The second in a series of posts that investigates using strongly-typed first-class functions with WordPress WP-API to create a composable, testable, verifiable, and productive method of REST API development.
Context switching is a productivity killer. What exactly constitutes a context switch though?
Moving to a ping in Slack away from a Vim window? Definitely a context switch.
Switching via cmd-tab between a source code editor and browser window? Also a context switch. Yes, even when duck-duck-going the error from the console.
Everything that reduces context switching during development is a productivity win.
Debugging is a Productivity Killer
Time spent searching logs and reconstructing failure cases from production bugs is time not spent shipping.
It is also time that was not accounted for in the 100% accurate development estimate given to the project manager to complete the task.
Passing a string value to a function that expects an int: bug. Typing the incorrect string name of a function in WordPress’s add_filter: another bug. Calling a method on a WP_Error instance because it was assumed to be a WP_User: bug.
They may all seem like small bugs but they can quickly add up to a non-trivial amount of time debugging. Perhaps these bugs will be discovered quickly at runtime, but that requires the correct codepaths are executed in a runtime. Is every code path in a project going to be executed between each source code change? No.
Static analysis will increase productivity by uncovering these bugs. But even with a 100% typed, fully analyzed codebase validating running code output is still necessary.
Automating runtime validation is another tool to increase productivity.
Runtime Verification
Psalm enforces correct types and API usage. Checking the correctness of the runtime code still requires some manual steps, like booting up an entire WordPress stack. Previously, wp-env was used to verify that the endpoint actually worked.
This isn’t going to scale well when the number of endpoints and the number of ways to call them increases. Jumping from an editor to a browser and back isn’t the best recipe for productive coding sessions either.
Time for automated tests.
In the world of PHP, that means PHPUnit.
The bare minimum code to test totes_not_buggy() is a single implementation of PHUnit\Framework\TestCase with a single test method. It will live in tests/Totes/TotesTest.php:
<?php
namespace Totes;
use WP_REST_Request;
use WP_REST_Server;
class TotesTest extends \PHPUnit\Framework\TestCase {
/**
* @return void
*/
function testTotesNotBuggy() {
$request = new WP_REST_Request( 'GET', '/totes/not-buggy' );
$response = totes_not_buggy( $request );
$this->assertEquals( [ 'status' => 'not buggy' ], $response->get_data( ) );
}
}
To run PHPUnit, the dependency needs to be installed.
composer --dev require phpunit/phpunit
Now run the test:
./vendor/bin/phpunit tests
// yadda yadda
ERRORS!
Tests: 1, Assertions: 0, Errors: 1.
The error shows that we don’t have WordPress APIs available to our run runtime:
1) Totes\TotesTest::testTotesNotBuggy
Error: Class 'WP_REST_Request' not found
WordPress is a dependency of this project. It won’t work without it. Time to install it:
composer require --dev johnpbloch/wordpress
The johnpbloch/wordpress package by default will install the WordPress source code in ./wordpress. Setting up a whole WordPress stack to work on some source code: productivity killer. “No install” is faster than any five minute install no matter how famous it is.
If WordPress were a PSR-4 compliant project there wouldn’t be anything left to do. But it isn’t. To illustrate, run the test again and observe the result is the same.
Since Composer doesn’t know how to autoload WordPress source code, PHPUnit needs to be taught how to find WordPress APIs during test execution. A perfect place for this is via PHPUnit’s "bootstrap" system.
Generate a config and tell PHPUnit to use a custom"bootstrap":
./vendor/bin/phpunit --generate-config
PHPUnit 9.0.1 by Sebastian Bergmann and contributors.
Generating phpunit.xml in /Users/beau/code/wp-api-fun
Bootstrap script (relative to path shown above; default: vendor/autoload.php): tests/bootstrap.php
Tests directory (relative to path shown above; default: tests):
Source directory (relative to path shown above; default: src):
Generated phpunit.xml in /Users/beau/code/wp-api-fun
This generates ./phpunit.xml and tells phpunit to run test/bootstrap.php before executing tests.
Time to hunt down all of the WordPress dependencies for this test.
One way to find which PHP files need to be included is to keep running the tests and including the files that define the missing classes and functions.
For example, the current error is that WP_REST_Request is not defined.
Now add wordpress/wp-includes/rest-api/class-wp-rest-request.php.
Keep going until it passes. This is the end result for now. Note that this is – at this time in our development – 100% of our plugin’s runtime dependencies.
Now that Composer can install WordPress and PHPUnit, the CI can run these tests too. Add it to the GitHub action:
+
+ - name: Unit Tests
+ run: vendor/bin/phpunit
Runtime verification of any new route can now be captured in a unit test. Once in a unit test it can be ran in all sorts of ways.
Bonus, with XDebug configured PHPUnit will also report coverage analysis when proper @covers annotations are added:
vendor/bin/phpunit test --coverage-html coverage-report
PHPUnit 9.0.1 by Sebastian Bergmann and contributors.
. 1 / 1 (100%)
Time: 68 ms, Memory: 8.00 MB
OK (1 test, 1 assertion)
Generating code coverage report in HTML format ... done [12 ms]
68 millisecond execution time with 100% coverage of a one-line function assigned a CRAP score of 1. Gotta love that new project smell.
Screen capture of a PHPUnit coverage report.
Safety Nets Engaged
Between Psalm and PHPUnit we now have static analysis and automated runtime tests.
Next up we’ll dive into Higher-Order Kinds with Psalm and start using them with WP-API to create a declarative, composable API.
When sharing some of this with a coworker to help figure out some type questions they quickly pointed out that this is in fact a Parser (thanks Dennis). These are things an informally trained developer (me) probably should have been able to identify at this point in their career.
Mapping the understanding of what a Parser is to what I had named it caused confusion. So all things Validator<T> have become Parser<T>. Naming: one of the two hard things.
Combining more than Two Parsers
In the Parser<T> library the function oneOf accepts two Parser<T> types and returns the union of them:
function oneOf<A,B>(a: Parser<A>, Parser<B>): Parser<(A|B)> {
return value => mapFailure(a(value), () => b(value));
}
A more complex Parser<T> is now created out of simpler ones.
Assuming isPerson is Parser<Person> and isAnimal is Parser<Animal>, const isThing is inferred by TypeScript to be:
type Parser<null | Person | Animal>
Each additional Parser<T> requires another call of oneOf. Writing a oneOf that takes one or more Parser<T> types is straight forward:
function oneOf(parser, ... parsers) {
return value => parsers(
(result, next) => mapFailure(result, () => next(result)),
parser(value)
)
}
However, writing the correct type signature for this function was beyond my grasp.
My first attempt I knew couldn’t work:
function oneOf<T>(parser: Parser<T>, ...parsers: Parser<T>[]): Parser<T> {
In use, TypeScript’s inference was not happy:
const example = oneOf(isString, isNumber, isBoolean);
Types of property 'value' are incompatible.
Type 'number' is not assignable to type 'string'.
The T was being captured as string because the first argument to oneOf is a Parser<string>. However isNumber is a Parser<number>, so the two T did not match and tsc was not happy. Removing the first parser: Parser<T> didn’t help.
If TypeScript is told what the union is, then everything is ok:
const example = oneOf<string|number|boolean>(isString, isNumber, isBoolean);
But the best API experience is one in which the correct type is inferred.
After varying attempts of picking out similar cases in TypeScript’s Advanced Types I gave up and posed the question in the company’s #typescript Slack channel.
The magical internet people debated about Parser<T> and Result<T> so I tried to simplify things to the “base case” and got rid of Result<T>:
type Machine<T> = () => T
Is it possible to create a function signature such that a list of Machine<*>s of differing <T>s via variadic type arguments could infer the union Machine<T1|T2|T3|...>:
function oneOf(... machines: Array<Machine<?>>>): Machine<(UNION of ?)> {
The magical internet people came up with a solution (thank you, Tal).
type MachineType<T> = T extends Machine<infer U> ? U : never;
function<M extends Machine<any>[]>(...machines: M): Machine<MachineType<M[number]>> {
After mapping it into the Parser domain, It worked!
type ParserType<T> = T extends Parser<infer U> ? U : never;
function<P extends Parser<any>[]>oneOf(...machines: P): Parser<ParserType<P[number]>> {
const example = oneOf(isNumber, isString, isBoolean);
Running tsc passed, and the inferred type of const example is:
const example: (value: any) => Result<string | number | boolean>
Now to understand why it works.
Conditional Types: ParserType<T>
The first thing to understand is ParserType<T>, which uses a Conditional Type:
type ParserType<T> = T extends Parser<infer U> ? U : never;
This is essentially a function within the type analysis stage of TypeScript (somewhat analogous to Flow’s $Call utility type). My first understanding of this reads as:
Given a type T, if it extends Parser<infer U> return U, otherwise never.
Using ParserType with any Parser<T> will give the type of T. So given any function that is a Parser<T>, the type of <T> can be inferred.
Within the extends clause of a conditional type, it is now possible to have infer declarations that introduce a type variable to be inferred. Such inferred type variables may be referenced in the true branch of the conditional type. It is possible to have multiple infer locations for the same type variable.
Take an example parsePerson parser which is defined using objectOf:
const parsePerson = objectOf({
name: isString,
email: isString,
metInPerson: isBoolean
});
type Person = ParserType<typeof parsePerson>;
// This is ok!
const valid: Person = {
name: 'Nausicaa',
email: 'nausica@valleyofthewind.website',
metInPerson: false,
};
// This fails!
const invalid: Person = {}; // Type Error
type Person is inferred to be:
type Person = {
name: string;
email: string;
metInPerson: boolean;
}
const invalid: Person fails because:
Type '{}' is missing the following properties from type '{ name: string; email: string; metInPerson: boolean; }': name, email, metInPerson
So now the return value of oneOf is almost understood:
: Parser<ParserType<P[number]>>
This says:
Returns a Parser<T> whose T is the ParserType of P[number].
Well what is P[number]?
Mapped Types
In TypeScript, Mapped Types allow one to take the key and value types of one type, and transform them into another.
If you’ve used Partial<T> or ReadOnly<T>, you have used a Mapped Type. The example implementations of those are given as:
type Readonly<T> = {
readonly [P in keyof T]: T[P];
}
type Partial<T> = {
[P in keyof T]?: T[P];
}
Given a type with an index, the type that is used for the index’s value can be accessed using its key type:
type MyIndexedType = {[key: number]: (number|boolean|string)};
type ValueType = MyIndexedType[number];
In this example ValueType will have the type (number|boolean|string).
In the return signature of oneOf there is a P[number].
: Parser<ParserType<P[number]>>
Assuming P is an indexed type with keys and values whose key type is a number, this gives the type of the value stored in P.
So what is P?
function<P extends Parser<any>[]>oneOf(
P is an array of Parser<any>[]. Well it extendsParser<any>[].
This is where the magic happens.
TypeScript captures the T of each Parser<any> and stores it in P. Because an Array is an indexed type whose key is number, the type of P can also be expressed like this:
type P = {[number]: (Parser<number>|Parser<string>|Parser<boolean>)};
There it is! The union is the value type at P[number].
Putting the Pieces Together
ParserType is a Conditional Type that given a Parser<T>, returns T.
What happens when ParserType is given a union of Parser<T> types.
type T = ParserType<(Parser<string> | Parser<number>)>
TypeScript infers the union for T:
type T = string | number
Given a Mapped Type P that extends Parser<T>[], the union of Parser<T> types is available at P[number].
It follows then that passing the P[number] into ParserType will provide the union of T types in Parser<T>. That is exactly what the return type in oneOf does.
Reading the new signature for oneOf is now less cryptic:
function oneOf<P extends Parser<any>[]>(
...parsers: P
): Parser<ParserType<P[number]>> {
Now to wrap up the implementation.
Using oneOf doesn’t work unless there is at least one Parser<T>. The signature can be updated to require one:
function oneOf<T, P extends Parser<any>[]>(
parser: Parser<T>,
...parsers: P
): Parser<T|ParserType<P[number]>> {
// no additional parsers, return the single parser to be used as is
if (parsers.length === 0) {
return parser;
}
return value => mapFailure(
parsers.reduce(
// with each reduction, only, try to parse when the previous was a Failure
(result, next) => mapFailure(result, () => next(value)),
// seed the result with the first parser
parser(value)
),
// if all parsers fail, indicate that there were multiple parsers attempted
() => failure(value, `'${value}' did not match any of ${parsers.length+1} validators`)
);
}
In my personal projects I have fallen in love with solving my problems via Type Driven Development.
Given a language has static types, generics, and first-class functions it hits the sweet spot for this kind of development. The only real requirement is first-class functions because it is an application of Lambda calculus principles.
The Problem with any
Typed languages provide safety. If the developer uses an API incorrectly, the computer will yell at them.
type Product = {
readonly name: string
}
function createProduct(name: string): Product {
return { name };
}
createProduct(5);
When calling createProduct with name of something other than a string the computer cries out:
Argument of type '5' is not assignable to parameter of type 'string'.
A problem I want to solve in one of my side-projects is JSON safety. Take Product as an example. When serializing it with JSON.stringify and then parsing it with JSON.parse, the type is lost:
type User = {
readonly username: string
}
function renameUser(name: string, user: User): void {
// implementation left blank
}
const product = createProduct('some product');
renameUser('some user', product);
renameUser('some user', JSON.parse(JSON.stringify(product)));
The second call to renameUser shows no error. The first call to renameUser shows:
Argument of type 'Product' is not assignable to parameter of type 'User'.
Property 'username' is missing in type 'Product' but required in type 'User'.
If we write the unit test I’m confident we can prove that product and JSON.parse(JSON.stringify(product)) are deeply equal.
The problem is that JSON.parse() returns any (in TypeScript and Flow).
A similar problem exists in all of the languages I have come across:
Java org.json.JSONObject and org.json.JSONArray
Swift/Objective-C have JSONSerialization/NSJSONSerializalion
PHP’s json_decode
Going from binary data to native object is inherently unsafe. When the JSON data comes in from an external system – like a REST API – the risk is real.
A Band-Aid
In a language like TypeScript or Flow the straight-forward way to safely deal with JSON values is through type refinement.
This results in an increasing number of type guards as different members within the any type are accessed. Assuming your chosen REST API layer does JSON marshaling for you:
const result = await api.get('http://example.dev/api/people');
if (result && result.people && Array.isArray(result.people) {
people.map(person => {
// more runtime type refining 💩
})
}
If both client and server are both under your control, or you feel somewhat confident enough in the REST API maintainers, one might feel brazen enough to force the situation:
type PeopleResponse = { people: Array<Person> };
const result: PeopleResponse = await api.get('http://example.dev/api/people');
// go along your merry way until your Runtime errors start popping up
This is madness. It assumes type safety when there isn’t any. Unfortunately, this is what I see most often in projects at work.
The prospect of writing lines and lines of type refinements for every possible JSON structure for every API response is a lot of work. In my “toy” project I already have 21 different REST API calls with varying shapes of responses and that’s only going to grow.
Can I write a JSON validation layer that’s as declarative as creating custom TypeScript types?
Let’s give it a shot.
Defining our Validation Types
Time to start practicing Type Driven Development.
What is Type Driven Development? Start with types, then write implementations to satisfy the type checker. It’s like Test Driven Development, but you don’t even have to write the tests.
Our current problem is pretty clear. We need a way to write functions that validate some JSON any type. That means we need a function that accepts a single any type as its input.
But which type does it return? That should be up to the implementation of the validation, and at this point, that implementation doesn’t exist. So we’ll use a generic type to stand in its place:
type Validator<T> = (value: any) => T;
This states that a Validator<T> is a Function that accepts a single any and returns a T.
This makes sense for success cases, but what about failure cases? What happens when validation fails?
At this point there are two options to deal with failure:
throw an Error
return a Union type to indicate success or failure modes.
Common usage of a Validator<T> expects failure. Using throw might feel simpler at the implementation level, but it forces the user of the Validator<T> to take on that complexity. TypeScript’s (or Flow’s) Union types allow for safe handling of success/failure modes.
Here’s what a Union type API looks like:
type Success<T> = {
readonly type: 'success'
readonly value: T
}
type Failure = {
readonly type: 'failure'
readonly value: any
readonly reason: string
}
type Result<T> = Success<T> | Failure;
type Validator<T> = (value: any) => Result<T>;
This looks like the complete set of types for a “validation” API. A function that accepts any thing and returns Success<T>orFailure. The Success<T> boxes the typed value with the refined type. The Failure contains the original value and the reason that validation failed.
Let’s write our first validator:
const isString: Validator<string> = (value) => {
if (typeof value === 'string') {
return { type: 'success', value }
} else {
return {
type: 'failure',
value,
reason: 'typeof value is ' + (typeof value)
};
}
}
With tsc and jest we can confirm that both type checking and runtime behavior match our expectations:
The remaining non-container (Array, and Object) types are equally as trivial. And to make things a little more convenient we can make Success<T> and Failure factories:
Now isString, isNumber, isNull, isUndefined, isObject, isArray, isUndefined and isBoolean can all follow this pattern:
const isNull: Validator<null> = value =>
value === null
? success(null)
: failure(value, 'typeof value is ' + (typeof value));
With each basic case we can write the corresponding set of tests to confirm the runtime characteristics and the static type checker’s ability to infer types.
But JSON is more complex than these base types, and our TypeScript types even more complicated with nullables and unions.
We need to be able to combine these base cases into something that can address our real world needs.
Combining Simple Types to Make Complicated Ones
Optional types in TypeScript and Flow are a Union type of null or some type T.
type Optional<T> = null | T;
If we wanted to validate an optional type our validator’s type would be Validator<null|T>.
An optional string validator would have the type Validator<null|string>. We have a Validator<string> already, so perhaps we can utilize that.
const isOptionalString: Validator<null|string> = value => {
if (value === null) {
return success(null);
}
return isString(value);
}
This works fine, but the idea of writing each isOptionalX sounds boring. And TypeScript types can be more complex than null|T. They can be string | number or any other set of unions.
Since we’re playing at leveraging Lamda calculus concepts, we can lift ourselves out of the minutiae of Validator<T> implementations and start working with validators themselves.
Given two different validators Validator<A> and Validator<B>, can we use what we know about validators to create a Validator<A|B>?
Using Type Driven Development, let’s stub out the function signature:
function oneOf<A,B>(a: Validator<A>, b: Validator<B>): Validator<A|B> {
}
At this point tsc is upset:
A function whose declared type is neither 'void' nor 'any' must return a value.
What should we return? A Validator<A|B> is like any other validator in that it accepts a single any argument. In Type Driven Development style, let’s return a function since that’s what it wants:
function oneOf<A,B>(a: Validator<A>, b: Validator<B>): Validator<A|B> {
return value => {
}
}
Now tsc says:
Type '(value: any) => void' is not assignable to type 'Validator<A | B>'.
Type 'void' is not assignable to type 'Result<A | B>'.
Our function isn’t correct yet. It has no return value (void) but a Validator<A | B> needs to return a Result<A | B>.
We now have all of the inputs we need do do that within the scope of this function. All we need to do is use them:
function oneOf<A,B>(a: Validator<A>, b: Validator<B>): Validator<A|B> {
return value => {
return a(value);
}
}
Now tsc is happy, but does it have the runtime characteristics we want?
expect(received).toEqual(expected) // deep equality
- Expected - 1
+ Received + 2
Object {
- "type": "success",
+ "reason": "typeof value is number",
+ "type": "failure",
"value": 1,
}
It failed with the number value as it should have, because we didn’t use both Validator<T>‘s.
function oneOf<A,B>(a: Validator<A>, b: Validator<B>): Validator<A|B> {
return value => {
const result_a = a(value);
if (result_a.type === 'success') {
return result_a;
}
return b(value);
}
}
If Validator<A> succeeds, we return a Success<A>. Otherwise return the result of Validator<B> which is Success<B> | Failure.
We’ve written a function that accepts two Validator<T> types and returns a new Validator<> by combining them. We wrote a combinator.
I have so far failed to create a variadic version of oneOf that can take “n” Validator<T>s and infer the union Validator<T1|T2|Tn> type. This means we need to use multiple calls to oneOf to build up inferred union types:
Since nullable types are so common – and because it’s so easy to do given our APIs – we can use oneOf to make a convenient combinator that takes a Validator<T> and turns it into a Validator<null | T>. I’ll name it optional.
The combinator we want to make here is objectOf. It will take a plain object who’s keys point to values of Validator<T>s and returns a Validator<Result<{...}>> that matches the shape of the validator.
Now that you know how to wrap the properties of a type, the next thing you’ll want to do is unwrap them. Fortunately, that’s pretty easy:
type Proxify<T> = {
[P in keyof T]: Proxy<T[P]>;
};
function unproxify<T>(t: Proxify<T>): T {
let result = {} as T;
for (const k in t) {
result[k] = t[k].get();
}
return result;
}
In terms of our domain we want to map the keys K of some generic object T into validators that validate the type at key K in T.
export function objectOf<T extends {}>(
validators: {[K in keyof T]: Validator<T[K]>}
): Validator<T> {
}
So far what does tsc think:
A function whose declared type is neither 'void' nor 'any' must return a value.
Time to implement the combinator:
Declare an instance of validatedT
Iterate through the keys of the mapped validators.
Validate the value at value[key] with its corresponding validators[key].
If Success<T[K]> set validated[key] = result.value
If Failure return the Failure
return success(validated)
export function objectOf<T extends {}>(
validators: {[K in keyof T]: Validator<T[K]>}
): Validator<T> {
let result = {} as T;
for (const key in validators) {
const validated = validators[key](value ? value[key] : undefined);
if (validated.type === 'failure') {
return validated;
}
result[key] = validated.value;
}
return success(result);
}
If you already have a type you know you need to validate for, you can use it as the generic argument to objectOf and tsc will enforce that all of the keys are present:
type Record = { id: number, name: string };
const validate = objectOf<Record>({});
The tsc error shows:
Argument of type '{}' is not assignable to parameter of type '{ id: Validator; name: Validator; }'.
Type '{}' is missing the following properties from type '{ id: Validator; name: Validator; }': id, name
It knows a validator for the Record type needs an id validator and a name validator.
It even knows which type of Validator<T> it needs:
id in Record has a type of number, but isString cannot validate to number:
(property) id: Validator
Type '(value: any) => Result' is not assignable to type 'Validator'.
Type 'Result' is not assignable to type 'Result'.
Type 'Readonly<{ type: "success"; value: string; }>' is not assignable to type 'Result'.
Type 'Readonly<{ type: "success"; value: string; }>' is not assignable to type 'Readonly<{ type: "success"; value: number; }>'.
Types of property 'value' are incompatible.
Type 'string' is not assignable to type 'number'
You can see how it worked out that the id validator of isString does not return a Result<T> that is compatible with number which is the type of Record['id'].
One last thing to make use of objectOf a little nicer. When it iterates through the keys of the validators and reaches a Failure type, it returns the Failure as is. This resulted in a somewhat opaque failure reason:
const invalid = {
name: 'Invalid',
child: { id: 'not-number' },
};
expect(validate(invalid)).toEqual(failure(invalid, 'typeof value is string' ));
The "typeof value is string" message failed because invalid.child.id was a string, not a number. Given we know which key was being validated when the Failure was returned, we can improve the error message:
Now the failure in objectOf can be passed through keyedFailure before returning:
for (const key in validators) {
const validated = validators[key](value ? value[key] : undefined);
if (validated.type === 'failure') {
return keyedFailure(value, key, validated);
}
The improved error message is now:
"Failed at 'child': Failed at 'id': typeof value is string"
The value at .child.id was a string, and that’s why there’s a failure. Much clearer.
We’re an arrayOf implementation away from a fully capable JSON validation library. But before we go there, we’re going to detour into more combinators.
Combinators
In Lamda calculus a combinator is an abstraction (function) whose identifiers are all bound within that abstraction. In short, no “global” variables.
If we consider the behavior of Validator<T> and how it returns one of two values Success<T> or Failure a natural branching control flow reveals itself.
In our example uses of Validator<T> instances, to continue using it, the next step is to first refine it by checking result.type for either success or failure.
Given how common this pattern is, we can write some combinators to make them slightly easier to work with.
In most uses of Validator<T> we want to do something with the boxed value of the Success<T> case of Result<T>.
This looks like:
const result: Result<Thing> = validate(thing);
if (result.type === 'success') {
const value: Thing = result.value;
// do something interesting with value
}
The pattern here is refining to the success case, then using the success value in a new domain. So if the user of validate had a function of type:
(thing: Thing) => OtherThing
It would be nice if they could forego the extra refinement work. We can define that pattern in a combinator.
We want to map the success case into a new domain.
function mapSuccess<A, B>(result: Result<A>, map: (value: A) => B): B|Failure {
if (result.type === 'success') {
return map(result.value);
}
return result;
}
Why would you want this? It allows you to write pure functions in your business domain, like isAdmin above, and then combine them with the Validator<T> domain, without using any glue code.
The fewer lines of code, the fewer variables to type. And we have tsc there to let us know when the function signatures don’t match.
For instance trying to use a function that takes something other than a User is going to fail type analysis when used with mapResult(Result<User>, ...).
The less often you need to cross domains within your APIs, the more decoupled they are.
Validating Array
A Validator<T> returns a Result<T>. What if we wanted to continue validating T and turn it into another type? Let’s consider Array.
The first step to turning an any type into an Array<T> is first checking if is in fact an Array.
This is similar to our other base validators:
const isArray: Validator<any[]> = value =>
Array.isArray(value) ? success(value) : failure('value is not an array');
The next step is iterating through each member in the Array<any> and validating the member. Since we’re practicing Type Driven Development, we’ll start with the type signature.
function arrayOf<T>(validator: Validator<T>): Validator<Array<T>> {
}
And jus like before tsc isn’t happy:
A function whose declared type is neither 'void' nor 'any' must return a value.
We just defined isArray. It would be neat if we could use it here. Thinking about it, it would be nice to be able to take the success case of isArray and then do more validation to it and return a mapped Result<Array<T>>.
Let’s write one more combinator that maps a Validator<A> into a Validator<B> given a function of (value: A) => Result<B>.
function mapValidator<A, B>(
validator: Validator<A>,
map: (value: A) => Result<B>
): Validator<B> {
}
If the Result<A> case is a Failure, it should be returned right away, but if it’s a Success<A> we want to unbox it and give it to (value: A) => Result<B>.
Does that sound familiar? We want to map the success result of Validator<A>. That’s mapSuccess. We can define mapValidator in terms of mapSuccess:
function mapValidator<A, B>(
validator: Validator<A>,
map: (value: A) => Result<B>
): Validator<B> {
return value => mapSuccess(validator(value), map);
}
Using mapValidator allows us to define a validation in terms of another Validator<T>.
So now we can define Validator<Array<T>> in terms of Validator<any[]>:
At this point tsc can determine that value is type any[]. But to satisfy Validator<Array<T>> we need to validate each member of any[] with Validator<T>.
If any item fails validation, the whole Array fails validation. So not only are we validating each member, but potentially returning a Failure case. We need to reduce any[] to Result<Array<T>>.
We can seed the reduce call with an empty success case:
But what to use for our reduce function? We’re declaring to Array.prototype.reduce that the first argument and return value is a Result<T[]>. That means the type of our reduce function needs to be of type:
Now that we are within an iteration of the array, we have enough context to use our Validator<T> on the member. If it’s successful, we want to concat it with the rest of items, if a failure, we’ll just return it (for now).
One last thing before we tie a ribbon on Validator<T>. The Falure case reason says:
"Failed at 'name': typeof value is number"
In the context of .reduce we know which index we are currently on while iterating. So when we validate the member, we can use mapFailure to enhance the Failure case. Here’s the new reducer:
Seattle, Washington. Tuesday, December 10th, 2019 the Collins household discovered the control panel of the LG DLE2516W did not respond to button pushes.
DuckDuckGo was consulted. LG support provides troubleshooting steps.
Unplug unit.
Press & hold power button for 5 seconds.
Press & hold start button for 5 seconds.
Plug in unit.
Instructions were followed with no change in behavior. Unit still unresponsive.
Multimeter is not on premises. Multimeter, door switch, and high temperature fuse purchased from Amazon.com.
Thursday, December 12th, 2019. Door switch arrives. Replaced on unit. No change. Collins leave on trip to Portland, Oregon.
Tuesday, December 17th, 2019. Multimeter and fuse arrive. High voltage outlet and unit power supply measure 240v.
To access high temperature fuse entire unit must be disassembled.
Unit disassembled. Multimeter finds fuse to be in working order. Replaced anyway. Ducts inspected and reveal no blockage.
Unit reassembled. Control board lights up when power button pressed. When start is pressed a relay clicks but motor does not engage (queue Picard).
Unit disassembled. Previous reassembly found to be performed by incompetent technician. Assembler received cursing. Curser and cursee roles were fulfilled by the same person.
Unit reassembled. Powers on. Motor starts.
There was much rejoicing.
Steps to prevent future outages: clean the lint from the filter.
