commenters focused on the practical aspects of using smallpond. Questions were raised about the deployment process, particularly around containerization and Kubernetes integration. There was also interest in the project's roadmap and its future development plans. One user inquired about support for window functions, suggesting it as a crucial feature for analytical workloads.
Finally, there was some discussion about the wider implications of bringing distributed computing to DuckDB. One commenter speculated on the potential for smallpond to democratize access to distributed query processing, making it easier for users to leverage the power of distributed computing. Another user noted the increasing interest in combining the strengths of single-node analytical databases like DuckDB with the scalability of distributed systems.
Overall, the comments section reflects a mixture of excitement and cautious optimism. While many users expressed enthusiasm for the potential of smallpond, there was also a healthy dose of skepticism and a desire for more concrete evidence to support the claims made in the original article. The discussion highlighted the importance of performance benchmarking, architectural choices, practical usability, and the broader context of the distributed computing landscape.