Napkin

Techniques and numbers for estimating system's performance from first-principles - sirupsen/napkin-math

Techniques and numbers for estimating system's performance from first-principles - sirupsen/napkin-math

We're joined by Simon Eskildsen, Principal Engineer at Shopify, talking about how he uses a concept called napkin math where you use first-principle thinking to estimate systems without writing any code. By the end of the show we were estimating pretty much everything using napkin math.

“640K ought to be enough for anybody.” - Supposedly Bill Gates, circa 1981.

The most interesting links I’ve read in December 2020 and January 2021.

Last week, at a conference, I had a random hallway conversation with another engineer. About AI.

When designing a system, it’s important to consider the limitations of the technologies chosen. Making some approximate calculations when the system is designed helps us decide on the tradeoffs of the different approaches. These approximations include: This article has a table with latency comparison numbers, common numbers used in system design calculations, and some challenges to put all of this info into practice.

Some performance myths resist debunking after being whispered among techies in cubicles and server rooms for generations. Let's try anyway.

Techniques and numbers for estimating system's performance from first-principles - sirupsen/napkin-math

Last week, at a conference, I had a random hallway conversation with another engineer. About AI.

Curated collection of books, papers, and articles for learning distributed systems and performance engineering

Latency Numbers Every Programmer Should Know. GitHub Gist: instantly share code, notes, and snippets.

Imagine this: