RethinkDB 2.1 is out: high availability

IMO picking a KV store makes the most sense if you have insane performance requirements (e.g. millions of ops/second) where you need to squeeze every last drop of performance and the query layer gets in the way.

(Also if you have any questions and don't want to share more info publicly, feel free to shoot me an email -- slava@rethinkdb.com)

If you wanted to stay closer to key/value with nice clustering and scaling options, I might suggest cassandra. To be honest, that would be overkill as a relatively small RethinkDB cluster would likely handle your load for several years without issue.

Unless you are a business where $5k/node looks reasonable to pay for peace of mind, you are probably fine with the Community Edition. A lot of the customers paying for Enterprise Edition are moving to Couchbase from Oracle, so they are paying less per node and running fewer nodes than they used to.

A couple of things -- the payload in Raft tends to be much higher (though it could probably be fixed with sufficient engineering effort), and in some scenarios this process would have to happen multiple times during netsplits (which may or may not be ok).

RethinkDB doesn't relax consistency guarantees; we implement them in a different way. Check out http://rethinkdb.com/docs/consistency/ for more details.

I'm a bit busy today, but this is a really interesting question. I'll see if we can do a technical blog post on this and go into all the details in depth.

In the Raft protocol, the leader sends AppendEntries; the follower writes the log entries to persistent storage, but doesn't apply them to the state machine yet; the leader sends another AppendEntries with a higher commit index; and then the follower applies the changes to the state machine. In RethinkDB's case, the "state machine" is the B-tree we store on disk. One of the guarantees we provide is that if the server acknowledges a write to the client, then all future reads should see that write; and we perform reads by querying the B-tree. So we can't acknowledge writes until they've been actually written to the B-tree, and we can't start writing to the B-tree until after the write has been committed via Raft. So that's where the latency would come from if we were using Raft to manage individual documents. We considered a couple of ways to work around this. One would be to make reads check both the B-tree and the Raft log; but that makes the read logic much more complicated. Another would be to start writing to the B-tree as soon as we put the write in the Raft log. The problem is that Raft expects to be able to roll back parts of its log at any time, and our storage engine's MVCC capabilities aren't good enough for that. The hybrid approach allows us to have good latency without major rewrites of the existing storage engine.

The other reason is that RethinkDB allows tables to be split into shards, and the number of shards can be changed while the databases is running and accepting queries. We considered having one Raft instance per shard, but we would have needed to modify the Raft algorithm to allow splitting and merging Raft instances while they are running and accepting queries. (This is approximately what CockroachDB is doing [1].) But the Raft algorithm is really tricky to get right even when you're not trying to modify it, and we wanted to stick as closely as possible to the official algorithm to minimize bugs. The approach we ended up going with allows us to have the performance and convenience of live resharding without having to modify the Raft algorithm.

In response to your question about consistency guarantees: RethinkDB gives users several options for trading off consistency and latency. The default is to acknowledge writes only once they're safely on disk on a majority of replicas, but to perform reads only on the primary replica (leader). This doesn't give perfect consistency; if the leader fails over, reads that hit the database around the time of the failover might see outdated data, or they might read writes that were rolled back as part of the failover. Unfortunately, the only way to get stronger consistency guarantees is to wait for a majority of replicas to acknowledge the read, which makes performance much worse. We offer a safe-but-slow mode for reads, but it's not the default because the performance is so bad. We also offer fast-but-unsafe modes for writes, for users that want better latency and are OK with losing the last few writes in the event of a failover. See the documentation [2] for more information.

[1] http://www.cockroachlabs.com/blog/scaling-raft/

[2] http://rethinkdb.com/docs/consistency/

However you're right that we don't pass stricter guarantees in fast-but-unsafe mode (but I'm not aware of any products in the space that do).