Engineering Digest 0x6
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💥 The Engineering Digest #6 is here💥
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Did I run out of space cats? Absolutely not! So, after a season break the digest is back at its normal cadence 😃
👉👉👉 Extreme Event Forecasting at Uber with Recurrent Neural Networks. At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time.
https://eng.uber.com/neural-networks/
👉👉👉 Network protocols for anyone who knows a programming language. The network stack does several seemingly-impossible things. It does reliable transmission over our unreliable networks, usually without any detectable hiccups.
https://www.destroyallsoftware.com/compendium/network-protocols
👉👉👉 A pattern language for microservices. The beginnings of a pattern language for microservice architectures.
http://microservices.io/patterns/index.html
👉👉👉 44 engineering management lessons from a co-founder of RethinkDB.
http://www.defmacro.org/2014/10/03/engman.html
👉👉👉 MUST READ — Recovering from Burnout. While that period of my life is a bit of a blur, I remember that moment clearly. It was the moment I knew something was deeply wrong — that I had no motivation left for my work, and that things were out of control.
https://kierantie.com/a/burnout/
👉👉👉 Processing a trillion-edge graph on a single machine. Unless your graph is bigger than Facebook’s, you can process it on a single machine.
https://blog.acolyer.org/2017/05/30/mosaic-processing-a-trillion-edge-graph-on-a-single-machine/
👉👉👉 Online reconstruction of structural information from datacenter logs. Reconstructing sessions from log files using batch processing is one thing, but the TS system in this paper does it in real time, while ingesting an entire datacenter’s worth of log output!
