TLA+建模技巧
TLA+ Modeling Tips

原始链接: http://muratbuffalo.blogspot.com/2025/12/tla-modeling-tips.html

## TLA+建模:摘要 有效的TLA+建模优先考虑**极简和清晰**,而非完整的实现细节。从一个核心、可工作的模型开始,**只有在省略明显破坏正确性时才增加复杂性**,专注于特定的行为切片(例如领导者选举),而不是整个系统。 **指定*应该*发生什么,而不是*如何*发生**,使用声明式不变式并避免类似代码的控制流。通过推导值而不是存储它们来最小化状态,并严格检查**非法知识**——进程“看到”它们在现实中无法获取的信息。 动作应该**细粒度且表达为带保护条件的命令**,清晰地定义它们何时可以执行。至关重要的是,**不要仅仅依赖模型检查器**。主动寻找潜在问题,例如:编写明确的**TypeOK不变式**,定义大量的**安全*和*进展属性**,并故意**“破坏”规范**以测试其鲁棒性。 最后,记住TLA+是一种**交流工具**——优先考虑可读性和充分的文档。针对模型检查的优化应该是*最后*一步,在确保规范准确反映系统行为之后进行。

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原文

Model minimalistically

Start from a tiny core, and always keep a working model as you extend. Your default should be omission. Add a component only when you can explain why leaving it out would not work. Most models are about a slice of behavior, not the whole system in full glory: E.g., Leader election, repair, reconfiguration. Cut entire layers and components if they do not affect that slice. Abstraction is the art of knowing what to cut. Deleting should spark joy. 

Model specification, not implementation

Write declaratively. State what must hold, not how it is achieved. If your spec mirrors control flow, loops, or helper functions, you are simulating code. Cut it out. Every variable must earn its keep. Extra variables multiply the state space (model checking time) and hide bugs. Ask yourself repeatedly: can I derive this instead of storing it? For example, you do not need to maintain a WholeSet variable if you can define it as a state function of existing variables: WholeSet == provisionalItems \union nonProvisionalItems.

Review the model for illegal knowledge

Do a full read-through of your model and check what each process can really see. TLA+ makes it easy to read global state (or another process's state) that no real distributed process could ever observe atomically. This is one of the most common modeling errors. Make a dedicated pass to eliminate illegal global knowledge.

Check atomicity granularity

Push actions to be as fine-grained as correctness allows. Overly large atomic actions hide races and invalidate concurrency arguments. Fine-grained actions expose the real interleavings your protocol must tolerate. 

Think in guarded commands, not procedures 

Each action should express one logical step in guarded-command style. The guard should ideally define the meaning of the action. Put all enablement conditions in the guard. If the guard holds, the action may fire at any time in true event-driven style. This is why I now prefer writing TLA+ directly over PlusCal: TLA+ forces you to think in guarded-command actions, which is how distributed algorithms are meant to be designed. Yes, PlusCal is easier for developers to read, but it also nudges you toward sequential implementation-shaped thinking. And recently, with tools like Spectacle, sharing and visually exploring TLA+ specs got much easier.

Step back and ask what you forgot to model

There is no substitute for thinking hard about your system. TLA+ modeling is only there to help you think hard about your system, and cannot substitute thinking about it. Check that you incorporated all relevant aspects: failures, message reordering, repair, reconfiguration.

Write TypeOK invariants 

TLA+ is not typed, so you should state types explicitly and early by writing TypeOK invariants. A good TypeOK invariant provides an executable documentation for your model. Writing this in seconds can save you many minutes of hunting runtime bugs through TLA+ counterexample logs.

Write as many invariants as you can

If a property matters, make it explicit as an invariant. Write them early. Expand them over time. Try to keep your invariants as tight as possible. Document your learnings about invariants and non-invariants. A TLA+ spec is a communication artifact. Write it for readers, not for the TLC model checker. Be explicit and boring for the sake of clarity.

Write progress properties

Safety invariants alone are not enough. Check that things eventually happen: requests complete, leaders emerge, and goals accomplished. Many "correct" models may quietly do nothing forever. Checking progress properties catch paths that stall.

Be suspicious of success

A successful TLC run proves nothing unless the model explores meaningful behavior. Low coverage or tiny state spaces usually mean the model is over-constrained or wrong. Break the spec on purpose to check that your spec is actually doing some real work, and not giving up in a vacuous/trivial way. Inject bugs on purpose. If your invariants do not fail, they are too weak. Test the spec by sabotaging it.

Optimize model checking efficiency last

Separate the model from the model checker. The spec should stand on its own. Using the cfg file, you can optimize for model checking by using appropriate configuration, constraints, bounds for counters, and symmetry terms.

You can find many examples and walkthroughs of TLA+ specifications on my blog.

There are many more in the TLA+ repo as well.

联系我们 contact @ memedata.com