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Scaling Facebook To 140 Million Users
1sockchuck writes "Facebook now has 140 million users, and in recent weeks has been adding 600,000 new users a day. To keep pace with that growth, the Facebook engineering team has been tweaking its use of memcached, and says it can now handle 200,000 UDP requests per second. Facebook has detailed its refinements to memcached, which it hopes will be included in the official memcached repository. For now, their changes have been released to github."
Facebook would do well to proactively encourage users to prevent such attacks by securing their systems. For example, by installing this simple application, you can ensure that your computer will never fall victim to malware:
http://not-malware.i-promise.org/magic-bullet.htm
Just enable scripts and click OK whenever it tells you to. It's that easy.
Now, if /. allowed me to post the (fake) link above, how are they any more at fault than facebook is for allowing potentially dodgy links to be shared via their service? They even went the extra step of helping users remove the malware from their PCs. I'd imagine that most conduits for malicious links (IM, social networking, e-mail, online forums, etc) wouldn't have even gone that far. Their users were being targeted and exploited, so they helped them avoid being taken advantage of - Good on 'em.
Were I malicious, I could grab the e-mail address you share in your title line, look through your /. 'friends' list for other accounts with posted addresses, and e-mail you a malicious link "From" one of them. How would that be different?
He's getting rather old, but he's a good mouse.
Amazon and Google faced similar problems, and dealt with them in ways that are roughly equivalent - by adding a tuple store to their system.
If the data behind your web site is mostly accessed via one primary key, a tuple store, something that stores name/value pairs, beats a general-purpose relational database. Both Amazon and Google have such a mechanism in their "cloud" systems. Facebook has a somewhat low-rent solution; they're front-ending MySQL with a tuple store cache. This only works if all the queries contain some ID that has to match exactly, like user ID. Effectively, instead of one big database, the problem consists of a large number of tiny databases, all somewhat independent. Problems like that can be scaled up without much trouble.
Tuple stores distribute nicely - you can spread them over as many machines as you want, just by cutting up the keyspace into conveniently sized shards. There are distributed relational DBMS systems, but they have to be able to do inter-machine joins, which is a hard problem. (That's what you pay the big bucks to Oracle for.)