Hey, it's HighScalability time:

It's not too late London. There's still time to make this happen. 

• 40%: energy Google saves in datacenters using machine learning; 2.3: times more energy knights in armor spend than when walking; 1000x: energy efficiency of 3D carbon nanotubes over silicon chips; 176,000: searchable documents from the Founding Fathers of the US; 93 petaflops: China’s Sunway TaihuLight; $800m: Azure's quarterly revenue; 500 Terabits per square inch: density when storing a bit with an atom; 2 billion: Uber rides; 46 months: jail time for accessing a database; 


• Quotable Quotes:
  • Lenin: There are decades where nothing happens; and there are weeks where decades happen.
  • Nitsan Wakart: I have it from reliable sources that incorrectly measuring latency can lead to losing ones job, loved ones, will to live and control of bowel movements.
  • Margaret Hamilton~ part of the culture on the Apollo program “was to learn from everyone and everything, including from that which one would least expect.”
  • @DShankar: Basically @elonmusk plans to compete with -all vehicle manufacturers (cars/trucks/buses) -all ridesharing companies -all utility companies
  • @robinpokorny: ‘Number one reason for types is to get idea what the hell is going on.’ @swannodette at #curryon
  • Dan Rayburn: Some have also suggested that the wireless carriers are seeing a ton of traffic because of Pokemon Go, but that’s not the case. Last week, Verizon Wireless said that Pokemon Go makes up less than 1% of its overall network data traffic.
  • @timbaldridge: When people say "the JVM is slow" I wonder to what dynamic, GC'd, runtime JIT'd, fully parallel, VM they are comparing it to.
  • @papa_fire: “Burnout is when long term exhaustion meets diminished interest.”  May be the best definition I’ve seen.
  • Sheena Josselyn: Linking two memories was very easy, but trying to separate memories that were normally linked became very difficult
  • @mstine: if your microservices must be deployed as a complete set in a specific order, please put them back in a monolith and save yourself some pain
  • teaearlgraycold: Some people, when confronted with a problem, think “I know, I'll use regular expressions.” Now they have two problems.
  • Erik Duindam:  I bake minimum viable scalability principles into my app.
  • Hassabis: It [DeepMind] controls about 120 variables in the data centers. The fans and the cooling systems and so on, and windows and other things. They were pretty astounded.
  • @WhatTheFFacts: In 1989, a new blockbuster store was opening in America every 17 hours.
  • praptak: It [SRE] changes the mindset from "Failure? Just log an error, restore some 'good'-ish state and move on to the next cool feature." towards "New cool feature? What possible failures will it cause? How about improving logging and monitoring on our existing code instead?"
  • plusepsilon: I transitioned from using Bayesian models in academia to using machine learning models in industry. One of the core differences in the two paradigms is the "feel" when constructing models. For a Bayesian model, you feel like you're constructing the model from first principles. You set your conditional probabilities and priors and see if it fits the data. I'm sure probabilistic programming languages facilitated that feeling. For machine learning models, it feels like you're starting from the loss function and working back to get the best configuration


• Isn't it time we admit Dark Energy and Dark Matter are simply optimizations in the algorithms running the sim of our universe? Occam's razor. Even the Eldritch engineers of our creation didn't have enough compute power to simulate an entire universe. So they fudged a bit. What's simpler than making 90 percent of matter in our galaxy invisible?


• Do you have one of these? Google has a Head of Applied AI.


• Uber with a great two article series on their stack. Part uno; Part deux: Our business runs on a hybrid cloud model, using a mix of cloud providers and multiple active data centers...We currently use Schemaless (built in-house on top of MySQL), Riak, and Cassandra...We use Redis for both caching and queuing. Twemproxy provides scalability of the caching layer without sacrificing cache hit rate via its consistent hashing algorithm. Celery workers process async workflow operations using those Redis instances...for logging, we use multiple Kafka clusters...This data is also ingested in real time by various services and indexed into an ELK stack for searching and visualizations...We use Docker containers on Mesos to run our microservices with consistent configurations scalably...Aurora for long-running services and cron jobs...Our service-oriented architecture (SOA) makes service discovery and routing crucial to Uber’s success...we’re moving to a pub-sub pattern (publishing updates to subscribers). HTTP/2 and SPDY more easily enable this push model. Several poll-based features within the Uber app will see a tremendous speedup by moving to push....we’re prioritizing long-term reliability over debuggability...Phabricator powers a lot of internal operations, from code review to documentation to process automation...We search through our code on OpenGrok...We built our own internal deployment system to manage builds. Jenkins does continuous integration. We combined Packer, Vagrant, Boto, and Unison to create tools for building, managing, and developing on virtual machines. We use Clusto for inventory management in development. Puppet manages system configuration...We use an in-house documentation site that autobuilds docs from repositories using Sphinx...Most developers run OSX on their laptops, and most of our production instances run Linux with Debian Jessie...At the lower levels, Uber’s engineers primarily write in Python, Node.js, Go, and Java...We rip out and replace older Python code as we break up the original code base into microservices. An asynchronous programming model gives us better throughput. And lots more.

Don't miss all that the Internet has to say on Scalability, click below and become eventually consistent with all scalability knowledge (which means this post has many more items to read so please keep on reading)...