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Recommend Stuff The Internet Says On Scalability For May 24, 2013 (Email)

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Hey, it's HighScalability time:

 

  • ~20K : Netflix AWS instances; 100 million hours per minute: Youtube video upload;
  • Quotable Quotes:
    • @sw17ch: Computer Science is thinking about thinking. Software Engineering is thinking about how to avoid thinking.
    • @neha: I am starting a distributed systems reading group at MIT. Suggestions on papers to read? Current list here.
    • John Sheehan: Services are the new process
    • @cheeseplus: Sharding isn’t a scalability strategy, it’s a failure mode in progress.
    • @basharatw: @adrianco Features in days, not months; hw in mins not weeks; incident response in secs not hours … there's a trade off for utopia #gluecon
    • @mgroeninger: @johnsheehan now telling a story about struggling against tools... quit to build a better hammer #gluecon < this is the heart of devops
    • @aneel: "you really have to do a reorg to do devops and you really have to do a reorg to do cloud-native" - @adrianco #gluecon
    • @voodoogeek: scalability. why is it so hard to understand? and please please do NOT tell me it was not foreseeable and the usual BS.
    • @joestump: Celery's queue routing key stuff is pretty swanky. If you don't need low latency messaging, highly recommend celery + SQS. 0 maintenance.

  • There are more kinds of programming in heaven and earth than are dreamt of in your data structure books...Cell-Based Computing Goes Analog: designing circuits in Escherichia coli that could perform functions based on a range of inputs, much like the temperature gauge on a thermostat. Specifically, the circuits were sensitive to levels of sugar arabinose or acyl homoserine lactone.

  • Startups are the new intentional communities attempting to do right by hacking human nature and founding a utopia. To see this in action take a look at Why I Spent 200 Hours Writing Culture Code Instead of Python Code. A Walden 3.0?

  • Horst Simon on Why we need Exascale and why we won’t get there by 2020: You could say that the end of the HPC world as we know it began in 2004, when we hit the inflection point of power use and clock speed. That’s when we realized that we could not keep increasing clock speed due to power demands (and heat), but needed to move to much greater parallelism. In “new” HPC, power is the primary design constraint for future HPC system design; data movement dominates costs, so we need to optimize to minimize data movement; to increase concurrency we look to exponential growth of parallelism within chips. This “new” reality fundamentally breaks our current programming paradigm and computing ecosystem.

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