Store Mapping

Overview

fritz2 uses stores to manage the application state by holding some data model. But quite often those data does not fit naturally to the needed UI-fragment. This is often due to the normalized form, where redundancy is avoided as much as possible. Also, HTML can only render Strings in the end, but the String representation of some data types might differ from case to case, and we do not want to store those all explicitly.

In order to support clean data management but also a good match between data and UI-shape, fritz2's store concept offers a powerful concept: Store mapping.

Like the known map-function from collections, where some source type T gets transformed to some other type R inside an expression, we can also map a store in order to change its source type to some other, better fitting type. There is one big difference between the classical map-function and the store's mapping functions: A store needs not only a function from T -> R (getter) but also from R -> T (setter) as a store manages changes!

Lenses

There is a universal concept in computer science for such a functionality called lens. You might have a look at the excellent documentation on lenses from the arrow-project.

fritz2 also offers the method lensOf() for a short-and-sweet-experience, which accepts a getter- and a setter-expression:

val nameLens: Lens<Person, String> = lensOf({ it.name }, { person, value -> person.copy(name = value) })

The lens can then be used to access the name-property of a Person or to create a new person with changed name:

val fritz2 = Person(1, "fritz2")
val nameOfFritz2: String = nameLens.get(person) // nameOfFritz2 = "fritz2"
val hugo: Person = nameLens.set(fritz2, "hugo") // hugo = Person(1, "hugo")

As you can see, there is no magic; just plain old function calling.

Let us take a step back and explore, how this concept of lenses can be used to map one store to another.

Mapping a Store

Imagine a use case, where we want to render the interests of a person like a kind of tags as comma seperated values. We would also like to change them by typing them as CSV.

In order to further process interests of a person, it makes more sense to store them a List<Interests> though. So that should be the canonical state representation in our application.

val interestsStore: Store<List<Interest>> = storeOf(emptyList())

It does not fit to the requirements of the specific UI-fragment though!

We can define a Lens that does the mapping between the list and the String based CSV representation:

val interestLens: Lens<List<Interest>, String> = lensOf(
List<Interest>::joinToString, // getter
{ it.split(",").map { Interest.valueOf(it.trim()) } } // setter
)

Armed with this lens, we can finally map the whole interest-store and use the resulting store for the UI:

val interestsStore: Store<List<Interest>> = storeOf(emptyList())

val interestLens: Lens<List<Interest>, String> = lensOf(
List<Interest>::joinToString,
{ it.split(",").map { Interest.valueOf(it.trim()) } }
)

val csvInterests: Store<String> = interestsStore.map(interestLens)
// ^^^^^^^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^
// We create a new store with the we use the `map` function to "map" the store
// desired type (2nd of the `Lens`) and provide the lens, that `map` uses to process the mapping

render {
h1 { +"Choose Interests from" }
p { +Interest.values().joinToString() }
input {
label { +"Interests:" }
// connect the input reactively to the mapped CSV representation store
value(csvInterests.data)
changes.values() handledBy csvInterests.update
}
h1 { +"Chosen Interests:" }
csvInterests.data.renderText()

// Just to demonstrate that the original store is always in sync with the mapped one:
interestsStore.data handledBy { interests ->
console.log(interests)
}
}

As you can see, the mapped store fits perfectly to the desired (yet a little artificial) requirements for the UI: There is no mapping inside the UI, nor are there any custom handler or data-flows in the store.

To be fair, the heavy work is done by the manual creation of the lens though.

fritz2 offers some more tools to make lens generation easier, especially for the use case of destructuring complex model types.

Essentials

Lenses in Depth

Most of the time, your model for a view will not be of just a simple data-type but a complex entity, like a person having a name, multiple addresses, an email, a date of birth, etc.

In those cases, you will most likely need Stores for the single properties of your main entity, and - later on - for the properties of the child-entity like the street in an address in our example from above.

fritz2 uses a mechanism called Lens to describe the relationship between an entity and its child-entities and properties.

A Lens is basically a way to describe the relation between an outer and inner entity in a structure. It focuses on the inner entity from the viewpoint of the outer entity, which is how it got its name. Lenses are especially useful when using immutable data-types like fritz2 does. A Lens needs to handle the following:

  • Getting the value of the inner entity from a given instance of the outer entity
  • Creating a new instance of the outer entity (immutable!) as a copy of a given one with a different value only for the inner entity

In fritz2, a Lens is defined by the following interface:

interface Lens<P,T> {
val id: String
fun get(parent: P): T
fun set(parent: P, value: T): P
}

You can easily use this interface by just implementing get() and set(). fritz2 also offers the method lensOf() for a short-and-sweet-experience:

val nameLens = lensOf("name", { it.name }, { person, value -> person.copy(name = value) })

No magic there. The first parameter sets an id for the Lens. When using Lenses with Stores, the id will be used to generate a valid HTML id representing the path through your model. This can be used to identify your elements semantically (for validation or automated ui-tests for example).

If you have deep nested structures or a lot of them, you may want to automate this behavior. fritz2 offers an annotation @Lenses you can add to your data-classes in the commonMain source-set of your multiplatform project:

@Lenses
data class Person(val name: String, val value: String) {
companion object // needs to be declared! The generated lens-factories are created within.
}

Using an annotation-processor, fritz2 builds factory methods for each public constructor property within the companion object of the data class from these annotations which contains all the Lenses you need. They are named exactly like the entities and properties, so it's easy to use:

val nameLens = Person.name()

You can see it in action at our nestedmodel-example.

Keep in mind that your annotated classes have to be in your commonMain source-set otherwise the automatic generation of the lenses will not work!

Have a look at the validation-example to see how to set it up.

This will also help you define a multiplatform project for sharing your model and validation code between the browser and backend.

Destructuring complex Models with mapped Stores and Lenses

Having a Lens available which points to some specific property makes it very easy to get a Store for that property from an original Store of the parent entity:

// given the following nested data classes...
@Lenses
data class Name(val firstname: String, val lastname: String) {
companion object
}

@Lenses
data class Person(val name: Name, description: String) {
companion object
}

// ... you can create a root-store...
val personStore = storeOf(Person(Name("first name", "last name"), "more text"))
// ... and a derived store using the automatic generated lens-factory `Person.name()`
val nameStore = personStore.map(Person.name())

Now you can use your nameStore exactly like any other Store to set up two-way data binding, call map(...) again to access the properties of Name. If a Store contains a List, you can of course iterate over it by using renderEach(). It's fully recursive from here on down to the deepest nested parts of your model.

Rememeber that you can also add Handlers to your Stores by simply calling the handle method:

val booleanChildStore = parentStore.map(someLens)
val switch = booleanChildStore.handle { model: Boolean ->
!model
}

render {
button {
+"switch state"
clicks handledBy switch
}
}

To keep your code well-structured, it is recommended to implement complex logic at your Store or inherit it by using interfaces. However, the code above is a decent solution for small (convenience-)handlers.

Calling map on a Store with nullable Content

To call map on a nullable Store only makes sense, when you have checked, that its state is not null:

@Lenses
data class Person(val name: String)

//...

val storedPerson = storeOf<Person?>(null)

//...

storedPerson.data.render { person ->
if (person != null) { // Avoid NullPointerExceptions reading or updating storedPerson
// by manually creating a safe scope ensuring that person is not null
val storedName = customerStore.map(Person.name())
input {
value(storedName.data)
changes.values() handledBy storedName.update
}
}
else {
p { + "No customer selected" }
}
}

Handling nullable States in Stores

If you have a Store with a nullable state, you can use mapNull to derive a non-nullable Store from it, that transparently translates a null-value from its parent Store to the given default-value and vice versa.

In the following case, when you enter some text in the input and remove it again, you will have a state of null in your nameStore:

val nameStore = storeOf<String?>(null)

render {
input {
nameStore.mapNull("").also { formStore ->
value(formStore.data)
changes.values() handledBy formStore.update
}
}
}

In real world, you will often come across nullable attributes of complex entities. Then you can call mapNull directly on the Store you create to use with your form elements:

@Lenses
data class Person(val name: String?)

//...

val personStore = storeOf(Person(null))

//...

val nameStore = personStore.map(Person.name()).mapNull("")

Combining Lenses

A Lens supports the plus-operator with another lens in order to create a new lens, that combines the two in such a way, that the get and set-functions are chained in natural order.

Imagine the following example:

data class Address(val street: String)
data class Person(val address: Address)

val addressOfPerson: Lens<Person, Address> = lensOf("address", Person::address) { p, v -> p.copy(address = v) }
val streetOfAddress: Lens<Address, String> = lensOf("street", Address::street) { p, v -> p.copy(street = v) }

// combine two lenses:
val streetOfPerson = address + street

// apply the combined lens to an example object:
val person = Person(Address("Lerchenweg"))
streetOfPerson.get(person) // -> "Lerchenweg"
streetOfPerson.set("Rosenstra├če") // Person(address = Address("Rosenstra├če"))

Let us recap, how this example would work with automatically generated lenses:

@Lenses
data class Address(val street: String) { companion object }

@Lenses
data class Person(val address: Address) { companion object }

val streetOfPerson = Person.address() + Address.street()

This works, but the syntax is quite cumbersome; especially for deeper nested models!

This is why our automatic @Lenses-annotation-processor has a dedicated support for deeper nested models as well and creates extension functions for all lenses, so you can chain the calls in a fluent way:

@Lenses
data class Address(val street: String) { companion object }

@Lenses
data class Person(val address: Address) { companion object }

val streetOfPerson = Person.address().street()

This fluent API looks much terser and cleaner compared to the canonical one above. Beware that under the hood nothing special happens! The generated code simply uses the plus operator the same way you can do so manually.

Combining lenses is i.a. very useful for formatting values like you will learn about in the next section.

Formatting Values

In html you can only use Strings in your attributes like in the value attribute of input {}. To use other data types in your model you have to specify how to represent a specific value as String (e.g. Number, Currency, Date). When you work with input {} you also need parse the entered text back to your data type. For all Kotlin basic types there is a convenience function asString() which generates a Lens from this type to String and vice versa. Therefore, it calls internally the T.toString() and String.toT() functions.

@Lenses
data class Person(val age: Int)

val ageLens: Lens<Person, Int> = Person.age() // cannot be used in tag attributes
val ageLensAsString: Lens<Person, String> = Person.age().asString() // now it is usable

fritz2 also provides a special lensOf() function for creating a Lens<P, String> for special types that are not basic:

fun <P> lensOf(format: (P) -> String, parse: (String) -> P): Lens<P, String>

If you use other types like kotlinx.datetime.LocalDate in your data classes, you have to specify a special lens for it. This lens then converts the value to a String and vice versa.

import kotlinx.datetime.*

@Lenses
data class Person(val birthday: LocalDate)

object Formats {
val date: Lens<LocalDate, String> = lensOf(LocalDate::toString, String::toLocalDate)
}

Now you can use the Formats.date lens for deriving appropriate stores:

val personStore = storeOf<Person>(Person(LocalDate(1990, 1, 1)))

val birthday: Store<String> = personStore.map(Person.birthday() + Formats.date)
// or when interim store is needed
val birthday: Store<String> = personStore.map(Person.birthday()).map(Formats.date)

Take a look at our complete validation example to get an impression on that topic.

Summary of Store-Mapping-Factories

Factory Use case
Store<P>.map(lens: Lens<P, T>): Store<T> Most generic map-function. Maps any Store given a Lens. Use for model destructuring with automatic generated lenses for example.
Store<P?>.map(lens: Lens<P & Any, T>): Store<T> Maps any nullable Store given a Lens to a Store of a definitely none nullable T. Use in render*-content expressions combined with some null check.
Store<List<T>>.mapByElement(element: T, idProvider): Store<T> Maps a Store of some List<T> to one element of that list. Works for entities, as a stable Id is needed.
Store<List<T>>.mapByIndex(index: Int): Store<T> Maps a Store of some List<T> to one element of that list using the index.
Store<Map<K, V>>.mapByKey(key: K): Store<V> Maps a Store of some Map<T> to one element of that map using the key
Store<T?>.mapNull(default: T): Store<T> Maps a Store of some nullable T to a Store of a definitely none nullable T using some default value in case of null in source-store.
MapRouter.mapByKey(key: String): Store<String> Maps a MapRouter to a Store. See chapter about routers for more information.

Summary Lens-Factories

Factory Use case
lensOf(id: String, getter: (P) -> T, setter: (P, T) -> P): Lens<P, T> Most generic lens (used by lenses-annotation-processor. Fits for complex model destructuring.
lensOf(parse: (String) -> P, format: (P) -> String): Lens<P, String> Formatting lens: Use for mapping into Strings.
lensForElement(element: T, idProvider: IdProvider<T, I>): Lens<List, T> Select one element from a list of entities, therefore some stable Id is needed.
lensForElement(index: Int): Lens<List, T> Select one element from a list by index. Useful for value objects
lensForElement(key: K): Lens<Map<K, V>, V> Select one element from a map by some key.

Advanced Topics

Reactive Rendering of Lists of Entities with automatically Mapped Element Store

There is a special convenience method for the reactive rendering of list of entities, that can only be explained with the already explained knowledge about Stores and Lenses.

On a store of List<T> an extension method called renderEach ia defined directly on the store. It is mandatory to pass an idProvider, so this is targeted to entity-types.

Inside the content-parameter expression of renderEach, instead of some T a whole Store<T> gets injected. So under the hood there is some store-mapping taking place, where for each element of the original list, a mapped store handling that element from the original store is created:

val storedPersons: Store<List<Person>> = storeOf(listOf(Person(1, "fritz2", emptySet())))

// needed for mapping an already mapped store to destructure the model further
val nameLens: Lens<Person, String> = lensOf("name", Person::name) { person, name -> person.copy(name = name) }

render {
div {
storedPersons.renderEach(Person::id) { storedPerson ->
// ^^^^^^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^
// call directly on the get a full `Store<Person>`
// store for each element

val storedName = storedPerson.map(nameLens) // create this store to map it further

// provide some input element in order to modify one property of that person
input {
value(storedName.data)
changes.values() handledBy storedName.update
}
}
}
}

Typical use cases are tables with editable cells for example.

With all the knowledge about reactive rendering of entities and lenses, we can demonstrate, what boilerplate code can be omitted, using the Flow<List<T>>.renderEach-function:

val storedPersons: Store<List<Person>> = storeOf(listOf(Person(1, "fritz2", emptySet())))

val nameLens: Lens<Person, String> = lensOf("name", Person::name) { person, name -> person.copy(name = name) }

render {
div {
storedPersons.data.renderEach(Person::id) { person -> // we only get some `T` ...

// ... thus we must create the mapped store manually:
val storedPerson = storedPersons.mapByElement(person, Person::id)

// the same as above
val storedName = storedPerson.map(nameLens)
input {
value(storedName.data)
changes.values() handledBy storedName.update
}
}
}
}

You might recognize that the parameters of renderEach and mapByElement are identically. That's why it is possible to encapsulate the store mapping directly into the former presented convenience function.

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