Content collections are the best way to manage content in any Astro project. Using the Content layer API we can define a collection to provide automatic TypeScript type-safety for all of our content.

What are Content Collections?

Let’s say we want to build our personal blog using Astro; using a collection we can turn a bunch of Markdown files (or MDX, Markdoc, YAML, or JSON files) stored locally in our project as the source of our blog content.

Defining a Collection

To define a collection, we must create a src/content.config.ts file in your project, which Astro will use to configure your content collections (we can define many collections).

import { z, defineCollection } from 'astro:content'

// Create the collection
export const posts = defineCollection({
  // type: 'content', // CAN'T USE THIS WITH LOADERS!
  loader: glob({ pattern: ['!_**/*.md','**/*.md'], base: './posts' }),
  schema: ({ image }) =>
    z.object({
      title: z.string(),
      author: z.string(),
      tags: z.array(z.string()),
      description: z.string(),
      date: z.date(),
    }),
})

// Export the collection
export const collections = {
  posts,
}

This file used to go in src/content/config.ts (the old location, Astro API moves fast), but apparently, now it has to be placed in src/content.config.ts. The glob function allows us to do two things:

• Set the folder for our collection, using base (relative to project root).
• Exclude some folder/files from being parsed, using the pattern option (!_**/*.md won’t match folders starting with un underscore)

link test

It’s not so often we get to set up a mac with the basic command line tools that make you productive, here I’ll leave what I do.

Command Line Tools

If you’re gonna be writing apps for iOS or macOS, most probably you should be installing Xcode, but if that’s not the case, probably it’s enough to install the command line tools:

xcode-select –-install

To verify that the command line tools have been installed, you can run:

xcode-select -p

The output of the command above should be the location of the **command line tools in our system, in my case /Library/Developer/CommandLineTools. If you’re curious about what tools exactly are we getting, run:

ls Library/Developer/CommandLineTools/usr/bin

Homebrew: the macOS Package Manager

Homebrew is the most popular package manager for macOS, which we can install with:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Oh My Zsh

Oh My Zsh is an open source, community-driven framework for managing your Zsh configuration. Installing it is super easy:

sh -c "$(curl -fsSL https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh)"

If you restart your shell, you’ll see you have a pretty cool new prompt. Next, let’s install some plugins:

• Autosuggestions plugin, which suggests commands as you type based on history and completions. Read installation instructions here
• zsh-syntax-highlighting plugin, which enables highlighting of commands whilst they are typed at a zsh prompt into an interactive terminal. This is super helpful for catching typos that would result in syntax errors. Read how to install it in oh my zsh here
• zsh-autocomplete plugin, which provides real-time type-ahead autocompletion to your command line. This one doesn’t include instructions about how to install in oh my zsh. Basically we just have to clone it in the plugins folder:

git clone --depth 1 -- https://github.com/marlonrichert/zsh-autocomplete.git $ZSH_CUSTOM/plugins/zsh-autocomplete

Once we’ve installed the plugins we want, we have to add them to the list of plugins in a our .zshrc file; this is what my list looks like:

plugins=(
  git
  zsh-autosuggestions
  zsh-syntax-highlighting
  zsh-autocomplete
)

To uninstall any of the plugins, we just have to remove it from the list of plugins above, and remove its folder; for example, to remove the zsh-syntax-highlighting folder:

rm -rf $ZSH_CUSTOM/plugins/zsh-syntax-highlighting

FZF

Fzf is a command-line fuzzy finder which I find super useful. Let’s install it with brew:

brew install fzf

Here I had some problems integrating this tool with zsh, but searching through the internets I found out that we have to run an installation script to generate the necessary configuration files:

$(brew --prefix)/opt/fzf/install

The output of the command above:

Downloading bin/fzf ...
  - Already exists
  - Checking fzf executable ... 0.61.3
Do you want to enable fuzzy auto-completion? ([y]/n)
Do you want to enable key bindings? ([y]/n)

Generate /Users/javi/.fzf.bash ... OK
Generate /Users/javi/.fzf.zsh ... OK

Do you want to update your shell configuration files? ([y]/n)

Update /Users/javi/.bashrc:
  - [ -f ~/.fzf.bash ] && source ~/.fzf.bash
    + Added

Update /Users/javi/.zshrc:
  - [ -f ~/.fzf.zsh ] && source ~/.fzf.zsh
    + Added

Finished. Restart your shell or reload config file.
   source ~/.bashrc  # bash  (.bashrc should be loaded from .bash_profile)
   source /Users/javi/.zshrc   # zsh

Use uninstall script to remove fzf.

At the end, we should end up with a line at the end of our .zshrc:

[ -f ~/.fzf.zsh ] && source ~/.fzf.zsh

Which checks that the generated ~/.fzf.zsh file exists, and source it.

Amazon Q

A friend of mine recommended me a (generative AI)-powered assistant named Amazon Q, which is quite easy to install in macOS:

brew install amazon-q

Or just download the .dmg file, and clickety-click until we have it running. Whatever way we choose, we can verify the installation with:

q --version

NVM

nvm is next:

curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.2/install.sh | bash

Running the command above appends the following lines to the bottom of our .zshrc:

export NVM_DIR="$HOME/.nvm"
[ -s "$NVM_DIR/nvm.sh" ] && \. "$NVM_DIR/nvm.sh"  # This loads nvm
[ -s "$NVM_DIR/bash_completion" ] && \. "$NVM_DIR/bash_completion"  # This loads nvm bash_completion

To verify the installation we have to run:

command -v nvm

which should output nvm if the installation was successful. Please note that which nvm will not work, since nvm is a sourced shell function, not an executable binary.

nvm install node # "node" is an alias for the latest version

In mathematics and computer science currying is the technique of translating a function that takes multiple arguments into a sequence of families of functions, each taking a single argument.

The Pipe Analogy

One constraint of FP is that functions should only receive one input, which are technically known as unary functions. We can think of a function as a pipe that takes in one input and produces one output. This is similar to how a water pipe takes in water and produces a flow of water out the other end.

But what if we need to write a function that takes in multiple input values? We could pass all of the inputs as properties of a single object and call it a day. For example:

const greet = ({name: string, greeting: string}) => {
  return `${greeting}, ${name}!`;
};

greet({name: 'John', greeting: 'Good morning'}); // => Good morning, John!

This is a perfectly valid approach, but it does introduce some potential issues that can make functions less predictable and harder to optimize in a purely functional paradigm:

• We introduce potential mutation risks if the object is modified elsewhere in the program.
• We’re requiring the caller to know the exact property names. If the function expects { name: string, greeting: string } but receives { name: 'Alice' }, it could break at runtime.

Curried Functions: Building a Pipeline

The FP approach to deal with this is to create an outer function that receives a single input, wrapping an inner function (also unary). For example, let’s rewrite the previous example in a curried way:

function greet(greeting: string) {
  return function(name: string) {
    return `${greeting}, ${name}!`
  }
}

const result = greet('Hello')('Bob')

console.log(result) // => Hello, Bob!

Note that greet is a function that returns another function. The first function takes in a single input, greeting, and returns another function that takes in a single input, name. The inner function then produces the final output. For example:

console.log(greet('Hello'))

We’re calling the only the outer function greet, this is the output:

function (name) {
  return `${greeting}, ${name}!`;
}

Where’s the pipeline?

Let’s rewrite the previous example in a more functional style:

const greet = (greeting: string) => (name: string) => `${greeting}, ${name}!`;

Using arrow functions this resembles a series of pipes that take in one of the inputs and pass the output to the next pipe, creating a sort of pipeline.

This is similar to how a water pipe can be connected to multiple other pipes, each taking in one input and producing one output. Let’s create another simple example:

type Sum = (a: number) => (b: number) => number;
const sum: Sum = (a) => (b) => a + b;

const result = sum(1)(2);
console.log(result); // => 3

You may be wondering, how this is useful? Well, imagine we want to write a function that increments a number by a certain value. We could write a function reusing our sum function:

type Increment = (value: number) => number;
const increment: Increment = (value: number) => sum(value)(1);

const result = increment(5);
console.log(result); // => 6

What about decrementing?

type Decrement = (value: number) => number;
const decrement: Decrement = (value: number) => sum(value)(-1);

const result = decrement(5);
console.log(result); // => 4

Currying Functions

Now imagine that we have a function that takes in multiple inputs, and we want to curry it so we can use it as a curried function:

// Just an usual binary function
const normalSum = (a: number, b: number) => a + b

type Curry2 = (f: (a: number, b: number) => number)
    => (a: number)
    => (b: number)
    => number
const curry2: Curry2 = f => a => b => f(a, b)

const curriedSum = curry2(normalSum)

const partialResult = curriedSum(1)
console.log(partialResult)  // b => f(a, b)

const finalResult = partialResult(41)
console.log(finalResult)    // 42

In the code above:

• normalSum is a normal function that takes in two inputs.
• curry2 is a function that takes in a function f and returns a curried version of it.
• curriedSum is the curried version of normalSum.
• partialResult is the result of calling curriedSum with the first input, which returns a function that takes in the second input.
• finalResult is the result of calling partialResult with the second input, which produces the final output.

So what’s the point of currying?

1. It allows us to create partial functions that can be reused in different contexts ().
2. It allows us to write multi-argument functions, which is easier to reason, then turn them into their curried versions.
3. We can use the partial functions to create other versions of the same function, like increment and decrement in the previous example.
4. Some libraries that include multi-argument functions can be curried, and the resulting functions can be used in a more functional way.

Developers often write regular functions first and curry them later if needed. Many libraries, like Lodash, provide a _.curry function so you don’t have to manually curry everything.

A Generic Curry Function

Let’s rewrite curry2 as a generic curry function that can curry any function, regardless of the type of their arguments. Here’s an example:

type Curry2 = <T, U, V>(f: (a: A, b: B) => C)
    => (a: A)
    => (b: B)
    => C

This is a generic function that takes in a function f that takes in two arguments of type A and B, and returns a value of type C. The curried version of the function takes in the first argument of type A, and returns a function that takes in the second argument of type B, and returns a value of type C. Let’s see how this works in practice:

const normalSum = (a: number, b: number) => a + b

type Curry2 = <TA, B, C>(f: (a: A, b: B) => C)
    => (a: A)
    => (b: B)
    => C

const curry2: Curry2 = f => a => b => f(a, b)

const curriedSum = curry2(normalSum)

const partialResult = curriedSum(1)
console.log(partialResult)  // b => f(a, b)

const finalResult = partialResult(41)
console.log(finalResult)    // 42

This works exactly the same as before, but now we can use it with any function that takes in two arguments of any type. For example:

const normalConcat = (a: string, b: string) => a + b

const curriedConcat = curry2(normalConcat)

const partialResult = curriedConcat('Hello')
console.log(partialResult)  // b => f(a, b)

const finalResult = partialResult('World')
console.log(finalResult)    // HelloWorld

Functional programming is a programming paradigm, which allows us to write our programs in a declarative and composable way by combining functions.

Functional programming in TypeScript?

Functional programming is language agnostic, meaning that we can apply it using almost any language. The only requirement is that the language treats functions as first-class citizens, which means that we can:

• Assign functions to variables.
• Pass functions as arguments to other functions.
• Use functions as return values from other functions.

All of the above is doable in TypeScript, which makes it a suitable choice to write code in the FP paradigm.

There are several libraries such as ramda or fp-ts, that include features to allow us to write in a more pure functional style. In this series of articles, we won’t be using any of these, just vanilla TS.

But what is Functional Programming about?

FP is about taking some input, and process it by using a chain of transformations (each transformations is accomplished by a function). When each of these functions is called with some given arguments, it will always return the same result (pure function).

In constrast, an impure function, like the ones we use in imperative programming, can have side effects (such as modifying the program’s state or taking input from a user). Side effects are considered undesirable in pure functional programming because they make functions less predictable and harder to test.

Some defining features of FP are:

• Functions are at the center of the paradigm. We end up with a bunch of reusable units of code, which leads to having to write less code to achive the same results.
• These functions are quite deterministic, meaning, given a given input, they always return the same output, regardless of the program’s state.
• Easy to test code: As a result, when writing tests for the functions, we don’t have to worry about program’s state.
• Easier to debug: FP aims to minimize or isolate side effects to make code more predictable, testable, and easier to debug.
• We’ll see that for or while loops are not used in FP; in these scenarios we use recursion.

As with any other approach, it will take time to become comfortable using it. As a result, noticing its benefits will take some time.

Pure Functions

A pure function has the following characteristics:

• It’s deterministic, meaning that given the same input (given in the arguments), the function always returns the same output. That implies that the function shouldn’t be affected by the state of the program (no side causes), only by its input.
• A pure function has no side effects, meaning that calling such a function should not mutate the state of the program.

In FP, we don’t use variables only constants. Each transformation (function call) returns a new value, which is assigned to a constant. This constant can be used as input (argument) in the next transformation.

Total vs Partial Functions

A total function is a function that is defined for all possible inputs in its domain. This means that for every input value in the domain, the function will produce a valid output. For example:

function add(a: number, b: number): number {
  return a + b
}

A partial function is a function that is defined for only a subset of inputs in its domain. For some inputs, the function may not produce a valid output or may result in an error. For example:

function divide(a: number, b: number): number {
  if (b === 0) {
    throw new Error('Division by zero is not allowed')
  }
  return a / b
}

So the main difference is that total functions map all of its inputs to some output value, whereas partial functions map only a subset of its inputs to some output:

Key Differences Between Total and Partial Functions

Why Total Functions Are Preferred in Functional Programming

1. Predictability: Total functions are easier to reason about because they are guaranteed to work for all inputs.

2. No Runtime Errors: Total functions eliminate the risk of runtime errors caused by invalid inputs.

3. Composability: Total functions can be composed more easily because they always produce valid outputs, which can be passed as inputs to other functions.

4. Immutability: Total functions align with the principles of functional programming, where functions are deterministic and side-effect-free.

How to Handle Partial Functions in FP

In functional programming, partial functions are often avoided or handled explicitly using techniques like:

1. Option Types (e.g., Maybe or Option): Wrap the result in a type that explicitly represents the possibility of failure. For example:

function safeDivide(a: number, b: number): number | null {
  return b === 0 ? null : a / b
}

2. Validation: Validate inputs before calling the function to ensure they are within the valid subset.

function divide(a: number, b: number): number {
  if (b === 0) {
    throw new Error('Division by zero is not allowed')
  }
  return a / b
}

Function Composition

Most of what we do in FP is composing functions, which consists in combining two or more functions to produce a new function; The output of one function becomes the input of the next one. Function composition allows us to build complex operations by combining smaller, reusable functions.

For example, imagine we have the function increment:

type Increment = (x: number) => number

const increment = (x: number): number => x + 1

And also the function stringify:

type Stringify = (x: number) => string

const stringify: Stringify = (x) => x.toString()

Using function composition we can define a higher-order function named incrementAndStringify, where we connect the output tof increment to the input of stringify:

type IncrementAndStringify = (x: number) => string
const incrementAndStringify: IncrementAndStringify = (x) => stringify(increment(x))

const result: string = incrementAndStringify(1)
console.log(result) // "2"

const stringify = (x: number): string => x.toString()

const add = (a: number, b: number): number => a + b

const result = stringify(add(2, 3))
const result2 = stringify(5) // We can replace add(2, 3) with 5

A Step Up

In the previous section, we were composing functions by hardcoding their names into the composed version. It would be nicer if we could create a third function, which takes two functions, and return their composition:

type Increment = (x: number) => number
const increment: Increment = (x) => x + 1

type Stringify = (x: number) => string
const stringify: Stringify = (x) => x.toString()

type Compose = (
    f: (x: number) => string,
    g: (x: number) => number
) => (x: number) => string

const compose: Compose = (f, g) => x =>  f(g(x))

const incrementAndStringify = compose(stringify, increment)
console.log(incrementAndStringify(33)) // "34"

Adding Generic Types

It would be even nicer, if we could define our compose function so that it’s not limited to the types string and number. Check this out:

type Increment = (x: number) => number
const increment: Increment = (x) => x + 1

type Stringify = (x: number) => string
const stringify: Stringify = (x) => x.toString()

type IncrementAndStringify = (x: number) => string

type Compose = <T, U>(
    f: (x: T) => U,
    g: (x: T) => T
) => (x: T) => U

const compose: Compose = (f, g) => x =>  f(g(x))

const incrementAndStringify: IncrementAndStringify = compose(stringify, increment)
console.log(incrementAndStringify(41)) // "42"

In the code above we are using two generics that allow us to replace T and U by any types. That way we can compose any two functions with different type signatures. In the way we defined the compose function above, we have only two constraints:

• The type of input of g, f, and our compose function is the same.
• The type of output of f and our compose function is the same.

Using function composition allows us to reuse a lot of small functions by composing them in different ways, to achieve different results. Testing each of these functions is easy.

A Generic compose Function

We could take it a step further, and define a super generic compose function, that could take any two functions, no matter their signature:

type Compose = <A, B, C>(
  f: (x: B) => C,
  g: (x: A) => B
) => (x: A) => C

const compose: Compose = (f, g) => x =>  f(g(x))

const incrementAndStringify: IncrementAndStringify = compose(stringify, increment)
console.log(incrementAndStringify(41)) // "42"

Here, we define a generic compose function compose with three generic types:

• A: The type of input to g (the first function in the composition), and to the compose function itself.
• B: The type of input to f, and the output from g.
• C: The type of output from f, and the output of compose (the final result).