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Another challenge is ensuring the timely delivery of the messages themselves.
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.
The way this is typically accomplished in a web application is by separating the model and the view: data is persisted in a database (perhaps with a separate in-memory cache), with each short-lived request retrieving only the parts relevant to that request.
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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.Chatliv provides you with an amazing real time and live online Face to Face chatting experience.Unlike other chat sites, you don't have to wait for Stranger to respond.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.
Both subsystems are clustered and partitioned for reliability and efficient failover. In short, because the problem domain fits Erlang like a glove.