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State machines

In this guide, we'll see what state machines are and how they are part of the Solidus toolbelt for dealing with business flows.

As a programmer, state is one of the most challenging parts to model in a system. For instance, if you ask a semaphore which light is turned on, it might answer red, green, or amber, depending on when you ask it and what happened before the request: the state of the semaphore depends on user-initiated and external events.

A finite state machine (FSM) is a design pattern where a system, or one of its components, can be in one and only one of a limited number of states at a given time. Transitions are allowed between states under well-known events. As long as the initial state and the list of past events are known, you can recreate a state machine at any point.

For instance, a semaphore can be in a red, green, or amber state. When a pedestrian presses a button, it transitions from green to amber. When on amber, it goes to red if 5 seconds pass. If red, it turns green after 180 seconds. If you know a semaphore was green at time zero, and the list of events has been button-press, 500 seconds, button-press, 3 seconds, you know it's amber now.

Several parts of an e-commerce system fit this paradigm well. An order can be in progress, waiting for payment, or completed. It goes to completed when the payment is made. At that point, reimbursement can still be processed o completed. A payment... well, you get the idea.

State machines in Solidus

Solidus defines a few state machines on top of some models.

Each state machine describes its valid states and the allowed transitions. It also defines event methods that can be called from the outside to trigger internal changes. Finally, they can also declare some hooks that run when specific transitions happen.

Internally, Solidus' state machines use the states_machine gem (more precisely, states_machine-activerecord) . Take a look at states_machine's README for more details on its usage and API.

Customizing state machines

State machines modules are included in the corresponding model, so all the strategies described in the core customization section are valid.

When customizing state machines, you should differentiate two different use cases:

  1. You need to modify the domain where the state machine flow belongs. For instance, you're working on the order checkout and want to prevent it from completing if some requirement is not met.
  2. You need to add orthogonal behavior to the flow, like sending an email or updating an external service when a payment is received.

It would be best if you kept in mind that state machine transitions (including their after_ hooks) are wrapped within a database transaction. On the first use case, adding new transition hooks is okay. However, if your requirement is tangential to the main flow, it's better to override the whole event method so that you can do your work when the primary database transaction is over.

A good example of adding orthogonal behavior is event subscribers. There's no point in blocking database access until your subscribers have finished running. In fact, it's a bad practice: a failed subscriber could roll back the whole DB transaction and potentially leave your system in an inconsistent state.

The customizations explained above allow surgical-precision changes to the state machine's flows. Still, sometimes, you may need to change extensive parts of how they act for more deep behavioral modifications. In that case, Solidus allows replacing the entire state machine with something custom. As always, with great power comes great responsibility. Replacing the whole state machine should be the outcome of an informed decision. Solidus relies on well-known state machine states and events in many areas of the core, so be prepared to adjust other parts of Solidus to work with your custom implementation.

danger

It's better to be conservative when customizing state machines. Try to apply the smallest possible set of changes, and if possible, avoid changing the defined states. You're dealing with the core of the domain model: large changes could branch out in unanticipated ways!

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