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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.

Each Facebook Chat user now needs to be notified whenever one of his/her friends (a) takes an action such as sending a chat message or loads a Facebook page (if tracking idleness via a last-active timestamp) or (b) transitions between idleness states (if representing idleness as a state machine with states like "idle-for-1-minute", "idle-for-2-minutes", "idle-for-5-minutes", "idle-for-10-minutes", etc.).

Note that approach (a) changes the sending a chat message / loading a Facebook page from a one-to-one communication into a multicast to all online friends, while approach (b) ensures that users who are neither chatting nor browsing Facebook are nonetheless generating server load.

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.

The project I'm currently working on, Facebook Chat, offered a nice set of software engineering challenges: The most resource-intensive operation performed in a chat system is not sending messages.

Another challenge is ensuring the timely delivery of the messages themselves.

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.While this architecture works pretty well in general, it isn't as successful in a chat application due to the high volume of long-lived requests, the non-relational nature of the data involved, and the statefulness of each request.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.Because the data is persisted, a failed read request can be re-attempted.Cache misses and database failure can be detected by the non-database layers and either reported to the user or worked around using replication.The results of inevitable programming bugs, hardware failures, et al., should be hidden from the user as much as possible and isolated from the rest of the system.

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