Raft implementations can choose to implement reads in various ways. In LogCabin reads are linearizable, meaning that the results are current as of sometime after the read request was initiated, and it doesn't rely on bounded clock drift to make this guarantee. That's about the best you can do in terms of freshness in a distributed system. Other implementations might offer weaker read semantics or give the client the option. Check out 6.3-6.4 in my dissertation for a ton more detail: https://github.com/ongardie/dissertation#readme
I'm still misunderstanding what happens when a network partition creates a two leader scenario. It is in a slide here http://thesecretlivesofdata.com/raft/ under "log replication" after creating a network partition that separates CDE from AB. Doesn't that mean a client of node B could read stale data? Or maybe an implementation like etcd would let the client choose to read or not in that case?
If the network is split into AB and CDE, then only CDE will be able to reach a quorum. If a client is on the AB side of that split, no consensus algorithm could guarantee freshness on a read. LogCabin clients will continually retry until they can talk to a functioning leader (and clients can put a timeout on that) so that you get a guaranteed fresh read every time; other systems could choose to return a stale result right away instead. It's up to the implementation, but I strongly recommend people not do stale reads by default.
