Akka persistence enables stateful actors to persist their internal state so that it can be recovered when an actor is started, restarted by a supervisor or migrated in a cluster. It also allows stateful actors to recover from JVM crashes, for example. The key concept behind Akka persistence is that only changes to an actor's internal state are persisted but never its current state directly (except for optional snapshots). These changes are only ever appended to storage, nothing is ever mutated, which allows for very high transaction rates and efficient replication. Stateful actors are recovered by replaying stored changes to these actors from which they can rebuild internal state. This can be either the full history of changes or starting from a snapshot of internal actor state which can dramatically reduce recovery times. Akka persistence also provides point-to-point communication channels with at-least-once message delivery guarantees.

Storage backends for state changes and snapshots are pluggable in Akka persistence. Currently, these are written to the local filesystem. Distributed and replicated storage, with the possibility of scaling writes, will be available soon.

Akka persistence is inspired by the eventsourced library. It follows the same concepts and architecture of eventsourced but significantly differs on API and implementation level.


This module is marked as “experimental” as of its introduction in Akka 2.3.0. We will continue to improve this API based on our users’ feedback, which implies that while we try to keep incompatible changes to a minimum the binary compatibility guarantee for maintenance releases does not apply to the contents of the akka.persistence package.


Akka persistence is a separate jar file. Make sure that you have the following dependency in your project:

"com.typesafe.akka" %% "akka-persistence-experimental" % "2.3-M2"


  • Processor: A processor is a persistent, stateful actor. Messages sent to a processor are written to a journal before its receive method is called. When a processor is started or restarted, journaled messages are replayed to that processor, so that it can recover internal state from these messages.
  • Channel: Channels are used by processors to communicate with other actors. They prevent that replayed messages are redundantly delivered to these actors and provide at-least-once message delivery guarantees, also in case of sender and receiver JVM crashes.
  • Journal: A journal stores the sequence of messages sent to a processor. An application can control which messages are stored and which are received by the processor without being journaled. The storage backend of a journal is pluggable.
  • Snapshot store: A snapshot store persists snapshots of a processor's internal state. Snapshots are used for optimizing recovery times. The storage backend of a snapshot store is pluggable.
  • Event sourcing. Based on the building blocks described above, Akka persistence provides abstractions for the development of event sourced applications (see section Event sourcing)


A processor can be implemented by extending the Processor trait and implementing the receive method.

import akka.persistence.{ Persistent, PersistenceFailure, Processor }

class MyProcessor extends Processor {
  def receive = {
    case Persistent(payload, sequenceNr) =>
    // message successfully written to journal
    case PersistenceFailure(payload, sequenceNr, cause) =>
    // message failed to be written to journal
    case other =>
    // message not written to journal

Processors only write messages of type Persistent to the journal, others are received without being persisted. When a processor's receive method is called with a Persistent message it can safely assume that this message has been successfully written to the journal. If a journal fails to write a Persistent message then the processor is stopped, by default. If an application wants that a processors continues to run on persistence failures it must handle PersistenceFailure messages. In this case, a processor may want to inform the sender about the failure, so that the sender can re-send the message, if needed, under the assumption that the journal recovered from a temporary failure.

A Processor itself is an Actor and can therefore be instantiated with actorOf.


val processor = actorOf(Props[MyProcessor], name = "myProcessor")

processor ! Persistent("foo") // will be journaled
processor ! "bar" // will not be journaled


By default, a processor is automatically recovered on start and on restart by replaying journaled messages. New messages sent to a processor during recovery do not interfere with replayed messages. New messages will only be received by that processor after recovery completes.

Recovery customization

Automated recovery on start can be disabled by overriding preStart with an empty implementation.

override def preStart() = ()

In this case, a processor must be recovered explicitly by sending it a Recover() message.

processor ! Recover()

If not overridden, preStart sends a Recover() message to self. Applications may also override preStart to define further Recover() parameters such as an upper sequence number bound, for example.

override def preStart() {
  self ! Recover(toSequenceNr = 457L)

Upper sequence number bounds can be used to recover a processor to past state instead of current state. Automated recovery on restart can be disabled by overriding preRestart with an empty implementation.

override def preRestart(reason: Throwable, message: Option[Any]) = ()

Recovery status

A processor can query its own recovery status via the methods

def recoveryRunning: Boolean
def recoveryFinished: Boolean

Failure handling

A persistent message that caused an exception will be received again by a processor after restart. To prevent a replay of that message during recovery it can be deleted.

override def preRestart(reason: Throwable, message: Option[Any]) {
  message match {
    case Some(p: Persistent) => deleteMessage(p.sequenceNr)
    case _                   =>
  super.preRestart(reason, message)

Message deletion

A processor can delete a single message by calling the deleteMessage method with the sequence number of that message as argument. An optional permanent parameter specifies whether the message shall be permanently deleted from the journal or only marked as deleted. In both cases, the message won't be replayed. Later extensions to Akka persistence will allow to replay messages that have been marked as deleted which can be useful for debugging purposes, for example. To delete all messages (journaled by a single processor) up to a specified sequence number, processors can call the deleteMessages method.


A processor must have an identifier that doesn't change across different actor incarnations. It defaults to the String representation of processor's path without the address part and can be obtained via the processorId method.

def processorId: String

Applications can customize a processor's id by specifying an actor name during processor creation as shown in section Processors. This changes that processor's name in its actor hierarchy and hence influences only part of the processor id. To fully customize a processor's id, the processorId method should be overridden.

override def processorId = "my-stable-processor-id"



There are further changes planned to the channel API that couldn't make it into the current milestone. One example is to have only a single destination per channel to allow gap detection and more advanced flow control.

Channels are special actors that are used by processors to communicate with other actors (channel destinations). Channels prevent redundant delivery of replayed messages to destinations during processor recovery. A replayed message is retained by a channel if its previous delivery has been confirmed by a destination.

import{ Actor, Props }
import akka.persistence.{ Channel, Deliver, Persistent, Processor }

class MyProcessor extends Processor {
  val destination = context.actorOf(Props[MyDestination])
  val channel = context.actorOf(Channel.props(), name = "myChannel")

  def receive = {
    case p @ Persistent(payload, _) =>
      channel ! Deliver(p.withPayload(s"processed ${payload}"), destination)

class MyDestination extends Actor {
  def receive = {
    case p @ ConfirmablePersistent(payload, sequenceNr, redeliveries) =>
      // ...

A channel is ready to use once it has been created, no recovery or further activation is needed. A Deliver request instructs a channel to send a Persistent message to a destination. Sender references are preserved by a channel, therefore, a destination can reply to the sender of a Deliver request.

If a processor wants to reply to a Persistent message sender it should use the sender reference as channel destination.

channel ! Deliver(p.withPayload(s"processed ${payload}"), sender)

Persistent messages delivered by a channel are of type ConfirmablePersistent. ConfirmablePersistent extends Persistent by adding the methods confirm method and redeliveries (see also Message re-delivery). Channel destinations confirm the delivery of a ConfirmablePersistent message by calling confirm() an that message. This asynchronously writes a confirmation entry to the journal. Replayed messages internally contain these confirmation entries which allows a channel to decide if a message should be retained or not.

A Processor can also be used as channel destination i.e. it can persist ConfirmablePersistent messages too.

Message re-delivery

Channels re-deliver messages to destinations if they do not confirm their receipt within a configurable timeout. This timeout can be specified as redeliverInterval when creating a channel, optionally together with the maximum number of re-deliveries a channel should attempt for each unconfirmed message.

  ChannelSettings(redeliverInterval = 30 seconds, redeliverMax = 15)),
  name = "myChannel")

Message re-delivery is done out of order with regards to normal delivery i.e. redelivered messages may arrive later than newer normally delivered messages. The number of re-delivery attempts can be obtained via the redeliveries method on ConfirmablePersistent or by pattern matching.

A channel keeps messages in memory until their successful delivery has been confirmed by their destination(s) or their maximum number of re-deliveries is reached. In the latter case, the application has to re-send the correspnding Deliver request to the channel so that the channel can start a new series of delivery attempts (starting again with a redeliveries count of 0).

Re-sending Deliver requests is done automatically if the sending processor replays messages: only Deliver requests of unconfirmed messages will be served again by the channel. A message replay can be enforced by an application by restarting the sending processor, for example. A replay will also take place if the whole application is restarted, either after normal termination or after a crash.

This combination of

  • message persistence by sending processors
  • message replays by sending processors
  • message re-deliveries by channels and
  • application-level confirmations (acknowledgements) by destinations

enables channels to provide at-least-once message delivery guarantees. Possible duplicates can be detected by destinations by tracking message sequence numbers. Message sequence numbers are generated per sending processor. Depending on how a processor routes outbound messages to destinations, they may either see a contiguous message sequence or a sequence with gaps.


If a processor emits more than one outbound message per inbound Persistent message it must use a separate channel for each outbound message to ensure that confirmations are uniquely identifiable, otherwise, at-least-once message delivery is not guaranteed. This rule has been introduced to avoid writing additional outbound message identifiers to the journal which would decrease the overall throughput. It is furthermore recommended to collapse multiple outbound messages to the same destination into a single outbound message, otherwise, if sent via multiple channels, their ordering is not defined. These restrictions are likely to be removed in the final release.

Whenever an application wants to have more control how sequence numbers are assigned to messages it should use an application-specific sequence number generator and include the generated sequence numbers into the payload of Persistent messages.

Persistent channels

Channels created with Channel.props do not persist messages. These channels are usually used in combination with a sending processor that takes care of persistence, hence, channel-specific persistence is not necessary in this case. They are referred to as transient channels in the following.

Applications may also use transient channels standalone (i.e. without a sending processor) if re-delivery attempts to destinations are required but message loss in case of a sender JVM crash is not an issue. If applications want to use standalone channels but message loss is not acceptable, they should use persistent channels. A persistent channel can be created with PersistentChannel.props and configured with a PersistentChannelSettings object.

val channel = context.actorOf(PersistentChannel.props(
  PersistentChannelSettings(redeliverInterval = 30 seconds, redeliverMax = 15)),
  name = "myPersistentChannel")

channel ! Deliver(Persistent("example"), destination)

A persistent channel is like a transient channel that additionally persists Deliver requests before serving it. Hence, it can recover from sender JVM crashes and provide the same message re-delivery semantics as a transient channel in combination with an application-defined processor.

By default, a persistent channel doesn't reply whether a Persistent message, sent with Deliver, has been successfully persisted or not. This can be enabled by creating the channel with the replyPersistent configuration parameter set to true:

PersistentChannelSettings(replyPersistent = true)

With this setting, either the successfully persisted message is replied to the sender or a PersistenceFailure. In case of a persistence failure, the sender should re-send the message.

Using a persistent channel in combination with an application-defined processor can make sense if destinations are unavailable for a long time and an application doesn't want to buffer all messages in memory (but write them to the journal only). In this case, delivery can be disabled by sending the channel a DisableDelivery message (to stop delivery and persist-only) and re-enabled again by sending it an EnableDelivery message. A disabled channel that receives an EnableDelivery message, processes all persisted, unconfirmed Deliver requests again before serving new ones.

Sender resolution

ActorRef s of Persistent message senders are also stored in the journal. Consequently, they may become invalid if an application is restarted and messages are replayed. For example, the stored ActorRef may then reference a previous incarnation of a sender and a new incarnation of that sender cannot receive a reply from a processor. This may be acceptable for many applications but others may require that a new sender incarnation receives the reply (to reliably resume a conversation between actors after a JVM crash, for example). Here, a channel may assist in resolving new sender incarnations by specifying a third Deliver argument:

  • Resolve.Destination if the sender of a persistent message is used as channel destination

    channel ! Deliver(p, sender, Resolve.Destination)
  • Resolve.Sender if the sender of a persistent message is forwarded to a destination.

    channel forward Deliver(p, destination, Resolve.Sender)

Default is Resolve.Off which means no resolution. Find out more in the Deliver API docs.


In the same way as Processors, channels also have an identifier that defaults to a channel's path. A channel identifier can therefore be customized by using a custom actor name at channel creation. This changes that channel's name in its actor hierarchy and hence influences only part of the channel identifier. To fully customize a channel identifier, it should be provided as argument Channel.props(String) or PersistentChannel.props(String).


Persistent messages


The payload of a Persistent message can be obtained via its

def payload: Any

method or by pattern matching

case Persistent(payload, _) =>

Inside processors, new persistent messages are derived from the current persistent message before sending them via a channel, either by calling p.withPayload(...) or Persistent.create(...) where the latter uses the implicit currentPersistentMessage made available by Processor.

implicit def currentPersistentMessage: Option[Persistent]

This is necessary for delivery confirmations to work properly. Both ways are equivalent but we recommend using p.withPayload(...) for clarity.

Sequence number

The sequence number of a Persistent message can be obtained via its

def sequenceNr: Long

method or by pattern matching

case Persistent(_, sequenceNr) =>

Persistent messages are assigned sequence numbers on a per-processor basis (or per channel basis if used standalone). A sequence starts at 1L and doesn't contain gaps unless a processor deletes a message.


Snapshots can dramatically reduce recovery times. Processors can save snapshots of internal state by calling the saveSnapshot method on Processor. If saving of a snapshot succeeds, the processor will receive a SaveSnapshotSuccess message, otherwise a SaveSnapshotFailure message

class MyProcessor extends Processor {
  var state: Any = _

  def receive = {
    case "snap"                                => saveSnapshot(state)
    case SaveSnapshotSuccess(metadata)         => // ...
    case SaveSnapshotFailure(metadata, reason) => // ...

where metadata is of type SnapshotMetadata:

case class SnapshotMetadata(processorId: String, sequenceNr: Long, timestamp: Long = 0L)

During recovery, the processor is offered a previously saved snapshot via a SnapshotOffer message from which it can initialize internal state.

class MyProcessor extends Processor {
  var state: Any = _

  def receive = {
    case SnapshotOffer(metadata, offeredSnapshot) => state = offeredSnapshot
    case Persistent(payload, sequenceNr)          => // ...

The replayed messages that follow the SnapshotOffer message, if any, are younger than the offered snapshot. They finally recover the processor to its current (i.e. latest) state.

In general, a processor is only offered a snapshot if that processor has previously saved one or more snapshots and at least one of these snapshots matches the SnapshotSelectionCriteria that can be specified for recovery.

processor ! Recover(fromSnapshot = SnapshotSelectionCriteria(
  maxSequenceNr = 457L,
  maxTimestamp = System.currentTimeMillis))

If not specified, they default to SnapshotSelectionCriteria.Latest which selects the latest (= youngest) snapshot. To disable snapshot-based recovery, applications should use SnapshotSelectionCriteria.None. A recovery where no saved snapshot matches the specified SnapshotSelectionCriteria will replay all journaled messages.

Snapshot deletion

A processor can delete a single snapshot by calling the deleteSnapshot method with the sequence number and the timestamp of the snapshot as argument. To bulk-delete snapshots that match a specified SnapshotSelectionCriteria argument, processors can call the deleteSnapshots method.

Event sourcing

In all the examples so far, messages that change a processor's state have been sent as Persistent messages by an application, so that they can be replayed during recovery. From this point of view, the journal acts as a write-ahead-log for whatever Persistent messages a processor receives. This is also known as command sourcing. Commands, however, may fail and some applications cannot tolerate command failures during recovery.

For these applications Event Sourcing is a better choice. Applied to Akka persistence, the basic idea behind event sourcing is quite simple. A processor 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 a processor's state. When the processor 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 processor, in contrast to commands. Eventsourced processors may of course also process commands that do not change application state, such as query commands, for example.

Akka persistence supports event sourcing with the EventsourcedProcessor trait (which implements event sourcing as a pattern on top of command sourcing). A processor that extends this trait does not handle Persistent messages directly but uses the persist method to persist and handle events. The behavior of an EventsourcedProcessor is defined by implementing receiveReplay and receiveCommand. This is best explained with an example (which is also part of akka-sample-persistence).

import akka.persistence._

case class Cmd(data: String)
case class Evt(data: String)

case class ExampleState(events: List[String] = Nil) {
  def update(evt: Evt) = copy( :: events)
  def size = events.length
  override def toString: String = events.reverse.toString

class ExampleProcessor extends EventsourcedProcessor {
  var state = ExampleState()

  def updateState(event: Evt): Unit =
    state = state.update(event)

  def numEvents =

  val receiveReplay: Receive = {
    case evt: Evt                                 => updateState(evt)
    case SnapshotOffer(_, snapshot: ExampleState) => state = snapshot

  val receiveCommand: Receive = {
    case Cmd(data) =>
      persist(Evt(s"${data}-${numEvents + 1}")) { event =>
        if (data == "foo") context.become(otherCommandHandler)
    case "snap"  => saveSnapshot(state)
    case "print" => println(state)

  val otherCommandHandler: Receive = {
    case Cmd("bar") =>
      persist(Evt(s"bar-${numEvents}")) { event =>
    case other => stash()

The example defines two data types, Cmd and Evt to represent commands and events, respectively. The state of the ExampleProcessor is a list of persisted event data contained in ExampleState.

The processor's receiveReplay method defines how state is updated during recovery by handling Evt and SnapshotOffer messages. The processor's receiveCommand method is a command handler. In this example, a command is handled by generating two events which are then persisted and handled. Events are persisted by calling persist with an event (or a sequence of events) as first argument and an event handler as second argument.

The persist method persists events asynchronously and the event handler is executed for successfully persisted events. Successfully persisted events are internally sent back to the processor as separate messages which trigger the event handler execution. An event handler may therefore close over processor state and mutate it. The sender of a persisted event is the sender of the corresponding command. This allows event handlers to reply to the sender of a command (not shown).

The main responsibility of an event handler is changing processor state using event data and notifying others about successful state changes by publishing events.

When persisting events with persist it is guaranteed that the processor will not receive new commands between the persist call and the execution(s) of the associated event handler. This also holds for multiple persist calls in context of a single command.

The example also demonstrates how to change the processor's default behavior, defined by receiveCommand, to another behavior, defined by otherCommandHandler, and back using context.become() and context.unbecome(). See also the API docs of persist for further details.

Reliable event delivery

Sending events from an event handler to another actor directly doesn't guarantee delivery of these events. To guarantee at-least-once delivery, Channels must be used. In this case, also replayed events (received by receiveReplay) must be sent to a channel, as shown in the following example:

class MyEventsourcedProcessor(destination: ActorRef) extends EventsourcedProcessor {
  val channel = context.actorOf(Channel.props("channel"))

  def handleEvent(event: String) = {
    // update state
    // ...
    // reliably deliver events
    channel ! Deliver(Persistent(event), destination)

  def receiveReplay: Receive = {
    case event: String => handleEvent(event)

  def receiveCommand: Receive = {
    case "cmd" => {
      // ...

In larger integration scenarios, channel destinations may be actors that submit received events to an external message broker, for example. After having successfully submitted an event, they should call confirm() on the received ConfirmablePersistent message.

Batch writes

To optimize throughput, a Processor internally batches received Persistent messages under high load before writing them to the journal (as a single batch). The batch size dynamically grows from 1 under low and moderate loads to a configurable maximum size (default is 200) under high load.

akka.persistence.journal.max-batch-size = 200

A new batch write is triggered by a processor as soon as a batch reaches the maximum size or if the journal completed writing the previous batch. Batch writes are never timer-based which keeps latencies as low as possible.

Applications that want to have more explicit control over batch writes and batch sizes can send processors PersistentBatch messages.

class MyProcessor extends Processor {
  def receive = {
    case Persistent("a", _) => // ...
    case Persistent("b", _) => // ...

val system = ActorSystem("example")
val processor = system.actorOf(Props[MyProcessor])

processor ! PersistentBatch(List(Persistent("a"), Persistent("b")))

Persistent messages contained in a PersistentBatch message are always written atomically, even if the batch size is greater than max-batch-size. Also, a PersistentBatch is written isolated from other batches. Persistent messages contained in a PersistentBatch are received individually by a processor.

PersistentBatch messages, for example, are used internally by an EventsourcedProcessor to ensure atomic writes of events. All events that are persisted in context of a single command are written as single batch to the journal (even if persist is called multiple times per command). The recovery of an EventsourcedProcessor will therefore never be done partially i.e. with only a subset of events persisted by a single command.

Storage plugins

Storage backends for journals and snapshot stores are plugins in akka-persistence. The default journal plugin writes messages to LevelDB (see Local LevelDB journal). The default snapshot store plugin writes snapshots as individual files to the local filesystem (see Local snapshot store). Applications can provide their own plugins by implementing a plugin API and activate them by configuration. Plugin development requires the following imports:

import scala.concurrent.Future
import scala.collection.immutable.Seq
import akka.persistence._
import akka.persistence.journal._
import akka.persistence.snapshot._

Journal plugin API

A journal plugin either extends SyncWriteJournal or AsyncWriteJournal. SyncWriteJournal is an actor that should be extended when the storage backend API only supports synchronous, blocking writes. The methods to be implemented in this case are:

 * Plugin API: synchronously writes a batch of persistent messages to the journal.
 * The batch write must be atomic i.e. either all persistent messages in the batch
 * are written or none.
def write(persistentBatch: immutable.Seq[PersistentRepr]): Unit

 * Plugin API: synchronously deletes all persistent messages within the range from
 * `fromSequenceNr` to `toSequenceNr` (both inclusive). If `permanent` is set to
 * `false`, the persistent messages are marked as deleted, otherwise they are
 * permanently deleted.
 * @see [[AsyncReplay]]
def delete(processorId: String, fromSequenceNr: Long, toSequenceNr: Long, permanent: Boolean): Unit

 * Plugin API: synchronously writes a delivery confirmation to the journal.
def confirm(processorId: String, sequenceNr: Long, channelId: String): Unit

AsyncWriteJournal is an actor that should be extended if the storage backend API supports asynchronous, non-blocking writes. The methods to be implemented in that case are:

 * Plugin API: asynchronously writes a batch of persistent messages to the journal.
 * The batch write must be atomic i.e. either all persistent messages in the batch
 * are written or none.
def writeAsync(persistentBatch: immutable.Seq[PersistentRepr]): Future[Unit]

 * Plugin API: asynchronously deletes all persistent messages within the range from
 * `fromSequenceNr` to `toSequenceNr` (both inclusive). If `permanent` is set to
 * `false`, the persistent messages are marked as deleted, otherwise they are
 * permanently deleted.
 * @see [[AsyncReplay]]
def deleteAsync(processorId: String, fromSequenceNr: Long, toSequenceNr: Long, permanent: Boolean): Future[Unit]

 * Plugin API: asynchronously writes a delivery confirmation to the journal.
def confirmAsync(processorId: String, sequenceNr: Long, channelId: String): Future[Unit]

Message replays are always asynchronous, therefore, any journal plugin must implement:

 * Plugin API: asynchronously replays persistent messages. Implementations replay
 * a message by calling `replayCallback`. The returned future must be completed
 * when all messages (matching the sequence number bounds) have been replayed. The
 * future `Long` value must be the highest stored sequence number in the journal
 * for the specified processor. The future must be completed with a failure if any
 * of the persistent messages could not be replayed.
 * The `replayCallback` must also be called with messages that have been marked
 * as deleted. In this case a replayed message's `deleted` method must return
 * `true`.
 * The channel ids of delivery confirmations that are available for a replayed
 * message must be contained in that message's `confirms` sequence.
 * @param processorId processor id.
 * @param fromSequenceNr sequence number where replay should start (inclusive).
 * @param toSequenceNr sequence number where replay should end (inclusive).
 * @param replayCallback called to replay a single message. Can be called from any
 *                       thread.
 * @see [[AsyncWriteJournal]]
 * @see [[SyncWriteJournal]]
def replayAsync(processorId: String, fromSequenceNr: Long, toSequenceNr: Long)(replayCallback: PersistentRepr  Unit): Future[Long]

A journal plugin can be activated with the following minimal configuration:

# Path to the journal plugin to be used
akka.persistence.journal.plugin = "my-journal"

# My custom journal plugin
my-journal {
  # Class name of the plugin.
  class = "docs.persistence.MyJournal"
  # Dispatcher for the plugin actor.
  plugin-dispatcher = ""

The specified plugin class must have a no-arg constructor. The plugin-dispatcher is the dispatcher used for the plugin actor. If not specified, it defaults to akka.persistence.dispatchers.default-plugin-dispatcher for SyncWriteJournal plugins and for AsyncWriteJournal plugins.

Snapshot store plugin API

A snapshot store plugin must extend the SnapshotStore actor and implement the following methods:

 * Plugin API: asynchronously loads a snapshot.
 * @param processorId processor id.
 * @param criteria selection criteria for loading.
def loadAsync(processorId: String, criteria: SnapshotSelectionCriteria): Future[Option[SelectedSnapshot]]

 * Plugin API: asynchronously saves a snapshot.
 * @param metadata snapshot metadata.
 * @param snapshot snapshot.
def saveAsync(metadata: SnapshotMetadata, snapshot: Any): Future[Unit]

 * Plugin API: called after successful saving of a snapshot.
 * @param metadata snapshot metadata.
def saved(metadata: SnapshotMetadata)

 * Plugin API: deletes the snapshot identified by `metadata`.
 * @param metadata snapshot metadata.

def delete(metadata: SnapshotMetadata)

 * Plugin API: deletes all snapshots matching `criteria`.
 * @param processorId processor id.
 * @param criteria selection criteria for deleting.
def delete(processorId: String, criteria: SnapshotSelectionCriteria)

A snapshot store plugin can be activated with the following minimal configuration:

# Path to the snapshot store plugin to be used
akka.persistence.snapshot-store.plugin = "my-snapshot-store"

# My custom snapshot store plugin
my-snapshot-store {
  # Class name of the plugin.
  class = "docs.persistence.MySnapshotStore"
  # Dispatcher for the plugin actor.
  plugin-dispatcher = "akka.persistence.dispatchers.default-plugin-dispatcher"

The specified plugin class must have a no-arg constructor. The plugin-dispatcher is the dispatcher used for the plugin actor. If not specified, it defaults to akka.persistence.dispatchers.default-plugin-dispatcher.

Pre-packaged plugins

Local LevelDB journal

The default journal plugin is akka.persistence.journal.leveldb which writes messages to a local LevelDB instance. The default location of the LevelDB files is a directory named journal in the current working directory. This location can be changed by configuration where the specified path can be relative or absolute:

akka.persistence.journal.leveldb.dir = "target/journal"

With this plugin, each actor system runs its own private LevelDB instance.

Shared LevelDB journal

A LevelDB instance can also be shared by multiple actor systems (on the same or on different nodes). This, for example, allows processors to failover to a backup node, assuming that the node, where the shared instance is runnning, is accessible from the backup node.


A shared LevelDB instance is a single point of failure and should therefore only be used for testing purposes.

A shared LevelDB instance can be created by instantiating the SharedLeveldbStore actor.

import akka.persistence.journal.leveldb.SharedLeveldbStore

val store = system.actorOf(Props[SharedLeveldbStore], "store")

By default, the shared instance writes journaled messages to a local directory named journal in the current working directory. The storage location can be changed by configuration: = "target/shared"

Actor systems that use a shared LevelDB store must activate the akka.persistence.journal.leveldb-shared plugin.

akka.persistence.journal.plugin = "akka.persistence.journal.leveldb-shared"

This plugin must be initialized by injecting the (remote) SharedLeveldbStore actor reference. Injection is done by calling the SharedLeveldbJournal.setStore method with the actor reference as argument.

trait SharedStoreUsage extends Actor {
  override def preStart(): Unit = {
    context.actorSelection("akka.tcp://[email protected]:2552/user/store") ! Identify(1)

  def receive = {
    case ActorIdentity(1, Some(store)) =>
      SharedLeveldbJournal.setStore(store, context.system)

Internal journal commands (sent by processors) are buffered until injection completes. Injection is idempotent i.e. only the first injection is used.

Local snapshot store

The default snapshot store plugin is akka.persistence.snapshot-store.local which writes snapshot files to the local filesystem. The default storage location is a directory named snapshots in the current working directory. This can be changed by configuration where the specified path can be relative or absolute:

akka.persistence.snapshot-store.local.dir = "target/snapshots"

Planned plugins

  • Shared snapshot store (SPOF, for testing purposes)
  • HA snapshot store backed by a distributed file system
  • HA journal backed by a distributed (NoSQL) data store

Custom serialization

Serialization of snapshots and payloads of Persistent messages is configurable with Akka's Serialization infrastructure. For example, if an application wants to serialize

  • payloads of type MyPayload with a custom MyPayloadSerializer and
  • snapshots of type MySnapshot with a custom MySnapshotSerializer

it must add {
  serializers {
    my-payload = "docs.persistence.MyPayloadSerializer"
    my-snapshot = "docs.persistence.MySnapshotSerializer"
  serialization-bindings {
    "docs.persistence.MyPayload" = my-payload
    "docs.persistence.MySnapshot" = my-snapshot

to the application configuration. If not specified, a default serializer is used, which is the JavaSerializer in this example.


When running tests with LevelDB default settings in sbt, make sure to set fork := true in your sbt project otherwise, you'll see an UnsatisfiedLinkError. Alternatively, you can switch to a LevelDB Java port by setting

akka.persistence.journal.leveldb.native = off

or = off

in your Akka configuration. The latter setting applies if you're using a Shared LevelDB journal. The LevelDB Java port is for testing purposes only.


State machines

State machines can be persisted by mixing in the FSM trait into processors.

import akka.persistence.{ Processor, Persistent }

class PersistentDoor extends Processor with FSM[String, Int] {
  startWith("closed", 0)

  when("closed") {
    case Event(Persistent("open", _), counter) =>
      goto("open") using (counter + 1) replying (counter)

  when("open") {
    case Event(Persistent("close", _), counter) =>
      goto("closed") using (counter + 1) replying (counter)