Introducing a distributed state on top of a single state machine instance running on a single jvm is a difficult and a complex topic. Distributed State Machine is introducing a few relatively complex problems on top of a simple state machine due to its run-to-completion model and generally because of its single thread execution model, though orthogonal regions can be executed parallel. One other natural problem is that a state machine transition execution is driven by triggers which are either event or timer based.

Distributed Spring State Machine is trying to solve problem of spanning a generic State Machine through a jvm boundary. Here we show that a generic State Machine concepts can be used in multiple jvm’s and Spring Application Contexts.

We found that if Distributed State Machine abstraction is carefully chosen and backing distributed state repository guarantees CP readiness, it is possible to create a consistent state machine which is able to share distributed state among other state machines in an ensemble.

Our results demonstrate that distributed state changes are consistent if backing repository is CP. We anticipate our distributed state machine to provide a foundation to applications which need to work with a shared distributed states. This model aims to provide a good methods for cloud applications to have much easier ways to communicate with each others without having a need to explicitly build these distributed state concepts.

Spring State Machine is not forced to use a single threaded execution model because once multiple regions are uses, regions can be executed parallel if necessary configuration is applied. This is an important topic because once user wants to have a parallel state machine execution it will make state changes faster for independent regions.

When state changes are no longer driven by a trigger in a local jvm or local state machine instance, transition logic needs to be controlled externally in an arbitrary persistent storage. This storage needs to have a ways to notify participating state machines when distributed state is changed.

CAP Theorem states that "it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees, consistency, availability and partition tolerance ". What this means is that whatever is chosen for a backing persistence storage is it advisable to be CP. In this context CP means consistency and partition tolerance. Naturally Distributed Spring Statemachine doesn’t care about what is its CAP level but in reality consistency and partition tolerance are more important than availability. This is an exact reason why i.e. Zookeeper is a CP storage.

All tests presented in this article are accomplished by running custom jepsen tests in a following environment:

All jepsen tests for Spring Distributed Statemachine are available from Jepsen Tests.

One design decision of a Distributed State Machine was not to make individual State Machine instance aware of that it is part of a distributed ensemble. Because main functions and features of a StateMachine can be accessed via its interface, it makes sense to wrap this instance using a DistributedStateMachine, which simply intercepts all state machine communication and collaborates with an ensemble to orchestrate distributed state changes.

One other important concept is to be able to persist enough information from a state machine order to reset a state machine state from arbitrary state into a new deserialized state. This is naturally needed when a new state machine instance is joining with an ensemble and it needs to synchronize its own internal state with a distributed state. Together with using concepts of distributed states and state persisting it is possible to create a distributed state machine. Currently only backing repository of a Distributed State Machine is implemented using a Zookeeper.

As mentioned in Chapter 23, Using Distributed States distibuted states are enabled by wrapping an instance of a StateMachine within a DistributedStateMachine. Specific StateMachineEnsemble implementation is ZookeeperStateMachineEnsemble providing integration with a Zookeeper.

We wanted to have a generic interface StateMachinePersist which is able to persist StateMachineContext into an arbitrary storage and ZookeeperStateMachinePersist is implementing this interface for a Zookeeper.

While distributed state machine is using one set of serialized contexts to update its own state, with zookeeper we’re having a conceptual problem how these context changes can be listened. We’re able to serialize context into a zookeeper znode and eventually listen when znode data is modified. However Zookeeper doesn’t guarantee that you will get notification for every data change because registered watcher for a znode is disabled once it fires and user need to re-register that watcher. During this short time a znode data can be changed thus resulting missing events. It is actually very easy to miss these events by just changing data from a multiple threads in a concurrent manner.

Order to overcome this issue we’re keeping individual context changes in a multiple znodes and we just use a simple integer counter to mark which znode is a current active one. This allows us to replay missed events. We don’t want to create more and more znodes and then later delete old ones, instead we’re using a simple concept of a circular set of znodes. This allows to use predefined set of znodes where a current can be determided with a simple integer counter. We already have this counter by tracking main znode data version which in Zookeeper is an integer.

Size of a circular buffer is mandated to be a power of two not to get trouble when interger is going to overflow thus we don’t need to handle any specific cases.

Order to show how a various distributed actions against a state machine work in a real life, we’re using a set of jepsen tests to simulate various conditions which may happen in a real distributed cluster. These include a brain split on a network level, parallel events with a multiple distributed state machines and changes in an extended state variables. Jepsen tests are based on a sample Chapter 34, Web where this sample instance is run on multiple hosts together with a Zookeeper instance on every node where state machine is run. Essentially every state machine sample will connect to local Zookeeper instance which allows use, via jepsen to simulate network conditions.

Plotted graphs below in this chapter contain states and events which directly maps to a state chart which can be found from Chapter 34, Web.

C.6.1 Isolated Events

Sending an isolated single event into exactly one state machine in an ensemble is the most simplest testing scenario and demonstrates that a state change in one state machine is properly propagated into other state machines in an ensemble.

In this test we will demonstrate that a state change in one machine will eventually cause a consistent state change in other machines.

What’s happening in above chart:

• All machines report state S21.
• Event I is sent to node n1 and all nodes report state change from S21 to S22.
• Event C is sent to node n2 and all nodes report state change from S22 to S211.
• Event I is sent to node n5 and all nodes report state change from S211 to S212.
• Event K is sent to node n3 and all nodes report state change from S212 to S21.
• We cycle events I, C, I and K one more time via random nodes.

C.6.2 Parallel Events

Logical problem with multiple distributed state machines is that if a same event is sent into a multiple state machine exactly at a same time, only one of those events will cause a distributed state transitions. This is somewhat expected scenario because a first state machine, for this event, which is able to change a distributed state will control the distributed transition logic. Effectively all other machines receiving this same event will silently discard the event because distributed state is no longer in a state where particular event can be processed.

In this test we will demonstrate that a state change caused by a parallel events throughout an ensemble will eventually cause a consistent state change in all machines.

What’s happening in above chart:

• We use exactly same event flow than in previous sample Section C.6.1, “Isolated Events” with a difference that events are always sent to all nodes.

C.6.3 Concurrent Extended State Variable Changes

Extended state machine variables are not guaranteed to be atomic at any given time but after a distributed state change, all state machines in an ensemble should have a synchronized extended state.

In this test we will demonstrate that a change in extended state variables in one distributed state machine will eventually be consistent in all distributed state machines.

What’s happening in above chart:

• Event J is send to node n5 with event variable testVariable having value v1. All nodes are then reporting having varible testVariable as value v1.
• Event J is repeated from variable v2 to v8 doing same checks.

C.6.4 Partition Tolerance

We need to always assume that sooner or later things in a cluster will go bad whether it is just a crash of a Zookeeper instance, a state machine or a network problem like a brain split. Brain split is a situation where existing cluster members are isolated so that only part of a hosts are able to see each others. Usual scenario is that a brain split will create a minority and majority partitions of an ensemble where hosts in a minority cannot participate in an ensemble anymore until network status has been healed.

In below tests we will demonstrate that various types of brain-split’s in an ensemble will eventually cause fully synchronized state of all distributed state machines.

There are two scenarios having a one straight brain split in a network where where Zookeeper and Statemachine instances are split in half, assuming each Statemachine will connect into a local Zookeeper instance:

• If current zookeeper leader is kept in a majority, all clients connected into majority will keep functioning properly.
• If current zookeeper leader is left in minority, all clients will disconnect from it and will try to connect back till previous minority members has successfully joined back to existing majority ensemble.

	Note
	In our current jepsen tests we can’t separate zookeeper split brains scenarios between leader left in majority or minority so we need to run tests multiple time to accomplish this situation.

	Note
	In below plots we have mapped a state machine error state into an error to indicate that state machine is in error state instead or a normal state. Please indicate this when interpreting chart states.

In this first test we show that when existing zookeeper leader was kept in majority, 3 out of 5 machines will continue as is.

What’s happening in above chart:

• First event C is sent to all machine leading a state change to S211.
• Jepsen nemesis will cause a brain-split which is causing partitions of n1/n2/n5 and n3/n4. Nodes n3/n4 are left in minority and nodes n1/n2/n5 construct a new healthy majority. Nodes in majority will keep function without problems but nodes in minority will get into error state.
• Jepsen will heal network and after some time nodes n3/n4 will join back into ensemble and synchronize its distributed status.
• Lastly event K1 is sent to all state machines to ensure that ensemble is working properly. This state change will lead back to state S21.

In this second test we show that when existing zookeeper leader was kept in minority, all machines will error out:

What’s happening in above chart:

• First event C is sent to all machine leading a state change to S211.
• Jepsen nemesis will cause a brain-split which is causing partitions so that existing Zookeeper leader is kept in minority and all instances are disconnected from ensemble.
• Jepsen will heal network and after some time all nodes will join back into ensemble and synchronize its distributed status.
• Lastly event K1 is sent to all state machines to ensure that ensemble is working properly. This state change will lead back to state S21.

C.6.5 Crash and Join Tolerance

In this test we will demonstrate that killing existing state machine and then joining new instance back into an ensemble will keep the distributed state healthy and newly joined state machines will synchronize their states properly.

	Note
	In this test, states are not checked between first X and last X, thus graph will will show flat line in between. States are checked exactly where state change happens between S21 and S211.

What’s happening in above chart:

• All state machines are transitioned from initial state S21 into S211 so that we can test proper state synchronize during join.
• X is marking when a specific node has been crashed and started.
• At a same time we request states from all machines and plot it.
• Finally we do a simple transition back to S21 from S211 to make sure that all state machines are still functioning properly.