Erlang The Movie II: The Sequel

And if you haven't seen it, here is some classic. “Erlang: the Movie”: — [LINK] And sequel “Erlang: the Movie II”: — [VIDEO] Don't think wtf. Just watch.

I don't care about downvotes, I thought that video was hilarious and enjoy sharing it. He's the same guy behind "MongoDB is web scale" and "Erlang the Movie II", which I'd also highly recommend: [LINK] [VIDEO]

I use Erlang the Movie and its Sequel [VIDEO] whenever I teach my classes at Oxford Uni. #studentlife :-)

And, after you've watched the original, check out "Erlang The Movie II: The Sequel", by Garrett Smith (aka gar1t): [VIDEO] (You are likely familiar with one of gar1t's earlier videos, the classic "Mongo DB is Web Scale": [LINK] And he's got more on his YouTube channel: [LINK] )

The sequel is shorter and more fun, “all erlang needs is an image upgrade.. we need something fresh, something edgy” [VIDEO]

It's funny you mention that, I came here just to post another wonderful remix, "Erlang The Movie II: The Sequel". [VIDEO]

Not at all. Have you ever thought about caching Twitter? It's an archetypical example of a site that's extremely difficult to cache well, which is the source of so many of its scaling problems. Every person's new feed is uniquely generated based on their followers. That means you can only at best cache at the individual tweet level -- you still need to uniquely generate a news feed for each user. This means that for every user you need to individually grab information simultaneously from the same high velocity tweets, while still being able to present information to each user uniquely based on conditions regarding how twitter should display tweets for that specific user individually. That's incredibly demanding data wise because it makes horizontal distribution of load much more difficult --- a huge number of people are all making demands on the same set of data resources, and you can't easily cache that data because of how real time and quickly updating it is. In comparison WhatsApp on Erlang is basically just an example of extremely dramatic real time horizontal scaling. Hosting hundreds of millions of simultaneous chats is relatively trivial in Erlang, given each one is operating under its own separately scalable chat process. Since very few chats share data with one another, horizontal scaling is easy because you don't have the issue of the same resource getting called simultaneously by millions of unique users at the same time, who all need that resource represented back

> (which doesn't bother to define what O, T, and P stand for.) It obviously stands for Outlaw Techno Psychobitch ;). [VIDEO] (Erlang The Movie II)