Build Your Python Data Processing Your Way And Run It Anywhere With Fugue

Feb 21, 2022

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Summary

Python has grown to be one of the top languages used for all aspects of data, from collection and cleaning, to analysis and machine learning. Along with that growth has come an explosion of tools and engines that help power these workflows, which introduces a great deal of complexity when scaling from single machines and exploratory development to massively parallel distributed computation. In answer to that challenge the Fugue project offers an interface to automatically translate across Pandas, Spark, and Dask execution environments without having to modify your logic. In this episode core contributor Kevin Kho explains how the slight differences in the underlying engines can lead to big problems, how Fugue works to hide those differences from the developer, and how you can start using it in your own work today.

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Your host is Tobias Macey and today I’m interviewing Kevin Kho about Fugue, a library that offers a unified interface for distributed computing that lets users execute Python, pandas, and SQL code on Spark and Dask without rewrites

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what Fugue is and the story behind it?
What are the core goals of the Fugue project?
Who are the target users for Fugue and how does that influence the feature priorities and API design?
How does Fugue compare to projects such as Modin, etc. for abstracting over the execution engine?
What are some of the sharp edges that contribute to the engineering effort required to migrate from a single machine to Spark or Dask?
What are some of the determining factors that will influence the decision of whether to use Pandas, Spark, or Dask?
Can you describe how Fugue is implemented?
- How have the design and goals of the project changed or evolved since you started working on it?
How do you ensure the consistency of logic across execution engines?
Can you describe the workflow of integrating Fugue into an existing or greenfield project?
How have you approached the work of automating logic optimization across execution contexts?
- What are some of the risks or error conditions that you have to guard against?
- How do you manage validation of those optimizations, particularly as the different engines release new versions or capabilities?
What are the most interesting, innovative, or unexpected ways that you have seen Fugue used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Fugue?
When is Fugue the wrong choice?
What do you have planned for the future of Fugue?

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Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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