DeepSeek AI unleashes astronomy: a lightweight data processing framework built on DuckDB and 3FS

Modern data workflows are increasingly burdened by the complexity of data set size and distributed processing. Many organizations have found that traditional systems work on longer processing times, memory constraints, and efficiently managing distributed tasks. In this environment, data scientists and engineers often spend too much time doing system maintenance rather than extracting insights from the data. It is obvious that tools are needed to simplify these processes (without sacrificing performance).

DeepSeek AI recently released Smleverpond, a lightweight data processing framework built on DuckDB and 3FS. SmplowPond is designed to extend DuckDB efficient, in-process SQL analysis to distributed settings. By connecting DuckDB to 3F (a high-performance, distributed file system optimized for modern SSD and RDMA networks), it provides a practical solution for handling large data sets without the complexity of long-running services or heavy infrastructure.

Technical details and benefits

Smortpond is designed to work seamlessly with Python, supporting versions 3.8 to 3.12. Its design philosophy is based on simplicity and modularity. Users can quickly install the framework through PIP and start processing data with minimal settings. A key feature is the ability to manually partition data. Whether it is counting by file, line number, or partitioning by specific column hashes, this flexibility allows users to adjust processing based on their specific data and infrastructure.

Under the hood, the antenna takes advantage of DuckDB’s robust, local-level performance in performing SQL queries. The framework is further integrated with Ray to enable parallel processing across distributed compute nodes. This combination not only simplifies scaling, but also ensures that workloads can be handled efficiently on multiple nodes. Furthermore, by avoiding the use of ongoing services, SmplyPond reduces the operating overhead that is often associated with distributed systems.

Install

Supports Python 3.8 to 3.12.

Start quickly

# Download example data
wget 

import smallpond

# Initialize session
sp = smallpond.init()

# Load data
df = sp.read_parquet("prices.parquet")

# Process data
df = df.repartition(3, hash_by="ticker")
df = sp.partial_sql("SELECT ticker, min(price), max(price) FROM {0} GROUP BY ticker", df)

# Save results
df.write_parquet("output/")
# Show results
print(df.to_pandas())

Performances and insights

In performance testing using Graysort benchmarks, Smplypond demonstrated its capability by sorting 110.5TIB in just 30 minutes, reaching an average throughput of 3.66 tib per minute. These results illustrate how the framework can effectively utilize the combined advantages of duck DB and 3F for computing and storage. Such performance metrics provide assurance that the software can meet the needs of tissues related to the data prior to the tobone. The open source nature of the project also means that users and developers can collaborate on further optimization and adapt the framework to various use cases.

in conclusion

Smallpox represents a measured and significant step in distributed data processing. It solves core challenges by extending the reliable efficiency of DuckDB into distributed environments, supporting the high-throughput capabilities of 3FS. Focusing on simplicity, flexibility and performance, Smplowpond provides data scientists and engineers with a practical tool responsible for handling large data sets. As an open source project, it invites community contributions and continuous improvements to make it a valuable addition to the modern data engineering toolkit. Whether managing moderate datasets or scaling to PBABYTE-level operations, Smplypond provides a powerful framework that is both efficient and easy to access.

Check Github repository. All credits for this study are to the researchers on the project. Also, please keep an eye on us twitter And don’t forget to join us 80k+ ml subcolumn count.

Recommended Reading – LG AI Research Unleashes Nexus: An Advanced System Integration Agent AI Systems and Data Compliance Standards to Address Legal Issues in AI Datasets