Split Brain Resolver

When operating an Akka cluster you must consider how to handle network partitions (a.k.a. split brain scenarios) and machine crashes (including JVM and hardware failures). This is crucial for correct behavior if you use Cluster Singleton or Cluster Sharding, especially together with Akka Persistence.

The Split Brain Resolver video is a good starting point for learning why it is important to use a correct downing provider and how the Split Brain Resolver works.

Module info

The Akka dependencies are available from Akka’s library repository. To access them there, you need to configure the URL for this repository.

sbt

resolvers += "Akka library repository".at("https://repo.akka.io/maven")

Maven

<project>
  ...
  <repositories>
    <repository>
      <id>akka-repository</id>
      <name>Akka library repository</name>
      <url>https://repo.akka.io/maven</url>
    </repository>
  </repositories>
</project>

Gradle

repositories {
    mavenCentral()
    maven {
        url "https://repo.akka.io/maven"
    }
}

To use Akka Split Brain Resolver is part of akka-cluster and you probably already have that dependency included. Otherwise, add the following dependency in your project:

sbt

val AkkaVersion = "2.9.1"
libraryDependencies += "com.typesafe.akka" %% "akka-cluster" % AkkaVersion

Maven

<properties>
  <scala.binary.version>2.13</scala.binary.version>
</properties>
<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.typesafe.akka</groupId>
      <artifactId>akka-bom_${scala.binary.version}</artifactId>
      <version>2.9.1</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>
<dependencies>
  <dependency>
    <groupId>com.typesafe.akka</groupId>
    <artifactId>akka-cluster_${scala.binary.version}</artifactId>
  </dependency>
</dependencies>

Gradle

def versions = [
  ScalaBinary: "2.13"
]
dependencies {
  implementation platform("com.typesafe.akka:akka-bom_${versions.ScalaBinary}:2.9.1")

  implementation "com.typesafe.akka:akka-cluster_${versions.ScalaBinary}"
}

Enable the Split Brain Resolver

You need to enable the Split Brain Resolver by configuring it as downing provider in the configuration of the ActorSystem (application.conf):

akka.cluster.downing-provider-class = "akka.cluster.sbr.SplitBrainResolverProvider"

You should also consider the different available downing strategies.

The Problem

A fundamental problem in distributed systems is that network partitions (split brain scenarios) and machine crashes are indistinguishable for the observer, i.e. a node can observe that there is a problem with another node, but it cannot tell if it has crashed and will never be available again or if there is a network issue that might or might not heal again after a while. Temporary and permanent failures are indistinguishable because decisions must be made in finite time, and there always exists a temporary failure that lasts longer than the time limit for the decision.

A third type of problem is if a process is unresponsive, e.g. because of overload, CPU starvation or long garbage collection pauses. This is also indistinguishable from network partitions and crashes. The only signal we have for decision is “no reply in given time for heartbeats” and this means that phenomena causing delays or lost heartbeats are indistinguishable from each other and must be handled in the same way.

When there is a crash, we would like to remove the affected node immediately from the cluster membership. When there is a network partition or unresponsive process we would like to wait for a while in the hope that it is a transient problem that will heal again, but at some point, we must give up and continue with the nodes on one side of the partition and shut down nodes on the other side. Also, certain features are not fully available during partitions so it might not matter that the partition is transient or not if it just takes too long. Those two goals are in conflict with each other and there is a trade-off between how quickly we can remove a crashed node and premature action on transient network partitions.

This is a difficult problem to solve given that the nodes on the different sides of the network partition cannot communicate with each other. We must ensure that both sides can make this decision by themselves and that they take the same decision about which part will keep running and which part will shut itself down.

Another type of problem that makes it difficult to see the “right” picture is when some nodes are not fully connected and cannot communicate directly to each other but information can be disseminated between them via other nodes.

The Akka cluster has a failure detector that will notice network partitions and machine crashes (but it cannot distinguish the two). It uses periodic heartbeat messages to check if other nodes are available and healthy. These observations by the failure detector are referred to as a node being unreachable and it may become reachable again if the failure detector observes that it can communicate with it again.

The failure detector in itself is not enough for making the right decision in all situations. The naive approach is to remove an unreachable node from the cluster membership after a timeout. This works great for crashes and short transient network partitions, but not for long network partitions. Both sides of the network partition will see the other side as unreachable and after a while remove it from its cluster membership. Since this happens on both sides the result is that two separate disconnected clusters have been created.

If you use the timeout based auto-down feature in combination with Cluster Singleton or Cluster Sharding that would mean that two singleton instances or two sharded entities with the same identifier would be running. One would be running: one in each cluster. For example when used together with Akka Persistence that could result in that two instances of a persistent actor with the same persistenceId are running and writing concurrently to the same stream of persistent events, which will have fatal consequences when replaying these events.

The default setting in Akka Cluster is to not remove unreachable nodes automatically and the recommendation is that the decision of what to do should be taken by a human operator or an external monitoring system. This is a valid solution, but not very convenient if you do not have this staff or external system for other reasons.

If the unreachable nodes are not downed at all they will still be part of the cluster membership. Meaning that Cluster Singleton and Cluster Sharding will not failover to another node. While there are unreachable nodes new nodes that are joining the cluster will not be promoted to full worthy members (with status Up). Similarly, leaving members will not be removed until all unreachable nodes have been resolved. In other words, keeping unreachable members for an unbounded time is undesirable.

With that introduction of the problem domain, it is time to look at the provided strategies for handling network partition, unresponsive nodes and crashed nodes.

Strategies

By default the Keep Majority strategy will be used because it works well for most systems. However, it’s worth considering the other available strategies and pick a strategy that fits the characteristics of your system. For example, in a Kubernetes environment the Lease strategy can be a good choice.

Every strategy has a failure scenario where it makes a “wrong” decision. This section describes the different strategies and guidelines of when to use what.

When there is uncertainty it selects to down more nodes than necessary, or even downing of all nodes. Therefore Split Brain Resolver should always be combined with a mechanism to automatically start up nodes that have been shutdown, and join them to the existing cluster or form a new cluster again.

You enable a strategy with the configuration property akka.cluster.split-brain-resolver.active-strategy.

Stable after

All strategies are inactive until the cluster membership and the information about unreachable nodes have been stable for a certain time period. Continuously adding more nodes while there is a network partition does not influence this timeout, since the status of those nodes will not be changed to Up while there are unreachable nodes. Joining nodes are not counted in the logic of the strategies.

copysource
# To enable the split brain resolver you first need to enable the provider in your application.conf:
# akka.cluster.downing-provider-class = "akka.cluster.sbr.SplitBrainResolverProvider"

akka.cluster.split-brain-resolver {
  # Select one of the available strategies (see descriptions below):
  # static-quorum, keep-majority, keep-oldest, down-all, lease-majority
  active-strategy = keep-majority

  # Time margin after which shards or singletons that belonged to a downed/removed
  # partition are created in surviving partition. The purpose of this margin is that
  # in case of a network partition the persistent actors in the non-surviving partitions
  # must be stopped before corresponding persistent actors are started somewhere else.
  # This is useful if you implement downing strategies that handle network partitions,
  # e.g. by keeping the larger side of the partition and shutting down the smaller side.
  # Decision is taken by the strategy when there has been no membership or
  # reachability changes for this duration, i.e. the cluster state is stable.
  stable-after = 20s

  # When reachability observations by the failure detector are changed the SBR decisions
  # are deferred until there are no changes within the 'stable-after' duration.
  # If this continues for too long it might be an indication of an unstable system/network
  # and it could result in delayed or conflicting decisions on separate sides of a network
  # partition.
  # As a precaution for that scenario all nodes are downed if no decision is made within
  # `stable-after + down-all-when-unstable` from the first unreachability event.
  # The measurement is reset if all unreachable have been healed, downed or removed, or
  # if there are no changes within `stable-after * 2`.
  # The value can be on, off, or a duration.
  # By default it is 'on' and then it is derived to be 3/4 of stable-after, but not less than
  # 4 seconds.
  down-all-when-unstable = on

}

Set akka.cluster.split-brain-resolver.stable-after to a shorter duration to have quicker removal of crashed nodes, at the price of risking too early action on transient network partitions that otherwise would have healed. Do not set this to a shorter duration than the membership dissemination time in the cluster, which depends on the cluster size. Recommended minimum duration for different cluster sizes:

The different strategies may have additional settings that are described below.

The side of the split that decides to shut itself down will use the cluster down command to initiate the removal of a cluster member. When that has been spread among the reachable nodes it will be removed from the cluster membership.

It’s good to terminate the ActorSystem and exit the JVM when the node is removed from the cluster.

That is handled by Coordinated Shutdown but to exit the JVM it’s recommended that you enable:

akka.coordinated-shutdown.exit-jvm = on

Keep Majority

The strategy named keep-majority will down the unreachable nodes if the current node is in the majority part based on the last known membership information. Otherwise down the reachable nodes, i.e. the own part. If the parts are of equal size the part containing the node with the lowest address is kept.

This strategy is a good choice when the number of nodes in the cluster change dynamically and you can therefore not use static-quorum.

This strategy also handles the edge case that may occur when there are membership changes at the same time as the network partition occurs. For example, the status of two members are changed to Up on one side but that information is not disseminated to the other side before the connection is broken. Then one side sees two more nodes and both sides might consider themselves having a majority. It will detect this situation and make the safe decision to down all nodes on the side that could be in minority if the joining nodes were changed to Up on the other side. Note that this has the drawback that if the joining nodes were not changed to Up and becoming a majority on the other side then each part will shut down itself, terminating the whole cluster.

Note that if there are more than two partitions and none is in majority each part will shut down itself, terminating the whole cluster.

If more than half of the nodes crash at the same time the other running nodes will down themselves because they think that they are not in majority, and thereby the whole cluster is terminated.

The decision can be based on nodes with a configured role instead of all nodes in the cluster. This can be useful when some types of nodes are more valuable than others. You might for example have some nodes responsible for persistent data and some nodes with stateless worker services. Then it probably more important to keep as many persistent data nodes as possible even though it means shutting down more worker nodes.

Configuration:

akka.cluster.split-brain-resolver.active-strategy=keep-majority

copysourceakka.cluster.split-brain-resolver.keep-majority {
  # if the 'role' is defined the decision is based only on members with that 'role'
  role = ""
}

Static Quorum

The strategy named static-quorum will down the unreachable nodes if the number of remaining nodes are greater than or equal to a configured quorum-size. Otherwise, it will down the reachable nodes, i.e. it will shut down that side of the partition. In other words, the quorum-size defines the minimum number of nodes that the cluster must have to be operational.

This strategy is a good choice when you have a fixed number of nodes in the cluster, or when you can define a fixed number of nodes with a certain role.

For example, in a 9 node cluster you will configure the quorum-size to 5. If there is a network split of 4 and 5 nodes the side with 5 nodes will survive and the other 4 nodes will be downed. After that, in the 5 node cluster, no more failures can be handled, because the remaining cluster size would be less than 5. In the case of another failure in that 5 node cluster all nodes will be downed.

Therefore, it is important that you join new nodes when old nodes have been removed.

Another consequence of this is that if there are unreachable nodes when starting up the cluster, before reaching this limit, the cluster may shut itself down immediately. This is not an issue if you start all nodes at approximately the same time or use the akka.cluster.min-nr-of-members to define required number of members before the leader changes member status of ‘Joining’ members to ‘Up’. You can tune the timeout after which downing decisions are made using the stable-after setting.

You should not add more members to the cluster than quorum-size * 2 - 1. A warning is logged if this recommendation is violated. If the exceeded cluster size remains when an SBR decision is needed it will down all nodes because otherwise there is a risk that both sides may down each other and thereby form two separate clusters.

For rolling updates, it’s best to leave the cluster gracefully via Coordinated Shutdown (SIGTERM). For successful leaving, SBR will not be used (no downing) but if there is an unreachability problem at the same time as the rolling update is in progress there could be an SBR decision. To avoid that the total number of members limit is not exceeded during the rolling update it’s recommended to leave and fully remove one node before adding a new one, when using static-quorum.

If the cluster is split into 3 (or more) parts each part that is smaller than then configured quorum-size will down itself and possibly shutdown the whole cluster.

If more nodes than the configured quorum-size crash at the same time the other running nodes will down themselves because they think that they are not in the majority, and thereby the whole cluster is terminated.

The decision can be based on nodes with a configured role instead of all nodes in the cluster. This can be useful when some types of nodes are more valuable than others. You might, for example, have some nodes responsible for persistent data and some nodes with stateless worker services. Then it probably more important to keep as many persistent data nodes as possible even though it means shutting down more worker nodes.

There is another use of the role as well. By defining a role for a few (e.g. 7) stable nodes in the cluster and using that in the configuration of static-quorum you will be able to dynamically add and remove other nodes without this role and still have good decisions of what nodes to keep running and what nodes to shut down in the case of network partitions. The advantage of this approach compared to keep-majority (described below) is that you do not risk splitting the cluster into two separate clusters, i.e. a split brain*. You must still obey the rule of not starting too many nodes with this role as described above. It also suffers the risk of shutting down all nodes if there is a failure when there are not enough nodes with this role remaining in the cluster, as described above.

Configuration:

akka.cluster.split-brain-resolver.active-strategy=static-quorum

copysourceakka.cluster.split-brain-resolver.static-quorum {
  # minimum number of nodes that the cluster must have
  quorum-size = undefined

  # if the 'role' is defined the decision is based only on members with that 'role'
  role = ""
}

Keep Oldest

The strategy named keep-oldest will down the part that does not contain the oldest member. The oldest member is interesting because the active Cluster Singleton instance is running on the oldest member.

There is one exception to this rule if down-if-alone is configured to on. Then, if the oldest node has partitioned from all other nodes the oldest will down itself and keep all other nodes running. The strategy will not down the single oldest node when it is the only remaining node in the cluster.

Note that if the oldest node crashes the others will remove it from the cluster when down-if-alone is on, otherwise they will down themselves if the oldest node crashes, i.e. shut down the whole cluster together with the oldest node.

This strategy is good to use if you use Cluster Singleton and do not want to shut down the node where the singleton instance runs. If the oldest node crashes a new singleton instance will be started on the next oldest node. The drawback is that the strategy may keep only a few nodes in a large cluster. For example, if one part with the oldest consists of 2 nodes and the other part consists of 98 nodes then it will keep 2 nodes and shut down 98 nodes.

This strategy also handles the edge case that may occur when there are membership changes at the same time as the network partition occurs. For example, the status of the oldest member is changed to Exiting on one side but that information is not disseminated to the other side before the connection is broken. It will detect this situation and make the safe decision to down all nodes on the side that sees the oldest as Leaving. Note that this has the drawback that if the oldest was Leaving and not changed to Exiting then each part will shut down itself, terminating the whole cluster.

The decision can be based on nodes with a configured role instead of all nodes in the cluster, i.e. using the oldest member (singleton) within the nodes with that role.

Configuration:

akka.cluster.split-brain-resolver.active-strategy=keep-oldest

copysourceakka.cluster.split-brain-resolver.keep-oldest {
  # Enable downing of the oldest node when it is partitioned from all other nodes
  down-if-alone = on

  # if the 'role' is defined the decision is based only on members with that 'role',
  # i.e. using the oldest member (singleton) within the nodes with that role
  role = ""
}

Down All

The strategy named down-all will down all nodes.

This strategy can be a safe alternative if the network environment is highly unstable with unreachability observations that can’t be fully trusted, and including frequent occurrences of indirectly connected nodes. Due to the instability there is an increased risk of different information on different sides of partitions and therefore the other strategies may result in conflicting decisions. In such environments it can be better to shutdown all nodes and start up a new fresh cluster.

Shutting down all nodes means that the system will be completely unavailable until nodes have been restarted and formed a new cluster. This strategy is not recommended for large clusters (> 10 nodes) because any minor problem will shutdown all nodes, and that is more likely to happen in larger clusters since there are more nodes that may fail.

See also Down all when unstable and indirectly connected nodes.

Lease

The strategy named lease-majority is using a distributed lease (lock) to decide what nodes that are allowed to survive. Only one SBR instance can acquire the lease and make the decision to remain up. The other side will not be able to aquire the lease and will therefore down itself.

Best effort is to keep the side that has most nodes, i.e. the majority side. This is achieved by adding a delay before trying to acquire the lease on the minority side.

There is currently one supported implementation of the lease which is backed by a Custom Resource Definition (CRD) in Kubernetes. It is described in the Kubernetes Lease documentation.

This strategy is very safe since coordination is added by an external arbiter. The trade-off compared to other strategies is that it requires additional infrastructure for implementing the lease and it reduces the availability of a decision to that of the system backing the lease store.

Similar to other strategies it is important that decisions are not deferred for too long because the nodes that couldn’t acquire the lease must decide to down themselves, see Down all when unstable.

In some cases the lease will be unavailable when needed for a decision from all SBR instances, e.g. because it is on another side of a network partition, and then all nodes will be downed.

Configuration:

akka {
  cluster {
    downing-provider-class = "akka.cluster.sbr.SplitBrainResolverProvider"
    split-brain-resolver {
      active-strategy = "lease-majority"
      lease-majority {
        lease-implementation = "akka.coordination.lease.kubernetes"
      }
    }
  }
}

copysourceakka.cluster.split-brain-resolver.lease-majority {
  lease-implementation = ""

  # The recommended format for the lease name is "<service-name>-akka-sbr".
  # When lease-name is not defined, the name will be set to "<actor-system-name>-akka-sbr"
  lease-name = ""

  # This delay is used on the minority side before trying to acquire the lease,
  # as an best effort to try to keep the majority side.
  acquire-lease-delay-for-minority = 2s

  # Release the lease after this duration.
  release-after = 40s

  # If the 'role' is defined the majority/minority is based only on members with that 'role'.
  role = ""
}

See also configuration and additional dependency in Kubernetes Lease

Indirectly connected nodes

In a malfunctioning network there can be situations where nodes are observed as unreachable via some network links, but they are still indirectly connected via other nodes, i.e. it’s not a clean network partition (or node crash).

When this situation is detected the Split Brain Resolvers will keep fully connected nodes and down all the indirectly connected nodes.

If there is a combination of indirectly connected nodes and a clean network partition it will combine the above decision with the ordinary decision, e.g. keep majority, after excluding suspicious failure detection observations.

Down all when unstable

When reachability observations by the failure detector are changed the SBR decisions are deferred until there are no changes within the stable-after duration. If this continues for too long it might be an indication of an unstable system/network and it could result in delayed or conflicting decisions on separate sides of a network partition.

As a precaution for that scenario all nodes are downed if no decision is made within stable-after + down-all-when-unstable from the first unreachability event. The measurement is reset if all unreachable have been healed, downed or removed, or if there are no changes within stable-after * 2.

This is enabled by default for all strategies and by default the duration is derived to be 3/4 of stable-after.

The below property can be defined as a duration of for how long the changes are acceptable to continue after the stable-after or it can be set to off to disable this feature.

akka.cluster.split-brain-resolver {
  down-all-when-unstable = 15s
  stable-after = 20s
}

Multiple data centers

Akka Cluster has support for multiple data centers, where the cluster membership is managed by each data center separately and independently of network partitions across different data centers. The Split Brain Resolver is embracing that strategy and will not count nodes or down nodes in another data center.

When there is a network partition across data centers the typical solution is to wait the partition out until it heals, i.e. do nothing. Other decisions should be performed by an external monitoring tool or human operator.

Cluster Singleton and Cluster Sharding

The purpose of Cluster Singleton and Cluster Sharding is to run at most one instance of a given actor at any point in time. When such an instance is shut down a new instance is supposed to be started elsewhere in the cluster. It is important that the new instance is not started before the old instance has been stopped. This is especially important when the singleton or the sharded instance is persistent, since there must only be one active writer of the journaled events of a persistent actor instance.

Since the strategies on different sides of a network partition cannot communicate with each other and they may take the decision at slightly different points in time there must be a time based margin that makes sure that the new instance is not started before the old has been stopped.

You would like to configure this to a short duration to have quick failover, but that will increase the risk of having multiple singleton/sharded instances running at the same time and it may take a different amount of time to act on the decision (dissemination of the down/removal). The duration is by default the same as the stable-after property (see Stable after above). It is recommended to leave this value as is, but it can also be separately overriden with the akka.cluster.down-removal-margin property.

Another concern for setting this stable-after/akka.cluster.down-removal-margin is dealing with JVM pauses e.g. garbage collection. When a node is unresponsive it is not known if it is due to a pause, overload, a crash or a network partition. If it is pause that lasts longer than stable-after * 2 it gives time for SBR to down the node and for singletons and shards to be started on other nodes. When the node un-pauses there will be a short time before it sees its self as down where singletons and sharded actors are still running. It is therefore important to understand the max pause time your application is likely to incur and make sure it is smaller than stable-margin.

If you choose to set a separate value for down-removal-margin, the recommended minimum duration for different cluster sizes are:

Expected Failover Time

As you have seen, there are several configured timeouts that add to the total failover latency. With default configuration those are:

• failure detection 5 seconds
• stable-after 20 seconds
• down-removal-margin (by default the same as stable-after) 20 seconds

In total, you can expect the failover time of a singleton or sharded instance to be around 45 seconds with default configuration. The default configuration is sized for a cluster of 100 nodes. If you have around 10 nodes you can reduce the stable-after to around 10 seconds, resulting in an expected failover time of around 25 seconds.