I just read this blog: https://mrotaru.wordpress.com/2013/10/10/scaling-to-12-million-concurrent-connections-how-migratorydata-did-it/ <https://mrotaru.wordpress.com/2013/10/10/scaling-to-12-million-concurrent-connections-how-migratorydata-did-it/> about scaling to 12 Million Concurrent Connections on a single server and it got me thinking.
Would it be possible to scale Dovecot IMAP server to 10 Million IMAP sessions on a single server? I think the current implementation of having a separate process manage each active IMAP session (w/ the possibility of moving idling sessions to a single hibernate process) will never be able to deploy a single server managing 10 Million IMAP sessions. But, would it be possible to implement a new IMAP server plugin that uses a fixed configurable pool of “worker” processes, much like NGINX or PHP-FPM does. These servers can probably scale to 10 Million TCP connections, if the server is carefully tuned and has enough cores/memory to support that many active sessions. I’m thinking that the new IMAP server could use some external database (e.g., Redis or Memcached) to save all the sessions state and have the “worker” processes poll the TCP sockets for new IMAP commands to process (fetching the session state from the external database when it has a command that is waiting on a response). The Dovecot IMAP proxies could even queue incoming commands to proxy many incoming requests to a smaller number of backend connections (like ProxySQL does for MySQL requests). That might allow each Dovecot proxy to support 10 Million IMAP sessions and a single backend could support multiple front end Dovecot proxies (to scale to 100 Million concurrent IMAP connections using 10 proxies for 100 Million connections and 1 backend server for 10 Million connections). Of course, the backend server may need to be beefy and have very fast NVMe SSDs for local storage, but changing the IMAP server to manage a pool of workers instead of requiring a process per active session, would allow bigger scale up and could save large sites a lot of money. Is this a good idea? Or, am I missing something? Kevin
