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We chose to simulate the impact of many real users hitting many machines by means of a "dark launch" period in which Facebook pages would make connections to the chat servers, query for presence information and simulate message sends without a single UI element drawn on the page.

With the "dark launch" bugs fixed, we hope that you enjoy Facebook Chat now that the UI lights have been turned on.

One of the things I like most about working at Facebook is the ability to launch products that are (almost) immediately used by millions of people.

Unlike a three-guys-in-a-garage startup, we don't have the luxury of scaling out infrastructure to keep pace with user growth; when your feature's userbase will go from 0 to 70 million practically overnight, scalability has to be baked in from the start.

There are new adult webcam services popping up almost every day.

With such an influx of sites it becomes hard to tell which ones are legitimate and the model pool becomes somewhat crowded.

For Facebook Chat, we rolled our own subsystem for logging chat messages (in C ) as well as an epoll-driven web server (in Erlang) that holds online users' conversations in-memory and serves the long-polled HTTP requests.

Both subsystems are clustered and partitioned for reliability and efficient failover. In short, because the problem domain fits Erlang like a glove.

Despite those advantages, using Erlang for a component of Facebook Chat had a downside: that component needed to communicate with the other parts of the system.The method we chose to get text from one user to another involves loading an iframe on each Facebook page, and having that iframe's Javascript make an HTTP GET request over a persistent connection that doesn't return until the server has data for the client.The request gets reestablished if it's interrupted or times out.The naive implementation of sending a notification to all friends whenever a user comes online or goes offline has a worst case cost of O(average friendlist size * peak users * churn rate) messages/second, where churn rate is the frequency with which users come online and go offline, in events/second.This is wildly inefficient to the point of being untenable, given that the average number of friends per user is measured in the hundreds, and the number of concurrent users during peak site usage is on the order of several millions.

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