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Python at Massive Scale - Stephen Simmons, Neil Slinger

PyData · 10K views · 2018-05-28 · 44:45

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Overview

A PyData London 2018 talk on how JPMorgan scaled its Athena Python platform over ten years to about 35 million lines of code and thousands of developers. It covers infrastructure, tooling and culture for analytics and visualization at that scale.

On Hacker News

Cited as the definitive public account of Python at massive scale, especially in discussions of monorepos and bank Python.

Recommended by 1 HN people across 1 months · HN mentions 2018–2022

Topics

Python, Ruby & scripting languagesSoftware practice, open source & careers
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jpmorganathenamonorepopythontrading systemsdeveloper tooling

What people said

stevesimmons · 2020-07 · Recommendation

As the comment by timsworkaccount says, this is definitely Athena at JPMorgan. For those interested, I gave a talk on Athena at PyData UK 2018 called "Python at Massive Scale". 4500 developers making 20k commits a week. Codebase with 35m LOC. The video is here: [VIDEO] It covers Athena's origins, what it is used for, application architecture, infrastructure, dev tooling and culture.

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Barrin92 · 2022-05 · Reference

>Python shines beautifully as a (somewhat) cross platform scripting language Python is much more than just a scripting language. I remember attending this talk[1] a few years about JPMorgan's 35 million LOC Python codebase. Python is being used to built seriously large software nowadays and I don't think performance is ever a minor issue. It should always be in the top 3 for any general purpose language because it directly translates into development speed, time and money. [1] [VIDEO]

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stevesimmons · 2022-01 · Reference

See [1] for a PyData London talk I did on "Python at Massive Scale", about JPMorgan's Athena trading and risk management system, used for FX, Commodities, Credit, Equities, etc trading. At that time, Athena had 4,500 Python developers doing 20,000 commits a week. Also see one recent HN discussion on "An Oral History of Bank Python" [2]. [1] [VIDEO] [2] [LINK]

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Sources

Video by PyData, played through YouTube.

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