Dias: Dynamic Rewriting of Pandas Code
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866913384365031424 |
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| author | Baziotis, Stefanos Kang, Daniel Mendis, Charith |
| author_facet | Baziotis, Stefanos Kang, Daniel Mendis, Charith |
| contents | In recent years, dataframe libraries, such as pandas have exploded in popularity. Due to their flexibility, they are increasingly used in ad-hoc exploratory data analysis (EDA) workloads. These workloads are diverse, including custom functions which can span libraries or be written in pure Python. The majority of systems available to accelerate EDA workloads focus on bulk-parallel workloads, which contain vastly different computational patterns, typically within a single library. As a result, they can introduce excessive overheads for ad-hoc EDA workloads due to their expensive optimization techniques. Instead, we identify program rewriting as a lightweight technique which can offer substantial speedups while also avoiding slowdowns. We implemented our techniques in Dias, which rewrites notebook cells to be more efficient for ad-hoc EDA workloads. We develop techniques for efficient rewrites in Dias, including dynamic checking of preconditions under which rewrites are correct and just-in-time rewrites for notebook environments. We show that Dias can rewrite individual cells to be 57$\times$ faster compared to pandas and 1909$\times$ faster compared to optimized systems such as modin. Furthermore, Dias can accelerate whole notebooks by up to 3.6$\times$ compared to pandas and 26.4$\times$ compared to modin. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2303_16146 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dias: Dynamic Rewriting of Pandas Code Baziotis, Stefanos Kang, Daniel Mendis, Charith Databases Programming Languages In recent years, dataframe libraries, such as pandas have exploded in popularity. Due to their flexibility, they are increasingly used in ad-hoc exploratory data analysis (EDA) workloads. These workloads are diverse, including custom functions which can span libraries or be written in pure Python. The majority of systems available to accelerate EDA workloads focus on bulk-parallel workloads, which contain vastly different computational patterns, typically within a single library. As a result, they can introduce excessive overheads for ad-hoc EDA workloads due to their expensive optimization techniques. Instead, we identify program rewriting as a lightweight technique which can offer substantial speedups while also avoiding slowdowns. We implemented our techniques in Dias, which rewrites notebook cells to be more efficient for ad-hoc EDA workloads. We develop techniques for efficient rewrites in Dias, including dynamic checking of preconditions under which rewrites are correct and just-in-time rewrites for notebook environments. We show that Dias can rewrite individual cells to be 57$\times$ faster compared to pandas and 1909$\times$ faster compared to optimized systems such as modin. Furthermore, Dias can accelerate whole notebooks by up to 3.6$\times$ compared to pandas and 26.4$\times$ compared to modin. |
| title | Dias: Dynamic Rewriting of Pandas Code |
| topic | Databases Programming Languages |
| url | https://arxiv.org/abs/2303.16146 |