# Cluster Usage
For introduction to the Akka Cluster concepts please see @ref:[Cluster Specification](common/cluster.md).
The core of Akka Cluster is the cluster membership, to keep track of what nodes are part of the cluster and
their health. There are several @ref:[Higher level Cluster tools](cluster-usage.md#higher-level-cluster-tools) that are built
on top of the cluster membership.
## Dependency
To use Akka Cluster, you must add the following dependency in your project:
@@dependency[sbt,Maven,Gradle] {
group="com.typesafe.akka"
artifact="akka-cluster_$scala.binary_version$"
version="$akka.version$"
}
## Sample project
You can look at the
@java[@extref[Cluster example project](samples:akka-samples-cluster-java)]
@scala[@extref[Cluster example project](samples:akka-samples-cluster-scala)]
to see what this looks like in practice.
## When and where to use Akka Cluster
An architectural choice you have to make is if you are going to use a microservices architecture or
a traditional distributed application. This choice will influence how you should use Akka Cluster.
### Microservices
Microservices has many attractive properties, such as the independent nature of microservices allows for
multiple smaller and more focused teams that can deliver new functionality more frequently and can
respond quicker to business opportunities. Reactive Microservices should be isolated, autonomous, and have
a single responsibility as identified by Jonas Bonér in the book
[Reactive Microsystems: The Evolution of Microservices at Scale](https://info.lightbend.com/ebook-reactive-microservices-the-evolution-of-microservices-at-scale-register.html).
In a microservices architecture, you should consider communication within a service and between services.
In general we recommend against using Akka Cluster and actor messaging between _different_ services because that
would result in a too tight code coupling between the services and difficulties deploying these independent of
each other, which is one of the main reasons for using a microservices architecture.
See the discussion on
@scala[[Internal and External Communication](https://www.lagomframework.com/documentation/current/scala/InternalAndExternalCommunication.html)]
@java[[Internal and External Communication](https://www.lagomframework.com/documentation/current/java/InternalAndExternalCommunication.html)]
in the docs of the [Lagom Framework](https://www.lagomframework.com) (where each microservice is an Akka Cluster)
for some background on this.
Nodes of a single service (collectively called a cluster) require less decoupling. They share the same code and
are deployed together, as a set, by a single team or individual. There might be two versions running concurrently
during a rolling deployment, but deployment of the entire set has a single point of control. For this reason,
intra-service communication can take advantage of Akka Cluster, failure management and actor messaging, which
is convenient to use and has great performance.
Between different services [Akka HTTP](https://doc.akka.io/docs/akka-http/current) or
[Akka gRPC](https://developer.lightbend.com/docs/akka-grpc/current/) can be used for synchronous (yet non-blocking)
communication and [Akka Streams Kafka](https://doc.akka.io/docs/akka-stream-kafka/current/home.html) or other
[Alpakka](https://developer.lightbend.com/docs/alpakka/current/) connectors for integration asynchronous communication.
All those communication mechanisms work well with streaming of messages with end-to-end back-pressure, and the
synchronous communication tools can also be used for single request response interactions. It is also important
to note that when using these tools both sides of the communication do not have to be implemented with Akka,
nor does the programming language matter.
### Traditional distributed application
We acknowledge that microservices also introduce many new challenges and it's not the only way to
build applications. A traditional distributed application may have less complexity and work well in many cases.
For example for a small startup, with a single team, building an application where time to market is everything.
Akka Cluster can efficiently be used for building such distributed application.
In this case, you have a single deployment unit, built from a single code base (or using traditional binary
dependency management to modularize) but deployed across many nodes using a single cluster.
Tighter coupling is OK, because there is a central point of deployment and control. In some cases, nodes may
have specialized runtime roles which means that the cluster is not totally homogenous (e.g., "front-end" and
"back-end" nodes, or dedicated master/worker nodes) but if these are run from the same built artifacts this
is just a runtime behavior and doesn't cause the same kind of problems you might get from tight coupling of
totally separate artifacts.
A tightly coupled distributed application has served the industry and many Akka users well for years and is
still a valid choice.
### Distributed monolith
There is also an anti-pattern that is sometimes called "distributed monolith". You have multiple services
that are built and deployed independently from each other, but they have a tight coupling that makes this
very risky, such as a shared cluster, shared code and dependencies for service API calls, or a shared
database schema. There is a false sense of autonomy because of the physical separation of the code and
deployment units, but you are likely to encounter problems because of changes in the implementation of
one service leaking into the behavior of others. See Ben Christensen's
[Don’t Build a Distributed Monolith](https://www.microservices.com/talks/dont-build-a-distributed-monolith/).
Organizations that find themselves in this situation often react by trying to centrally coordinate deployment
of multiple services, at which point you have lost the principal benefit of microservices while taking on
the costs. You are in a halfway state with things that aren't really separable being built and deployed
in a separate way. Some people do this, and some manage to make it work, but it's not something we would
recommend and it needs to be carefully managed.
## A Simple Cluster Example
The following configuration enables the `Cluster` extension to be used.
It joins the cluster and an actor subscribes to cluster membership events and logs them.
The `application.conf` configuration looks like this:
```
akka {
actor {
provider = "cluster"
}
remote.artery {
canonical {
hostname = "127.0.0.1"
port = 2551
}
}
cluster {
seed-nodes = [
"akka://ClusterSystem@127.0.0.1:2551",
"akka://ClusterSystem@127.0.0.1:2552"]
# auto downing is NOT safe for production deployments.
# you may want to use it during development, read more about it in the docs.
#
# auto-down-unreachable-after = 10s
}
}
```
To enable cluster capabilities in your Akka project you should, at a minimum, add the @ref:[Remoting](remoting-artery.md)
settings, but with `cluster`.
The `akka.cluster.seed-nodes` should normally also be added to your `application.conf` file.
@@@ note
If you are running Akka in a Docker container or the nodes for some other reason have separate internal and
external ip addresses you must configure remoting according to @ref:[Akka behind NAT or in a Docker container](remoting-artery.md#remote-configuration-nat-artery)
@@@
The seed nodes are configured contact points for initial, automatic, join of the cluster.
Note that if you are going to start the nodes on different machines you need to specify the
ip-addresses or host names of the machines in `application.conf` instead of `127.0.0.1`
An actor that uses the cluster extension may look like this:
Scala
: @@snip [SimpleClusterListener.scala](/akka-docs/src/test/scala/docs/cluster/SimpleClusterListener.scala) { type=scala }
Java
: @@snip [SimpleClusterListener.java](/akka-docs/src/test/java/jdocs/cluster/SimpleClusterListener.java) { type=java }
The actor registers itself as subscriber of certain cluster events. It receives events corresponding to the current state
of the cluster when the subscription starts and then it receives events for changes that happen in the cluster.
The easiest way to run this example yourself is to try the
@scala[@extref[Akka Cluster Sample with Scala](samples:akka-samples-cluster-scala)]@java[@extref[Akka Cluster Sample with Java](samples:akka-samples-cluster-java)].
It contains instructions on how to run the `SimpleClusterApp`.
## Joining to Seed Nodes
@@@ note
When starting clusters on cloud systems such as Kubernetes, AWS, Google Cloud, Azure, Mesos or others which maintain
DNS or other ways of discovering nodes, you may want to use the automatic joining process implemented by the open source
[Akka Cluster Bootstrap](https://developer.lightbend.com/docs/akka-management/current/bootstrap/index.html) module.
@@@
### Joining configured seed nodes
You may decide if joining to the cluster should be done manually or automatically
to configured initial contact points, so-called seed nodes. After the joining process
the seed nodes are not special and they participate in the cluster in exactly the same
way as other nodes.
When a new node is started it sends a message to all seed nodes and then sends join command to the one that
answers first. If no one of the seed nodes replied (might not be started yet)
it retries this procedure until successful or shutdown.
You define the seed nodes in the [configuration](#cluster-configuration) file (application.conf):
```
akka.cluster.seed-nodes = [
"akka://ClusterSystem@host1:2552",
"akka://ClusterSystem@host2:2552"]
```
This can also be defined as Java system properties when starting the JVM using the following syntax:
```
-Dakka.cluster.seed-nodes.0=akka://ClusterSystem@host1:2552
-Dakka.cluster.seed-nodes.1=akka://ClusterSystem@host2:2552
```
The seed nodes can be started in any order and it is not necessary to have all
seed nodes running, but the node configured as the **first element** in the `seed-nodes`
configuration list must be started when initially starting a cluster, otherwise the
other seed-nodes will not become initialized and no other node can join the cluster.
The reason for the special first seed node is to avoid forming separated islands when
starting from an empty cluster.
It is quickest to start all configured seed nodes at the same time (order doesn't matter),
otherwise it can take up to the configured `seed-node-timeout` until the nodes
can join.
Once more than two seed nodes have been started it is no problem to shut down the first
seed node. If the first seed node is restarted, it will first try to join the other
seed nodes in the existing cluster. Note that if you stop all seed nodes at the same time
and restart them with the same `seed-nodes` configuration they will join themselves and
form a new cluster instead of joining remaining nodes of the existing cluster. That is
likely not desired and should be avoided by listing several nodes as seed nodes for redundancy
and don't stop all of them at the same time.
### Automatically joining to seed nodes with Cluster Bootstrap
Instead of manually configuring seed nodes, which is useful in development or statically assigned node IPs, you may want
to automate the discovery of seed nodes using your cloud providers or cluster orchestrator, or some other form of service
discovery (such as managed DNS). The open source Akka Management library includes the
[Cluster Bootstrap](https://developer.lightbend.com/docs/akka-management/current/bootstrap/index.html) module which handles
just that. Please refer to its documentation for more details.
### Programatically joining to seed nodes with `joinSeedNodes`
You may also use @scala[`Cluster(system).joinSeedNodes`]@java[`Cluster.get(system).joinSeedNodes`] to join programmatically,
which is attractive when dynamically discovering other nodes at startup by using some external tool or API.
When using `joinSeedNodes` you should not include the node itself except for the node that is
supposed to be the first seed node, and that should be placed first in the parameter to
`joinSeedNodes`.
Scala
: @@snip [ClusterDocSpec.scala](/akka-docs/src/test/scala/docs/cluster/ClusterDocSpec.scala) { #join-seed-nodes }
Java
: @@snip [ClusterDocTest.java](/akka-docs/src/test/java/jdocs/cluster/ClusterDocTest.java) { #join-seed-nodes-imports #join-seed-nodes }
Unsuccessful attempts to contact seed nodes are automatically retried after the time period defined in
configuration property `seed-node-timeout`. Unsuccessful attempt to join a specific seed node is
automatically retried after the configured `retry-unsuccessful-join-after`. Retrying means that it
tries to contact all seed nodes and then joins the node that answers first. The first node in the list
of seed nodes will join itself if it cannot contact any of the other seed nodes within the
configured `seed-node-timeout`.
The joining of given seed nodes will by default be retried indefinitely until
a successful join. That process can be aborted if unsuccessful by configuring a
timeout. When aborted it will run @ref:[Coordinated Shutdown](actors.md#coordinated-shutdown),
which by default will terminate the ActorSystem. CoordinatedShutdown can also be configured to exit
the JVM. It is useful to define this timeout if the `seed-nodes` are assembled
dynamically and a restart with new seed-nodes should be tried after unsuccessful
attempts.
```
akka.cluster.shutdown-after-unsuccessful-join-seed-nodes = 20s
akka.coordinated-shutdown.terminate-actor-system = on
```
If you don't configure seed nodes or use `joinSeedNodes` you need to join the cluster manually, which can be performed by using [JMX](#cluster-jmx) or [HTTP](#cluster-http).
You can join to any node in the cluster. It does not have to be configured as a seed node.
Note that you can only join to an existing cluster member, which means that for bootstrapping some
node must join itself,and then the following nodes could join them to make up a cluster.
An actor system can only join a cluster once. Additional attempts will be ignored.
When it has successfully joined it must be restarted to be able to join another
cluster or to join the same cluster again. It can use the same host name and port
after the restart, when it come up as new incarnation of existing member in the cluster,
trying to join in, then the existing one will be removed from the cluster and then it will
be allowed to join.
@@@ note
The name of the `ActorSystem` must be the same for all members of a cluster. The name is given when you start the `ActorSystem`.
@@@
## Downing
When a member is considered by the failure detector to be unreachable the
leader is not allowed to perform its duties, such as changing status of
new joining members to 'Up'. The node must first become reachable again, or the
status of the unreachable member must be changed to 'Down'. Changing status to 'Down'
can be performed automatically or manually. By default it must be done manually, using
[JMX](#cluster-jmx) or [HTTP](#cluster-http).
It can also be performed programmatically with @scala[`Cluster(system).down(address)`]@java[`Cluster.get(system).down(address)`].
If a node is still running and sees its self as Down it will shutdown. @ref:[Coordinated Shutdown](actors.md#coordinated-shutdown) will automatically
run if `run-coordinated-shutdown-when-down` is set to `on` (the default) however the node will not try
and leave the cluster gracefully so sharding and singleton migration will not occur.
A pre-packaged solution for the downing problem is provided by
[Split Brain Resolver](http://developer.lightbend.com/docs/akka-commercial-addons/current/split-brain-resolver.html),
which is part of the [Lightbend Reactive Platform](http://www.lightbend.com/platform).
If you don’t use RP, you should anyway carefully read the [documentation](http://developer.lightbend.com/docs/akka-commercial-addons/current/split-brain-resolver.html)
of the Split Brain Resolver and make sure that the solution you are using handles the concerns
described there.
### Auto-downing (DO NOT USE)
There is an automatic downing feature that you should not use in production. For testing purpose you can enable it with configuration:
```
akka.cluster.auto-down-unreachable-after = 120s
```
This means that the cluster leader member will change the `unreachable` node
status to `down` automatically after the configured time of unreachability.
This is a naïve approach to remove unreachable nodes from the cluster membership.
It can be useful during development but in a production environment it will eventually breakdown the cluster. When a network partition occurs, both sides of the
partition will see the other side as unreachable and remove it from the cluster.
This results in the formation of two separate, disconnected, clusters
(known as *Split Brain*).
This behaviour is not limited to network partitions. It can also occur if a node
in the cluster is overloaded, or experiences a long GC pause.
@@@ warning
We recommend against using the auto-down feature of Akka Cluster in production. It
has multiple undesirable consequences for production systems.
If you are using @ref:[Cluster Singleton](cluster-singleton.md) or
@ref:[Cluster Sharding](cluster-sharding.md) it can break the contract provided by
those features. Both provide a guarantee that an actor will be unique in a cluster.
With the auto-down feature enabled, it is possible for multiple independent clusters
to form (*Split Brain*). When this happens the guaranteed uniqueness will no
longer be true resulting in undesirable behaviour in the system.
This is even more severe when @ref:[Akka Persistence](persistence.md) is used in
conjunction with Cluster Sharding. In this case, the lack of unique actors can
cause multiple actors to write to the same journal. Akka Persistence operates on a
single writer principle. Having multiple writers will corrupt the journal
and make it unusable.
Finally, even if you don't use features such as Persistence, Sharding, or Singletons,
auto-downing can lead the system to form multiple small clusters. These small
clusters will be independent from each other. They will be unable to communicate
and as a result you may experience performance degradation. Once this condition
occurs, it will require manual intervention in order to reform the cluster.
Because of these issues, auto-downing should **never** be used in a production environment.
@@@
## Leaving
There are two ways to remove a member from the cluster.
You can stop the actor system (or the JVM process). It will be detected
as unreachable and removed after the automatic or manual downing as described
above.
A more graceful exit can be performed if you tell the cluster that a node shall leave.
This can be performed using [JMX](#cluster-jmx) or [HTTP](#cluster-http).
It can also be performed programmatically with:
Scala
: @@snip [ClusterDocSpec.scala](/akka-docs/src/test/scala/docs/cluster/ClusterDocSpec.scala) { #leave }
Java
: @@snip [ClusterDocTest.java](/akka-docs/src/test/java/jdocs/cluster/ClusterDocTest.java) { #leave }
Note that this command can be issued to any member in the cluster, not necessarily the
one that is leaving.
The @ref:[Coordinated Shutdown](actors.md#coordinated-shutdown) will automatically run when the cluster node sees itself as
`Exiting`, i.e. leaving from another node will trigger the shutdown process on the leaving node.
Tasks for graceful leaving of cluster including graceful shutdown of Cluster Singletons and
Cluster Sharding are added automatically when Akka Cluster is used, i.e. running the shutdown
process will also trigger the graceful leaving if it's not already in progress.
Normally this is handled automatically, but in case of network failures during this process it might still
be necessary to set the node’s status to `Down` in order to complete the removal.
## WeaklyUp Members
If a node is `unreachable` then gossip convergence is not possible and therefore any
`leader` actions are also not possible. However, we still might want new nodes to join
the cluster in this scenario.
`Joining` members will be promoted to `WeaklyUp` and become part of the cluster if
convergence can't be reached. Once gossip convergence is reached, the leader will move `WeaklyUp`
members to `Up`.
This feature is enabled by default, but it can be disabled with configuration option:
```
akka.cluster.allow-weakly-up-members = off
```
You can subscribe to the `WeaklyUp` membership event to make use of the members that are
in this state, but you should be aware of that members on the other side of a network partition
have no knowledge about the existence of the new members. You should for example not count
`WeaklyUp` members in quorum decisions.
## Subscribe to Cluster Events
You can subscribe to change notifications of the cluster membership by using
@scala[`Cluster(system).subscribe`]@java[`Cluster.get(system).subscribe`].
Scala
: @@snip [SimpleClusterListener2.scala](/akka-docs/src/test/scala/docs/cluster/SimpleClusterListener2.scala) { #subscribe }
Java
: @@snip [SimpleClusterListener2.java](/akka-docs/src/test/java/jdocs/cluster/SimpleClusterListener2.java) { #subscribe }
A snapshot of the full state, `akka.cluster.ClusterEvent.CurrentClusterState`, is sent to the subscriber
as the first message, followed by events for incremental updates.
Note that you may receive an empty `CurrentClusterState`, containing no members,
followed by `MemberUp` events from other nodes which already joined,
if you start the subscription before the initial join procedure has completed.
This may for example happen when you start the subscription immediately after `cluster.join()` like below.
This is expected behavior. When the node has been accepted in the cluster you will
receive `MemberUp` for that node, and other nodes.
Scala
: @@snip [SimpleClusterListener2.scala](/akka-docs/src/test/scala/docs/cluster/SimpleClusterListener2.scala) { #join #subscribe }
Java
: @@snip [SimpleClusterListener2.java](/akka-docs/src/test/java/jdocs/cluster/SimpleClusterListener2.java) { #join #subscribe }
To avoid receiving an empty `CurrentClusterState` at the beginning, you can use it like shown in the following example,
to defer subscription until the `MemberUp` event for the own node is received:
Scala
: @@snip [SimpleClusterListener2.scala](/akka-docs/src/test/scala/docs/cluster/SimpleClusterListener2.scala) { #join #register-on-memberup }
Java
: @@snip [SimpleClusterListener2.java](/akka-docs/src/test/java/jdocs/cluster/SimpleClusterListener2.java) { #join #register-on-memberup }
If you find it inconvenient to handle the `CurrentClusterState` you can use
@scala[`ClusterEvent.InitialStateAsEvents`] @java[`ClusterEvent.initialStateAsEvents()`] as parameter to `subscribe`.
That means that instead of receiving `CurrentClusterState` as the first message you will receive
the events corresponding to the current state to mimic what you would have seen if you were
listening to the events when they occurred in the past. Note that those initial events only correspond
to the current state and it is not the full history of all changes that actually has occurred in the cluster.
Scala
: @@snip [SimpleClusterListener.scala](/akka-docs/src/test/scala/docs/cluster/SimpleClusterListener.scala) { #subscribe }
Java
: @@snip [SimpleClusterListener.java](/akka-docs/src/test/java/jdocs/cluster/SimpleClusterListener.java) { #subscribe }
The events to track the life-cycle of members are:
* `ClusterEvent.MemberJoined` - A new member has joined the cluster and its status has been changed to `Joining`
* `ClusterEvent.MemberUp` - A new member has joined the cluster and its status has been changed to `Up`
* `ClusterEvent.MemberExited` - A member is leaving the cluster and its status has been changed to `Exiting`
Note that the node might already have been shutdown when this event is published on another node.
* `ClusterEvent.MemberRemoved` - Member completely removed from the cluster.
* `ClusterEvent.UnreachableMember` - A member is considered as unreachable, detected by the failure detector
of at least one other node.
* `ClusterEvent.ReachableMember` - A member is considered as reachable again, after having been unreachable.
All nodes that previously detected it as unreachable has detected it as reachable again.
There are more types of change events, consult the API documentation
of classes that extends `akka.cluster.ClusterEvent.ClusterDomainEvent`
for details about the events.
Instead of subscribing to cluster events it can sometimes be convenient to only get the full membership state with
@scala[`Cluster(system).state`]@java[`Cluster.get(system).state()`]. Note that this state is not necessarily in sync with the events published to a
cluster subscription.
### Worker Dial-in Example
Let's take a look at an example that illustrates how workers, here named *backend*,
can detect and register to new master nodes, here named *frontend*.
The example application provides a service to transform text. When some text
is sent to one of the frontend services, it will be delegated to one of the
backend workers, which performs the transformation job, and sends the result back to
the original client. New backend nodes, as well as new frontend nodes, can be
added or removed to the cluster dynamically.
Messages:
Scala
: @@snip [TransformationMessages.scala](/akka-docs/src/test/scala/docs/cluster/TransformationMessages.scala) { #messages }
Java
: @@snip [TransformationMessages.java](/akka-docs/src/test/java/jdocs/cluster/TransformationMessages.java) { #messages }
The backend worker that performs the transformation job:
Scala
: @@snip [TransformationBackend.scala](/akka-docs/src/test/scala/docs/cluster/TransformationBackend.scala) { #backend }
Java
: @@snip [TransformationBackend.java](/akka-docs/src/test/java/jdocs/cluster/TransformationBackend.java) { #backend }
Note that the `TransformationBackend` actor subscribes to cluster events to detect new,
potential, frontend nodes, and send them a registration message so that they know
that they can use the backend worker.
The frontend that receives user jobs and delegates to one of the registered backend workers:
Scala
: @@snip [TransformationFrontend.scala](/akka-docs/src/test/scala/docs/cluster/TransformationFrontend.scala) { #frontend }
Java
: @@snip [TransformationFrontend.java](/akka-docs/src/test/java/jdocs/cluster/TransformationFrontend.java) { #frontend }
Note that the `TransformationFrontend` actor watch the registered backend
to be able to remove it from its list of available backend workers.
Death watch uses the cluster failure detector for nodes in the cluster, i.e. it detects
network failures and JVM crashes, in addition to graceful termination of watched
actor. Death watch generates the `Terminated` message to the watching actor when the
unreachable cluster node has been downed and removed.
The easiest way to run **Worker Dial-in Example** example yourself is to try the
@scala[@extref[Akka Cluster Sample with Scala](samples:akka-samples-cluster-scala)]@java[@extref[Akka Cluster Sample with Java](samples:akka-samples-cluster-java)].
It contains instructions on how to run the **Worker Dial-in Example** sample.
## Node Roles
Not all nodes of a cluster need to perform the same function: there might be one sub-set which runs the web front-end,
one which runs the data access layer and one for the number-crunching. Deployment of actors—for example by cluster-aware
routers—can take node roles into account to achieve this distribution of responsibilities.
The roles of a node is defined in the configuration property named `akka.cluster.roles`
and it is typically defined in the start script as a system property or environment variable.
The roles of the nodes is part of the membership information in `MemberEvent` that you can subscribe to.
## How To Startup when Cluster Size Reached
A common use case is to start actors after the cluster has been initialized,
members have joined, and the cluster has reached a certain size.
With a configuration option you can define required number of members
before the leader changes member status of 'Joining' members to 'Up'.:
```
akka.cluster.min-nr-of-members = 3
```
In a similar way you can define required number of members of a certain role
before the leader changes member status of 'Joining' members to 'Up'.:
```
akka.cluster.role {
frontend.min-nr-of-members = 1
backend.min-nr-of-members = 2
}
```
You can start the actors in a `registerOnMemberUp` callback, which will
be invoked when the current member status is changed to 'Up', i.e. the cluster
has at least the defined number of members.
Scala
: @@snip [FactorialFrontend.scala](/akka-docs/src/test/scala/docs/cluster/FactorialFrontend.scala) { #registerOnUp }
Java
: @@snip [FactorialFrontendMain.java](/akka-docs/src/test/java/jdocs/cluster/FactorialFrontendMain.java) { #registerOnUp }
This callback can be used for other things than starting actors.
## How To Cleanup when Member is Removed
You can do some clean up in a `registerOnMemberRemoved` callback, which will
be invoked when the current member status is changed to 'Removed' or the cluster have been shutdown.
An alternative is to register tasks to the @ref:[Coordinated Shutdown](actors.md#coordinated-shutdown).
@@@ note
Register a OnMemberRemoved callback on a cluster that have been shutdown, the callback will be invoked immediately on
the caller thread, otherwise it will be invoked later when the current member status changed to 'Removed'. You may
want to install some cleanup handling after the cluster was started up, but the cluster might already be shutting
down when you installing, and depending on the race is not healthy.
@@@
## Higher level Cluster tools
### Cluster Singleton
For some use cases it is convenient and sometimes also mandatory to ensure that
you have exactly one actor of a certain type running somewhere in the cluster.
This can be implemented by subscribing to member events, but there are several corner
cases to consider. Therefore, this specific use case is covered by the
@ref:[Cluster Singleton](cluster-singleton.md).
### Cluster Sharding
Distributes actors across several nodes in the cluster and supports interaction
with the actors using their logical identifier, but without having to care about
their physical location in the cluster.
See @ref:[Cluster Sharding](cluster-sharding.md).
### Distributed Publish Subscribe
Publish-subscribe messaging between actors in the cluster, and point-to-point messaging
using the logical path of the actors, i.e. the sender does not have to know on which
node the destination actor is running.
See @ref:[Distributed Publish Subscribe in Cluster](distributed-pub-sub.md).
### Cluster Client
Communication from an actor system that is not part of the cluster to actors running
somewhere in the cluster. The client does not have to know on which node the destination
actor is running.
See @ref:[Cluster Client](cluster-client.md).
### Distributed Data
*Akka Distributed Data* is useful when you need to share data between nodes in an
Akka Cluster. The data is accessed with an actor providing a key-value store like API.
See @ref:[Distributed Data](distributed-data.md).
### Cluster Aware Routers
All @ref:[routers](routing.md) can be made aware of member nodes in the cluster, i.e.
deploying new routees or looking up routees on nodes in the cluster.
When a node becomes unreachable or leaves the cluster the routees of that node are
automatically unregistered from the router. When new nodes join the cluster, additional
routees are added to the router, according to the configuration.
See @ref:[Cluster Aware Routers](cluster-routing.md).
### Cluster Metrics
The member nodes of the cluster can collect system health metrics and publish that to other cluster nodes
and to the registered subscribers on the system event bus.
See @ref:[Cluster Metrics](cluster-metrics.md).
## Failure Detector
In a cluster each node is monitored by a few (default maximum 5) other nodes, and when
any of these detects the node as `unreachable` that information will spread to
the rest of the cluster through the gossip. In other words, only one node needs to
mark a node `unreachable` to have the rest of the cluster mark that node `unreachable`.
The failure detector will also detect if the node becomes `reachable` again. When
all nodes that monitored the `unreachable` node detects it as `reachable` again
the cluster, after gossip dissemination, will consider it as `reachable`.
If system messages cannot be delivered to a node it will be quarantined and then it
cannot come back from `unreachable`. This can happen if the there are too many
unacknowledged system messages (e.g. watch, Terminated, remote actor deployment,
failures of actors supervised by remote parent). Then the node needs to be moved
to the `down` or `removed` states and the actor system of the quarantined node
must be restarted before it can join the cluster again.
The nodes in the cluster monitor each other by sending heartbeats to detect if a node is
unreachable from the rest of the cluster. The heartbeat arrival times is interpreted
by an implementation of
[The Phi Accrual Failure Detector](http://www.jaist.ac.jp/~defago/files/pdf/IS_RR_2004_010.pdf).
The suspicion level of failure is given by a value called *phi*.
The basic idea of the phi failure detector is to express the value of *phi* on a scale that
is dynamically adjusted to reflect current network conditions.
The value of *phi* is calculated as:
```
phi = -log10(1 - F(timeSinceLastHeartbeat))
```
where F is the cumulative distribution function of a normal distribution with mean
and standard deviation estimated from historical heartbeat inter-arrival times.
In the [configuration](#cluster-configuration) you can adjust the `akka.cluster.failure-detector.threshold`
to define when a *phi* value is considered to be a failure.
A low `threshold` is prone to generate many false positives but ensures
a quick detection in the event of a real crash. Conversely, a high `threshold`
generates fewer mistakes but needs more time to detect actual crashes. The
default `threshold` is 8 and is appropriate for most situations. However in
cloud environments, such as Amazon EC2, the value could be increased to 12 in
order to account for network issues that sometimes occur on such platforms.
The following chart illustrates how *phi* increase with increasing time since the
previous heartbeat.
Phi is calculated from the mean and standard deviation of historical
inter arrival times. The previous chart is an example for standard deviation
of 200 ms. If the heartbeats arrive with less deviation the curve becomes steeper,
i.e. it is possible to determine failure more quickly. The curve looks like this for
a standard deviation of 100 ms.
To be able to survive sudden abnormalities, such as garbage collection pauses and
transient network failures the failure detector is configured with a margin,
`akka.cluster.failure-detector.acceptable-heartbeat-pause`. You may want to
adjust the [configuration](#cluster-configuration) of this depending on your environment.
This is how the curve looks like for `acceptable-heartbeat-pause` configured to
3 seconds.
Death watch uses the cluster failure detector for nodes in the cluster, i.e. it detects
network failures and JVM crashes, in addition to graceful termination of watched
actor. Death watch generates the `Terminated` message to the watching actor when the
unreachable cluster node has been downed and removed.
If you encounter suspicious false positives when the system is under load you should
define a separate dispatcher for the cluster actors as described in [Cluster Dispatcher](#cluster-dispatcher).
@@@ div { .group-scala }
## How to Test
@ref:[Multi Node Testing](multi-node-testing.md) is useful for testing cluster applications.
Set up your project according to the instructions in @ref:[Multi Node Testing](multi-node-testing.md) and @ref:[Multi JVM Testing](multi-jvm-testing.md), i.e.
add the `sbt-multi-jvm` plugin and the dependency to `akka-multi-node-testkit`.
First, as described in @ref:[Multi Node Testing](multi-node-testing.md), we need some scaffolding to configure the `MultiNodeSpec`.
Define the participating roles and their [configuration](#cluster-configuration) in an object extending `MultiNodeConfig`:
@@snip [StatsSampleSpec.scala](/akka-cluster-metrics/src/multi-jvm/scala/akka/cluster/metrics/sample/StatsSampleSpec.scala) { #MultiNodeConfig }
Define one concrete test class for each role/node. These will be instantiated on the different nodes (JVMs). They can be
implemented differently, but often they are the same and extend an abstract test class, as illustrated here.
@@snip [StatsSampleSpec.scala](/akka-cluster-metrics/src/multi-jvm/scala/akka/cluster/metrics/sample/StatsSampleSpec.scala) { #concrete-tests }
Note the naming convention of these classes. The name of the classes must end with `MultiJvmNode1`, `MultiJvmNode2`
and so on. It is possible to define another suffix to be used by the `sbt-multi-jvm`, but the default should be
fine in most cases.
Then the abstract `MultiNodeSpec`, which takes the `MultiNodeConfig` as constructor parameter.
@@snip [StatsSampleSpec.scala](/akka-cluster-metrics/src/multi-jvm/scala/akka/cluster/metrics/sample/StatsSampleSpec.scala) { #abstract-test }
Most of this can be extracted to a separate trait to avoid repeating this in all your tests.
Typically you begin your test by starting up the cluster and let the members join, and create some actors.
That can be done like this:
@@snip [StatsSampleSpec.scala](/akka-cluster-metrics/src/multi-jvm/scala/akka/cluster/metrics/sample/StatsSampleSpec.scala) { #startup-cluster }
From the test you interact with the cluster using the `Cluster` extension, e.g. `join`.
@@snip [StatsSampleSpec.scala](/akka-cluster-metrics/src/multi-jvm/scala/akka/cluster/metrics/sample/StatsSampleSpec.scala) { #join }
Notice how the *testActor* from @ref:[testkit](testing.md) is added as [subscriber](#cluster-subscriber)
to cluster changes and then waiting for certain events, such as in this case all members becoming 'Up'.
The above code was running for all roles (JVMs). `runOn` is a convenient utility to declare that a certain block
of code should only run for a specific role.
@@snip [StatsSampleSpec.scala](/akka-cluster-metrics/src/multi-jvm/scala/akka/cluster/metrics/sample/StatsSampleSpec.scala) { #test-statsService }
Once again we take advantage of the facilities in @ref:[testkit](testing.md) to verify expected behavior.
Here using `testActor` as sender (via `ImplicitSender`) and verifying the reply with `expectMsgPF`.
In the above code you can see `node(third)`, which is useful facility to get the root actor reference of
the actor system for a specific role. This can also be used to grab the `akka.actor.Address` of that node.
@@snip [StatsSampleSpec.scala](/akka-cluster-metrics/src/multi-jvm/scala/akka/cluster/metrics/sample/StatsSampleSpec.scala) { #addresses }
@@@
@@@ div { .group-java }
## How to Test
Currently testing with the `sbt-multi-jvm` plugin is only documented for Scala.
Go to the corresponding Scala version of this page for details.
@@@
## Management
### HTTP
Information and management of the cluster is available with a HTTP API.
See documentation of [Akka Management](http://developer.lightbend.com/docs/akka-management/current/).
### JMX
Information and management of the cluster is available as JMX MBeans with the root name `akka.Cluster`.
The JMX information can be displayed with an ordinary JMX console such as JConsole or JVisualVM.
From JMX you can:
* see what members that are part of the cluster
* see status of this node
* see roles of each member
* join this node to another node in cluster
* mark any node in the cluster as down
* tell any node in the cluster to leave
Member nodes are identified by their address, in format *akka.**protocol**://**actor-system-name**@**hostname**:**port***.
### Command Line
@@@ warning
**Deprecation warning** - The command line script has been deprecated and is scheduled for removal
in the next major version. Use the [HTTP management](#cluster-http) API with [curl](https://curl.haxx.se/)
or similar instead.
@@@
The cluster can be managed with the script `akka-cluster` provided in the Akka GitHub repository @extref[here](github:akka-cluster/jmx-client). Place the script and the `jmxsh-R5.jar` library in the same directory.
Run it without parameters to see instructions about how to use the script:
```
Usage: ./akka-cluster ...
Supported commands are:
join - Sends request a JOIN node with the specified URL
leave - Sends a request for node with URL to LEAVE the cluster
down - Sends a request for marking node with URL as DOWN
member-status - Asks the member node for its current status
members - Asks the cluster for addresses of current members
unreachable - Asks the cluster for addresses of unreachable members
cluster-status - Asks the cluster for its current status (member ring,
unavailable nodes, meta data etc.)
leader - Asks the cluster who the current leader is
is-singleton - Checks if the cluster is a singleton cluster (single
node cluster)
is-available - Checks if the member node is available
Where the should be on the format of
'akka.://@:'
Examples: ./akka-cluster localhost 9999 is-available
./akka-cluster localhost 9999 join akka://MySystem@darkstar:2552
./akka-cluster localhost 9999 cluster-status
```
To be able to use the script you must enable remote monitoring and management when starting the JVMs of the cluster nodes,
as described in [Monitoring and Management Using JMX Technology](http://docs.oracle.com/javase/8/docs/technotes/guides/management/agent.html).
Make sure you understand the security implications of enabling remote monitoring and management.
## Configuration
There are several configuration properties for the cluster. We refer to the
@ref:[reference configuration](general/configuration.md#config-akka-cluster) for more information.
### Cluster Info Logging
You can silence the logging of cluster events at info level with configuration property:
```
akka.cluster.log-info = off
```
You can enable verbose logging of cluster events at info level, e.g. for temporary troubleshooting, with configuration property:
```
akka.cluster.log-info-verbose = on
```
### Cluster Dispatcher
Under the hood the cluster extension is implemented with actors. To protect them against
disturbance from user actors they are by default run on the internal dispatcher configured
under `akka.actor.internal-dispatcher`. The cluster actors can potentially be isolated even
further onto their own dispatcher using the setting `akka.cluster.use-dispatcher`.
### Configuration Compatibility Check
Creating a cluster is about deploying two or more nodes and make then behave as if they were one single application. Therefore it's extremely important that all nodes in a cluster are configured with compatible settings.
The Configuration Compatibility Check feature ensures that all nodes in a cluster have a compatible configuration. Whenever a new node is joining an existing cluster, a subset of its configuration settings (only those that are required to be checked) is sent to the nodes in the cluster for verification. Once the configuration is checked on the cluster side, the cluster sends back its own set of required configuration settings. The joining node will then verify if it's compliant with the cluster configuration. The joining node will only proceed if all checks pass, on both sides.
New custom checkers can be added by extending `akka.cluster.JoinConfigCompatChecker` and including them in the configuration. Each checker must be associated with a unique key:
```
akka.cluster.configuration-compatibility-check.checkers {
my-custom-config = "com.company.MyCustomJoinConfigCompatChecker"
}
```
@@@ note
Configuration Compatibility Check is enabled by default, but can be disabled by setting `akka.cluster.configuration-compatibility-check.enforce-on-join = off`. This is specially useful when performing rolling updates. Obviously this should only be done if a complete cluster shutdown isn't an option. A cluster with nodes with different configuration settings may lead to data loss or data corruption.
This setting should only be disabled on the joining nodes. The checks are always performed on both sides, and warnings are logged. In case of incompatibilities, it is the responsibility of the joining node to decide if the process should be interrupted or not.
If you are performing a rolling update on cluster using Akka 2.5.9 or prior (thus, not supporting this feature), the checks will not be performed because the running cluster has no means to verify the configuration sent by the joining node, nor to send back its own configuration.
@@@