akka-persistence-kafka

by krasserm

A replicated Akka Persistence journal backed by Apache Kafka

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Kafka Plugins for Akka Persistence

Replicated Akka Persistence journal and snapshot store backed by Apache Kafka.

Build Status

Dependency

To include the Kafka plugins into your

sbt
project, add the following lines to your
build.sbt
file:
resolvers += "krasserm at bintray" at "http://dl.bintray.com/krasserm/maven"

libraryDependencies += "com.github.krasserm" %% "akka-persistence-kafka" % “0.4”

This version of

akka-persistence-kafka
depends on Kafka 0.8.2.1, Akka 2.3.11 and is cross-built against Scala 2.10.4 and 2.11.6. A complete list of released versions is here.

Usage hints

Kafka does not permanently store log entries but rather deletes them after a configurable retention time which defaults to 7 days in Kafka 0.8.x. Therefore, applications need to take snapshots of their persistent actors at intervals that are smaller than the configured retention time (for example, every 3 days). This ensures that persistent actors can always be recovered successfully.

Alternatively, the retention time can be set to a maximum value so that Kafka will never delete old entries. In this case, all events written by a single persistent actor must fit on a single node. This is a limitation of the current implementation which may be removed in later versions. However, this limitation is likely not relevant when running Kafka with default (or comparable) retention times and taking snapshots.

The latest snapshot of a persistent actor is never deleted if log compaction is enabled. See also section Configuration hints for details how to properly configure Kafka for being used with the storage plugins.

Journal plugin

Activation

To activate the journal plugin, add the following line to

application.conf
:
akka.persistence.journal.plugin = "kafka-journal"

This will run the journal plugin with default settings and connect to a Zookeeper instance running on

localhost:2181
. The Zookeeper connect string can be customized with the
kafka-journal.zookeeper.connect
configuration key (see also section Kafka cluster). Recommended Kafka broker configurations are given in section Configuration hints.

Use cases

Journal topics

For each persistent actor, the plugin creates a Kafka topic where the topic name equals the actor's

persistenceId
(only if it contains alphanumeric,
.
,
-
or
_
characters, otherwise, all other characters are replaced by
_
). Events published to these topics are serialized
akka.persistence.PersistentRepr
objects (see journal plugin API). Serialization of
PersistentRepr
objects can be customized. Journal topics are mainly intended for internal use (for recovery of persistent actors) but can also be consumed externally.

User-defined topics

The journal plugin can also publish events to user-defined topics. By default, all events generated by all persistent actors are published to a single

events
topic. This topic is intended for external consumption only. Events published to user-defined topics are serialized
Event
objects
package akka.persistence.kafka

/**

  • Event published to user-defined topics.
  • @param persistenceId Id of the persistent actor that generates event data.
  • @param sequenceNr Sequence number of the event.
  • @param data Event data generated by a persistent actor.
  • / case class Event(persistenceId: String, sequenceNr: Long, data: Any)

where

data
is the actual event written by a persistent actor (by calling
persist
or
persistAsync
),
sequenceNr
is the event's sequence number and
persistenceId
the id of the persistent actor.
Event
objects are serialized with a protobuf serializer and event
data
serialization can be customized with a user-defined serializer in the same way as for journal topics. Custom serializer configurations always apply to both, journal topics and user-defined topics.

For publishing events to user-defined topics the journal plugin uses an

EventTopicMapper
:
package akka.persistence.kafka

/**

  • Defines a mapping of events to user-defined topics.
  • / trait EventTopicMapper { /**
    • Maps an event to zero or more topics.
    • @param event event to be mapped.
    • @return a sequence of topic names.
    • / def topicsFor(event: Event): immutable.Seq[String] }

The default mapper is

DefaultEventTopicMapper
which maps all events to the
events
topic. It is configured in the reference configuration as follows:
kafka-journal.event.producer.topic.mapper.class = "akka.persistence.kafka.DefaultEventTopicMapper"

To customize the mapping of events to user-defined topics, applications can implement and configure a custom

EventTopicMapper
. For example, in order to publish
  • events from persistent actor
    a
    to topics
    topic-a-1
    and
    topic-a-2
    and
  • events from persistent actor
    b
    to topic
    topic-b

and to turn of publishing of events from all other actors, one would implement the following

ExampleEventTopicMapper
package akka.persistence.kafka.example

class ExampleEventTopicMapper extends EventTopicMapper { def topicsFor(event: Event): Seq[String] = event.persistenceId match { case "a" => List("topic-a-1", "topic-a-2") case "b" => List("topic-b") case _ => Nil }

and configure it in

application.conf
:
kafka-journal.event.producer.topic.mapper.class = "akka.persistence.kafka.example.ExampleEventTopicMapper"

To turn off publishing events to user-defined topics, the

EmptyEventTopicMapper
should be configured.
kafka-journal.event.producer.topic.mapper.class = "akka.persistence.kafka.EmptyEventTopicMapper"

External consumers

The following example shows how to consume

Event
s from a user-defined topic with name
topic-a-2
(see previous example) using Kafka's high-level consumer API:
import java.util.Properties

import akka.persistence.kafka.{EventDecoder, Event}

import kafka.consumer.{Consumer, ConsumerConfig} import kafka.serializer.StringDecoder

val props = new Properties() props.put("group.id", "consumer-1") props.put("zookeeper.connect", "localhost:2181") // ...

val system = ActorSystem("consumer")

val consConn = Consumer.create(new ConsumerConfig(props)) val streams = consConn.createMessageStreams(Map("topic-a-2" -> 1), keyDecoder = new StringDecoder, valueDecoder = new EventDecoder(system))

streams("topic-a-2")(0).foreach { mm => val event: Event = mm.message println(s"consumed ${event}") }

Applications may also consume serialized

PersistentRepr
objects from journal topics and deserialize them with Akka's serialization extension:
import java.util.Properties

import akka.actor._ import akka.persistence.PersistentRepr import akka.serialization.SerializationExtension

import com.typesafe.config.ConfigFactory

import kafka.consumer.{Consumer, ConsumerConfig} import kafka.serializer.{DefaultDecoder, StringDecoder}

val props = new Properties() props.put("group.id", "consumer-2") props.put("zookeeper.connect", "localhost:2181") // ...

val system = ActorSystem("example") val extension = SerializationExtension(system)

val consConn = Consumer.create(new ConsumerConfig(props)) val streams = consConn.createMessageStreams(Map("a" -> 1), keyDecoder = new StringDecoder, valueDecoder = new DefaultDecoder)

streams("a")(0).foreach { mm => val persistent: PersistentRepr = extension.deserialize(mm.message, classOf[PersistentRepr]).get println(s"consumed ${persistent}") }

There are many other libraries that can be used to consume (event) streams from Kafka topics, such as Spark Streaming, to mention only one example.

Implementation notes

  • During initialization, the journal plugin fetches cluster metadata from Zookeeper which may take up to a few seconds.
  • The journal plugin always writes
    PersistentRepr
    entries to partition 0 of journal topics. This ensures that all events written by a single persistent actor are stored in correct order. Later versions of the plugin may switch to a higher partition after having written a configurable number of events to the current partition.
  • The journal plugin distributes
    Event
    entries to all available partitions of user-defined topics. The partition key is the event's
    persistenceId
    so that a partial ordering of events is preserved when consuming events from user-defined topics. In other words, events written by a single persistent actor are always consumed in correct order but the relative ordering of events from different persistent actors is not defined.

Current limitations

  • The journal plugin does not support features that have been deprecated in Akka 2.3.4 (channels and single event deletions).
  • Range deletions are not persistent (which may not be relevant for applications that configure Kafka with reasonably small retention times).

Example source code

The complete source code of all examples from previous sections is in Example.scala, the corresponding configuration in example.conf.

Snapshot store plugin

Activation

To activate the snapshot store plugin, add the following line to

application.conf
:
akka.persistence.snapshot-store.plugin = "kafka-snapshot-store"

This will run the snapshot store plugin with default settings and connect to a Zookeeper instance running on

localhost:2181
. The Zookeeper connect string can be customized with the
kafka-snapshot-store.zookeeper.connect
configuration key (see also section Kafka cluster). Recommended Kafka broker configurations are given in section Configuration hints.

Snapshot topics

For each persistent actor, the plugin creates a Kafka topic where the topic name equals the actor's

persistenceId
, prefixed by the value of the
kafka-snapshot-store.prefix
configuration key which defaults to
snapshot-
. For example, if an actor's
persistenceId
is
example
, its snapshots are published to topic
snapshot-example
. For persistent views, the
viewId
is taken instead of the
persistenceId
.

Implementation notes

  • During initialization, the journal plugin fetches cluster metadata from Zookeeper which may take up to a few seconds.
  • The journal plugin always writes snapshots to partition 0 of snapshot topics.

Current limitations

  • Deletions are not persistent (which may not be relevant for applications that configure Kafka with reasonably small retention times).

Kafka

Kafka cluster

To connect to an existing Kafka cluster, an application must set a value for the

kafka-journal.zookeeper.connect
key in its
application.conf
:
kafka-journal.zookeeper.connect = ":,:,..."

If you want to run a Kafka cluster on a single node, you may find this article useful.

Test server

To use the test server, the following additional dependencies must be added to

build.sbt
:
libraryDependencies ++= Seq(
  "com.github.krasserm" %% "akka-persistence-kafka" % "0.4" % "test" classifier "tests",
  "org.apache.curator" % "curator-test" % "2.7.1" % "test"
)

This makes the

TestServer
class available which can be used to start a single Kafka and Zookeeper instance:
import akka.persistence.kafka.server.TestServer

// start a local Kafka and Zookeeper instance val server = new TestServer()

// use the local instance // ...

// and stop it server.stop()

The

TestServer
configuration can be customized with the
test-server.*
configuration keys (see reference configuration for details).

Configuration hints

The following broker configurations are recommended for being used with the storage plugins:

  • num.partitions
    should be set to
    1
    by default because the plugins only write to partition 0 of journal topics and snapshot topics. If a higher number of partitions is needed for user-defined topics (e.g. for scalability or throughput reasons) then this should be configured manually with the
    kafka-topics
    command line tool.
  • default.replication.factor
    should be set to at least
    2
    for high-availability of topics created by the plugins.
  • message.max.bytes
    and
    replica.fetch.max.bytes
    should be set to a value that is larger than the largest snapshot size. The default value is
    1024 * 1024
    which may be large enough for journal entries but likely to small for snapshots. When changing these settings make sure to also set
    kafka-snapshot-store.consumer.fetch.message.max.bytes
    and
    kafka-journal.consumer.fetch.message.max.bytes
    to this value.
  • log.cleanup.policy
    must be set to
    "compact"
    otherwise the most recent snapshot may be deleted if the retention time is exceeded and complete state recovery of persistent actors is not possible any more.

See also section Usage hints.

Reference configuration

kafka-journal {

FQCN of the Kafka journal plugin

class = "akka.persistence.kafka.journal.KafkaJournal"

Dispatcher for the plugin actor

plugin-dispatcher = "kafka-journal.default-dispatcher"

Number of concurrent writers (should be <= number of available threads in

dispatcher).

write-concurrency = 8

The partition to use when publishing to and consuming from journal topics.

partition = 0

Default dispatcher for plugin actor.

default-dispatcher { type = Dispatcher executor = "fork-join-executor" fork-join-executor { parallelism-min = 2 parallelism-max = 8 } }

consumer { # ------------------------------------------------------------------- # Simple consumer configuration (used for message replay and reading # metadata). # # See http://kafka.apache.org/documentation.html#consumerconfigs # See http://kafka.apache.org/documentation.html#simpleconsumerapi # -------------------------------------------------------------------

socket.timeout.ms = 30000

socket.receive.buffer.bytes = 65536

fetch.message.max.bytes = 1048576

}

producer { # ------------------------------------------------------------------- # PersistentRepr producer (to journal topics) configuration. # # See http://kafka.apache.org/documentation.html#producerconfigs # # The metadata.broker.list property is set dynamically by the journal. # No need to set it here. # -------------------------------------------------------------------

request.required.acks = 1

# DO NOT CHANGE!
producer.type = "sync"

# DO NOT CHANGE!
partitioner.class = "akka.persistence.kafka.StickyPartitioner"

# DO NOT CHANGE!
key.serializer.class = "kafka.serializer.StringEncoder"

# Increase if hundreds of topics are created during initialization.
message.send.max.retries = 5

# Increase if hundreds of topics are created during initialization.
retry.backoff.ms = 100

# Add further Kafka producer settings here, if needed.
# ...

}

event.producer { # ------------------------------------------------------------------- # Event producer (to user-defined topics) configuration. # # See http://kafka.apache.org/documentation.html#producerconfigs # -------------------------------------------------------------------

producer.type = "sync"

request.required.acks = 0

topic.mapper.class = "akka.persistence.kafka.DefaultEventTopicMapper"

key.serializer.class = "kafka.serializer.StringEncoder"

# Add further Kafka producer settings here, if needed.
# ...

}

zookeeper { # ------------------------------------------------------------------- # Zookeeper client configuration # -------------------------------------------------------------------

connect = "localhost:2181"

session.timeout.ms = 6000

connection.timeout.ms = 6000

sync.time.ms = 2000

} }

kafka-snapshot-store {

FQCN of the Kafka snapshot store plugin

class = "akka.persistence.kafka.snapshot.KafkaSnapshotStore"

Dispatcher for the plugin actor.

plugin-dispatcher = "kafka-snapshot-store.default-dispatcher"

The partition to use when publishing to and consuming from snapshot topics.

partition = 0

Topic name prefix (which prepended to persistenceId)

prefix = "snapshot-"

Default dispatcher for plugin actor.

default-dispatcher { type = Dispatcher executor = "fork-join-executor" fork-join-executor { parallelism-min = 2 parallelism-max = 8 } }

consumer { # ------------------------------------------------------------------- # Simple consumer configuration (used for loading snapshots and # reading metadata). # # See http://kafka.apache.org/documentation.html#consumerconfigs # See http://kafka.apache.org/documentation.html#simpleconsumerapi # -------------------------------------------------------------------

socket.timeout.ms = 30000

socket.receive.buffer.bytes = 65536

fetch.message.max.bytes = 1048576

}

producer { # ------------------------------------------------------------------- # Snapshot producer configuration. # # See http://kafka.apache.org/documentation.html#producerconfigs # # The metadata.broker.list property is set dynamically by the journal. # No need to set it here. # -------------------------------------------------------------------

request.required.acks = 1

producer.type = "sync"

# DO NOT CHANGE!
partitioner.class = "akka.persistence.kafka.StickyPartitioner"

# DO NOT CHANGE!
key.serializer.class = "kafka.serializer.StringEncoder"

# Add further Kafka producer settings here, if needed.
# ...

}

zookeeper { # ------------------------------------------------------------------- # Zookeeper client configuration # -------------------------------------------------------------------

connect = "localhost:2181"

session.timeout.ms = 6000

connection.timeout.ms = 6000

sync.time.ms = 2000

} }

test-server {

-------------------------------------------------------------------

Test Kafka and Zookeeper server configuration.

#

See http://kafka.apache.org/documentation.html#brokerconfigs

-------------------------------------------------------------------

zookeeper {

port = 2181

dir = "data/zookeeper"

}

kafka {

broker.id = 1

port = 6667

num.partitions = 2

log.cleanup.policy = "compact"

log.dirs = data/kafka

log.index.size.max.bytes = 1024

} }

akka { actor { serializers { kafka-snapshot = "akka.persistence.kafka.snapshot.KafkaSnapshotSerializer" }

serialization-bindings {
  "akka.persistence.kafka.snapshot.KafkaSnapshot" = kafka-snapshot
}

} }

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