I'll start by saying I dig Redis. I wish we had a storage engine that offered some of the kinds of flexibility that Redis does. If I were making Volt from scratch I would embed Redis or something with flexible schema and secondary indexes and spend less time working on SQL and more time working on distributed functionality.

>These benchmarks don't mean much to me -- what matters is performance for your application.

Very true. Think about how you would implement an application like Voter in Redis. Voter is an event counting app that applies business logic server side to do what is basically fraud detection.

Let's take a look at what each of these transactions is.
https://github.com/VoltDB/voltdb/blob/master/examples/voter/...
3 statements and an entire round trip to validate data is kosher.
1 insert/update statement that is actually updating two different summary views

You would need to implement your own locking to get the isolation necessary to implement the business logic and there would be at least two round trips for each vote. You would also have to write your own code to maintain the materialized view used to print the summary. Sure you could implement the validation logic client side, but then you lose the ability to transactionally update said logic and the state it depends on.

With the new scripting functionality in Redis you would be able to do it in one round trip but you would still end up writing your own code for the view and rollback. How you will attach a debugger to debug your server side logic is also worth considering. I mistakenly wrote that Redis doesn't cache Lua scripts, but it actually does.

Now that you have server side logic you will also probably want to start maintaining replicated state to make some of your transactions single shard transactions. With Redis you are on your own for updating replicated state in a way that stays consistent across the cluster even when restoring different shards.

Also, no matter how fast Redis is you may reach a point where you have run out of disk/network IO, or even CPU. Now you are looking at sharding Redis. You are probably going to want to maintain replicated state to make more of your transactions single shard transactions. With Redis you are on your own for updating replicated state in a way that stays consistent across the cluster even when restoring different shards.

Don't forget re-sharding and backups which are now your problem. You definitely want to be able to roll back to before that last application update that corrupted your data. Don't forget that you will need to bring the replicas up in the correct state as well. Preferably in parallel with bringing up the master since you are down right now, and it would be nice if it resharded at the same time since you took this opportunity to add capacity. Redis cluster is coming, but it isn't here today and depending on the application you may not want to risk it not being ready when the time comes.

You should at least double the number of ops (where an op is roundtrip to the DB) done by Volt if not more. That said, doing half as much work inside the procedure doesn't make it twice as fast and doing twice as much doesn't make it twice as slow. The dominant cost for such a trivial operation is not the operation itself. Redis is much better at this because it is single threaded and written in C. My bet is that the number of instructions and cache misses to accept and execute a command in Redis is much smaller because the execution engine isn't dealing with stored procedures, schema, undo logging, SQL, and code expecting to deal with distributed transactions even if none are in play.

>I'm not sure if I am supposed to be impressed or not.

If you haven't tl;dr-ed already I'd call it good.