A lightweight and unopinionated library with minimal dependencies for semantic validation in Rust.
Without any macro magic, at least not now.
TL;DR If you need to validate complex data structures at runtime then this crate may empower you to enrich your domain model with semantic validation.
How do you recursively validate complex data structures, collect all violations along the way, and finally report or evaluate those findings? Validating external data at runtime before feeding it into further processing stages is crucial to avoid inconsistencies and even more to prevent physical damage.
Assume you are creating a web service for managing reservations in a restaurant. Customers can place reservations for a certain start time and a number of guests. As contact data they need to leave their phone number or e-mail address, at least one of both.
The JSON request body for creating a new reservation may look like in this example:
{
"start": "2019-07-30T18:00:00Z",
"number_of_guests": 4,
"customer": {
"name": "slowtec GmbH",
"contact_data": {
"phone": "+49 711 500 716 72",
"email": "[email protected]"
}
}
}Let's focus on the contact data. The corresponding type-safe data model in Rust might look like this:
struct PhoneNumber(String);
struct EmailAddress(String);
struct ContactData {
pub phone: Option<PhoneNumber>,
pub email: Option<EmailAddress>,
}In this example, both phone number and e-mail address are still represented by strings, but wrapped into tuple structs with a single member. This commonly used newtype pattern establishes type safety at compile time and enables us to add behavior to these types.
Our reservation business requires that contact data entities are only accepted if all of the following conditions are satisfied:
- The e-mail address is valid
- The phone number is valid
- Either e-mail address, or phone number, or both are present
Let's develop a software design for the reservation example use case. It should empower us to validate domain entities according to our business requirements.
We will solely focus on the contact data entity for simplicity. This is sufficient to deduce the basic principles. The complete code can be found in the file reservation.rs that is provided as an example in the repository.
What are the possible outcomes of a validation? If the validation succeeds we are done and processing continues as if nothing happened, i.e. validation is typically an idempotent operation. If the validation fails we somehow want to understand why it failed to resolve conflicts or to fix inconsistencies. Finally, we may need to report any unresolved findings back to the caller.
Reasons for a failed validation are expressed in terms of invalidity. An invalidity is basically the inverse of some validation condition.
The invalidity variants for contact data are:
- The e-mail address is invalid
- The phone number is invalid
- Both e-mail address and phone number are missing
Please note that different invalidity variants may apply at the same time, e.g. both e-mail address and phone number might be invalid for the same entity.
We already realized that the successful result of a validation is essentially nothing. In Rust
this nothing is represented by the unit type ().
Any invalidity will cause the validation to fail. Does this mean we should fail early and abort the validation when detecting the first invalidity? Not necessarily. Consider the use case of form validation with direct user interaction. If the user submits a form with multiple invalid or missing fields we should report all of them to reduce the number of unsuccessful retries and round trips.
This leads us to a preliminary definition for validation results:
type NaiveValidationResult = Result<(), Vec<Invalidity>>We will refine it in a moment.
Validation is a recursive operation that needs to traverse deeply nested data structures. The current state during such a traversal defines a context for the validation with a certain level of abstraction.
At the ContactData level we need to recursively validate both phone number and e-mail address if
present. Those subordinate validations are performed on a lower level of abstraction, unaware of the
upper-level context.
Additionally, we check if both members are missing and then reject the ContactData as
incomplete. This is the only validation that is actually implemented on the current level without
recursion.
Let's encode all possible variants in Rust by using sum types:
enum PhoneNumberInvalidity {
...lower abstraction level...
}
enum EmailAddressInvalidity {
...lower abstraction level...
}
enum ContactDataInvalidity {
Phone(PhoneNumberInvalidity),
Email(EmailAddressInvalidity),
Incomplete,
}Please note that each validation result refers to only a single Invalidity type. The recursive
nesting of validation results from lower-level contexts is achieved by wrapping their Invalidity
types into subordinate variants. The names of those variants typically resemble the role names
within the current context.
With the preliminary considerations, we are now able to finalize our definition of a generic validation result:
struct ValidationContext<V: Invalidity> {
...implementation details...
}
type ValidationResult<V: Invalidity> = Result<(), ValidationContext<V>>The ValidationContext is responsible for collecting validation results in the form of multiple
variants of the associated Invalidity type. Each item represents a violation of some validation
condition, i.e. a single invalidity that has been detected. The concrete implementation of how
invalidities are collected is hidden.
We enhance our domain entities by implementing the generic Validate trait:
pub trait Validate {
type Invalidity: Invalidity;
fn validate(&self) -> ValidationResult<Self::Invalidity>;
}The associated type Invalidity is typically defined as a companion type of the corresponding
domain entity, as we have seen above. Don't get confused by the trait bound of the same name that is
just an alias for Any + Debug.
Provided that all components of our composite entity ContactData already implement this trait the
implementation becomes straightforward:
impl Validate for ContactData {
type Invalidity = ContactDataInvalidity;
fn validate(&self) -> ValidationResult<Self::Invalidity> {
ValidationContext::new()
.validate_with(&self.email, ContactDataInvalidity::EmailAddress)
.validate_with(&self.phone, ContactDataInvalidity::PhoneNumber)
.invalidate_if(
// Either email or phone must be present
self.email.is_none() && self.phone.is_none(),
ContactDataInvalidity::Incomplete,
)
.into()
}
}The validation function starts by creating a new, empty context. Then it continues by recursively collecting results from subordinate validations as well as executing own validations rules. Finally, it transforms the context into a result for passing it back to the caller.
The fluent interface has proven to be useful and readable for the majority of use cases, even if more complex validations may require to break the control flow at certain points.
We have translated the validation rules for our business requirements into a few lines of comprehensive code. This code is associated with the corresponding domain entity and only needs to consider a single level of abstraction. Recursive composition enables us to validate complex data structures and to trace back the cause of failed validations.
The validation code is independent of infrastructure components and an ideal candidate for including it in the functional core of a system. With simple unit tests we can verify that the validation works as expected and reliably protects us from accepting invalid data.
We didn't cover
- how to enhance
Invaliditytypes with additional, context-sensitive data by defining them as tagged variants and - how to route and interpret validation results.
The answers to both questions depend on each other, require use case-specific solutions, and are not restricted by this library in any way.
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