Event Sourcing
You are viewing the documentation for the new actor APIs, to view the Akka Classic documentation, see Classic Akka Persistence.
Module info
To use Akka Persistence, add the module to your project:
- sbt
val AkkaVersion = "2.6.21" libraryDependencies ++= Seq( "com.typesafe.akka" %% "akka-persistence-typed" % AkkaVersion, "com.typesafe.akka" %% "akka-persistence-testkit" % AkkaVersion % Test )
- Maven
- Gradle
You also have to select journal plugin and optionally snapshot store plugin, see Persistence Plugins.
[+] Show project infoProject Info: Akka Event Sourcing (typed) | |
---|---|
Artifact | com.typesafe.akka
akka-persistence-typed
2.6.21
|
JDK versions | Adopt OpenJDK 8 Adopt OpenJDK 11 |
Scala versions | 2.13.8, 2.12.16, 3.1.2 |
JPMS module name | akka.persistence.typed |
License | |
Readiness level | Supported, Lightbend Subscription provides support
Since 2.6.0, 2019-11-06
|
Home page | https://akka.io/ |
API documentation | |
Forums | |
Release notes | akka.io blog |
Issues | Github issues |
Sources | https://github.com/akka/akka |
Introduction
Akka Persistence enables stateful actors to persist their state so that it can be recovered when an actor is either restarted, such as after a JVM crash, by a supervisor or a manual stop-start, or migrated within a cluster. The key concept behind Akka Persistence is that only the events that are persisted by the actor are stored, not the actual state of the actor (although actor state snapshot support is available). The events are persisted by appending to storage (nothing is ever mutated) which allows for very high transaction rates and efficient replication. A stateful actor is recovered by replaying the stored events to the actor, allowing it to rebuild its state. This can be either the full history of changes or starting from a checkpoint in a snapshot, which can dramatically reduce recovery times.
Akka Persistence also supports Durable State Behaviors, which is based on persistence of the latest state of the actor. In this implementation, the latest state is persisted, instead of events. Hence this is more similar to CRUD based applications.
The Event Sourcing with Akka 2.6 video is a good starting point for learning Event Sourcing, together with the Microservices with Akka tutorial that illustrates how to implement an Event Sourced CQRS application with Akka Persistence and Akka Projections.
The General Data Protection Regulation (GDPR) requires that personal information must be deleted at the request of users. Deleting or modifying events that carry personal information would be difficult. Data shredding can be used to forget information instead of deleting or modifying it. This is achieved by encrypting the data with a key for a given data subject id (person) and deleting the key when that data subject is to be forgotten. Lightbend’s GDPR for Akka Persistence provides tools to facilitate in building GDPR capable systems.
Event Sourcing concepts
See an introduction to Event Sourcing at MSDN.
Another excellent article about “thinking in Events” is Events As First-Class Citizens by Randy Shoup. It is a short and recommended read if you’re starting developing Events based applications.
What follows is Akka’s implementation via event sourced actors.
An event sourced actor (also known as a persistent actor) receives a (non-persistent) command which is first validated if it can be applied to the current state. Here validation can mean anything, from simple inspection of a command message’s fields up to a conversation with several external services, for example. If validation succeeds, events are generated from the command, representing the effect of the command. These events are then persisted and, after successful persistence, used to change the actor’s state. When the event sourced actor needs to be recovered, only the persisted events are replayed of which we know that they can be successfully applied. In other words, events cannot fail when being replayed to a persistent actor, in contrast to commands. Event sourced actors may also process commands that do not change application state such as query commands for example.
Example and core API
Let’s start with a simple example. The minimum required for a EventSourcedBehavior
is:
- Scala
-
source
import akka.persistence.typed.scaladsl.EventSourcedBehavior import akka.persistence.typed.PersistenceId object MyPersistentBehavior { sealed trait Command sealed trait Event final case class State() def apply(): Behavior[Command] = EventSourcedBehavior[Command, Event, State]( persistenceId = PersistenceId.ofUniqueId("abc"), emptyState = State(), commandHandler = (state, cmd) => throw new NotImplementedError("TODO: process the command & return an Effect"), eventHandler = (state, evt) => throw new NotImplementedError("TODO: process the event return the next state")) }
- Java
The first important thing to notice is the Behavior
of a persistent actor is typed to the type of the Command
because this is the type of message a persistent actor should receive. In Akka this is now enforced by the type system.
The components that make up an EventSourcedBehavior
are:
persistenceId
is the stable unique identifier for the persistent actor.emptyState
defines theState
when the entity is first created e.g. a Counter would start with 0 as state.commandHandler
defines how to handle command by producing Effects e.g. persisting events, stopping the persistent actor.eventHandler
returns the new state given the current state when an event has been persisted.
Next we’ll discuss each of these in detail.
PersistenceId
The PersistenceId
is the stable unique identifier for the persistent actor in the backend event journal and snapshot store.
Cluster Sharding is typically used together with EventSourcedBehavior
to ensure that there is only one active entity for each PersistenceId
(entityId
). There are techniques to ensure this uniqueness, an example of which can be found in the Persistence example in the Cluster Sharding documentation. This illustrates how to construct the PersistenceId
from the entityTypeKey
and entityId
provided by the EntityContext
.
The entityId
in Cluster Sharding is the business domain identifier of the entity. The entityId
might not be unique enough to be used as the PersistenceId
by itself. For example two different types of entities may have the same entityId
. To create a unique PersistenceId
the entityId
should be prefixed with a stable name of the entity type, which typically is the same as the EntityTypeKey.name
that is used in Cluster Sharding. There are PersistenceId.apply
factory methods to help with constructing such PersistenceId
from an entityTypeHint
and entityId
.
The default separator when concatenating the entityTypeHint
and entityId
is |
, but a custom separator is supported.
The |
separator is also used in Lagom’s scaladsl.PersistentEntity
but no separator is used in Lagom’s javadsl.PersistentEntity
. For compatibility with Lagom’s javadsl.PersistentEntity
you should use ""
as the separator.
A custom identifier can be created with PersistenceId.ofUniqueId
.
Command handler
The command handler is a function with 2 parameters, the current State
and the incoming Command
.
A command handler returns an Effect
directive that defines what event or events, if any, to persist. Effects are created using the Effect
factory.
The two most commonly used effects are:
persist
will persist one single event or several events atomically, i.e. all events are stored or none of them are stored if there is an errornone
no events are to be persisted, for example a read-only command
More effects are explained in Effects and Side Effects.
In addition to returning the primary Effect
for the command EventSourcedBehavior
s can also chain side effects that are to be performed after successful persist which is achieved with the thenRun
function e.g. Effect.persist(..).thenRun
.
Event handler
When an event has been persisted successfully the new state is created by applying the event to the current state with the eventHandler
. In the case of multiple persisted events, the eventHandler
is called with each event in the same order as they were passed to Effect.persist(..)
.
The state is typically defined as an immutable class and then the event handler returns a new instance of the state. You may choose to use a mutable class for the state, and then the event handler may update the state instance and return the same instance. Both immutable and mutable state is supported.
The same event handler is also used when the entity is started up to recover its state from the stored events.
The event handler must only update the state and never perform side effects, as those would also be executed during recovery of the persistent actor. Side effects should be performed in thenRun
from the command handler after persisting the event or from the RecoveryCompleted
after Recovery.
Completing the example
Let’s fill in the details of the example.
Command and event:
- Scala
-
source
sealed trait Command final case class Add(data: String) extends Command case object Clear extends Command sealed trait Event final case class Added(data: String) extends Event case object Cleared extends Event
- Java
State is a List containing the 5 latest items:
The command handler persists the Add
payload in an Added
event:
- Scala
-
source
import akka.persistence.typed.scaladsl.Effect val commandHandler: (State, Command) => Effect[Event, State] = { (state, command) => command match { case Add(data) => Effect.persist(Added(data)) case Clear => Effect.persist(Cleared) } }
- Java
The event handler appends the item to the state and keeps 5 items. This is called after successfully persisting the event in the database:
- Scala
-
source
val eventHandler: (State, Event) => State = { (state, event) => event match { case Added(data) => state.copy((data :: state.history).take(5)) case Cleared => State(Nil) } }
- Java
These are used to create an EventSourcedBehavior
:
- Scala
-
source
import akka.persistence.typed.scaladsl.EventSourcedBehavior import akka.persistence.typed.PersistenceId def apply(id: String): Behavior[Command] = EventSourcedBehavior[Command, Event, State]( persistenceId = PersistenceId.ofUniqueId(id), emptyState = State(Nil), commandHandler = commandHandler, eventHandler = eventHandler)
- Java
Effects and Side Effects
A command handler returns an Effect
directive that defines what event or events, if any, to persist. Effects are created using the Effect
factory and can be one of:
persist
will persist one single event or several events atomically, i.e. all events are stored or none of them are stored if there is an errornone
no events are to be persisted, for example a read-only commandunhandled
the command is unhandled (not supported) in current statestop
stop this actorstash
the current command is stashedunstashAll
process the commands that were stashed withEffect.stash
reply
send a reply message to the givenActorRef
Note that only one of those can be chosen per incoming command. It is not possible to both persist and say none/unhandled.
In addition to returning the primary Effect
for the command EventSourcedBehavior
s can also chain side effects that are to be performed after successful persist which is achieved with the thenRun
function e.g. Effect.persist(..).thenRun
.
In the example below the state is sent to the subscriber
ActorRef. Note that the new state after applying the event is passed as parameter of the thenRun
function. In the case where multiple events have been persisted, the state passed to thenRun
is the updated state after all events have been handled.
All thenRun
registered callbacks are executed sequentially after successful execution of the persist statement (or immediately, in case of none
and unhandled
).
In addition to thenRun
the following actions can also be performed after successful persist:
thenStop
the actor will be stoppedthenUnstashAll
process the commands that were stashed withEffect.stash
thenReply
send a reply message to the givenActorRef
Example of effects:
- Scala
-
source
def onCommand(subscriber: ActorRef[State], state: State, command: Command): Effect[Event, State] = { command match { case Add(data) => Effect.persist(Added(data)).thenRun(newState => subscriber ! newState) case Clear => Effect.persist(Cleared).thenRun((newState: State) => subscriber ! newState).thenStop() } }
- Java
Most of the time this will be done with the thenRun
method on the Effect
above. You can factor out common side effects into functions and reuse for several commands. For example:
- Scala
-
source
// Example factoring out a chained effect to use in several places with `thenRun` val commonChainedEffects: Mood => Unit = _ => println("Command processed") // Then in a command handler: Effect .persist(Remembered("Yep")) // persist event .thenRun(commonChainedEffects) // add on common chained effect
- Java
Side effects ordering and guarantees
Any side effects are executed on an at-most-once basis and will not be executed if the persist fails.
Side effects are not run when the actor is restarted or started again after being stopped. You may inspect the state when receiving the RecoveryCompleted
signal and execute side effects that have not been acknowledged at that point. That may possibly result in executing side effects more than once.
The side effects are executed sequentially, it is not possible to execute side effects in parallel, unless they call out to something that is running concurrently (for example sending a message to another actor).
It’s possible to execute a side effects before persisting the event, but that can result in that the side effect is performed but the event is not stored if the persist fails.
Atomic writes
It is possible to store several events atomically by using the persist
effect with a list of events. That means that all events passed to that method are stored or none of them are stored if there is an error.
The recovery of a persistent actor will therefore never be done partially with only a subset of events persisted by a single persist
effect.
Some journals may not support atomic writes of several events and they will then reject the persist
with multiple events. This is signalled to an EventSourcedBehavior
via an EventRejectedException
(typically with a UnsupportedOperationException
) and can be handled with a supervisor.
Cluster Sharding and EventSourcedBehavior
Cluster Sharding is an excellent fit to spread persistent actors over a cluster, addressing them by id. It makes it possible to have more persistent actors exist in the cluster than what would fit in the memory of one node. Cluster sharding improves the resilience of the cluster. If a node crashes, the persistent actors are quickly started on a new node and can resume operations.
The EventSourcedBehavior
can then be run as with any plain actor as described in actors documentation, but since Akka Persistence is based on the single-writer principle the persistent actors are typically used together with Cluster Sharding. For a particular persistenceId
only one persistent actor instance should be active at one time. If multiple instances were to persist events at the same time, the events would be interleaved and might not be interpreted correctly on replay. Cluster Sharding ensures that there is only one active entity for each id. The Cluster Sharding example illustrates this common combination.
Accessing the ActorContext
If the EventSourcedBehavior
needs to use the ActorContext
, for example to spawn child actors, it can be obtained by wrapping construction with Behaviors.setup
:
- Scala
-
source
import akka.persistence.typed.scaladsl.Effect import akka.persistence.typed.scaladsl.EventSourcedBehavior.CommandHandler def apply(): Behavior[String] = Behaviors.setup { context => EventSourcedBehavior[String, String, State]( persistenceId = PersistenceId.ofUniqueId("myPersistenceId"), emptyState = State(), commandHandler = CommandHandler.command { cmd => context.log.info("Got command {}", cmd) Effect.none }, eventHandler = { case (state, _) => state }) }
- Java
Changing Behavior
After processing a message, actors are able to return the Behavior
that is used for the next message.
As you can see in the above examples this is not supported by persistent actors. Instead, the state is returned by eventHandler
. The reason a new behavior can’t be returned is that behavior is part of the actor’s state and must also carefully be reconstructed during recovery. If it would have been supported it would mean that the behavior must be restored when replaying events and also encoded in the state anyway when snapshots are used. That would be very prone to mistakes and thus not allowed in Akka Persistence.
For basic actors you can use the same set of command handlers independent of what state the entity is in, as shown in above example. For more complex actors it’s useful to be able to change the behavior in the sense that different functions for processing commands may be defined depending on what state the actor is in. This is useful when implementing finite state machine (FSM) like entities.
The next example demonstrates how to define different behavior based on the current State
. It shows an actor that represents the state of a blog post. Before a post is started the only command it can process is to AddPost
. Once it is started then one can look it up with GetPost
, modify it with ChangeBody
or publish it with Publish
.
The state is captured by:
- Scala
-
source
sealed trait State case object BlankState extends State final case class DraftState(content: PostContent) extends State { def withBody(newBody: String): DraftState = copy(content = content.copy(body = newBody)) def postId: String = content.postId } final case class PublishedState(content: PostContent) extends State { def postId: String = content.postId }
- Java
The commands, of which only a subset are valid depending on the state:
- Scala
-
source
sealed trait Command final case class AddPost(content: PostContent, replyTo: ActorRef[StatusReply[AddPostDone]]) extends Command final case class AddPostDone(postId: String) final case class GetPost(replyTo: ActorRef[PostContent]) extends Command final case class ChangeBody(newBody: String, replyTo: ActorRef[Done]) extends Command final case class Publish(replyTo: ActorRef[Done]) extends Command final case class PostContent(postId: String, title: String, body: String)
- Java
The command handler to process each command is decided by first looking at the state and then the command. It typically becomes two levels of pattern matching, first on the state and then on the command. Delegating to methods is a good practice because the one-line cases give a nice overview of the message dispatch.
- Scala
-
source
private val commandHandler: (State, Command) => Effect[Event, State] = { (state, command) => state match { case BlankState => command match { case cmd: AddPost => addPost(cmd) case _ => Effect.unhandled } case draftState: DraftState => command match { case cmd: ChangeBody => changeBody(draftState, cmd) case Publish(replyTo) => publish(draftState, replyTo) case GetPost(replyTo) => getPost(draftState, replyTo) case AddPost(_, replyTo) => Effect.unhandled.thenRun(_ => replyTo ! StatusReply.Error("Cannot add post while in draft state")) } case publishedState: PublishedState => command match { case GetPost(replyTo) => getPost(publishedState, replyTo) case AddPost(_, replyTo) => Effect.unhandled.thenRun(_ => replyTo ! StatusReply.Error("Cannot add post, already published")) case _ => Effect.unhandled } } } private def addPost(cmd: AddPost): Effect[Event, State] = { val evt = PostAdded(cmd.content.postId, cmd.content) Effect.persist(evt).thenRun { _ => // After persist is done additional side effects can be performed cmd.replyTo ! StatusReply.Success(AddPostDone(cmd.content.postId)) } } private def changeBody(state: DraftState, cmd: ChangeBody): Effect[Event, State] = { val evt = BodyChanged(state.postId, cmd.newBody) Effect.persist(evt).thenRun { _ => cmd.replyTo ! Done } } private def publish(state: DraftState, replyTo: ActorRef[Done]): Effect[Event, State] = { Effect.persist(Published(state.postId)).thenRun { _ => println(s"Blog post ${state.postId} was published") replyTo ! Done } } private def getPost(state: DraftState, replyTo: ActorRef[PostContent]): Effect[Event, State] = { replyTo ! state.content Effect.none } private def getPost(state: PublishedState, replyTo: ActorRef[PostContent]): Effect[Event, State] = { replyTo ! state.content Effect.none }
- Java
The event handler:
- Scala
-
source
private val eventHandler: (State, Event) => State = { (state, event) => state match { case BlankState => event match { case PostAdded(_, content) => DraftState(content) case _ => throw new IllegalStateException(s"unexpected event [$event] in state [$state]") } case draftState: DraftState => event match { case BodyChanged(_, newBody) => draftState.withBody(newBody) case Published(_) => PublishedState(draftState.content) case _ => throw new IllegalStateException(s"unexpected event [$event] in state [$state]") } case _: PublishedState => // no more changes after published throw new IllegalStateException(s"unexpected event [$event] in state [$state]") } }
- Java
And finally the behavior is created from the EventSourcedBehavior.apply
:
- Scala
-
source
object BlogPostEntity { // commands, events, state defined here def apply(entityId: String, persistenceId: PersistenceId): Behavior[Command] = { Behaviors.setup { context => context.log.info("Starting BlogPostEntity {}", entityId) EventSourcedBehavior[Command, Event, State](persistenceId, emptyState = BlankState, commandHandler, eventHandler) } } // commandHandler and eventHandler defined here }
- Java
This can be taken one or two steps further by defining the event and command handlers in the state class as illustrated in event handlers in the state and command handlers in the state.
There is also an example illustrating an optional initial state.
Replies
The Request-Response interaction pattern is very common for persistent actors, because you typically want to know if the command was rejected due to validation errors and when accepted you want a confirmation when the events have been successfully stored.
Therefore you typically include a ActorRef
[ReplyMessageType]
. If the command can either have a successful response or a validation error returned, the generic response type StatusReply
[ReplyType]
can be used. If the successful reply does not contain a value but is more of an acknowledgement a pre defined StatusReply.Ack
of type StatusReply[Done]
can be used.
After validation errors or after persisting events, using a thenRun
side effect, the reply message can be sent to the ActorRef
.
- Scala
-
source
final case class AddPost(content: PostContent, replyTo: ActorRef[StatusReply[AddPostDone]]) extends Command final case class AddPostDone(postId: String)
- Java
- Scala
-
source
val evt = PostAdded(cmd.content.postId, cmd.content) Effect.persist(evt).thenRun { _ => // After persist is done additional side effects can be performed cmd.replyTo ! StatusReply.Success(AddPostDone(cmd.content.postId)) }
- Java
Since this is such a common pattern there is a reply effect for this purpose. It has the nice property that it can be used to enforce that replies are not forgotten when implementing the EventSourcedBehavior
. If it’s defined with EventSourcedBehavior.withEnforcedReplies
there will be compilation errors if the returned effect isn’t a ReplyEffect
, which can be created with Effect.reply
, Effect.noReply
, Effect.thenReply
, or Effect.thenNoReply
.
- Scala
-
source
def apply(accountNumber: String, persistenceId: PersistenceId): Behavior[Command] = { EventSourcedBehavior.withEnforcedReplies(persistenceId, EmptyAccount, commandHandler(accountNumber), eventHandler) }
- Java
The commands must have a field of ActorRef
[ReplyMessageType]
that can then be used to send a reply.
- Scala
-
source
sealed trait Command extends CborSerializable final case class Withdraw(amount: BigDecimal, replyTo: ActorRef[StatusReply[Done]]) extends Command
- Java
The ReplyEffect
is created with Effect.reply
, Effect.noReply
, Effect.thenReply
, or Effect.thenNoReply
.
- Scala
-
source
private def withdraw(acc: OpenedAccount, cmd: Withdraw): ReplyEffect[Event, Account] = { if (acc.canWithdraw(cmd.amount)) Effect.persist(Withdrawn(cmd.amount)).thenReply(cmd.replyTo)(_ => StatusReply.Ack) else Effect.reply(cmd.replyTo)( StatusReply.Error(s"Insufficient balance ${acc.balance} to be able to withdraw ${cmd.amount}")) }
- Java
These effects will send the reply message even when EventSourcedBehavior.withEnforcedReplies
is not used, but then there will be no compilation errors if the reply decision is left out.
Note that the noReply
is a way of making conscious decision that a reply shouldn’t be sent for a specific command or the reply will be sent later, perhaps after some asynchronous interaction with other actors or services.
Serialization
The same serialization mechanism as for actor messages is also used for persistent actors. When picking a serialization solution for the events you should also consider that it must be possible to read old events when the application has evolved. Strategies for that can be found in the schema evolution.
You need to enable serialization for your commands (messages), events, and state (snapshot). Serialization with Jackson is a good choice in many cases and our recommendation if you don’t have other preference.
Recovery
An event sourced actor is automatically recovered on start and on restart by replaying journaled events. New messages sent to the actor during recovery do not interfere with replayed events. They are stashed and received by the EventSourcedBehavior
after the recovery phase completes.
The number of concurrent recoveries that can be in progress at the same time is limited to not overload the system and the backend data store. When exceeding the limit the actors will wait until other recoveries have been completed. This is configured by:
akka.persistence.max-concurrent-recoveries = 50
The event handler is used for updating the state when replaying the journaled events.
It is strongly discouraged to perform side effects in the event handler, so side effects should be performed once recovery has completed as a reaction to the RecoveryCompleted
signal in the receiveSignal
handler
- Scala
-
source
EventSourcedBehavior[Command, Event, State]( persistenceId = persistenceId, emptyState = State(), commandHandler = (state, cmd) => throw new NotImplementedError("TODO: process the command & return an Effect"), eventHandler = (state, evt) => throw new NotImplementedError("TODO: process the event return the next state")) .receiveSignal { case (state, RecoveryCompleted) => throw new NotImplementedError("TODO: add some end-of-recovery side-effect here") }
- Java
The RecoveryCompleted
contains the current State
.
The actor will always receive a RecoveryCompleted
signal, even if there are no events in the journal and the snapshot store is empty, or if it’s a new persistent actor with a previously unused PersistenceId
.
Snapshots can be used for optimizing recovery times.
Replay filter
There could be cases where event streams are corrupted and multiple writers (i.e. multiple persistent actor instances) journaled different messages with the same sequence number. In such a case, you can configure how you filter replayed messages from multiple writers, upon recovery.
In your configuration, under the akka.persistence.journal.xxx.replay-filter
section (where xxx
is your journal plugin id), you can select the replay filter mode
from one of the following values:
- repair-by-discard-old
- fail
- warn
- off
For example, if you configure the replay filter for leveldb plugin, it looks like this:
# The replay filter can detect a corrupt event stream by inspecting
# sequence numbers and writerUuid when replaying events.
akka.persistence.journal.leveldb.replay-filter {
# What the filter should do when detecting invalid events.
# Supported values:
# `repair-by-discard-old` : discard events from old writers,
# warning is logged
# `fail` : fail the replay, error is logged
# `warn` : log warning but emit events untouched
# `off` : disable this feature completely
mode = repair-by-discard-old
}
Disable recovery
You can also completely disable the recovery of events and snapshots:
- Scala
-
source
EventSourcedBehavior[Command, Event, State]( persistenceId = PersistenceId.ofUniqueId("abc"), emptyState = State(), commandHandler = (state, cmd) => throw new NotImplementedError("TODO: process the command & return an Effect"), eventHandler = (state, evt) => throw new NotImplementedError("TODO: process the event return the next state")) .withRecovery(Recovery.disabled)
- Java
Please refer to snapshots if you need to disable only the snapshot recovery, or you need to select specific snapshots.
In any case, the highest sequence number will always be recovered so you can keep persisting new events without corrupting your event log.
Tagging
Persistence allows you to use event tags without using an EventAdapter
:
- Scala
-
source
EventSourcedBehavior[Command, Event, State]( persistenceId = PersistenceId.ofUniqueId("abc"), emptyState = State(), commandHandler = (state, cmd) => throw new NotImplementedError("TODO: process the command & return an Effect"), eventHandler = (state, evt) => throw new NotImplementedError("TODO: process the event return the next state")) .withTagger(_ => Set("tag1", "tag2"))
- Java
Event adapters
Event adapters can be programmatically added to your EventSourcedBehavior
s that can convert from your Event
type to another type that is then passed to the journal.
Defining an event adapter is done by extending an EventAdapter:
- Scala
-
source
case class Wrapper[T](event: T) class WrapperEventAdapter[T] extends EventAdapter[T, Wrapper[T]] { override def toJournal(e: T): Wrapper[T] = Wrapper(e) override def fromJournal(p: Wrapper[T], manifest: String): EventSeq[T] = EventSeq.single(p.event) override def manifest(event: T): String = "" }
- Java
Then install it on an EventSourcedBehavior
:
- Scala
-
source
EventSourcedBehavior[Command, Event, State]( persistenceId = PersistenceId.ofUniqueId("abc"), emptyState = State(), commandHandler = (state, cmd) => throw new NotImplementedError("TODO: process the command & return an Effect"), eventHandler = (state, evt) => throw new NotImplementedError("TODO: process the event return the next state")) .eventAdapter(new WrapperEventAdapter[Event])
- Java
Wrapping EventSourcedBehavior
When creating an EventSourcedBehavior
, it is possible to wrap EventSourcedBehavior
in other behaviors such as Behaviors.setup
in order to access the ActorContext
object. For instance to access the actor logging upon taking snapshots for debug purpose.
- Scala
-
source
Behaviors.setup[Command] { context => EventSourcedBehavior[Command, Event, State]( persistenceId = PersistenceId.ofUniqueId("abc"), emptyState = State(), commandHandler = (state, cmd) => throw new NotImplementedError("TODO: process the command & return an Effect"), eventHandler = (state, evt) => throw new NotImplementedError("TODO: process the event return the next state")) .snapshotWhen((state, _, _) => { context.log.info2("Snapshot actor {} => state: {}", context.self.path.name, state) true }) }
- Java
Journal failures
By default an EventSourcedBehavior
will stop if an exception is thrown from the journal. It is possible to override this with any BackoffSupervisorStrategy
. It is not possible to use the normal supervision wrapping for this as it isn’t valid to resume
a behavior on a journal failure as it is not known if the event was persisted.
- Scala
-
source
EventSourcedBehavior[Command, Event, State]( persistenceId = PersistenceId.ofUniqueId("abc"), emptyState = State(), commandHandler = (state, cmd) => throw new NotImplementedError("TODO: process the command & return an Effect"), eventHandler = (state, evt) => throw new NotImplementedError("TODO: process the event return the next state")) .onPersistFailure( SupervisorStrategy.restartWithBackoff(minBackoff = 10.seconds, maxBackoff = 60.seconds, randomFactor = 0.1))
- Java
If there is a problem with recovering the state of the actor from the journal, a RecoveryFailed
signal is emitted to the receiveSignal
handler and the actor will be stopped (or restarted with backoff).
Journal rejections
Journals can reject events. The difference from a failure is that the journal must decide to reject an event before trying to persist it e.g. because of a serialization exception. If an event is rejected it definitely won’t be in the journal. This is signalled to an EventSourcedBehavior
via an EventRejectedException
and can be handled with a supervisor. Not all journal implementations use rejections and treat these kind of problems also as journal failures.
Stash
When persisting events with persist
it is guaranteed that the EventSourcedBehavior
will not receive further commands until after the events have been confirmed to be persisted and additional side effects have been run. Incoming messages are stashed automatically until the persist
is completed.
Commands are also stashed during recovery and will not interfere with replayed events. Commands will be received when recovery has been completed.
The stashing described above is handled automatically, but there is also a possibility to stash commands when they are received to defer processing of them until later. One example could be waiting for some external condition or interaction to complete before processing additional commands. That is accomplished by returning a stash
effect and later use thenUnstashAll
.
Let’s use an example of a task manager to illustrate how the stashing effects can be used. It handles three commands; StartTask
, NextStep
and EndTask
. Those commands are associated with a given taskId
and the manager processes one taskId
at a time. A task is started when receiving StartTask
, and continues when receiving NextStep
commands until the final EndTask
is received. Commands with another taskId
than the one in progress are deferred by stashing them. When EndTask
is processed a new task can start and the stashed commands are processed.
- Scala
-
source
object TaskManager { sealed trait Command final case class StartTask(taskId: String) extends Command final case class NextStep(taskId: String, instruction: String) extends Command final case class EndTask(taskId: String) extends Command sealed trait Event final case class TaskStarted(taskId: String) extends Event final case class TaskStep(taskId: String, instruction: String) extends Event final case class TaskCompleted(taskId: String) extends Event final case class State(taskIdInProgress: Option[String]) def apply(persistenceId: PersistenceId): Behavior[Command] = EventSourcedBehavior[Command, Event, State]( persistenceId = persistenceId, emptyState = State(None), commandHandler = (state, command) => onCommand(state, command), eventHandler = (state, event) => applyEvent(state, event)) .onPersistFailure(SupervisorStrategy.restartWithBackoff(1.second, 30.seconds, 0.2)) private def onCommand(state: State, command: Command): Effect[Event, State] = { state.taskIdInProgress match { case None => command match { case StartTask(taskId) => Effect.persist(TaskStarted(taskId)) case _ => Effect.unhandled } case Some(inProgress) => command match { case StartTask(taskId) => if (inProgress == taskId) Effect.none // duplicate, already in progress else // other task in progress, wait with new task until later Effect.stash() case NextStep(taskId, instruction) => if (inProgress == taskId) Effect.persist(TaskStep(taskId, instruction)) else // other task in progress, wait with new task until later Effect.stash() case EndTask(taskId) => if (inProgress == taskId) Effect.persist(TaskCompleted(taskId)).thenUnstashAll() // continue with next task else // other task in progress, wait with new task until later Effect.stash() } } } private def applyEvent(state: State, event: Event): State = { event match { case TaskStarted(taskId) => State(Option(taskId)) case TaskStep(_, _) => state case TaskCompleted(_) => State(None) } } }
- Java
You should be careful to not send more messages to a persistent actor than it can keep up with, otherwise the stash buffer will fill up and when reaching its maximum capacity the commands will be dropped. The capacity can be configured with:
akka.persistence.typed.stash-capacity = 10000
Note that the stashed commands are kept in an in-memory buffer, so in case of a crash they will not be processed.
- Stashed commands are discarded in case the actor (entity) is passivated or rebalanced by Cluster Sharding.
- Stashed commands are discarded in case the actor is restarted (or stopped) due to a thrown exception while processing a command or side effect after persisting.
- Stashed commands are preserved and processed later in case of a failure while storing events but only if an
onPersistFailure
backoff supervisor strategy is defined.
It’s allowed to stash messages while unstashing. Those newly added commands will not be processed by the unstashAll
effect that was in progress and have to be unstashed by another unstashAll
.
Scaling out
In a use case where the number of persistent actors needed is higher than what would fit in the memory of one node or where resilience is important so that if a node crashes the persistent actors are quickly started on a new node and can resume operations Cluster Sharding is an excellent fit to spread persistent actors over a cluster and address them by id.
Akka Persistence is based on the single-writer principle. For a particular PersistenceId
only one EventSourcedBehavior
instance should be active at one time. If multiple instances were to persist events at the same time, the events would be interleaved and might not be interpreted correctly on replay. Cluster Sharding ensures that there is only one active entity (EventSourcedBehavior
) for each id within a data center. Replicated Event Sourcing supports active-active persistent entities across data centers.
Configuration
There are several configuration properties for the persistence module, please refer to the reference configuration.
The journal and snapshot store plugins have specific configuration, see reference documentation of the chosen plugin.
Example project
Persistence example project is an example project that can be downloaded, and with instructions of how to run. This project contains a Shopping Cart sample illustrating how to use Akka Persistence.
The Shopping Cart sample is expanded further in the Microservices with Akka tutorial. In that sample the events are tagged to be consumed by even processors to build other representations from the events, or publish the events to other services.
Multi-DC Persistence example project illustrates how to use Replicated Event Sourcing that supports active-active persistent entities across data centers.