Building Software Systems At Google and Lessons Learned

In addition to Linus's git talk, I really enjoyed Jeff Dean's EE380 retrospective on Building Systems at Google ( [VIDEO] ). Many people have mentioned Jeff's basic premise elsewhere ("Design a system for 10x your current need, but not 100x, rewrite it before then") but this talk gave several useful examples where tipping points occurred (at least with Search).

Also recommend Jeff Dean's tech talk Building Software Systems At Google and Lessons Learned which references those latency numbers. Slides: [LINK] Video: [VIDEO] Also, a previous thread on latency numbers: [LINK]

There is a lecture from Jeff Dean about it: [VIDEO] It's from 2010, but the fundamentals probably haven't changed.

Probably this one: [LINK] Or this one: [VIDEO] (terrible audio) Both are very old, but extremely well aged. IMO, the secret behind Google Search is not the smartness of the algorithms, but how much of it is baked in those O(100ms) which takes Google machines to answer your query. That's why the links above are the true reason Google Search performs well. Natural Language Processing and Information Retrieval state of the art is far beyond what Google/Bing/Yahoo/Yandex/Baidu employ. But, it's far too expensive to serve it at QPS & latency required for decent UX.

Google is famous for publishing a lot of material every year. A couple of related publications you might be interested in: 1. The Datacenter as a computer: [LINK] 2. The SRE book: [LINK] A couple of dated presentations: 1. Jeff Dean on building software systems at Google (2010) at Stanford: [VIDEO] 2. Notes by James Hamilton on a talk by Jeff Dean on Google Datacenters in 2008: [LINK] -- Microsoft Research pretty much work on bleeding edge tech, as well and publish about some of their research pretty regularly. Here's a staring point to help anyone interested find relevant publications: [LINK]