Zustand, devtools and immer wrapper

I’m considering a Vue 3 to React migration at work, and while writing a proof of concept, I’m standardizing the Zustand stores creation, which will all use devtools and immer middlewares. And this results in convoluted and error-prone code, which I’m trying to abstract away with a generator function. Such a function, however, has proven to be very hard to write with perfect TypeScript typings.

I had to resort to the great Daishi Kato again, which very patiently helped me to write. It indeed has a deep level of TypeScript black magic which I’m not familiar with:

import {create, StateCreator} from 'zustand';
import {devtools} from 'zustand/middleware';
import {immer} from 'zustand/middleware/immer';

export function createFullStore<T extends object, U extends object>(
	name: string,
	state: T,
	actions: StateCreator<
		T,
		[['zustand/devtools', never], ['zustand/immer', never]],
		[['zustand/immer', never], ['zustand/devtools', never]],
		U
	>,
) {
	return create<T & U>()(
		devtools(
			immer((...a) => Object.assign({}, state, (actions as any)(...a))),
			{name},
		),
	);
}

It works perfectly, with all the type inferences one would expect. I’m really thankful to the guy.

By the way this should’ve been published yesterday, but after a whole day without internet at home, I finally can do it.

TypeScript function to set value by key

While researching React stores automation, I wondered whether it would be possible to have a TypeScript function to set the value of an object by the name of its property, with correctly typed parameters, of course.

Turns out, it is

export function setField<
	T extends object,
	K extends keyof T,
>(o: T, k: K, v: T[K]): void {
	o[k] = v;
}

The function above works perfectly with interfaces:

interface Person {
	name: string;
	age: number;
}

const p: Person = { name: 'Joe', age: 42 };
setField(p, 'name', 'foo');

Jotai utilities for read-only and write-only atoms

Three months ago I wrote an utility function to deal with write-only atoms in Jotai. To go with it, today I wrote a function to deal with read-only atoms, so now I have:

import {Atom, useAtom, WritableAtom} from 'jotai';

/**
 * Syntactic sugar to useAtom() for read-only atoms.
 */
export function useReadAtom<V>(a: Atom<V>) {
	const [val] = useAtom(a);
	return val;
}

/**
 * Syntactic sugar to useAtom() for write-only atoms.
 */
export function useWriteAtom<V, A extends unknown[], R>(a: WritableAtom<V, A, R>) {
	const [_, setFunc] = useAtom(a);
	return setFunc;
}

It’s worth mentioning that useReadAtom also works for derived atoms, used for computed values.

Zustand computed values, pt. 2

I’ve been trying to find a way for computed values in Zustand for a while. The general recommended way is using a custom hook, according to Daishi Kato himself. The issue with this approach, however, is that your computed logic will be completely alienated from the store itself.

I just found a neat approach which seems to work: using a nested object inside the store itself, as pointed in this highly cheered comment. Below is a fully typed example:

import {create} from 'zustand';
import {combine} from 'zustand/middleware';
import {immer} from 'zustand/middleware/immer';

export interface Person {
	name: string;
	age: number;
}

const usePeople = create(immer(
	combine({
		people: [] as Person[],
	},
	(set, get) => ({
		$computed: {
			get count(): number {
				return get().people.length;
			},
		},
		addNew(name: string, age: number): void {
			set(state => {
				state.people.push({name, age});
			});
		},
	})),
));

export default usePeople;

The tradeoff, as benchmarked by myself, is that this $computed getters are not cached and will run every time the state changes, in addition to every time they are requested – which happens when the component re-renders. The ordinary custom hook approach runs only when they are requested. So, there is a performance penalty, which can be huge if the logic is too demanding.

Cross-compiling Rust for x32

While analyzing a bug in WinSafe, I had to test its compilation on the i686 platform. Without knowing, I already had it installed together with my MSVC toolchain, and I just needed a few commands in order to pull it out:

List all available toolchains:

rustup toolchain list

Which gave me:

stable-i686-pc-windows-msvc
stable-x86_64-pc-windows-msvc (default)
nightly-x86_64-pc-windows-msvc

Then choose other than the default toolchain:

cargo +stable-i686-pc-windows-msvc c

After performing all the tests, it’s a good idea to clean all the build artifacts:

cargo clean

Note that, if the toolchain is not present, it may have to be installed.

Calling write-only atoms in Jotai

Every atom in Jotai, when called through its useAtom primitive, returns a tuple of value + setter, just like React’s own useState. In a Jotai write-only atom, however, there is no value, so the canonical call is:

const [_, setVal] = useAtom(myValAtom);

In order to get rid of the verbose underscore, and thus the whole useless tuple, I wrote the following function:

import {WritableAtom} from 'jotai';

/**
 * Syntactic sugar to useAtom() for write-only atoms.
 */
export function useWriteAtom<V, A extends unknown[], R>(a: WritableAtom<V, A, R>) {
	const [_, setFunc] = useAtom(a);
	return setFunc;
}

The atom type was copied straight from Jotai’s TypeScript definitions, so the type inference works seamlessly. The example now can be written as:

const setVal = useWriteAtom(myValAtom);

Delegating Display/Debug trait implementations

Suppose you want implement Display and Debug traits for your struct, but the output is the same. Instead of copy & paste the implementation, you can delegate the implementation by casting the Self type to the trait type:

impl std::fmt::Debug for MyStruct {
	fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
		write!(f, "MyStruct");
	}
}

impl std::fmt::Display for HRESULT {
	fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
		<Self as std::fmt::Debug>::fmt(self, f) // delegate
	}
}

As a reminder, the Self casting can be useful in other situations.

A generic hook for useState and Immer

While prospecting the use of useState with the great Immer, I ended up finding the small use-immer package, which unifies both things. However, it doesn’t have proper TypeScript typings.

So I wrote my own hook to perform this task: just like useState, it returns a value and a setter, but the setter’s argument is a mutable state, automatically wired to produce, and all that using a generic argument for proper typing:

import {useCallback, useState} from 'react';
import {Draft, produce} from 'immer';

export function useStateImmer<T>(initialState: T | (() => T)): [
	T,
	(updater: (draft: Draft<T>) => void) => void,
] {
	const [obj, setObj] = useState(initialState);
	const setObjProduce = useCallback((updater: (draft: Draft<T>) => void): void => {
		setObj(curState => {
			return produce(curState, draft => {
				updater(draft);
			});
		});
	}, [setObj]);
	return [obj, setObjProduce];
}

Note that the returned setter function is identity, since it’s guarded by an useCallback call.

Java Either class

Today, at work, I had the dire need of discriminated unions in Java, which of course doesn’t support it. Then I found the idea of the Either type, which I implemented myself using the Optional class from Java 8:

import java.util.Optional;
import java.util.function.Consumer;
	
/**
 * Guarda 3 elementos mutuamente exclusivos, isto é, apenas 1 dos 3 pode existir
 * dentro do objeto.
 */
public class Either3<T1, T2, T3> {
	
	private Optional<T1> v1 = Optional.empty();
		
	private Optional<T2> v2 = Optional.empty();
		
	private Optional<T3> v3 = Optional.empty();
	
	/** Cria um objeto com o valor do tipo 1. */
	public static <<1, T2, T3> Either3<T1, T2, T3> of1(T1 v1) {
		Either3<T1, T2, T3> obj = new Either3<>();
		obj.v1 = Optional.of(v1);
		return obj;
	}
	
	/** Cria um objeto com o valor do tipo 1. */
	public static <T1, T2, T3> Either3<T1, T2, T3> of2(T2 v2) {
		Either3<T1, T2, T3> obj = new Either3<>();
		obj.v2 = Optional.of(v2);
		return obj;
	}
	
	/** Cria um objeto com o valor do tipo 1. */
	public static <T1, T2, T3> Either3<T1, T2, T3> of3(T3 v3) {
		Either3<T1, T2, T3> obj = new Either3<
		obj.v3 = Optional.of(v3);
		return obj;
	}
	
	/** Retorna o valor do tipo 1; caso não exista, lança {@code NoSuchElementException}. */
	public T1 get1() {
		return v1.get();
	}
	
	/** Retorna o valor do tipo 2; caso não exista, lança {@code NoSuchElementException}. */
	public T2 get2() {
		return v2.get();
	}
	
	/** Retorna o valor do tipo 3; caso não exista, lança {@code NoSuchElementException}. */
	public T3 get3() {
		return v3.get();
	}
	
	/** Diz se o objeto possui um valor do tipo 1. */
	public boolean has1() {
		return v1.isPresent();
	}
	
	/** Diz se o objeto possui um valor do tipo 2. */
	public boolean has2() {
		return v2.isPresent();
	}
	
	/** Diz se o objeto possui um valor do tipo 3. */
	public boolean has3() {
		return v3.isPresent();
	}
	
	/** Executa a função caso haja um valor do tipo 1. */
	public void if1(Consumer<? super T1> consumer) {
		v1.ifPresent(consumer);
	}
	
	/** Executa a função caso haja um valor do tipo 2. */
	public void if2(Consumer<? super T2> consumer) {
		v2.ifPresent(consumer);
	}
	
	/** Executa a função caso haja um valor do tipo 3. */
	public void if3(Consumer<? super T3> consumer) {
		v3.ifPresent(consumer);
	}
}

Since Java doesn’t have variadic generic parameters, having one class to each amount is needed. Nonetheless, it’s doable and remarkably ergonomic.

Using Zustand, TypeScript and Immer together

In React land, Redux nightmare finally came to an end with the inception of Zustand, which throws away all the chores we had to previously do, leaving us with a simple workflow of minimal code. On top of that, TypeScript allows us to relax our brains with some degree of strong typing.

When dealing with our Zustand store, just like Redux, we have to manipulate a chunk of immutable data, which becomes more and more annoying as we deepen the object structure. And, when it comes to dealing immutable data, Immer is everyone’s best friend – just slap a produce call and you can go home earlier.

Now here comes the question: how to use Immer inside our Zustand store?

Turns out Zustand provides a few middlewares – functions which lie between the data and your manipulation functions –, and among them an Immer middleware. Using the middleware has two advantages:

• you don’t need to explicitly call produce, because the state argument already comes to you as a WritableDraft;
• you don’t need to explicitly type the state argument, because it already comes to you properly typed.

To put everything together, with automatic type inference, we also need the combine middleware, so we don’t need to write the store type by hand. Here is the full template that can be used as the starting point of your store:

import {create} from 'zustand';
import {combine} from 'zustand/middleware';
import {immer} from 'zustand/middleware/immer';

interface Person {
	name: string;
	age: number;
}

const useStore = create(immer(
	combine({
		people: [] as Person[],
	},
	(set, get) => ({
		add(name: string, age: number) {
			set(state => {
				state.people.push({name, age});
			});
		},
		remove(name: string) {
			set(state => {
				state.people = state.people.filter(p => p.name === name);
			});
		},
	})),
));

export default useStore;

Notice how you can simply mutate the state inside the actions.

TypeScript map for objects

In another round of React experiments, after another Vue frustration with Volar not typing event arguments, I was again into the state normalization land. When dealing with objects with IDs as keys, every operation requires a tedious reduce, in contrast to the intuitive map we use with arrays.

So I wrote a map for objects:

function mapObj<T>(obj: Record<string, T>, callback: (elem: T, key: string) => T): Record<string, T> {
	return Object.keys(obj).reduce((accum, key) => {
		accum[key] = callback(obj[key], key);
		return accum;
	}, {} as Record<string, T>);
}

It’s fully typed, and it was surprisingly easy to write. TypeScript is a great language.

Testing GitHub pull requests before merging

My personal projects on GitHub oftentimes. It’s very flattering when other people get interested in your personal work, and actively use it. GitHub pull request is how other people suggest changes to our code.

Pull requests can be merged in GitHub with just one click, but it’s also possible – and recommended – trying the changes locally first. The usual way to do this is creating a local branch, then pulling the modified code onto it.

As an example, let’s take pull request #75 of WinSafe. We create another branch called foo and check the pull request onto it by using:

git fetch origin pull/75/head:foo
git checkout foo

Simple enough, and for some reason it took me years to try this for the first time today.