Well there are many possible responses to this, and you can't really know what's actually going on at Reddit specifically without being part of Reddit's tech team. Speaking broadly and generally, one main issue is that a typical app consists of app servers which talk to a database. Scaling up the app servers is often easy, because they're (ideally) stateless and interchangeable, and you can just add capacity by adding more of them. But as your traffic increases, all those app servers will eventually get bottlenecked on the shared database, and scaling up the database is harder. You can't just add more independent databases because the data needs to be distributed and replicated across them. This can be done, but there are a lot of theoretical and practical issues with it. For starters, the CAP theorem says there's a fundamental tradeoff between how consistent your replicas are with each other, and how resilient the whole system is to failures. In practice, there are a lot of ways for issues with one machine to cause temporary downtime -- maybe not for the entire site, but at least for some small fraction of users. If you have 1000 sharded database servers storing user profile data, then whenever one of them crashes and restarts, the website might seem to be down for 0.1% of users, even though it was never completely "down" for everyone. This is why on status pages, you often see messages like "elevated API error rates" rather than "everything's broken". It's not trivial to measure or even define what "downtime" means in this kind of scenario. In more specific terms, all servers are failure-prone, so the more replicas you have, the more frequently there's something broken somewhere. Servers are not necessarily independent, because they're all communicating; you can get effects like the "thundering herd problem" where a failure in one place cascades to others, and you don't end up having as much redundancy as you thought you did. And of course, there's always the possibility of human error (e.g. bugs and configuration issues) that takes down all the servers at once, no matter how many redundant servers you have. You can't completely prevent mistakes by just throwing money at them. Check out Google's online "Site Reliability Engineering" book as a starting point to read more about what goes into making big distributed software systems reliable, and what kinds of things can go wrong. More on reddit.com

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I love that the post is 1 minute old and it already has 5 upvotes. We all unite in pain More on reddit.com

Nope, up and running for me chief. More on reddit.com