Event Sourcing: How to combine divergent states? - event-sourcing

Question

Suppose:
The events are A perceived, B perceived or Ping perceived.
A possible sequence of events could be A,A,A,B,Ping.
The states are InA, InB, PingMissing.
The rules are
No Ping in all events -> PingMissing.
A -> InA
B -> InB
(Only Ping events -> InA)
I would like to have one recommended action/ state.
I see three possibilities for the transition function f(s,e)->s:
Create a pseudo event likePingMissing perceived. Hence everything is in one function.
Two separate transition functions and combining the result.
One transition function with two states as a tuple and combining the result.
Any thoughts? Best practices?
Implementation of 2. in F# (language doesn't really matter):
type Event =
| A
| B
| Ping
type State1 =
| InA
| InB
type State2 =
| PingReceived
| PingMissing
type StateCombined =
| InA'
| InB'
| PingMissing'
let f1 s e :State1 =
match s,e with
| _, A -> InA
| _, B -> InB
| _, _ -> s
let f2 s e :State2 =
match s,e with
| _, Ping -> PingReceived
| _, _ -> s
let fCombined events =
let finalState1 = events |> Seq.fold f1 InA
let finalState2 = events |> Seq.fold f2 PingMissing
match finalState1, finalState2 with
| _, PingMissing -> PingMissing'
| InA, _ -> InA'
| InB, _ -> InB'
fCombined [A;A;A;B]
// PingMissing'
fCombined [A;A;A;B;Ping]
// InB'

Answer 1

I would tend to model the unified state as a tuple of the two substates (broadly in this case: "has a ping been received" and "if a ping has been received, was the last perception an A or a B"). A convenience function can then distill that into a recommendation.
This has the advantage of not reusing the sequence of events, so is a bit more compatible with a view of the events as a stream: at the very least this results in not having to refetch the events from an event store or keep the entire sequence of events in memory.
For example, in Scala (and explicitly modeling the situation where no A nor B has been perceived yet):
sealed trait Event
case object A extends Event
case object B extends Event
case object Ping extends Event
sealed trait PingState
case object PingReceived extends Event // Don't strictly need...
case object PingMissing extends Event
sealed trait LastPerceivedState
case object InA extends Event
case object InB extends Event
// ... could just as well be (Option[PingMissing], Option[LastPerceivedState])...
type State = (PingState, Option[LastPerceivedState])
// ... in which case, this is (Some(PingMissing), None)
val InitialState = PingMissing -> None
def distilledState(state: State): Either[PingMissing, Option[LastPerceivedState]] =
state match {
case (PingMissing, _) => Left(PingMissing)
case (_, lpsOpt) => Right(lpsOpt)
}
The transition function could then be written directly (taking advantage of the fact that the events can be partitioned into events which affect PingState or LastPerceivedState but never both):
val transitionF = { (state: State, evt: Event) =>
val (ps, lpsOpt) = state
evt match {
case A => ps -> Some(InA)
case B => ps -> Some(InB)
case Ping => PingReceived -> lpsOpt
}
}
In the event that there are events which affect both, then decomposing into subhandlers might simplify the code (at the expense of some possibly redundant invocations):
val pingStateTransition = { (ps: PingState, evt: Event) =>
if (ps == PingReceived) PingReceived
else if (evt == Ping) PingReceived
else ps
}
val lastPerceivedStateTransition = { (lpsOpt: Option[LastPerceivedState], evt: Event) =>
evt match {
case A => Some(InA)
case B => Some(InB)
case _ => lpsOpt
}
}
val transitionF = { (state: State, evt: Evt) =>
pingStateTransition(state._1, evt) -> lastPerceivedStateTransition(state._2, evt)
}