DuckDB vs Polars Comparison Summary

1. Introduction

The article discusses the performance of two emerging data tools, DuckDB and Polars. The benchmark tests were designed with moderately-sized datasets to reflect real-world scenarios and straightforward queries for comprehensibility and reproducibility.

2. Test Environment

For the benchmark tests, the 2021 Yellow Taxi Trip dataset with 30 million records was used, totaling about 3GB in storage. The tests were conducted on a 2021 MacBook Pro with an Apple M1 MAX chip, 64GB RAM, 1TB SSD, and a 10-core CPU. DuckDB version 0.10.0 and Polars version 0.20.15 were used, following a clear and understandable benchmarking method by Marc Garcia.

3. Test Methods

The benchmark consisted of various operations like reading CSV files, simple aggregation (sum, average, min, max), group aggregation, window functions, and joins, with source code provided for each query in both DuckDB and Polars.

4. Test Results

Contrary to expectations that DuckDB would outperform in most queries, Polars showed a significant advantage, especially in reading CSV files (three times faster) and window functions (over seven times faster). However, DuckDB was faster in join operations, with about a 1.3 times speed advantage—a critical aspect of data integration and analysis.

5. Test Procedure Guide

A step-by-step procedure is provided for replicating the benchmark tests, including downloading the dataset, setting up the correct file paths, working within a virtual environment, installing dependencies, and running benchmark scripts. Running unit tests is also suggested to verify code correctness.

6. Considerations/Limitations

The article highlights that certain result collection methods like .arrow(), .pl(), .df(), and .fetchall() used in DuckDB's benchmark tests could introduce non-core system factors that may affect the accuracy of the results. Polars, on the other hand, uses the .collect() method for building data frames.

7. Conclusion

The test aimed to be impartial and showed that both DuckDB and Polars have comparable performance, with both demonstrating excellent speed and efficiency. The insights provided can help readers choose the right tool for their projects. The article also recommends a book for Python data analysis and highlights previous discussions on various technology topics.

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