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#eventualconsistency#concurrency#caching
Why Computers Can't Count Sometimes
Tom Scott
6.7M subscribers • 28 videos on Hallway
- Recommended by 2 people: 1 on Lobsters and 1 in engineering communities.
- Presented by Tom Scott
Beginner. No background in the topic needed.
4.8M views7 years ago2 recommendations
Distributed systemsSystems programmingReplication and consistencyConcurrency and parallelismScaling and large-scale architecture
Tom Scott explains how concurrency, caching, and eventual consistency make accurate counting difficult in distributed systems.
Sometimes, numbers on sites like YouTube and Twitter jump up and down; subscriber counts lag, like-counts bounce all over the place. Why is it so hard for computers to count? To answer that, we need to talk about threading, eventual consistency, and caching. Thanks to my proofreading team, and to Tomek on camera! The Cambridge Centre for Computing History: computinghistory.org.uk/ 🟥 MORE FROM TOM: tomscott.com/ (you can find contact details and social links there too) ❓ LATERAL, free weekly podcast: lateralcast.com/ youtube.com/lateralcast/ 👥 THE TECHNICAL DIFFICULTIES: youtube.com/techdifWhy it's here
2 people recommend thisWhat people say. Recommended as an exceptionally clear explanation of eventual consistency and concurrency issues in distributed systems.