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Title: Flexible rule-based decomposition and metadata independence in modin: a parallel dataframe system
Dataframes have become universally popular as a means to represent data in various stages of structure, and manipulate it using a rich set of operators---thereby becoming an essential tool in the data scientists' toolbox. However, dataframe systems, such as pandas, scale poorly---and are non-interactive on moderate to large datasets. We discuss our experiences developing Modin, our first cut at a parallel dataframe system, which already has users across several industries and over 1M downloads. Modin translates pandas functions into a core set of operators that are individually parallelized via columnar, row-wise, or cell-wise decomposition rules that we formalize in this paper. We also introduce metadata independence to allow metadata---such as order and type---to be decoupled from the physical representation and maintained lazily. Using rule-based decomposition and metadata independence, along with careful engineering, Modin is able to support pandas operations across both rows and columns on very large dataframes---unlike Koalas and Dask DataFrames that either break down or are unable to support such operations, while also being much faster than pandas.
Authors:
; ; ; ; ; ;
Award ID(s):
1940757
Publication Date:
NSF-PAR ID:
10324483
Journal Name:
Proceedings of the VLDB Endowment
Volume:
15
Issue:
3
Page Range or eLocation-ID:
739 to 751
ISSN:
2150-8097
Sponsoring Org:
National Science Foundation
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