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Scaling a web stack

Scaling a web stack

A talk on scaling a typical web stack, based on lessons learned at 99designs, given to an RMIT Systems Architecture class.

Lars Yencken

April 29, 2013

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  1. App

  2. Cache App App Cache App App App App Memcache Queue

    Worker remove single points of failure DB DB*
  3. Cache App App Cache App App App App Memcache Queue

    Worker Balancer DB add flexibility to the cache layer DB*
  4. • "Cloud" hosting on Amazon Web Services • Instead of

    few, highly-tuned servers, have many disposable servers • Tradeoff that favours flexibility
  5. Costs of instability • Lost customer business (direct & indirect)

    • Support burden and costs • Ops burden and costs
  6. Asynchronous tasks DB App App App App App App DB

    Queue Worker 3rd party services
  7. • Less traffic (experiments by Yahoo, Microsoft, Google; ranking) •

    Worse user experience • Higher hardware costs Costs of slow sites
  8. Cacheing DB Cache App App Cache App App App App

    DB Memcache whole pages & images whole files on disk db queries & page fragments
  9. Difficulties • Norms for browsers and internet connections constantly change

    • Some strategies conflict with each-other • Measure, measure, measure!