Speaker 1: Welcome everyone to another deep dive. You know, our goal
Speaker 1: here is always the same, to help you get a
Speaker 1: really thorough, multi perspective understanding of complex topics without feeling
Speaker 1: totally overwhelmed by information overload.
Speaker 2: Right, because there there's just so much noise out there, exactly.
Speaker 1: And today's mission is unpacking a really fascinating book. It's
Speaker 1: called Functional Programming in JavaScript by Lewis Atensio. A great read, Yeah,
Speaker 1: it really is. And the goal today is to demystify
Speaker 1: functional programming, or FP as we'll call it, and figure
Speaker 1: out how it acts as a well a shortcut to
Speaker 1: writing cleaner, less buggy.
Speaker 2: Code, specifically in JavaScript, which, as anyone who writes it knows,
Speaker 2: can be a bit of a wild West.
Speaker 1: Oh absolutely. I mean, as web apps get more complex,
Speaker 1: JavaScript's dynamic, mutable nature can just turn your code into
Speaker 1: a totally tangled mess.
Speaker 2: Yeah, complete house of cards.
Speaker 1: Right. So we're going to explore how shifting to a
Speaker 1: functional mindset can actually protect you from JavaScript itself.
Speaker 2: It's a fundamental paradigm shift, really, moving from an imperative
Speaker 2: way of thinking to a declarative one.
Speaker 1: And there's this brilliant quote from Michael feathers and the
Speaker 1: text that really nails this. He says, Ooh, object oriented
Speaker 1: programming makes code understandable by encapsulating moving parts, and FP.
Speaker 2: Makes code understandable by minimizing moving parts.
Speaker 1: Yes, minimizing the moving parts, which is just I mean,
Speaker 1: it's such a perfect way to frame it.
Speaker 2: What's fascinating here is that it completely changes how you
Speaker 2: approach problem solving. In imperative programming, you're detailing exactly how
Speaker 2: a computer.
Speaker 1: Should do a task, like manually managing a loop counter
Speaker 1: just to square an array of numbers.
Speaker 2: It's highly mechanical. But with declarative programming, you just describe
Speaker 2: what the logic is without worrying about the control flow.
Speaker 2: It's a lot like writing a SEQL query.
Speaker 1: You just asked for the data. You don't tell the
Speaker 1: database how to spin the hard drive to go find it.
Speaker 2: Precisely, you separate the program's description from its evaluation.
Speaker 1: I always like to think of imperative programming like micromanaging
Speaker 1: a new employee to build a presentation. You're hovering over them, saying,
Speaker 1: click this menu, type this word, make it bold, exactly right.
Speaker 1: But declarative is like giving the task to a highly
Speaker 1: competent assistant. You just give them the final goal and
Speaker 1: trust their process.
Speaker 2: And that trust lets developers focus on the actual business logic,
Speaker 2: not the plumbing.
Speaker 1: Yeah, the plumbing is where the bugs live. So to
Speaker 1: build this trust, the book talks about two core pillars,
Speaker 1: pure functions and immutability.
Speaker 2: The foundational building blocks of FP.
Speaker 1: Let's talk about pure functions first. These are functions that
Speaker 1: depend only on the inputs you give them, and they
Speaker 1: inflict zero changes beyond their scope.
Speaker 2: Right, they operate in total isolation.
Speaker 1: The book gives some great examples of impure functions to
Speaker 1: contrast this, like date dot now.
Speaker 2: Oh yeah, date dot now is completely unpredictable.
Speaker 1: Because it relies on time. You call it twice, you
Speaker 1: get two different answers. Or like modifying a global counter variable.
Speaker 2: Which is affect. You're reaching outside the function and messing
Speaker 2: with the global state.
Speaker 1: Which leads us to referential transparency. Meaning if a function
Speaker 1: always yields the exact same result for the same input,
Speaker 1: it can literally be mathematically substituted.
Speaker 2: Which makes the code incredibly predictable.
Speaker 1: Predictability is exactly what we want. But the second pillar, immutability,
Speaker 1: that's where things get tricky in JavaScript, very.
Speaker 2: Tricky JavaScript loves mutation.
Speaker 1: Yeah, the book points out this native flaw array dot sort.
Speaker 1: It doesn't give you a nice new, sordid array, No.
Speaker 2: It sorts it in place, right.
Speaker 1: It modifies the original reference, creating this massive, dangerous side
Speaker 1: effect which.
Speaker 2: Can break entirely unrelated parts of your app that we're
Speaker 2: relying on that original array.
Speaker 1: Okay, let's unpack this. Yeah, because if we can't mutate objects,
Speaker 1: and we can't change global variables or even print to
Speaker 1: a screen without causing a quote unquote side.
Speaker 2: Effect, I know where you're going with this.
Speaker 1: I mean, what practical value does a pure program actually have?
Speaker 1: Isn't an entire app just one giant side effect?
Speaker 2: It is without side effects. Your program is basically just
Speaker 2: a space heater for your room. It just burns CPU cycles.
Speaker 1: Exactly, So how do we actually build anything useful?
Speaker 2: Practical FP doesn't forbid side effects, it just manages them.
Speaker 2: The goal is to tightly isolate the pure logic from
Speaker 2: the impure interactions.
Speaker 1: Oh okay, so like dom manipulation or database calls. Right.
Speaker 2: The book uses this monolithic show student function as an example.
Speaker 2: It queries the database, formats the data, and renders the
Speaker 2: HTML all in one giant block.
Speaker 1: The total nightmare to test.
Speaker 2: I bet absolutely so the functional approach decomposes that into smaller,
Speaker 2: single purpose pieces. You put all the impure database and
Speaker 2: UI stuff at the very edges of your app.
Speaker 1: Leaving the core logic perfectly pure, intestable exactly. Okay, that
Speaker 1: makes sense. But enforcing that immutability in a language like JavaScript,
Speaker 1: which naturally just lacks it, right, how do we tame
Speaker 1: this wild nature?
Speaker 2: Well, the book introduces a few strategies, like the value
Speaker 2: object pattern.
Speaker 1: Write the zip code example.
Speaker 2: Yeah, By returning an object literal interface with pseudo private
Speaker 2: variables inside a closure, you basically create a primitive like object.
Speaker 1: It has no mutating methods. You can read it, but
Speaker 1: you can't change it exactly.
Speaker 2: It's a really solid defense.
Speaker 1: But then there's object dot freeze, which is a native tool.
Speaker 2: It is, but it has a major limitation. It's only
Speaker 2: a shallow freeze.
Speaker 1: Right, So, if I freeze a person object, the top
Speaker 1: level stuff like their name is safe.
Speaker 2: But if they have a nested property like address dot country.
Speaker 1: That nest to property can still be completely mutated.
Speaker 2: Yeah, it leaves you vulnerable unless you write recursive deep
Speaker 2: freeze functions. To lock down every single level.
Speaker 1: Which brings up a big concern. Isn't deep freezing every
Speaker 1: single object or you know, costly making deep copies of
Speaker 1: massive data structures just a total nightmare for performance?
Speaker 2: It sounds like it would be.
Speaker 1: Yeah, why go through all this trouble? I mean copying
Speaker 1: huge JSON trees every time a single value changes.
Speaker 2: Well, this connects to the bigger picture. You're avoiding cascading
Speaker 2: bugs caused by a shared mutable state. The peace of
Speaker 2: mind and testability you gain is huge.
Speaker 1: Well, what about the performance hit.
Speaker 2: That's where lenses come in. The book introduces them via
Speaker 2: the ramda dot js library. They act as functional references
Speaker 2: that let you read and immutably update nested properties.
Speaker 1: Oh, using copy on write semantics exactly.
Speaker 2: It gives you structural sharing for free. You aren't doing
Speaker 2: heavy deep copies. You're just sharing the unchanged memory branches.
Speaker 1: Oh wow, So you get the safety without the massive
Speaker 1: memory overhead. That's really clever it is.
Speaker 2: The syntax is unobtrusive too.
Speaker 1: So JavaScript has some flaws, but its design actually naturally
Speaker 1: enables functional programming too.
Speaker 2: Right, Oh, definitely. In fact, functions in JavaScript are first.
Speaker 1: Class citizens, meaning they can be assigned to variables passed.
Speaker 2: Around, right, and that supports higher order functions, functions that
Speaker 2: can accept other functions as arguments or return them like a.
Speaker 1: Ray dot sort going on to that it's dangerous, but
Speaker 1: it is a higher order function.
Speaker 2: Yeah, And the book points out how JS natively converts
Speaker 2: numbers to strings for sorting based on Unicode, right.
Speaker 1: Which is why ten gets sorted before two, which is
Speaker 1: just hilarious and awful.
Speaker 2: It's the classic JS bug. But because sort is a
Speaker 2: higher order function, you can pass a custom comparator function
Speaker 2: into it to fix that behavior.
Speaker 1: It's treating behavior as data.
Speaker 2: Exactly, and that leads directly into closures and scopes. A
Speaker 2: closure is basically a data structure that binds a function
Speaker 2: to its lexical.
Speaker 1: Environment at the exact moment it's declared. Yes, here's where
Speaker 1: it gets really interesting. I like to picture a closure
Speaker 1: like a snow globe.
Speaker 2: Snow globe.
Speaker 1: Yeah, the function is the little house inside, and the
Speaker 1: closure traps all the surrounding snow, the variables in the
Speaker 1: outer scope. Oh, I see, It traps them exactly as
Speaker 1: they were when the globe was sealed. It prevents the
Speaker 1: outside world from melting them or messing with them.
Speaker 2: That's a great visualization and understanding that snow globe is
Speaker 2: crucial because of the notorious loop counter ambiguity.
Speaker 1: Bug, oh Man, the vara bug.
Speaker 2: Yep. If you declare a loop with var, it gets
Speaker 2: hoisted to the top of the function, and.
Speaker 1: If you're creating closures inside that loop, they all end
Speaker 1: up sharing that exact same vari reference.
Speaker 2: So it mutates unexpectedly. By the time the loop is done,
Speaker 2: all your closures point to the final maximum value of
Speaker 2: varas it causes so many bugs.
Speaker 1: The closures can also fix this right like using four eights.
Speaker 2: Exactly for each creates a fresh scope for every iteration,
Speaker 2: or you know, just using e S six's let qword
Speaker 2: solves it by properly block scoping the variable.
Speaker 1: It all comes back to managing states safely, and that
Speaker 1: transitions perfectly into how we process data declaratively. We're fluent
Speaker 1: chains and high level operation.
Speaker 2: Yeah, moving away from manual loops entirely.
Speaker 1: In standard OP you use method chaining like string dot
Speaker 1: substring dot to lower case.
Speaker 2: It reads left to right. But in FP you use
Speaker 2: function chaining and lambda expressions.
Speaker 1: A fat arrow function give you.
Speaker 2: A really terse one line syntax that implicitly returns values, which.
Speaker 1: Perfectly aligns with the FP rule that every function must
Speaker 1: return a usable result.
Speaker 2: The book shows this incredible example using dot map from
Speaker 2: the low dash library.
Speaker 1: Oh, the one extracting student full names.
Speaker 2: Right, you extract the names from a whole collection of
Speaker 2: objects without writing a single for loop.
Speaker 1: You're just treating the data and the control flow as
Speaker 1: simple connections between high level components.
Speaker 2: It's so clean.
Speaker 1: But wait, if we're purely nesting functional calls inside out. Yeah,
Speaker 1: like concat to lowercase, say.
Speaker 2: Right on the Lisp style nesting.
Speaker 1: Right, that looks incredibly unreadable to me. How is this
Speaker 1: actually better for developers if they can't easily read what
Speaker 1: the code is doing.
Speaker 2: This raises an important question, because you're right, deep nesting
Speaker 2: is awful for human readability, and that is exactly why
Speaker 2: functional programmers use tools like low dash or.
Speaker 1: REMDA to create fluent chains.
Speaker 2: Exactly, they create pipelines. The data flows sequentially from one
Speaker 2: black box operation to the next, so.
Speaker 1: It reads top to bottom or left to right again.
Speaker 2: Yes, you preserve the mathematical purity, but you keep it
Speaker 2: readable for the humans maintaining the code.
Speaker 1: So what does this all mean, I mean FP and
Speaker 1: JavaScript isn't just a trendy API you plug in.
Speaker 2: No, it's not.
Speaker 1: It's a profound shift in thinking. By embracing pure functions,
Speaker 1: treating objects as immutable values, and leveraging those higher order
Speaker 1: functions and closures we talked about, you build modular, highly
Speaker 1: predictable applications.
Speaker 2: And you don't even have to abandon object oriented programming entirely.
Speaker 1: To do it right. The text mentions that sweet spot.
Speaker 2: Yeah, JavaScript is a hybrid language. It allows you to
Speaker 2: blend a rich op domain model for the broad architecture
Speaker 2: with pure functional operations handling the data transformations inside.
Speaker 1: It's the best of both worlds, truly. You know, before
Speaker 1: we wrap up, I want to leave you the listener
Speaker 1: with the final thought. We briefly touched on this concept
Speaker 1: from the text called lazy evaluation right.
Speaker 2: Deferring execution until the data is actually needed, just to
Speaker 2: save cpusite.
Speaker 1: Exactly, only computing data at the exact moment it's strictly
Speaker 1: required to form an output.
Speaker 2: It's highly efficient.
Speaker 1: But what if we applied that same architectural philosophy to
Speaker 1: our own human productivity?
Speaker 2: Oh that's interesting.
Speaker 1: Think about our information consumption? What if we stopped eagerly
Speaker 1: processing the massive flood of daily data, the emails, the news,
Speaker 1: the social media, and only evaluated the information strictly required
Speaker 1: for the immediate decision in front of us.
Speaker 2: Wow, we'd probably have a lot less mental burnout.
Speaker 1: Right, Just like saving CPU cycles, maybe we need to
Speaker 1: save our cognitive cycles. A functional mindset for the brain.
Speaker 1: I love that something for you to moul over. Thanks
Speaker 1: for tuning into this deep dive and we'll see you
Speaker 1: next time.