trait StateReader[F[_], S] {
  def read: F[Option[S]]
trait Passivator[F[_]] {
  def enablePassivation(after: FiniteDuration = Duration.Zero): F[Unit]
  def disablePassivation: F[Unit]
trait Self[F[_], Alg[_[_]]] {
  def self: F[Alg[F]]
trait Effector[F[_], S] extends StateReader[F, S] with Passivator[F] with Self[F]

Effector is a typeclass used to describe side effects occurring after event persistence and entity recovery.

Side-effects are typically asynchronous operations such as kafka writes, outgoing REST requests, and entity passivation (flushing out of memory). Effector is used in a Effector => F[Unit] function provided upon entity deployment (e.g. BookingEffector). In the provided Akka runtime, the resulting F[Unit] is executed in run & forget mode so that command reply is not delayed by any lengthy side-effect (Self can be used to notify success or failure of asynchronous operations back to the entity).


In the provided Akka runtime, read-only commands (commands that do not generate events) do not trigger side-effects, which corresponds to sound practice.


Defining an effector is entirely optional with the Akka runtime, pass-in (_, _) => EffectorT.unit in deployEntity to disable effector.

State-derived side-effects

StateReader allows reading the updated entity state after event persistence or recovery.


Passivator allow fine grain control over passivation. In certain domains, entities can evolve into “dormant” states (e.g. after a BookingCancelled event) for which it is beneficial to trigger passivation, either immediately or after a certain delay. This enables proactive optimization of cluster resources.

Self & process definition

Self exposes the algebra of the entity within the effector context. This allows definition of asynchronous processes that involve interaction with the very same entity, typically to define entities acting as process managers (see below for more detail).

At least once delivery with zero latency

For most processes, at least once delivery guarantees are required. This can be achieved with a projection, however at the cost of some incurred latency. Actual latency depends on the database and event journal implementation used, as well as the projection throughput. One must also make sure to distribute the projection across the cluster to avoid creating a central choke point. Even so, if a projector process gets stalled for some reason, this can create a cascade effect with events pending processing building up.

An effective alternative to using a projection is to track process completion in the entity state itself. Launching asynchronous operations directly as a side-effect of an event has zero latency overhead and also the added advantage that the process launches within the node of the entity which triggered it, thus benefiting from inherent distribution.

By enabling remember-entities, we can achieve guaranteed at-least-once completion of asynchronous processes thanks to effector running right after recovery (thus withstanding node crash or shard rebalancing).

endless makes it easy to implement this pattern with Self. Here’s the recipe, as illustrated in the example application example:

  1. BookingPlaced event gets persisted. At this point, entity state represents pending acceptation of the booking Booking(..., status = Pending)
  2. Effector function inspects the state, and in case of Pending status, asks a third-party service for availability and notifies the entity of the result:
val availabilityProcess: Booking => F[Unit] = booking =>
      booking.status match {
        case Status.Pending =>
          for {
            isAvailable <- availabilityAlg.isCapacityAvailable(booking.time, booking.passengerCount)
            entity <- self
            _ <- entity.notifyCapacity(isAvailable)
          } yield ()
        case _ => ().pure

3.BookingAccepted or BookingRejected events are persisted and entity state is updated accordingly.