Design a state machine by giving states, context, guards, actions, and persistence distinct jobs. States describe meaningful modes; context stores values that vary within a mode; guards decide synchronously whether a transition is allowed; actions mark effect boundaries. For durable workflows, also decide what happens when an effect succeeds but saving state fails—or the reverse.
What belongs in a state, and what belongs in context?
A state names the system’s qualitative mode. Context holds data used while the system is in that mode. A retry count, form value, selected item, or request identifier usually belongs in context. A meaningful phase such as loading, ready, or failed is usually clearer as a state, especially when a user or another component needs to distinguish it.
Do not create a separate state for every possible value. If you make each counter value or form input its own state, the model can grow rapidly. Statecharts’ discussion of state explosion explains how combinations and dependencies can make a model harder to manage. Treat this as a design heuristic, not a formal limit: use states for distinct modes and context for changing data within them.
What makes a good guard?
A guard is a boolean condition that determines whether a candidate transition is enabled. Keep it quick, synchronous, deterministic for its inputs, and free of externally visible mutation. The Statecharts.dev glossary puts the rule plainly: “A guard function must not have any side effects.” A guard should return immediately; it should not wait on a future or promise.
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If deciding whether to proceed requires a network lookup or other asynchronous work, model that work as behavior rather than hiding it in a guard:
- Start the request at an explicit action or service boundary.
- Represent its completion as an event or service result, including the data needed to decide what happens next.
- Use a guard on the resulting transition if a decision is still needed, using data already available to the machine.
Some implementations evaluate multiple guarded alternatives for the same event in order. In the cited Statecharts material, the first true guard wins. Make alternatives mutually exclusive where possible. If priority is intentional, document the order as part of the behavior contract. Test the observable transition for each relevant event and context case; do not make correctness depend on guards being called exactly once.
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Where should side effects go?
Keep transition selection separate from the operation it triggers. Statechart actions may be attached to transitions or to state entry and exit; some libraries also provide invoked services or actors suited to longer-running work. Use these effect boundaries for I/O, messages, external-system updates, and logging—not guards.
An effect boundary should make clear what inputs the operation receives, how errors are reported, what retry behavior applies, and how its activity can be observed. Represent asynchronous completion or failure as an event or service result that the machine can handle. This separation makes the transition logic easier to test and prevents external work from being entangled with a decision that may be evaluated more than once.
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The XState introduction describes actions as effects or side effects and covers entry and exit actions. That documentation is an older API reference: use it for the general concept, not as a source of current, copy-and-paste syntax. Check the documentation for the specific library and version you use.
How should persistence interact with effects?
First establish what the chosen runtime actually saves and restores. Depending on the implementation, a snapshot may include the current state value and context, but you should verify whether it also covers history, timers, child actors, pending events, and a state or schema version. Do not assume that a state-machine library makes an external operation and a durable snapshot atomic.
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Then map the failure cases across the effect and the save. The documentation for the Python project xstate-statemachine says its external action effects occur before snapshot save and may therefore happen at least once if saving fails or the process dies. It recommends idempotency or an outbox as practical responses. This is a guarantee of that Python project only; it does not establish the persistence behavior of JavaScript XState or other engines.
- Effect succeeds, save fails: recovery may retry the effect unless the operation can be recognized as already completed.
- Save succeeds, delivery fails: the stored state may advance even though a message or command was not delivered.
- Recovery replays work: a retried charge, email, or command may happen more than once without idempotency or deduplication.
- State shape changes: a durable snapshot may need migration if the state or context schema changes between deployments.
- Timers are involved: confirm whether they are persisted, recreated, or lost on restart.
Choose a recovery design that matches the transaction boundaries of the actual system. Idempotency keys and deduplication can make replay safe; an outbox/inbox design can coordinate durable state changes with message delivery. Which approach fits depends on the database, broker, and runtime. There is no universal persistence recipe established across state-machine libraries.
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How do you compare a flat FSM, a statechart, or a library?
Choose based on the behavior and the team’s ability to reason about it, not on a claim that one structure is always superior. A flat finite-state machine can be straightforward for a small set of modes. Hierarchy and parallel regions can reduce duplicated transitions, but can also make ownership less obvious. Evaluate the actual model against these questions:
- Does hierarchy or parallelism remove meaningful duplication, or make it harder to tell which part owns a transition?
- How are context initialized and updated? Can the language’s type system express which context values are valid in each state?
- Are guard ordering, purity, and synchronous evaluation rules clear?
- Where do actions execute, and how do errors and service completion become events?
- What does the runtime snapshot, how is it versioned, and how is it restored?
- What delivery guarantees apply across effects and persistence, including duplicate execution after retries?
- Can the team visualize and test the model, and is the runtime familiar enough to operate reliably?
The relevant documentation supports discussion of hierarchy, parallel states, guards, and actions, but does not establish a current head-to-head benchmark across libraries. Verify implementation-specific details in the current official documentation before relying on a particular syntax or runtime guarantee.
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