"Hints for Computer System Design" is amazing, in part because it was written in '83! The paper is a survey of learnings from Butler Lampson's design of many systems, on interface design, premature optimization, and many more. https://s3.amazonaws.com/systemsandpapers/papers/acrobat-17.pdf …
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It also features this lethal quote from C Hoare, "Algol 60 was not only an improvement on its predecessors, but also on nearly all its successors." Shade thrown.
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"Big Ball of Mud" is a reaction against papers celebrating grandiose design patterns, arguing that the defining pattern for most applications is THE BIG BALL OF MUD. Why is this pattern so popular, if we all agree it's a tire fire? https://s3.amazonaws.com/systemsandpapers/papers/bigballofmud.pdf …
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"The Google File System" looks at GFS, which was potentially *the* defining infrastructure abstraction that underpinned Google's systems until the development of Colossus. (Colossus is really cool to read about too!) https://s3.amazonaws.com/systemsandpapers/papers/gfs.pdf …
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"On Designing and Deploying Internet-Scale Services" is James Hamilton dropping a huge amount of wisdom around how to run large-scale systems, even more impressive for being written in '07 and I still learn more each time I read it. https://s3.amazonaws.com/systemsandpapers/papers/hamilton.pdf …
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I mean, how many infrastructure teams in 2018 are tracking their "system-to-administrator ratio" as Hamilton discusses? Do you have a target ratio that you're trying to hit? (Me? Oh, yeah, of course I do... eek.)
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"Harvest, Yield and Scalable Tolerant Systems" is a nuanced look at CAP, and how the vocabulary how breaks down in discussing how systems degrade. h/t to
@kiranb who introduced me to this one! https://s3.amazonaws.com/systemsandpapers/papers/FOX_Brewer_99-Harvest_Yield_and_Scalable_Tolerant_Systems.pdf …2 replies 0 retweets 7 likesShow this thread -
Ok, and there are like twenty other papers as well! What are some of your favorite papers?
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Replying to @Lethain
A couple of nice surveys of ML sub-topics: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.297.3086 … https://arxiv.org/pdf/1710.06068.pdf … A neat security application of probabilistic data structures: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/popularityISeverything.pdf … ML eng best practices: http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf …
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Replying to @avibryant
Thank you! These are fantastic, in particular the ML survey articles are really helpful for me.
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I mean, they’re very *narrow* surveys but I happen to be interested in those sub-topics. That last link, OTOH, is full of super-practical broadly applicable advice.
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