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Building Software Systems At Google and Lessons Learned

Name: Building Software Systems At Google and Lessons Learned
Description: Jeff Dean of Google describes his experiences building Google's systems and how its infrastructure evolved over time. The Stanford talk covers search, scaling tipping points and lessons from building large-scale software.

Stanford Lectures intermediate

Open on YouTube First HN thread

4 HN users recommended this lecture in 4 different months between 2012 and 2022.

2007–2026

1 strong recommendation 3 recommendations 1 citation HackerTube score 4.5

HN recommends it as a classic look at systems engineering at Google, whose fundamentals have held up well despite its 2010 date.

Jeff Dean of Google describes his experiences building Google's systems and how its infrastructure evolved over time. The Stanford talk covers search, scaling tipping points and lessons from building large-scale software.

Pull quote: “It's from 2010, but the fundamentals probably haven't changed.” (HN, 2020)

Also tagged: large-scale systems design, jeff dean, google, distributed systems, web search, scaling

What Hacker News said

5 labeled comments from the HN archive; up to 20 are kept per video, favoring different commenters. Each one links back to the original thread.

Strongly recommends boulos October 2016 under Ask HN: What's your favorite tech talk?

Concrete examples of scaling tipping points in Google Search.

In addition to Linus's git talk, I really enjoyed Jeff Dean's EE380 retrospective on Building Systems at Google ( [this 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).

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Recommends kqr2 December 2012 under Latency Numbers Every Programmer Should Know

Jeff Dean talk referencing the latency numbers and Google systems design.

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

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Recommends ma2rten September 2020 under Web Performance Profiling: Google.com

Jeff Dean lecture whose fundamentals still hold.

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

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Recommends mrazomor August 2022 under We're improving search results when you use quotes

Explains why Google Search's speed comes from systems engineering.

Probably this one: [link] Or this one: [this 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.

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Citations, criticism and jokes

Cites it ignoramous February 2019 under AWS Nitro System

Dated Jeff Dean presentation on building software at Google.

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: [this 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]

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