I agree that machines go down, but there are sane (and safe!) ways to build this sort of thing without adding in cassandra and Redis. Additionally, the max plac

I agree that machines go down, but there are sane (and safe!) ways to build this sort of thing without adding in cassandra and Redis. Additionally, the max placement rate of 333/s is reaaaaally slow! Maybe that's due to the websocket frontends, not the DB, but, that doesn't mean that's the most obvious way to build it. The crux of the problem is that they need to mutate a relatively tiny amount of memory and have a rolling log of events for which only the last 5 minutes needs fast access. Also, if you can put all your state on one machine its far less likely that the one machine will die, than it is that at least one will die in a cluster of machines. Given the nature of the problem keeping all state on one machine seems pretty rational to me, so long as you have the ability to switch to a hot spare within a few minutes or so. If I were to architect this for speed I would have two tiers: a websocket tier, and secondly a 'database' tier. The database would be a custom program that would: 0. Provide a simple Websocket API that would receive a write request and return either success if the user's write timer allowed it to write or failure if it didn't. This would also broadcast the state + deltas. 1. Keep the image in memory as a bitmap 2. Use rocksdb for tracking last user writes to enforce the 5m constraint. You could use an in memory map, but the nice thing about rocksdb is that it shouldn't blow up your heap. 3. Periodically flush the bitmap out to disk to timestamped files for snapshots 4. Keep the hashmap size small by evicting any keys past their time limit 5. Write rotating log files rotated every 5m or so to record the history of events for DR and also later analysis Backing this sort of thing up is very simple. You just replicate the files using rsync or something like it. You may have some corruption on files that are partially written, but since we're opening and closing new files often you can choose how much data-loss you want to tolerate. Restoration is as simple as re-reading the bitmap and reading the log files in reverse up to 5 minutes ago to see who still isn't allowed to write yet (thus reconstituting the hashmap). Let's remember, redis replication is async, so this has the same tradeoffs.