Speaker 1: For many of us in software development, the way we
Speaker 1: build systems is constantly evolving. It's not just about adopting
Speaker 1: a new tool, it's about shifting our fundamental approach. You know,
Speaker 1: how we tackle complex problems like concurrency, fault tolerance, maintainability. Today,
Speaker 1: we're taking a deep dive into just such a powerful paradigm,
Speaker 1: functional programming, and we're using Elixer as our expert guide.
Speaker 1: We're inviting you to explore a different mindset, one that
Speaker 1: offers a really fresh and powerful perspective on how you
Speaker 1: approach development exactly.
Speaker 2: Our mission for this deep dive is well to lay
Speaker 2: out the essence of functional programming, explore wides become increasingly
Speaker 2: important in modern software, and demonstrate how Elixer serves as
Speaker 2: just a fantastic vehicle for exploring this style. And even
Speaker 2: if you don't switch to Alizer for your next project,
Speaker 2: you'll uncover concepts here, concepts that are already supported in
Speaker 2: many mainstream languages, hopefully providing those aha moments they quickly
Speaker 2: deepen your understanding.
Speaker 1: Okay, let's unpack this, then, what's the fundamental shift in
Speaker 1: thinking for functional program It really is a different mindset
Speaker 1: from what many of us are used to. Right.
Speaker 2: Yeah. The core idea is that functional programming is a
Speaker 2: distinct paradigm. It moves away from explicit, step by step instructions.
Speaker 2: So instead of focusing on how to achieve a result
Speaker 2: through a sequence of changing states, we describe what the
Speaker 2: software must do. We build systems by composing functions that
Speaker 2: transform data.
Speaker 1: And a cornerstone of this paradigm, I hear, is something
Speaker 1: called immutability. Now, for those of us accustomed to directly
Speaker 1: changing data, how does that work? And maybe more importantly,
Speaker 1: why is it such a game changer, especially for something
Speaker 1: like parallelism.
Speaker 2: Right, So, conventional languages often rely on mutating shared values,
Speaker 2: which then requires complex mechanisms you know, locks semaphores to
Speaker 2: prevent raise conditions when you're working concurrently. The profound impact
Speaker 2: of immutability in functional programming is that all values, once created,
Speaker 2: cannot be changed. Ever, this means each function operates on
Speaker 2: a stable input, which eliminates the need for those intricate
Speaker 2: lock mechanisms and drastically simplifies concurrent and parallel work.
Speaker 1: Okay, but doesn't creating new values all the time slow
Speaker 1: things down?
Speaker 2: That's a common thought. Yeah, But languages like Elixir use
Speaker 2: smart immutable data structures, they efficiently reuse memory behind the scenes. So,
Speaker 2: for example, if you have a list like one, two, three, four,
Speaker 2: and you call say list dot delaty, list manage one,
Speaker 2: or list plus plus one the original list value that one, two,
Speaker 2: three four, it stays exactly the same. These operations produce
Speaker 2: new lists, and because the original data is untouched, the
Speaker 2: compiler can safely run these operations in parallel without worrying
Speaker 2: about side effects messing with the input.
Speaker 1: Okay, okay, So if data is immutable, what then becomes
Speaker 1: the primary building block, the star of the show? So
Speaker 1: to speak? How are functions themselves different here than what
Speaker 1: we might be used to in, say, oh languages.
Speaker 2: Great question. In functional programming, functions are first class citizens,
Speaker 2: which means they're treated just like any other value. You
Speaker 2: can assign them to variables, pass them as our arguments
Speaker 2: to other functions, even return them from other functions. What
Speaker 2: data exactly like data? They explicitly receive input, process it,
Speaker 2: and return output. Now, if you use to an object
Speaker 2: oriented approach, where operations are methods tied to an object's
Speaker 2: changing state, functional programming offers an alternative. Operations exist independently.
Speaker 2: You send the data to the function. A classic example
Speaker 2: is something like enom dot map dogs, cats, flowers and
Speaker 2: strained out up case one. Here we're passing the string
Speaker 2: dot upcase function itself as an argument to transform each
Speaker 2: item in the.
Speaker 1: List that brings us to a key distinction. Then the
Speaker 1: difference between a pure function and an impure one. Why
Speaker 1: does that distinction matter so much for predictability, especially when
Speaker 1: we're trying to reason about our code.
Speaker 2: Ah purity. Yes, pure functions are incredibly predictable. Given the
Speaker 2: exact same input, they will always produce the same consistent
Speaker 2: output always, and critically, they have no observable effects outside
Speaker 2: of their own scope, no side effects. This is often
Speaker 2: called referential transparency.
Speaker 3: Like a mathematical function, pretty much, take ad two op
Speaker 3: l nabn plus two N. If you call add two
Speaker 3: o two, you will always get four every single time,
Speaker 3: no surprises. And the real aha here isn't just the
Speaker 3: predictability itself, it's the profound impact on debugging. Imagine finding
Speaker 3: a bug with pure functions, you can isolate the problem
Speaker 3: to a single function call you know it won't be
Speaker 3: affected by hidden system state or alter anything outside his control.
Speaker 3: It transforms debugging from this complex detective hunt into a
Speaker 3: much more straightforward analysis.
Speaker 1: Okay, so what about impure functions then.
Speaker 2: Well, impure functions are the unpredictable ones. They might return
Speaker 2: different results for the same input, or they produce side
Speaker 2: effects by interacting with the outside world. Think about reading
Speaker 2: user input from the terminal like io dot gets what's
Speaker 2: the meaning of life that depends on the user, or
Speaker 2: getting the current time daytime dot uacat now that changes constantly.
Speaker 2: They're absolutely necessary for any useful software. Of course, you
Speaker 2: need to interact with the world, but the key is
Speaker 2: to isolate these impure parts, handle them carefully, often at
Speaker 2: the edges of your application.
Speaker 1: So pushing the messy stuff to the boundaries.
Speaker 2: Kind of yeah, containing the unpredictability.
Speaker 1: So what does this all mean for how we actually
Speaker 1: write the code? Does it lead to a completely different style?
Speaker 2: It definitely fosters a declarative style of programming. You focus
Speaker 2: on what needs to be done, rather than explicitly defining
Speaker 2: how to do it step by step. This contrast pretty
Speaker 2: sharply with an imperative approach. Consider transforming a list of
Speaker 2: strings to uppercase. An imperative JavaScript example might use a
Speaker 2: for loop manually incrementing e, pushing results to a new
Speaker 2: array that gets verbose.
Speaker 1: Yeah, I can picture that.
Speaker 2: In Alixir using a declarative style, you might use recursion,
Speaker 2: which we'll talk about yeah, or more commonly, a higher
Speaker 2: order function like enom dot map in string dot case one.
Speaker 2: This approach makes the crucial aspects of the task, the
Speaker 2: parts that really matter, explicit, and it often generates much simpler,
Speaker 2: more readable code.
Speaker 1: Okay, that makes sense. Now let's turn our attention to
Speaker 1: Elixir itself. How does Elixer enable this functional way of thinking?
Speaker 1: Right from its foundations?
Speaker 2: Right? So, Elixer is a dynamic functional programming language. It's
Speaker 2: built on the erline VM beam, which is renowned for
Speaker 2: its capabilities in building distributed fault tolerant systems rock solid foundation.
Speaker 2: Elixer was designed to offer a modern kind of fun syntax,
Speaker 2: a vibrant community, and really robust production ready tooling. You
Speaker 2: can interact with it in an interactive shell called IIx,
Speaker 2: which is great for experimenting, or you compile dot e's
Speaker 2: files for larger projects, or run dot x or scripts
Speaker 2: for quick tasks. Elissa's basic types include integers, strings, booleans, atoms, lists, tuples,
Speaker 2: and even functions themselves. As we said, operators work mostly
Speaker 2: as you'd expect for arithmetic, plus IDs and comparisons, and
Speaker 2: invalid expressions like trying to add a string in a
Speaker 2: number will predictably fail with clear errors, no silent failures.
Speaker 2: Good Now, Variables and Elixir function a bit differently than
Speaker 2: in many other languages. When you write X equal forty two,
Speaker 2: you're performing binding. The operator binds the value forty two
Speaker 2: to the name X. But what's crucial time back to
Speaker 2: immutability is that values are immutable. So if you later
Speaker 2: write xquel six.
Speaker 1: You're not changing the forty two exactly.
Speaker 2: You're not changing the original forty two in memory. You're
Speaker 2: simply rebinding the name X to point to a new
Speaker 2: value six. The old forty two might still be referenced
Speaker 2: elsewhere or get garbage collected later. This prevents those accidental
Speaker 2: side effects and makes reasoning about your code much much simpler,
Speaker 2: because you know of value once bound won't just change
Speaker 2: out from under you unexpectedly, and it's good practice to
Speaker 2: use descriptive snake case names like total cost rather than
Speaker 2: just x or y helps clarity. Oh, and remember variables
Speaker 2: starting with capital letters or deserved for modules or atoms.
Speaker 2: Got it. Anonymous functions are also essential to Elixir. These
Speaker 2: are functions without a global name, typically created on the fly.
Speaker 2: You often bind them to a variable if you want
Speaker 2: to reuse them. The syntax is sn parameter is body
Speaker 2: end for instance sn name ss HeLa hashtag name dot
Speaker 2: end simple enough. You then invoke it using a dot
Speaker 2: helo and they can be multiling take multiple arguments. Very flexible,
Speaker 2: and this flexibility leads us to an important concept. How
Speaker 2: values are shared with these functions, even without being explicitly
Speaker 2: passed as arguments, which brings us to closures.
Speaker 1: Right, you mentioned closures earlier. It sounds a bit abstract.
Speaker 1: How do they actually help us share values in a
Speaker 1: way we can't otherwise control. What's the practical benefit here?
Speaker 2: Okay? Think of a closure like this. When you create
Speaker 2: an anonymous function, it takes all the variables that are
Speaker 2: currently visible in its surrounding environment and it puts them
Speaker 2: into its own little backpack. No matter where that function
Speaker 2: goes or when it gets called later, even in a
Speaker 2: completely different process, it always carries that backpack with its
Speaker 2: remembered values.
Speaker 1: So it closes over the environment it was born in.
Speaker 2: Exactly. A closure is an anonymous function that remembers values
Speaker 2: from its lexical scope, that specific block of code where
Speaker 2: it was defined, even if it's executed much later or
Speaker 2: somewhere else entirely. This is super useful when you're working
Speaker 2: with functions that take other functions as arguments, like an
Speaker 2: ENEM dot map sometimes or quickly when starting asynchronous processes
Speaker 2: where you can't explicitly pass arguments in the traditional way.
Speaker 2: For example, if you define message hello world outside and
Speaker 2: then salo fna a l process dot sleep one thousand
Speaker 2: eiotut's message end, that SALEO function will remember the message
Speaker 2: value even when you run it asynchronously in a separate
Speaker 2: process using spawn salo, the innfunction can see variables defined
Speaker 2: outside its immediate body. It's a powerful way to handle
Speaker 2: state without needing explicit objects.
Speaker 1: Sometimes that's pretty cool. Okay, now that we've covered the basics,
Speaker 1: Elixir has some truly powerful features that make functional programming
Speaker 1: really sing. Let's talk about organizing code and some very
Speaker 1: clever ways to control program flow.
Speaker 3: Yeah.
Speaker 2: Absolutely. For larger, more organized applications, Elixir uses named functions.
Speaker 2: These are defined inside modules using deaf module and DEAF
Speaker 2: standard stuff. For organization modules help structure your code logically,
Speaker 2: and you can test them using namespaces like e commerce
Speaker 2: check out for better organization as things grow. Functions within
Speaker 2: a module can also be made private using death that
Speaker 2: restricts their access just to within that module.
Speaker 1: Good encapsulation and importing functions.
Speaker 2: You can import functions from other modules for brevity, sure,
Speaker 2: but many Elixer developers actually prefer explicitly calling module name
Speaker 2: dot function. It keeps the code really clear, but exactly
Speaker 2: where each function is coming from less magic, and what's
Speaker 2: need is you can treat these name functions as values
Speaker 2: themselves using the function capturing operator. The ampersand for instance,
Speaker 2: factorial am equals and factorial dot f one captures a
Speaker 2: reference to the factorial dot f function that takes one
Speaker 2: argument that's the one. This lets you pass named functions
Speaker 2: around just like any other value, slotting them into places
Speaker 2: expecting an anonymous function. It's quite elegant, okay.
Speaker 1: That brings us to a really innovative approach Elixer takes
Speaker 1: to controlling program flow. You mentioned the equals sign. It
Speaker 1: changes how you think about entirely, doesn't it.
Speaker 2: It really does? So how do we control program flow effectively?
Speaker 2: Without mutable state and traditional loops. The answer in large
Speaker 2: part is pattern matching. It's absolutely central to Elixir. The
Speaker 2: operator isn't just for assignment like in many languages, it's
Speaker 2: actually the match operator. It attempts to make both sides equivalent.
Speaker 2: So one nixel one matches. That succeeds, but two equals
Speaker 2: one fails. It throws a.
Speaker 1: Matcher because they aren't equivalent right now.
Speaker 2: When you use a variable on the left like x
Speaker 2: isn't bound. It binds value one to X. That looks
Speaker 2: like assignment. But if x already has a value, say
Speaker 2: x's two, then two x would succeed because they match,
Speaker 2: but three xles would fail with a matcher. It's not
Speaker 2: reassignment in that context. It's an assertion of equality. And
Speaker 2: you can use the pin operator like percent strength value
Speaker 2: to force elixer to use the current value of strength
Speaker 2: value for matching rather than trying to rebind.
Speaker 1: It useful to prevent accidental rebinding.
Speaker 2: Within a match exactly, and pattern matching is incredibly powerful
Speaker 2: for destructuring two. That means pulling values out of complay
Speaker 2: data types like two poles okay, answer okay, this bind's
Speaker 2: forty two to answer or lists headtail, one two three.
Speaker 2: This binds one to head and two to three to tail.
Speaker 2: Super common for recursion maps. Done percent strength and strength
Speaker 2: valuabilities pulls out the value associated with the strength key
Speaker 2: and strucks work. Similarly, you can also use a wildcard
Speaker 2: to ignore parts of a match you don't need.
Speaker 1: Wow, Okay, that's a lot packed into the sign it.
Speaker 2: Is and building directly on pattern matching, Elixer uses function
Speaker 2: clauses as a primary way to control program flow. You
Speaker 2: can find multiple functions with the same name but with
Speaker 2: different argument patterns. ELSTA will then automatically execute the first
Speaker 2: function clause whose arguments successfully match the incoming values when
Speaker 2: you call it.
Speaker 1: So instead of a big IFL sort switch.
Speaker 2: Inside the function, you just define different function bodies for
Speaker 2: different input shapes. It's very declarative. For example, a number
Speaker 2: compared dot greater function might internally call different check clauses
Speaker 2: based on a comparison. Maybe check true number handles the
Speaker 2: case where the first number is greater, and check false
Speaker 2: other number handles the case where the second is greater.
Speaker 2: Each check clause only matches its specific scenario, and to
Speaker 2: add even more power, you have guard clauses. See these
Speaker 2: use the when keyword right after the function definition to
Speaker 2: add boolean checks like def greater number other number when
Speaker 2: number or the other number two number. This clause only
Speaker 2: matches if number is actually greater than or equal to
Speaker 2: other number. Guards are incredibly useful for enforcing conditions or types,
Speaker 2: like when price equals zero and tax rate equals zero.
Speaker 2: It often reduces the need for nested if statements are
Speaker 2: separate helper functions. Just note only pure and generally fast
Speaker 2: functions are allowed inside guard clauses to keep them efficient
Speaker 2: and predictable.
Speaker 1: That's really elegant. Okay, so repetition, that's fundamental in programming.
Speaker 1: But if we're avoiding traditional loops, how do we repeat
Speaker 1: tasks in a purely functional way.
Speaker 2: Recursion? That's the core mechanism. A recursive function is simply
Speaker 2: one that calls itself repeatedly. The crucial part is that
Speaker 2: it must have a stop condition or boundary clause a
Speaker 2: base case, to prevent it from looping forever.
Speaker 1: Yeah, it got to stop somewhere exactly.
Speaker 2: Take SMIng numbers up to n. The base case is
Speaker 2: some dot up two m it, which is just zero,
Speaker 2: and then some dot up to mber for any other
Speaker 2: n is n plus some dot up to n one.
Speaker 2: See how each call reduces the problem and one until
Speaker 2: it hits the base case zero. The same principle applies
Speaker 2: to complex tasks like navigating lists. You might sum a
Speaker 2: list using some headtail, which calculates head plus some tail
Speaker 2: until you hit the empty lisk mware where some return
Speaker 2: zero the base again.
Speaker 1: So you process one element and recurse on.
Speaker 2: The rest precisely, and two common recursive patterns emerge. Decrease
Speaker 2: and conquer simplifies the problem incrementally down to the base case,
Speaker 2: then builds the solution back up. Factorial is a classic example.
Speaker 2: Factorial dot fFN is n an factorial dom n one
Speaker 2: until you hit factorial dot of zero, which is one.
Speaker 2: Then there's divideing Concker. This splits the problem in a
Speaker 2: smaller independent subproblems, solves those recursively, and then combines the
Speaker 2: res the famous merge sort algorithm for lists. It's a
Speaker 2: perfect example. You keep splitting the list until you have
Speaker 2: lists of one element which are inherently sorted. Then you
Speaker 2: merge them back together in sorted order.
Speaker 1: That sounds incredibly powerful for handling tasks iteratively. But wait,
Speaker 1: with all these function calls stacking up. Doesn't recursion just
Speaker 1: gobble up memory on the call stack? Isn't that a problem?
Speaker 1: Is there a trick to make it more memory efficient?
Speaker 2: That is a very perceptive question, and you're absolutely right.
Speaker 2: Now you've recursion can consume significant memory. It can lead
Speaker 2: to stack overflows for deep recursions. The crucial solution the
Speaker 2: trick is tail call optimization or TCO. Okay, So a
Speaker 2: body recursive function has its recursive call, but it's not
Speaker 2: the very last thing it does. Like our factorial dot
Speaker 2: nfactorialp fn one, there's still a multiplication after the recursive
Speaker 2: call returns. This means the intermediate results the context of
Speaker 2: each call have to be kept on the call stack
Speaker 2: stack grows A tail recursive function, however, performs a recursive
Speaker 2: call as its absolute final action. There's nothing left to
Speaker 2: do after it returns. Often this involves passing an accumulator
Speaker 2: argument to carry the state forward. So factorial might become
Speaker 2: factorial off n one n acc where aci holds the
Speaker 2: result calculated so far. The beauty is Elixir's compiler and
Speaker 2: the Erlang VM can optimize these tail recursive calls. It
Speaker 2: effectively transforms them into iterative loops under the hood like
Speaker 2: a go to. This results in constant low memory consumption,
Speaker 2: just like a loop.
Speaker 1: Wow, so tail recursion gets optimized away.
Speaker 2: Yes, it avoids growing the call stack. It's a trade off.
Speaker 2: Sometimes the tail recursive version might look slightly more complex
Speaker 2: to read initially, but the memory efficiency game is often
Speaker 2: essential for deep or potentially infinite recursions, which brings us
Speaker 2: to unbounded recursion.
Speaker 1: Yeah.
Speaker 2: Sometimes you can't predict the number of repetitions. Think of
Speaker 2: a web crawler following links or navigating a file system
Speaker 2: that might have circular references or symbolic links.
Speaker 1: Yeah, that could go on forever.
Speaker 2: Right, So strategies there include adding explicit boundaries like setting
Speaker 2: a maximum depth for direct navigation, or actively detecting and
Speaker 2: avoiding circular references, perhaps by keeping track of visited nodes
Speaker 2: or checking for symbolic links using something like file dot
Speaker 2: l stat one.
Speaker 1: Okay, makes sense. So how do we use these functional
Speaker 1: principles then, to abstract away the tedious stuff? How do
Speaker 1: we build those better functions with simple interfaces that developers
Speaker 1: actually enjoy using.
Speaker 2: This is where higher order functions or hos truly shine.
Speaker 2: Remember these are functions that either take other functions as
Speaker 2: arguments or they return functions, or maybe both. They are
Speaker 2: incredibly powerful for hiding verbose or laborious routines behind elegant
Speaker 2: simple interfaces. Think about file dot open three Intelixir takes
Speaker 2: a path, options, and a function to execute with the
Speaker 2: open file handle. The file dot open function handles opening
Speaker 2: the file, running your function, and crucially ensuring the file
Speaker 2: is closed afterwards even if errors occur. It abstracts away
Speaker 2: all that tricky resource.
Speaker 1: Management nice so it hides the boilerplate exactly.
Speaker 2: And common list operations are prime examples. Etam dot each
Speaker 2: takes a function and applies it to every item, just
Speaker 2: for side effects. Enom dot map takes a function, applies
Speaker 2: it to transform each item, and returns a new list
Speaker 2: of the results, like increasing prices. Enom dot reduce takes
Speaker 2: a function and an initial accumulator and boils a list
Speaker 2: down to a single value, like summing total income. Enom
Speaker 2: dot filter takes a function that returns truer falls and
Speaker 2: selects only the items for which it's true. These are
Speaker 2: all higher order functions found in Elixer's awesome Enom module,
Speaker 2: and the Enom functions work seamlessly with any data type
Speaker 2: that implements the innumerable protocol lists, maps, ranges, streams, incredibly
Speaker 2: versatile protocols. I'll get to those, we will an Elixer
Speaker 2: also provides comprehensions using the inneverrable inn acol for special form.
Speaker 2: It offers a concise, really readable fintax that often bundles
Speaker 2: common enom operations like iterating, mapping, and filtering into a
Speaker 2: single expressive statement. Very Python like.
Speaker 1: If you know that the four comprehensions make a lot
Speaker 1: of sense. Yeah, but the pipe operator. I hear about
Speaker 1: this a lot with Elixer. It seems like a standout feature.
Speaker 1: What's its superpower? What makes it so effective for code readability?
Speaker 3: Oh?
Speaker 2: The pipe operator? Yes, yes, it's arguably Elixer's most famous
Speaker 2: feature for transforming data. Its purpose is simple but profound.
Speaker 2: It makes combining function calls in sequence incredibly readable, almost
Speaker 2: like reading plain English. Its superpower is just this. It
Speaker 2: takes the result of the expression on its left and
Speaker 2: passes it as the first argument to the function on
Speaker 2: its right. That's it. So remember that example of capitalizing
Speaker 2: words in a title instead of a nested mess like
Speaker 2: enom dot join enom dot maps string dot capitalize one.
Speaker 3: Yeah.
Speaker 1: Hard to read inside out exactly.
Speaker 2: With the pipe. You write a clear left to right
Speaker 2: pipeline well title string dot, split enom dot, map, enom
Speaker 2: dot join. It reads like take the title, then split
Speaker 2: it the map, capitalize over it, then join it with spaces.
Speaker 2: This chaining clearly expresses a step by step daty transformation.
Speaker 2: It dramatically improves readability and maintainability. People love it.
Speaker 1: I can see why. Okay. And speaking of processing data efficiently,
Speaker 1: you mentioned lazy evaluation. The advice was be lazy. What
Speaker 1: is that I actually mean in programming? And how does
Speaker 1: being lazy help us work with potentially huge or even
Speaker 1: endless streams of data without blowing up our memory?
Speaker 3: Right?
Speaker 2: Lazy evaluation basically means a series of instructions won't be
Speaker 2: executed immediately. They'll just sit there waiting for a trigger.
Speaker 2: They only do the work when it's absolutely necessary, usually
Speaker 2: when you ask for the final result. In Elixir, streams
Speaker 2: are the primary mechanism for lazy collections. For instance, creating
Speaker 2: a range like one point one zero zero zero zero
Speaker 2: zero zero zero zero zero thinks roughly the same tiny
Speaker 2: amount of memory is creating one point ten because the
Speaker 2: numbers aren't generated up front, they're only generated one by
Speaker 2: one on demand. When you actually try to use them,
Speaker 2: maybe by piping the stream into an ENOM function. Functions
Speaker 2: like stream dot iterat two can create an endlessly expanding
Speaker 2: stream like stream dot iterate zero in and goesway one
Speaker 2: two three forever. Lazily stream dot cycle one can endlessly
Speaker 2: repeat a list. Stream dot cycle one two three gives
Speaker 2: one two three, one two three. When you combine these
Speaker 2: infinite streams with something finite like ENOM dot take five,
Speaker 2: you gain precise control. You only pull out and process
Speaker 2: the first five elements. The infinite potential remains unrealized until needed.
Speaker 2: This naturally raises the question, what's the practical difference between
Speaker 2: eager processing like ENEM and lazy processing stream in a
Speaker 2: data pipeline.
Speaker 1: Yeah, when would you choose one over the other?
Speaker 2: Think of an assembly line in eager evaluation, which is
Speaker 2: how ENOM dot map or ENOM dot filter work. Each
Speaker 2: machine each step in your pipeline processes all the items
Speaker 2: the whole batch before sending anything to the next machine.
Speaker 2: If you have a thousand screws to process through three machines,
Speaker 2: machine one does all thousand, then machine two does all thousand,
Speaker 2: the machine three. You wait for the entire batch at
Speaker 2: each step.
Speaker 1: Okay, batch processing right now.
Speaker 2: In lazy evaluation using stream dot mac or stream dot filter,
Speaker 2: each machine processes a small number of items, maybe just one,
Speaker 2: and immediately passes it to the next machine in the line.
Speaker 2: So one processed item can go to machine two while
Speaker 2: machine one is already starting on the next item.
Speaker 1: It's true pipelining, ah, so you get results sooner.
Speaker 2: It's exactly. This pipeline provides much faster feedback, especially if
Speaker 2: any step is slow, and it's far more memory efficient
Speaker 2: when dealing with tasks that take time like network requests,
Speaker 2: file io, or processing massive potentially infinite data sets. Because
Speaker 2: you only hold a small amount of data and memory
Speaker 2: at any one time, memory usage stays constant. Functions like
Speaker 2: stream dot chunk every two or stream dot flatMap two
Speaker 2: allow for really efficient batch processing within these lazy pipelines too.
Speaker 2: For when you need to work on.
Speaker 1: Small groups, that distinction between eager enom and lazy stream
Speaker 1: seems really important. Okay, we explored so many elegant concepts
Speaker 1: for writing pure predictable code. This is great. How do
Speaker 1: we take all this beautiful functional thinking and actually apply it?
Speaker 1: How do we build robust real world applications, especially when
Speaker 1: we have to deal with the unpredictable wild world of
Speaker 1: events and all their uncertainties, errors, side effects.
Speaker 2: Right, let's tie these concepts together. Let's imagine we're building
Speaker 2: a simple e commerce application. Maybe first off, for building
Speaker 2: a elixerra Apple Cations from the ground up, you'll use mix.
Speaker 2: Mix is your essential command line tool. It handles everything
Speaker 2: creating new projects, mixed new my app, compiling your code,
Speaker 2: running tests, mixed test, managing external libraries, dependencies.
Speaker 1: It's your bill tool, standard toolkit stuff pretty much.
Speaker 2: Now, when designing the core entities within our e commerce app,
Speaker 2: say a product or a user, Elixer offers structs. Structs
Speaker 2: are basically extensions of maps, but they provide consistent structures
Speaker 2: and importantly, compile time guarantees. This means you define the
Speaker 2: allowed fields upfront and you can't accidentally add an unexpected
Speaker 2: key later. Helps prevent common bugs. They're excellent for representing
Speaker 2: related data like a products truct needing name, price, and
Speaker 2: skew fields clear definition.
Speaker 1: Okay, so structs give us defined shapes for our data exactly.
Speaker 2: Now, what if we want to create polymorphic functions. That
Speaker 2: means a single function interface that works consistently across different
Speaker 2: data types. Like maybe we want to print a product
Speaker 2: or a customer in a user friend way using the
Speaker 2: sameio dot putz call. You know how object oriented languages
Speaker 2: use interfaces or abstract classes to define a contract. Yeah,
Speaker 2: In Elixer, we achieve similar powerful polymorphism using protocols. For instance,
Speaker 2: there's a built in string chars protocol. It defines a
Speaker 2: contract for converting something into a string via toastring one function.
Speaker 2: By implementing this protocol for your customs structs like our
Speaker 2: product struct you enable them to be directly interpolated into
Speaker 2: strings like displaying product hashtag product, Elixer will automatically call
Speaker 2: the toasting one implementation you provided for product, maybe just
Speaker 2: returning its name instead of printing the whole messy internal structure.
Speaker 2: It makes your code much cleaner and more flexible.
Speaker 1: So protocols define how different data types can behave in
Speaker 1: a certain.
Speaker 2: Way precisely, and related to that, for defining clear contracts
Speaker 2: that modules must adhere to, Elixers provides behaviors. The behaviors
Speaker 2: like interfaces for modules, you use the behavior directive at
Speaker 2: the top of a module to specify that it must
Speaker 2: implement certain functions defined by that behavior. It acts like
Speaker 2: a blueprint for our e commerce app. Maybe we have
Speaker 2: different payment gateways Stripe, PayPal, whatever. We could define a
Speaker 2: paid gateway behavior that requires any implementing module to have
Speaker 2: a process payment two function. If a module declares the
Speaker 2: behavior payment gateway but forgets to define process payment two,
Speaker 2: the compiler will issue a warning. This is invaluable for
Speaker 2: ensuring consistency in your applications design, especially when you have
Speaker 2: interchangeable components.
Speaker 1: Right enforcing the contract at compile time very useful. So
Speaker 1: we know impure functions are necessary but unpredictable. We need
Speaker 1: them for io errors, etc. How does alixer help us
Speaker 1: isolate their effects and make them more predictable and manageable,
Speaker 1: especially when things inevitably go wrong.
Speaker 2: Yeah, this is absolutely critical for building reliable software. You
Speaker 2: can't live entirely in the pure world now. Simple conditional
Speaker 2: structures like case and if we're fine for handling straightforward
Speaker 2: impure results like parsing user input for a number, it
Speaker 2: might succeed or fail. A case statement handles that nicely.
Speaker 2: But if you have a the sequence of operations that
Speaker 2: might fail. Combining many case statements can quickly lead to really.
Speaker 1: Deep nesting, ugly conditional nesting.
Speaker 2: The pyramid of doom exactly hard to read, hard to maintain,
Speaker 2: so Elixer provides more robust and idiomatic strategies. One option
Speaker 2: which might feel familiar if you come from other languages
Speaker 2: is the try statement. It has rescue for handling exceptions errors,
Speaker 2: and catch throw for non error control flow jumps. You'll
Speaker 2: often see functions that might raise exceptions end with a
Speaker 2: bang like file dot CD dot one, It signals danger.
Speaker 2: You can define your own custom exceptions too, using def exception,
Speaker 2: while try rescue gives you a clear happy path and
Speaker 2: handles exceptional cases. Elixir developers generally tend to avoid using
Speaker 2: raise and throw for normal control flow. They're usually reserved
Speaker 2: for truly exceptional, unrecoverable errors because they introduce hidden jumps
Speaker 2: in the call stack, making flow harder to reason about.
Speaker 1: Okay, so exceptions are for exceptional circumstances, not regular flow
Speaker 1: control makes sense? What about other approaches? I sometimes hear
Speaker 1: the term monad thrown around, often with a bit of mystique.
Speaker 1: Can you demystify what something like an error monad actually does?
Speaker 1: For us in practical terms, why is it valuable for
Speaker 1: handling this unpredictability.
Speaker 2: Yeah, the M word can sound scary, but you really
Speaker 2: don't need to dive into deep category theory to use
Speaker 2: them effectively. Simple monads like a common result monad, often
Speaker 2: found in community libraries like monot x, or even just
Speaker 2: implemented with tag Tuples like value or dot error reason
Speaker 2: are incredibly practical. They essentially wrap a value in a context,
Speaker 2: either a success context like okay the value, or an
Speaker 2: error context the dot error or their reason. You then
Speaker 2: use special operators, maybe like often called the bind operator
Speaker 2: in a library, to chain operations together. Here's the magic.
Speaker 2: If any operation in that chain results in an error context,
Speaker 2: the rest of the pipeline is automatically skipped. That error
Speaker 2: value just gets propagated straight through the end. This provides
Speaker 2: a really clear, pipeline like way to handle sequences of
Speaker 2: potentially failing operations without resorting to those deeply nested eifles
Speaker 2: or case statements. Your air handling becomes explicit and linear.
Speaker 1: So it's like the pipe operator but aware of success
Speaker 1: failure states.
Speaker 2: That's a great way to think about it. Yeah, it
Speaker 2: chains computations conditionally and Finally, there's elixers built in with statement.
Speaker 2: This is often the pragmatic and preferred choice in the
Speaker 2: elixer community for handling a series of uncertain values or
Speaker 2: operations that might fail. With gamps allows you to combine
Speaker 2: multiple pattern matching clauses sequentially, so you am I right
Speaker 2: with okay user fetch user weaponcams withgaans offt cofail, drack,
Speaker 2: pick chooser, grack of catch profile user word. If all
Speaker 2: those clauses successfully match, meaning fetch user returned art, then
Speaker 2: the DO block executes with all the successfully bound variables.
Speaker 2: But if any clause fails to match. For example, ifet
Speaker 2: chooser returns air not found, the width statement immediately stops
Speaker 2: and it returns to value that didn't match and not found.
Speaker 2: In this case, you can then optionally handle these non
Speaker 2: matching cases great fully. In an LS block, its real
Speaker 2: power comes from leveraging pattern matching, so you can check
Speaker 2: for very specific values or complex data structures and easy
Speaker 2: step its main consideration. It doesn't directly chain with the
Speaker 2: regular pipe operator, which can sometimes break the visual flow
Speaker 2: if you're in the middle of a long data transformation pipeline.
Speaker 1: Okay, So with with way gives you sequential pattern matching
Speaker 1: with automatic short circuiting on failure. That sounds very useful.
Speaker 2: It's incredibly useful and very common in adiomatic Elixir.
Speaker 1: Wow, what an eye opening deep dive. We've really journeyed
Speaker 1: from a fundamental paradigm shift of functional programming all the
Speaker 1: way to Elixer's core building blocks, exploring powerful abstractions like
Speaker 1: higher order functions and lazy streams, and even tackled the
Speaker 1: very real challenge of handling unpredictable, impure functions with elegance
Speaker 1: using things like with and protocols. You the listener, now
Speaker 1: have the conceptual tools to think differently about structuring your
Speaker 1: code for robustness and maintainability. Yeah.
Speaker 4: Putting this all in perspective, what you've learned is that
Speaker 4: fp empowers you to build software using mus data, pure
Speaker 4: functions where possible, and a declarative style. This tends to
Speaker 4: lead to more predictable, often more maintainable, and definitely more
Speaker 4: easily concurrent code. You've seen how Elixer's powerful pattern matching,
Speaker 4: its approach to recursion with TCO, higher order functions in
Speaker 4: ENEM and stream and tools like mulka with provide really
Speaker 4: elegant solutions to common programming problems, all within a very
Speaker 4: pragmatic and productive framework.
Speaker 1: So what does this all mean for you, the listener?
Speaker 1: You've just gained a fantastic shortcut to being well informed
Speaker 1: on this topic with practical insights into its power. Now
Speaker 1: for a final provocative thought to leave you with. Consider
Speaker 1: how this new way of thinking about immutability, about functions
Speaker 1: as first class citizens, about declarative style. How could that
Speaker 1: transform not just the new code you write, but how
Speaker 1: you approach debugging and refactoring your existing projects. Even in
Speaker 1: languages that aren't purely functional, You'll definitely start seeing your
Speaker 1: old code from a very different and perhaps a more
Speaker 1: optimistic perspective. This is really just the beginning of your
Speaker 1: path to mastering functional pro gramming with the Elixir