Four short links: 26 May 2017

Service Availability, Data Share, Eventual Consistency Explained, and Reproducible Deep Learning

By Nat Torkington
May 26, 2017
  1. The Calculus of Service AvailabilityA service cannot be more available than the intersection of all its critical dependencies. If your service aims to offer 99.99% availability, then all of your critical dependencies must be significantly more than 99.99% available. Internally at Google, we use the following rule of thumb: critical dependencies must offer one additional 9 relative to your service—in the example case, 99.999% availability—because any service will have several critical dependencies, as well as its own idiosyncratic problems. This is called the “rule of the extra 9.”
  2. datproject — open source crypto—guaranteed distributed data share, designed for versioned data sets.
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  4. How Your Data is Stored — eventual consistency VERY LUCIDLY explained. It follows the original (entertaining) paper by Leslie Lamport but spells everything out clearly for non-computer-scientists.
  5. OpenAI Baselines — open source implementations of the interesting published algorithms in deep learning. The papers often gloss over some of the details, so a full and working implementation truly lets others build on research. It’s like the reproducibility project for deep learning.
Post topics: Four Short Links