akka.persistence.journal.japi

Class AsyncWriteJournal

java.lang.Object

akka.persistence.journal.japi.AsyncRecovery

akka.persistence.journal.japi.AsyncWriteJournal

All Implemented Interfaces:

Actor, AsyncRecovery, AsyncWriteJournal, WriteJournalBase

public abstract class AsyncWriteJournal
extends AsyncRecovery
implements AsyncWriteJournal

Java API: abstract journal, optimized for asynchronous, non-blocking writes.

Nested Class Summary

Nested classes/interfaces inherited from interface akka.persistence.journal.AsyncWriteJournal

AsyncWriteJournal.Desequenced, AsyncWriteJournal.Desequenced$, AsyncWriteJournal.Resequencer

Nested classes/interfaces inherited from interface akka.actor.Actor

Actor.emptyBehavior$, Actor.ignoringBehavior$

Constructor Summary

Constructors

Constructor and Description
AsyncWriteJournal()

Method Summary

Modifier and Type	Method and Description
scala.concurrent.Future<scala.runtime.BoxedUnit>	asyncDeleteMessagesTo(java.lang.String persistenceId, long toSequenceNr) Plugin API: asynchronously deletes all persistent messages up to toSequenceNr (inclusive).
scala.concurrent.Future<scala.collection.immutable.Seq<scala.util.Try<scala.runtime.BoxedUnit>>>	asyncWriteMessages(scala.collection.immutable.Seq<AtomicWrite> messages) Plugin API: asynchronously writes a batch (Seq) of persistent messages to the journal.
ActorContext	context() Stores the context for this actor, including self, and sender.
scala.concurrent.Future<java.lang.Void>	doAsyncDeleteMessagesTo(java.lang.String persistenceId, long toSequenceNr) Java API, Plugin API: synchronously deletes all persistent messages up to `toSequenceNr`.
scala.concurrent.Future<java.lang.Long>	doAsyncReadHighestSequenceNr(java.lang.String persistenceId, long fromSequenceNr) Java API, Plugin API: asynchronously reads the highest stored sequence number for the given `persistenceId`.
scala.concurrent.Future<java.lang.Void>	doAsyncReplayMessages(java.lang.String persistenceId, long fromSequenceNr, long toSequenceNr, long max, java.util.function.Consumer<PersistentRepr> replayCallback) Java API, Plugin API: asynchronously replays persistent messages.
scala.concurrent.Future<java.lang.Iterable<java.util.Optional<java.lang.Exception>>>	doAsyncWriteMessages(java.lang.Iterable<AtomicWrite> messages) Java API, Plugin API: asynchronously writes a batch (`Iterable`) of persistent messages to the journal.
Persistence	persistence()
scala.PartialFunction<java.lang.Object,scala.runtime.BoxedUnit>	receiveWriteJournal()
ActorRef	self() The 'self' field holds the ActorRef for this actor.

Methods inherited from class akka.persistence.journal.japi.AsyncRecovery

asyncReadHighestSequenceNr, asyncReplayMessages

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface akka.persistence.journal.AsyncWriteJournal

isReplayFilterEnabled, receive, receivePluginInternal

Methods inherited from interface akka.actor.Actor

aroundPostRestart, aroundPostStop, aroundPreRestart, aroundPreStart, aroundReceive, postRestart, postStop, preRestart, preStart, sender, supervisorStrategy, unhandled

Methods inherited from interface akka.persistence.journal.WriteJournalBase

adaptFromJournal, adaptToJournal, preparePersistentBatch

Methods inherited from interface akka.persistence.journal.AsyncRecovery

asyncReadHighestSequenceNr, asyncReplayMessages

Constructor Detail

AsyncWriteJournal

public AsyncWriteJournal()

Method Detail

receiveWriteJournal

public final scala.PartialFunction<java.lang.Object,scala.runtime.BoxedUnit> receiveWriteJournal()

Specified by:

receiveWriteJournal in interface AsyncWriteJournal

persistence

public Persistence persistence()

Specified by:

persistence in interface WriteJournalBase

context

public ActorContext context()

Description copied from interface: Actor

Stores the context for this actor, including self, and sender. It is implicit to support operations such as forward.

WARNING: Only valid within the Actor itself, so do not close over it and publish it to other threads!

ActorContext is the Scala API. getContext returns a UntypedActorContext, which is the Java API of the actor context.

Specified by:

context in interface Actor

Returns:

(undocumented)

self

public final ActorRef self()

Description copied from interface: Actor

The 'self' field holds the ActorRef for this actor.

Can be used to send messages to itself:

 self ! message
 

Specified by:

self in interface Actor

Returns:

(undocumented)

asyncWriteMessages

public final scala.concurrent.Future<scala.collection.immutable.Seq<scala.util.Try<scala.runtime.BoxedUnit>>> asyncWriteMessages(scala.collection.immutable.Seq<AtomicWrite> messages)

Description copied from interface: AsyncWriteJournal

Plugin API: asynchronously writes a batch (Seq) of persistent messages to the journal.

The batch is only for performance reasons, i.e. all messages don't have to be written atomically. Higher throughput can typically be achieved by using batch inserts of many records compared to inserting records one-by-one, but this aspect depends on the underlying data store and a journal implementation can implement it as efficient as possible. Journals should aim to persist events in-order for a given persistenceId as otherwise in case of a failure, the persistent state may be end up being inconsistent.

Each AtomicWrite message contains the single PersistentRepr that corresponds to the event that was passed to the persist method of the PersistentActor, or it contains several PersistentRepr that corresponds to the events that were passed to the persistAll method of the PersistentActor. All PersistentRepr of the AtomicWrite must be written to the data store atomically, i.e. all or none must be stored. If the journal (data store) cannot support atomic writes of multiple events it should reject such writes with a Try Failure with an UnsupportedOperationException describing the issue. This limitation should also be documented by the journal plugin.

If there are failures when storing any of the messages in the batch the returned Future must be completed with failure. The Future must only be completed with success when all messages in the batch have been confirmed to be stored successfully, i.e. they will be readable, and visible, in a subsequent replay. If there is uncertainty about if the messages were stored or not the Future must be completed with failure.

Data store connection problems must be signaled by completing the Future with failure.

The journal can also signal that it rejects individual messages (AtomicWrite) by the returned immutable.Seq[Try[Unit}. It is possible but not mandatory to reduce number of allocations by returning Future.successful(Nil) for the happy path, i.e. when no messages are rejected. Otherwise the returned Seq must have as many elements as the input messages Seq. Each Try element signals if the corresponding AtomicWrite is rejected or not, with an exception describing the problem. Rejecting a message means it was not stored, i.e. it must not be included in a later replay. Rejecting a message is typically done before attempting to store it, e.g. because of serialization error.

Data store connection problems must not be signaled as rejections.

It is possible but not mandatory to reduce number of allocations by returning Future.successful(Nil) for the happy path, i.e. when no messages are rejected.

Calls to this method are serialized by the enclosing journal actor. If you spawn work in asynchronous tasks it is alright that they complete the futures in any order, but the actual writes for a specific persistenceId should be serialized to avoid issues such as events of a later write are visible to consumers (query side, or replay) before the events of an earlier write are visible. A PersistentActor will not send a new WriteMessages request before the previous one has been completed.

Please note that the sender field of the contained PersistentRepr objects has been nulled out (i.e. set to ActorRef.noSender) in order to not use space in the journal for a sender reference that will likely be obsolete during replay.

Please also note that requests for the highest sequence number may be made concurrently to this call executing for the same persistenceId, in particular it is possible that a restarting actor tries to recover before its outstanding writes have completed. In the latter case it is highly desirable to defer reading the highest sequence number until all outstanding writes have completed, otherwise the PersistentActor may reuse sequence numbers.

This call is protected with a circuit-breaker.

Specified by:

asyncWriteMessages in interface AsyncWriteJournal

Parameters:

messages - (undocumented)

Returns:

(undocumented)

asyncDeleteMessagesTo

public final scala.concurrent.Future<scala.runtime.BoxedUnit> asyncDeleteMessagesTo(java.lang.String persistenceId,
                                                                                    long toSequenceNr)

Description copied from interface: AsyncWriteJournal

Plugin API: asynchronously deletes all persistent messages up to toSequenceNr (inclusive).

This call is protected with a circuit-breaker. Message deletion doesn't affect the highest sequence number of messages, journal must maintain the highest sequence number and never decrease it.

Specified by:

asyncDeleteMessagesTo in interface AsyncWriteJournal

Parameters:

persistenceId - (undocumented)

toSequenceNr - (undocumented)

Returns:

(undocumented)

doAsyncWriteMessages

public scala.concurrent.Future<java.lang.Iterable<java.util.Optional<java.lang.Exception>>> doAsyncWriteMessages(java.lang.Iterable<AtomicWrite> messages)

Java API, Plugin API: asynchronously writes a batch (`Iterable`) of persistent messages to the journal. The batch is only for performance reasons, i.e. all messages don't have to be written atomically. Higher throughput can typically be achieved by using batch inserts of many records compared to inserting records one-by-one, but this aspect depends on the underlying data store and a journal implementation can implement it as efficient as possible. Journals should aim to persist events in-order for a given `persistenceId` as otherwise in case of a failure, the persistent state may be end up being inconsistent. Each `AtomicWrite` message contains the single `PersistentRepr` that corresponds to the event that was passed to the `persist` method of the `PersistentActor`, or it contains several `PersistentRepr` that corresponds to the events that were passed to the `persistAll` method of the `PersistentActor`. All `PersistentRepr` of the `AtomicWrite` must be written to the data store atomically, i.e. all or none must be stored. If the journal (data store) cannot support atomic writes of multiple events it should reject such writes with an `Optional` with an `UnsupportedOperationException` describing the issue. This limitation should also be documented by the journal plugin. If there are failures when storing any of the messages in the batch the returned `Future` must be completed with failure. The `Future` must only be completed with success when all messages in the batch have been confirmed to be stored successfully, i.e. they will be readable, and visible, in a subsequent replay. If there is uncertainty about if the messages were stored or not the `Future` must be completed with failure. Data store connection problems must be signaled by completing the `Future` with failure. The journal can also signal that it rejects individual messages (`AtomicWrite`) by the returned `Iterable<Optional<Exception>>`. The returned `Iterable` must have as many elements as the input `messages` `Iterable`. Each `Optional` element signals if the corresponding `AtomicWrite` is rejected or not, with an exception describing the problem. Rejecting a message means it was not stored, i.e. it must not be included in a later replay. Rejecting a message is typically done before attempting to store it, e.g. because of serialization error. Data store connection problems must not be signaled as rejections. Note that it is possible to reduce number of allocations by caching some result `Iterable` for the happy path, i.e. when no messages are rejected. Calls to this method are serialized by the enclosing journal actor. If you spawn work in asynchronous tasks it is alright that they complete the futures in any order, but the actual writes for a specific persistenceId should be serialized to avoid issues such as events of a later write are visible to consumers (query side, or replay) before the events of an earlier write are visible. This can also be done with consistent hashing if it is too fine grained to do it on the persistenceId level. Normally a `PersistentActor` will only have one outstanding write request to the journal but it may emit several write requests when `persistAsync` is used and the max batch size is reached. This call is protected with a circuit-breaker.

doAsyncDeleteMessagesTo

public scala.concurrent.Future<java.lang.Void> doAsyncDeleteMessagesTo(java.lang.String persistenceId,
                                                                       long toSequenceNr)

Java API, Plugin API: synchronously deletes all persistent messages up to `toSequenceNr`. This call is protected with a circuit-breaker.

See Also:

AsyncRecoveryPlugin

doAsyncReplayMessages

public scala.concurrent.Future<java.lang.Void> doAsyncReplayMessages(java.lang.String persistenceId,
                                                                     long fromSequenceNr,
                                                                     long toSequenceNr,
                                                                     long max,
                                                                     java.util.function.Consumer<PersistentRepr> replayCallback)

Java API, 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 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 `toSequenceNr` is the lowest of what was returned by doAsyncReadHighestSequenceNr(java.lang.String, long) and what the user specified as recovery Recovery parameter.

Parameters:

persistenceId - id of the persistent actor.

fromSequenceNr - sequence number where replay should start (inclusive).

toSequenceNr - sequence number where replay should end (inclusive).

max - maximum number of messages to be replayed.

replayCallback - called to replay a single message. Can be called from any thread.

doAsyncReadHighestSequenceNr

public scala.concurrent.Future<java.lang.Long> doAsyncReadHighestSequenceNr(java.lang.String persistenceId,
                                                                            long fromSequenceNr)

Java API, Plugin API: asynchronously reads the highest stored sequence number for the given `persistenceId`. The persistent actor will use the highest sequence number after recovery as the starting point when persisting new events. This sequence number is also used as `toSequenceNr` in subsequent call to [[#asyncReplayMessages]] unless the user has specified a lower `toSequenceNr`.

Parameters:

persistenceId - id of the persistent actor.

fromSequenceNr - hint where to start searching for the highest sequence number.