Couldnt Join Seed Nodes Will Try Again

Cluster Usage

For introduction to the Akka Cluster concepts please run across Cluster Specification.

The core of Akka Cluster is the cluster membership, to go along track of what nodes are part of the cluster and their health. There are several Higher level Cluster tools that are congenital on top of the cluster membership.

Dependency

To use Akka Cluster, you lot must add the following dependency in your project:

sbt

                libraryDependencies += "com.typesafe.akka" %% "akka-cluster" % "2.five.32"              

Maven

                <dependency>   <groupId>com.typesafe.akka</groupId>   <artifactId>akka-cluster_2.12</artifactId>   <version>2.5.32</version> </dependency>              

Gradle

                dependencies {   compile grouping: 'com.typesafe.akka', name: 'akka-cluster_2.12', version: '2.5.32' }              

Sample projection

Yous can look at the Cluster example project Cluster case project to come across what this looks like in do.

When and where to utilize Akka Cluster

An architectural pick you have to make is if you are going to use a microservices compages or a traditional distributed application. This choice will influence how you should utilize 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 ofttimes and can respond quicker to business opportunities. Reactive Microservices should exist isolated, democratic, and have a unmarried responsibleness as identified by Jonas Bonér in the volume Reactive Microsystems: The Evolution of Microservices at Scale.

In a microservices compages, you should consider advice within a service and between services.

In general nosotros 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. Run into the discussion on Internal and External Communication Internal and External Communication in the docs of the Lagom Framework (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 aforementioned lawmaking and are deployed together, as a fix, by a single team or individual. There might be ii versions running concurrently during a rolling deployment, but deployment of the entire set has a unmarried point of command. For this reason, intra-service advice can take advantage of Akka Cluster, failure management and actor messaging, which is user-friendly to utilize and has cracking performance.

Between different services Akka HTTP or Akka gRPC tin be used for synchronous (nevertheless not-blocking) communication and Akka Streams Kafka or other Alpakka connectors for integration asynchronous communication. All those communication mechanisms work well with streaming of messages with end-to-end back-pressure, and the synchronous advice tools can also be used for single request response interactions. It is also of import to note that when using these tools both sides of the advice do non take to exist implemented with Akka, nor does the programming language matter.

Traditional distributed application

We acknowledge that microservices as well introduce many new challenges and it'due south not the simply way to build applications. A traditional distributed application may have less complexity and work well in many cases. For case for a small startup, with a single team, building an application where time to market place is everything. Akka Cluster tin efficiently be used for edifice such distributed application.

In this instance, you have a single deployment unit of measurement, built from a single code base (or using traditional binary dependency management to modularize) just deployed across many nodes using a single cluster. Tighter coupling is OK, because there is a central signal of deployment and command. In some cases, nodes may have specialized runtime roles which means that the cluster is non totally homogenous (e.m., "front-end" and "back-finish" nodes, or dedicated master/worker nodes) merely if these are run from the same congenital artifacts this is simply a runtime behavior and doesn't cause the same kind of problems you might become from tight coupling of totally separate artifacts.

A tightly coupled distributed application has served the manufacture and many Akka users well for years and is notwithstanding a valid pick.

Distributed monolith

At that place is besides an anti-design that is sometimes called "distributed monolith". Yous accept multiple services that are built and deployed independently from each other, just they take 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 simulated sense of autonomy because of the physical separation of the lawmaking and deployment units, but you are probable to meet bug because of changes in the implementation of ane service leaking into the behavior of others. Encounter Ben Christensen's Don't 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 master benefit of microservices while taking on the costs. You are in a halfway state with things that aren't really separable existence built and deployed in a separate fashion. Some people practise this, and some manage to make information technology work, merely information technology'due south not something nosotros would recommend and information technology needs to exist advisedly managed.

A Simple Cluster Example

The post-obit configuration enables the Cluster extension to exist 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 {     log-remote-lifecycle-events = off     netty.tcp {       hostname = "127.0.0.one"       port = 0     }   }    cluster {     seed-nodes = [       "akka.tcp://[email protected]:2551",       "akka.tcp://[electronic mail protected]:2552"]      # auto downing is Not rubber for production deployments.     # you may want to use it during development, read more near it in the docs.     #     # car-down-unreachable-later on = 10s   } }  # Enable metrics extension in akka-cluster-metrics. akka.extensions=["akka.cluster.metrics.ClusterMetricsExtension"]  # Sigar native library extract location during tests. # Note: use per-jvm-instance folder when running multiple jvm on ane host. akka.cluster.metrics.native-library-excerpt-folder=${user.dir}/target/native                      

To enable cluster capabilities in your Akka project you should, at a minimum, add the Remoting settings, but with cluster. The akka.cluster.seed-nodes should normally also be added to your application.conf file.

Note

If you lot are running Akka in a Docker container or the nodes for another reason have split up internal and external ip addresses you lot must configure remoting co-ordinate to Akka behind NAT or in a Docker container

The seed nodes are configured contact points for initial, automated, join of the cluster.

Note that if y'all are going to start the nodes on unlike machines you need to specify the ip-addresses or host names of the machines in application.conf instead of 127.0.0.1

An thespian that uses the cluster extension may expect like this:

Scala

                /*  * Copyright (C) 2018-2019 Lightbend Inc. <https://world wide web.lightbend.com>  */  packet scala.docs.cluster  import akka.cluster.Cluster import akka.cluster.ClusterEvent._ import akka.actor.ActorLogging import akka.actor.Actor  class SimpleClusterListener extends Histrion with ActorLogging {    val cluster = Cluster(context.system)    // subscribe to cluster changes, re-subscribe when restart   override def preStart(): Unit of measurement = {     //#subscribe     cluster.subscribe(self, initialStateMode = InitialStateAsEvents, classOf[MemberEvent], classOf[UnreachableMember])     //#subscribe   }   override def postStop(): Unit of measurement = cluster.unsubscribe(self)    def receive = {     case MemberUp(fellow member) =>       log.info("Member is Upwards: {}", member.address)     case UnreachableMember(fellow member) =>       log.info("Fellow member detected as unreachable: {}", member)     case MemberRemoved(fellow member, previousStatus) =>       log.info("Fellow member is Removed: {} after {}", member.address, previousStatus)     case _: MemberEvent => // ignore   } }              

Java

                /*  * Copyright (C) 2018-2019 Lightbend Inc. <https://www.lightbend.com>  */  package jdocs.cluster;  import akka.actor.AbstractActor; import akka.cluster.Cluster; import akka.cluster.ClusterEvent; import akka.cluster.ClusterEvent.MemberEvent; import akka.cluster.ClusterEvent.MemberUp; import akka.cluster.ClusterEvent.MemberRemoved; import akka.cluster.ClusterEvent.UnreachableMember; import akka.result.Logging; import akka.event.LoggingAdapter;  public form SimpleClusterListener extends AbstractActor {   LoggingAdapter log = Logging.getLogger(getContext().getSystem(), this);   Cluster cluster = Cluster.go(getContext().getSystem());    // subscribe to cluster changes   @Override   public void preStart() {     // #subscribe     cluster.subscribe(         getSelf(), ClusterEvent.initialStateAsEvents(), MemberEvent.class, UnreachableMember.class);     // #subscribe   }    // re-subscribe when restart   @Override   public void postStop() {     cluster.unsubscribe(getSelf());   }    @Override   public Receive createReceive() {     render receiveBuilder()         .friction match(             MemberUp.class,             mUp -> {               log.info("Member is Up: {}", mUp.fellow member());             })         .match(             UnreachableMember.course,             mUnreachable -> {               log.info("Member detected as unreachable: {}", mUnreachable.fellow member());             })         .friction match(             MemberRemoved.grade,             mRemoved -> {               log.info("Fellow member is Removed: {}", mRemoved.fellow member());             })         .match(             MemberEvent.class,             message -> {               // ignore             })         .build();   } }              

The histrion registers itself as subscriber of certain cluster events. It receives events respective to the current state of the cluster when the subscription starts and so it receives events for changes that happen in the cluster.

The easiest way to run this case yourself is to attempt the Akka Cluster Sample with Scala Akka Cluster Sample with Java. Information technology contains instructions on how to run the SimpleClusterApp.

Joining to Seed Nodes

Note

When starting clusters on cloud systems such as Kubernetes, AWS, Google Deject, Azure, Mesos or others which maintain DNS or other ways of discovering nodes, you may desire to use the automatic joining process implemented by the open source Akka Cluster Bootstrap module.

Joining configured seed nodes

You may decide if joining to the cluster should be washed manually or automatically to configured initial contact points, and then-called seed nodes. Later on the joining process the seed nodes are non special and they participate in the cluster in exactly the same way as other nodes.

When a new node is started it sends a bulletin 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 exist started notwithstanding) it retries this procedure until successful or shutdown.

You define the seed nodes in the configuration file (application.conf):

            akka.cluster.seed-nodes = [   "akka.tcp://[email protected]:2552",   "akka.tcp://[email protected]:2552"]                      

This can also be defined as Java system backdrop when starting the JVM using the following syntax:

            -Dakka.cluster.seed-nodes.0=akka.tcp://[email protected]:2552 -Dakka.cluster.seed-nodes.one=akka.tcp://[email protected]:2552                      

The seed nodes tin exist started in whatsoever 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 listing must exist 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 start 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 aforementioned time (lodge doesn't matter), otherwise it tin accept upwards to the configured seed-node-timeout until the nodes tin can join.

Once more than ii seed nodes have been started it is no problem to close downwards the offset seed node. If the first seed node is restarted, it will outset endeavor to join the other seed nodes in the existing cluster. Note that if you lot stop all seed nodes at the same time and restart them with the same seed-nodes configuration they volition bring together themselves and course a new cluster instead of joining remaining nodes of the existing cluster. That is probable not desired and should exist avoided by listing several nodes equally seed nodes for redundancy and don't stop all of them at the aforementioned 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 desire to automate the discovery of seed nodes using your deject providers or cluster orchestrator, or some other course of service discovery (such equally managed DNS). The open source Akka Direction library includes the Cluster Bootstrap module which handles only that. Please refer to its documentation for more than details.

Programatically joining to seed nodes with joinSeedNodes

You may also use Cluster(organization).joinSeedNodes Cluster.become(organization).joinSeedNodes to join programmatically, which is attractive when dynamically discovering other nodes at startup past 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 exist placed outset in the parameter to joinSeedNodes.

Scala

                import akka.actor.Accost import akka.cluster.Cluster  val cluster = Cluster(system) val list: Listing[Address] = ??? //your method to dynamically become seed nodes cluster.joinSeedNodes(list)              

Java

                import akka.player.Address; import akka.cluster.Cluster;  final Cluster cluster = Cluster.get(system); List<Accost> list =     new LinkedList<>(); // replace this with your method to dynamically get seed nodes cluster.joinSeedNodes(list);              

Unsuccessful attempts to contact seed nodes are automatically retried after the time period defined in configuration holding seed-node-timeout. Unsuccessful endeavour to join a specific seed node is automatically retried later on the configured retry-unsuccessful-join-later on. 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 bring together. That process can be aborted if unsuccessful by configuring a timeout. When aborted it will run Coordinated Shutdown, which by default volition end the ActorSystem. CoordinatedShutdown tin likewise be configured to go out the JVM. It is useful to ascertain this timeout if the seed-nodes are assembled dynamically and a restart with new seed-nodes should exist tried after unsuccessful attempts.

            akka.cluster.shutdown-later on-unsuccessful-join-seed-nodes = 20s akka.coordinated-shutdown.end-actor-system = on                      

If yous don't configure seed nodes or use joinSeedNodes you need to join the cluster manually, which can be performed by using JMX or HTTP.

Yous can join to any node in the cluster. It does not have to be configured as a seed node. Note that y'all tin can only join to an existing cluster member, which means that for bootstrapping some node must join itself,so the following nodes could join them to brand up a cluster.

An actor organisation tin can only join a cluster in one case. Additional attempts will be ignored. When information technology has successfully joined it must be restarted to exist able to join another cluster or to bring together the same cluster again. It can use the same host name and port after the restart, when it come as new incarnation of existing member in the cluster, trying to join in, so the existing one will be removed from the cluster so it will exist immune to join.

Note

The name of the ActorSystem must be the same for all members of a cluster. The proper noun is given when you kickoff the ActorSystem.

Downing

When a member is considered past the failure detector to exist unreachable the leader is not allowed to perform its duties, such as irresolute condition of new joining members to 'Upwards'. The node must first become reachable again, or the status of the unreachable member must be inverse to 'Down'. Irresolute status to 'Down' can be performed automatically or manually. By default it must be done manually, using JMX or HTTP.

It can besides be performed programmatically with Cluster(arrangement).down(accost) Cluster.go(arrangement).down(accost) .

If a node is still running and sees its cocky as Downwards information technology will shutdown. Coordinated Shutdown will automatically run if run-coordinated-shutdown-when-down is set to on (the default) however the node will non try and get out the cluster gracefully so sharding and singleton migration volition not occur.

A pre-packaged solution for the downing problem is provided by Split Brain Resolver, which is part of the Lightbend Reactive Platform. If you don't use RP, you should anyhow carefully read the documentation of the Split up Brain Resolver and make sure that the solution you lot are using handles the concerns described there.

Auto-downing (DO NOT USE)

There is an automatic downing feature that you should non use in production. For testing purpose you can enable it with configuration:

            akka.cluster.car-down-unreachable-after = 120s                      

This means that the cluster leader member will modify the unreachable node status to downwards 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 product environment it volition eventually breakdown the cluster. When a network sectionalisation occurs, both sides of the segmentation volition see the other side as unreachable and remove it from the cluster. This results in the germination of two separate, disconnected, clusters (known as Dissever Brain).

This behaviour is non limited to network partitions. It tin also occur if a node in the cluster is overloaded, or experiences a long GC suspension.

Warning

We recommend against using the car-down feature of Akka Cluster in product. It has multiple undesirable consequences for production systems.

If you are using Cluster Singleton or Cluster Sharding it tin break the contract provided past those features. Both provide a guarantee that an role player will be unique in a cluster. With the auto-down characteristic enabled, it is possible for multiple independent clusters to class (*Split Brain*). When this happens the guaranteed uniqueness will no longer exist true resulting in undesirable behaviour in the system.

This is even more severe when Akka Persistence is used in conjunction with Cluster Sharding. In this instance, the lack of unique actors can cause multiple actors to write to the same journal. Akka Persistence operates on a unmarried writer principle. Having multiple writers will decadent the journal and make information technology unusable.

Finally, fifty-fifty if you don't use features such every bit Persistence, Sharding, or Singletons, auto-downing can pb the organization to form multiple pocket-size clusters. These minor clusters will be independent from each other. They will be unable to communicate and every bit a result you may experience performance degradation. Once this condition occurs, it will require manual intervention in order to reform the cluster.

Considering of these issues, motorcar-downing should never be used in a production environs.

Leaving

There are 2 ways to remove a member from the cluster.

Y'all can stop the actor system (or the JVM procedure). It will be detected as unreachable and removed subsequently the automated or manual downing as described above.

A more graceful get out tin exist performed if you lot tell the cluster that a node shall leave. This can be performed using JMX or HTTP. It can besides exist performed programmatically with:

Scala

                val cluster = Cluster(system) cluster.get out(cluster.selfAddress)              

Java

                concluding Cluster cluster = Cluster.become(system); cluster.leave(cluster.selfAddress());              

Annotation that this command can be issued to any member in the cluster, not necessarily the one that is leaving.

The 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 svelte leaving of cluster including graceful shutdown of Cluster Singletons and Cluster Sharding are added automatically when Akka Cluster is used, i.east. running the shutdown process will besides trigger the graceful leaving if it's not already in progress.

Commonly 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 Downwardly in order to consummate the removal.

WeaklyUp Members

If a node is unreachable then gossip convergence is not possible and therefore whatever leader actions are also not possible. However, nosotros still might want new nodes to join the cluster in this scenario.

Joining members will exist promoted to WeaklyUp and go part of the cluster if convergence can't exist reached. In one case gossip convergence is reached, the leader volition motility WeaklyUp members to Up.

This feature is enabled by default, but it tin can be disabled with configuration option:

            akka.cluster.permit-weakly-upward-members = off                      

You can subscribe to the WeaklyUp membership event to make use of the members that are in this country, but you should be aware of that members on the other side of a network sectionalization take no knowledge almost the being of the new members. You should for example non count WeaklyUp members in quorum decisions.

Subscribe to Cluster Events

You can subscribe to change notifications of the cluster membership by using Cluster(arrangement).subscribe Cluster.become(system).subscribe .

Scala

                cluster.subscribe(self, classOf[MemberEvent], classOf[UnreachableMember])              

Coffee

                cluster.subscribe(getSelf(), MemberEvent.class, UnreachableMember.form);              

A snapshot of the total country, akka.cluster.ClusterEvent.CurrentClusterState, is sent to the subscriber as the first bulletin, followed past events for incremental updates.

Note that y'all may receive an empty CurrentClusterState, containing no members, followed past MemberUp events from other nodes which already joined, if y'all start the subscription earlier the initial bring together procedure has completed. This may for example happen when you beginning the subscription immediately after cluster.join() like below. This is expected behavior. When the node has been accustomed in the cluster y'all will receive MemberUp for that node, and other nodes.

Scala

                val cluster = Cluster(context.system)   cluster.bring together(cluster.selfAddress) cluster.subscribe(cocky, classOf[MemberEvent], classOf[UnreachableMember])              

Coffee

                Cluster cluster = Cluster.get(getContext().getSystem());   cluster.bring together(cluster.selfAddress()); cluster.subscribe(getSelf(), MemberEvent.class, UnreachableMember.class);              

To avert receiving an empty CurrentClusterState at the beginning, you can employ it like shown in the following example, to defer subscription until the MemberUp upshot for the own node is received:

Scala

                val cluster = Cluster(context.organization)   cluster.bring together(cluster.selfAddress) cluster.registerOnMemberUp {   cluster.subscribe(cocky, classOf[MemberEvent], classOf[UnreachableMember]) }              

Java

                Cluster cluster = Cluster.get(getContext().getSystem());   cluster.join(cluster.selfAddress()); cluster.registerOnMemberUp(     () -> cluster.subscribe(getSelf(), MemberEvent.class, UnreachableMember.form));              

If you lot find it inconvenient to handle the CurrentClusterState you tin can apply ClusterEvent.InitialStateAsEvents ClusterEvent.initialStateAsEvents() equally parameter to subscribe. That means that instead of receiving CurrentClusterState as the first bulletin y'all volition receive the events corresponding to the current state to mimic what you would have seen if yous were listening to the events when they occurred in the past. Note that those initial events merely correspond to the current state and information technology is not the full history of all changes that really has occurred in the cluster.

Scala

                cluster.subscribe(cocky, initialStateMode = InitialStateAsEvents, classOf[MemberEvent], classOf[UnreachableMember])              

Java

                cluster.subscribe(     getSelf(), ClusterEvent.initialStateAsEvents(), MemberEvent.class, UnreachableMember.form);              

The events to track the life-cycle of members are:

• ClusterEvent.MemberJoined - A new member has joined the cluster and its status has been inverse to Joining
• ClusterEvent.MemberUp - A new fellow member has joined the cluster and its condition has been changed to Up
• ClusterEvent.MemberExited - A member is leaving the cluster and its status has been changed to Exiting Annotation that the node might already have been shutdown when this event is published on some other 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 ane other node.
• ClusterEvent.ReachableMember - A fellow member is considered as reachable again, afterwards having been unreachable. All nodes that previously detected it as unreachable has detected information technology as reachable once more.

At that place are more than types of modify 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 exist convenient to merely get the full membership state with Cluster(system).state Cluster.go(system).state() . Note that this state is not necessarily in sync with the events published to a cluster subscription.

Worker Dial-in Instance

Let's take a look at an example that illustrates how workers, here named backend, tin detect and register to new master nodes, here named frontend.

The instance 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 equally new frontend nodes, can be added or removed to the cluster dynamically.

Letters:

Scala

                concluding case class TransformationJob(text: String) last case class TransformationResult(text: Cord) final case class JobFailed(reason: Cord, job: TransformationJob) case object BackendRegistration              

Java

                public interface TransformationMessages {    public static class TransformationJob implements Serializable {     private final String text;      public TransformationJob(String text) {       this.text = text;     }      public String getText() {       render text;     }   }    public static grade TransformationResult implements Serializable {     private final String text;      public TransformationResult(String text) {       this.text = text;     }      public String getText() {       return text;     }      @Override     public String toString() {       render "TransformationResult(" + text + ")";     }   }    public static class JobFailed implements Serializable {     private final String reason;     individual final TransformationJob job;      public JobFailed(String reason, TransformationJob job) {       this.reason = reason;       this.job = job;     }      public Cord getReason() {       return reason;     }      public TransformationJob getJob() {       return job;     }      @Override     public Cord toString() {       return "JobFailed(" + reason + ")";     }   }    public static final String BACKEND_REGISTRATION = "BackendRegistration"; }              

The backend worker that performs the transformation job:

Scala

                class TransformationBackend extends Thespian {    val cluster = Cluster(context.arrangement)    // subscribe to cluster changes, MemberUp   // re-subscribe when restart   override def preStart(): Unit = cluster.subscribe(self, classOf[MemberUp])   override def postStop(): Unit = cluster.unsubscribe(self)    def receive = {     case TransformationJob(text) => sender() ! TransformationResult(text.toUpperCase)     case land: CurrentClusterState =>       state.members.filter(_.status == MemberStatus.Up).foreach(annals)     example MemberUp(1000) => register(m)   }    def register(member: Member): Unit =     if (member.hasRole("frontend"))       context.actorSelection(RootActorPath(fellow member.address) / "user" / "frontend") !       BackendRegistration }              

Java

                public class TransformationBackend extends AbstractActor {    Cluster cluster = Cluster.become(getContext().getSystem());    // subscribe to cluster changes, MemberUp   @Override   public void preStart() {     cluster.subscribe(getSelf(), MemberUp.class);   }    // re-subscribe when restart   @Override   public void postStop() {     cluster.unsubscribe(getSelf());   }    @Override   public Receive createReceive() {     render receiveBuilder()         .match(             TransformationJob.class,             job -> {               getSender().tell(new TransformationResult(chore.getText().toUpperCase()), getSelf());             })         .match(             CurrentClusterState.class,             land -> {               for (Member member : state.getMembers()) {                 if (member.status().equals(MemberStatus.up())) {                   register(member);                 }               }             })         .match(             MemberUp.class,             mUp -> {               annals(mUp.member());             })         .build();   }    void register(Member member) {     if (fellow member.hasRole("frontend"))       getContext()           .actorSelection(member.accost() + "/user/frontend")           .tell(BACKEND_REGISTRATION, getSelf());   } }              

Annotation that the TransformationBackend actor subscribes to cluster events to notice 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

                class TransformationFrontend extends Histrion {    var backends = IndexedSeq.empty[ActorRef]   var jobCounter = 0    def receive = {     case job: TransformationJob if backends.isEmpty =>       sender() ! JobFailed("Service unavailable, try again later", job)      case job: TransformationJob =>       jobCounter += ane       backends(jobCounter % backends.size).frontwards(job)      example BackendRegistration if !backends.contains(sender()) =>       context.watch(sender())       backends = backends :+ sender()      case Terminated(a) =>       backends = backends.filterNot(_ == a)   } }              

Java

                public course TransformationFrontend extends AbstractActor {    List<ActorRef> backends = new ArrayList<ActorRef>();   int jobCounter = 0;    @Override   public Receive createReceive() {     return receiveBuilder()         .lucifer(             TransformationJob.class,             job -> backends.isEmpty(),             job -> {               getSender()                   .tell(new JobFailed("Service unavailable, try over again later", job), getSender());             })         .match(             TransformationJob.form,             job -> {               jobCounter++;               backends.get(jobCounter % backends.size()).forward(job, getContext());             })         .matchEquals(             BACKEND_REGISTRATION,             x -> {               getContext().spotter(getSender());               backends.add(getSender());             })         .match(             Terminated.class,             terminated -> {               backends.remove(terminated.getActor());             })         .build();   } }              

Note that the TransformationFrontend actor picket the registered backend to be able to remove information technology from its list of available backend workers. Decease picket 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 bulletin to the watching actor when the unreachable cluster node has been downed and removed.

The easiest way to run Worker Dial-in Example instance yourself is to try the Akka Cluster Sample with Scala Akka Cluster Sample with Java. It contains instructions on how to run the Worker Dial-in Example sample.

Node Roles

Non all nodes of a cluster demand to perform the same function: at that place might be one sub-set which runs the spider web front end-end, ane which runs the information access layer and one for the number-crunching. Deployment of actors—for instance by cluster-aware routers—tin take node roles into account to achieve this distribution of responsibilities.

The roles of a node is divers in the configuration belongings named akka.cluster.roles and it is typically defined in the get-go script every bit a system belongings or environment variable.

The roles of the nodes is function of the membership information in MemberEvent that you lot can subscribe to.

How To Startup when Cluster Size Reached

A common use case is to first actors later on the cluster has been initialized, members have joined, and the cluster has reached a certain size.

With a configuration option you can ascertain required number of members before the leader changes fellow member condition of 'Joining' members to 'Up'.:

            akka.cluster.min-nr-of-members = 3                      

In a similar way yous can define required number of members of a sure role before the leader changes member status of 'Joining' members to 'Up'.:

            akka.cluster.role {   frontend.min-nr-of-members = ane   backend.min-nr-of-members = ii }                      

You lot can start the actors in a registerOnMemberUp callback, which will be invoked when the current member status is inverse to 'Up', i.east. the cluster has at least the defined number of members.

Scala

                Cluster(arrangement).registerOnMemberUp {   organization.actorOf(Props(classOf[FactorialFrontend], upToN, true), name = "factorialFrontend") }              

Java

                Cluster.go(organization)     .registerOnMemberUp(         new Runnable() {           @Override           public void run() {             organisation.actorOf(                 Props.create(FactorialFrontend.class, upToN, true), "factorialFrontend");           }         });              

This callback can be used for other things than starting actors.

How To Cleanup when Member is Removed

Y'all tin exercise some clean up in a registerOnMemberRemoved callback, which will be invoked when the current member condition is changed to 'Removed' or the cluster have been shutdown.

An alternative is to register tasks to the Coordinated Shutdown.

Note

Register a OnMemberRemoved callback on a cluster that have been shutdown, the callback volition be invoked immediately on the caller thread, otherwise information technology will be invoked afterwards when the current member condition inverse to 'Removed'. You may desire to install some cleanup handling after the cluster was started upwardly, simply the cluster might already be shutting down when yous installing, and depending on the race is not healthy.

College level Cluster tools

Cluster Singleton

For some use cases it is user-friendly and sometimes too mandatory to ensure that yous have exactly 1 histrion of a certain blazon running somewhere in the cluster.

This can be implemented past subscribing to member events, but there are several corner cases to consider. Therefore, this specific utilize case is covered by the Cluster Singleton.

Cluster Sharding

Distributes actors beyond several nodes in the cluster and supports interaction with the actors using their logical identifier, but without having to care well-nigh their concrete location in the cluster.

See Cluster Sharding.

Distributed Publish Subscribe

Publish-subscribe messaging between actors in the cluster, and bespeak-to-point messaging using the logical path of the actors, i.e. the sender does not take to know on which node the destination role player is running.

Meet Distributed Publish Subscribe in Cluster.

Cluster Client

Communication from an role player system that is non part of the cluster to actors running somewhere in the cluster. The client does non have to know on which node the destination actor is running.

See Cluster Client.

Distributed Data

Akka Distributed Data is useful when you need to share data betwixt nodes in an Akka Cluster. The information is accessed with an actor providing a central-value store similar API.

See Distributed Data.

Cluster Aware Routers

All routers can be fabricated enlightened of fellow 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, boosted routees are added to the router, according to the configuration.

See Cluster Aware Routers.

Cluster Metrics

The member nodes of the cluster can collect organisation health metrics and publish that to other cluster nodes and to the registered subscribers on the organization upshot bus.

Run into Cluster Metrics.

Failure Detector

In a cluster each node is monitored by a few (default maximum v) other nodes, and when any of these detects the node every bit unreachable that data will spread to the rest of the cluster through the gossip. In other words, simply one node needs to marking a node unreachable to accept 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 once again the cluster, after gossip dissemination, will consider it equally reachable.

If system letters cannot be delivered to a node it will be quarantined and then it cannot come back from unreachable. This can happen if the at that place are likewise many unacknowledged system messages (due east.g. sentry, 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 role player system of the quarantined node must exist restarted before it tin can bring together the cluster again.

The nodes in the cluster monitor each other by sending heartbeats to detect if a node is unreachable from the residuum of the cluster. The heartbeat inflow times is interpreted by an implementation of The Phi Accrual Failure Detector.

The suspicion level of failure is given by a value chosen phi. The basic idea of the phi failure detector is to express the value of phi on a scale that is dynamically adapted to reflect current network conditions.

The value of phi is calculated as:

            phi = -log10(1 - F(timeSinceLastHeartbeat))                      

where F is the cumulative distribution role of a normal distribution with mean and standard deviation estimated from historical heartbeat inter-arrival times.

In the configuration you tin 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 loftier threshold generates fewer mistakes merely needs more than time to observe actual crashes. The default threshold is 8 and is appropriate for most situations. All the same in cloud environments, such as Amazon EC2, the value could exist increased to 12 in gild to account for network problems 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 difference of 200 ms. If the heartbeats arrive with less deviation the curve becomes steeper, i.e. it is possible to decide failure more quickly. The bend looks similar this for a standard divergence of 100 ms.

To be able to survive sudden abnormalities, such equally garbage collection pauses and transient network failures the failure detector is configured with a margin, akka.cluster.failure-detector.acceptable-heartbeat-pause. You may desire to adjust the configuration of this depending on your environment. This is how the curve looks like for acceptable-heartbeat-pause configured to iii seconds.

Death sentinel uses the cluster failure detector for nodes in the cluster, i.east. 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 see suspicious imitation positives when the arrangement is under load you should define a separate dispatcher for the cluster actors as described in Cluster Dispatcher.

How to Test

Multi Node Testing is useful for testing cluster applications.

Set upwardly your project according to the instructions in Multi Node Testing and Multi JVM Testing, i.e. add the sbt-multi-jvm plugin and the dependency to akka-multi-node-testkit.

Get-go, as described in Multi Node Testing, we demand some scaffolding to configure the MultiNodeSpec. Define the participating roles and their configuration in an object extending MultiNodeConfig:

              import akka.remote.testkit.MultiNodeConfig import com.typesafe.config.ConfigFactory  object StatsSampleSpecConfig extends MultiNodeConfig {   // annals the named roles (nodes) of the examination   val first = part("first")   val second = office("2d")   val third = role("tertiary")    def nodeList = Seq(first, second, third)    // Excerpt individual sigar library for every node.   nodeList.foreach { function =>     nodeConfig(role) {       ConfigFactory.parseString(s"""       # Enable metrics extension in akka-cluster-metrics.       akka.extensions=["akka.cluster.metrics.ClusterMetricsExtension"]       # Sigar native library extract location during tests.       akka.cluster.metrics.native-library-extract-folder=target/native/${role.name}       """)     }   }    // this configuration will be used for all nodes   // note that no fixed host names and ports are used   commonConfig(ConfigFactory.parseString("""     akka.role player.provider = cluster     akka.remote.log-remote-lifecycle-events = off     akka.cluster.roles = [compute]     akka.actor.deployment {       /statsService/workerRouter {           router = consistent-hashing-group           routees.paths = ["/user/statsWorker"]           cluster {             enabled = on             allow-local-routees = on             use-roles = ["compute"]           }         }     }     """))  }            

Define ane concrete test class for each part/node. These will exist instantiated on the different nodes (JVMs). They can be implemented differently, simply often they are the same and extend an abstract test class, as illustrated here.

              // need one physical test class per node grade StatsSampleSpecMultiJvmNode1 extends StatsSampleSpec class StatsSampleSpecMultiJvmNode2 extends StatsSampleSpec class StatsSampleSpecMultiJvmNode3 extends StatsSampleSpec            

Annotation the naming convention of these classes. The name of the classes must end with MultiJvmNode1, MultiJvmNode2 and and then on. It is possible to define some other suffix to be used past the sbt-multi-jvm, but the default should exist fine in most cases.

Then the abstruse MultiNodeSpec, which takes the MultiNodeConfig equally constructor parameter.

              import akka.remote.testkit.MultiNodeSpec import akka.testkit.ImplicitSender import org.scalatest.{ BeforeAndAfterAll, Matchers, WordSpecLike }  abstract grade StatsSampleSpec     extends MultiNodeSpec(StatsSampleSpecConfig)     with WordSpecLike     with Matchers     with BeforeAndAfterAll     with ImplicitSender {    import StatsSampleSpecConfig._    override def initialParticipants = roles.size    override def beforeAll() = multiNodeSpecBeforeAll()    override def afterAll() = multiNodeSpecAfterAll()                          

Most of this tin be extracted to a dissever trait to avoid repeating this in all your tests.

Typically yous brainstorm your examination by starting up the cluster and permit the members join, and create some actors. That tin can be done like this:

              "illustrate how to startup cluster" in within(15 seconds) {   Cluster(organization).subscribe(testActor, classOf[MemberUp])   expectMsgClass(classOf[CurrentClusterState])    val firstAddress = node(commencement).address   val secondAddress = node(second).address   val thirdAddress = node(third).accost    Cluster(system).join(firstAddress)    system.actorOf(Props[StatsWorker], "statsWorker")   system.actorOf(Props[StatsService], "statsService")    receiveN(3).collect { case MemberUp(m) => m.address }.toSet should be(     Set(firstAddress, secondAddress, thirdAddress))    Cluster(system).unsubscribe(testActor)    testConductor.enter("all-up") }            

From the test you collaborate with the cluster using the Cluster extension, e.one thousand. join.

              Cluster(system).join(firstAddress)            

Notice how the testActor from testkit is added as subscriber to cluster changes and so waiting for certain events, such as in this case all members condign 'Up'.

The above code was running for all roles (JVMs). runOn is a convenient utility to declare that a certain block of code should just run for a specific office.

              "evidence usage of the statsService from one node" in within(fifteen seconds) {   runOn(second) {     assertServiceOk()   }    testConductor.enter("done-2") }  def assertServiceOk(): Unit = {   val service = system.actorSelection(node(tertiary) / "user" / "statsService")   // eventually the service should exist ok,   // kickoff attempts might fail because worker actors not started withal   awaitAssert {     service ! StatsJob("this is the text that will be analyzed")     expectMsgType[StatsResult](1.2nd).meanWordLength should be(iii.875 +- 0.001)   }  }            

One time again we take reward of the facilities in testkit to verify expected behavior. Here using testActor as sender (via ImplicitSender) and verifying the answer with expectMsgPF.

In the above lawmaking yous can see node(third), which is useful facility to get the root actor reference of the histrion system for a specific role. This can too exist used to take hold of the akka.histrion.Address of that node.

              val firstAddress = node(start).accost val secondAddress = node(second).address val thirdAddress = node(third).address            

How to Test

Currently testing with the sbt-multi-jvm plugin is only documented for Scala. Get 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.

JMX

Information and management of the cluster is available as JMX MBeans with the root name akka.Cluster. The JMX information can exist displayed with an ordinary JMX panel such as JConsole or JVisualVM.

From JMX you can:

• see what members that are part of the cluster
• see status of this node
• come across roles of each fellow member
• bring together this node to some other node in cluster
• mark any node in the cluster equally down
• tell any node in the cluster to leave

Member nodes are identified by their accost, in format akka.*protocol**://**actor-system-name**@**hostname**:**port***.

Command Line

Alarm

Deprecation warning - The command line script has been deprecated and is scheduled for removal in the next major version. Apply the HTTP direction API with curl or similar instead.

The cluster can be managed with the script akka-cluster provided in the Akka GitHub repository here. Place the script and the jmxsh-R5.jar library in the same directory.

Run information technology without parameters to come across instructions nearly how to apply the script:

            Usage: ./akka-cluster <node-hostname> <jmx-port> <command> ...  Supported commands are:            join <node-url> - Sends request a Join node with the specified URL           get out <node-url> - Sends a request for node with URL to LEAVE the cluster            down <node-url> - Sends a asking for marking node with URL every bit DOWN              member-status - Asks the member node for its current status                    members - Asks the cluster for addresses of electric current members                unreachable - Asks the cluster for addresses of unreachable members             cluster-status - Asks the cluster for its current status (member band,                              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 bachelor Where the <node-url> should be on the format of   'akka.<protocol>://<actor-arrangement-proper noun>@<hostname>:<port>'  Examples: ./akka-cluster localhost 9999 is-available           ./akka-cluster localhost 9999 join akka.tcp://[electronic mail protected]:2552           ./akka-cluster localhost 9999 cluster-status                      

To be able to employ the script you must enable remote monitoring and management when starting the JVMs of the cluster nodes, every bit described in Monitoring and Direction Using JMX Technology. Make sure you lot understand the security implications of enabling remote monitoring and management.

Configuration

There are several configuration properties for the cluster. We refer to the reference configuration for more data.

Cluster Info Logging

Yous can silence the logging of cluster events at info level with configuration holding:

            akka.cluster.log-info = off                      

You can enable verbose logging of cluster events at info level, e.k. for temporary troubleshooting, with configuration property:

            akka.cluster.log-info-verbose = on                      

Cluster Dispatcher

Under the hood the cluster extension is implemented with actors and it can be necessary to create a bulkhead for those actors to avoid disturbance from other actors. Peculiarly the heartbeating actors that is used for failure detection tin can generate simulated positives if they are not given a chance to run at regular intervals. For this purpose you can define a separate dispatcher to be used for the cluster actors:

            akka.cluster.use-dispatcher = cluster-dispatcher  cluster-dispatcher {   blazon = "Dispatcher"   executor = "fork-join-executor"   fork-join-executor {     parallelism-min = 2     parallelism-max = 4   } }                      

Annotation

Normally it should not exist necessary to configure a carve up dispatcher for the Cluster. The default-dispatcher should be sufficient for performing the Cluster tasks, i.due east. akka.cluster.use-dispatcher should not be changed. If you have Cluster related issues when using the default-dispatcher that is typically an indication that you are running blocking or CPU intensive actors/tasks on the default-dispatcher. Utilise dedicated dispatchers for such actors/tasks instead of running them on the default-dispatcher, considering that may starve system internal tasks. Related config backdrop: akka.cluster.use-dispatcher = akka.cluster.cluster-dispatcher. Corresponding default values: akka.cluster.utilize-dispatcher =.

Configuration Compatibility Check

Creating a cluster is nearly deploying ii or more nodes and make then behave as if they were one single awarding. Therefore it'due south extremely important that all nodes in a cluster are configured with compatible settings.

The Configuration Compatibility Cheque characteristic 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 ain set up of required configuration settings. The joining node volition and then verify if information technology's compliant with the cluster configuration. The joining node will merely go along if all checks pass, on both sides.

New custom checkers tin exist added past extending akka.cluster.JoinConfigCompatChecker and including them in the configuration. Each checker must be associated with a unique cardinal:

            akka.cluster.configuration-compatibility-check.checkers {   my-custom-config = "com.company.MyCustomJoinConfigCompatChecker" }                      

Annotation

Configuration Compatibility Check is enabled by default, merely tin can exist disabled by setting akka.cluster.configuration-compatibility-check.enforce-on-bring together = off. This is especially useful when performing rolling updates. Obviously this should only be washed if a complete cluster shutdown isn't an option. A cluster with nodes with unlike configuration settings may pb to data loss or data corruption.

This setting should only be disabled on the joining nodes. The checks are ever 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.v.nine or prior (thus, non supporting this feature), the checks volition 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.

Establish an error in this documentation? The source code for this folio tin be constitute here. Please feel free to edit and contribute a pull request.

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Source: https://doc.akka.io/docs/akka/2.5/cluster-usage.html