Speaker 1: You know Python. You love its incredible flexibility, its massive ecosystem,
Speaker 1: and how quickly you can bring an idea to life,
Speaker 1: right from concept to prototype.
Speaker 2: Absolutely, it's fantastic for getting things done.
Speaker 1: But what happens when that speed, the execution speed, becomes
Speaker 1: absolutely critical. What if your Python code starts hitting a wall,
Speaker 1: becoming the bottleneck in your system?
Speaker 2: Yeah, that's a common problem.
Speaker 1: Today we're diving deep into a fascinating, powerful solution, supercharging
Speaker 1: your Python applications with Rust.
Speaker 2: It's a really interesting combination.
Speaker 1: Welcome to the deep Dive, the show where we take
Speaker 1: your sources, the articles, the research, your own notes and
Speaker 1: extract the most important nuggets of knowledge to make you
Speaker 1: truly well informed.
Speaker 2: Fast, getting you up to speed quickly.
Speaker 1: Our mission today is to unpack the practical art of
Speaker 1: fusing Python's legendary agility with rusts well unparalleled performance and safety.
Speaker 1: We're not just going to tell you how they work together.
Speaker 1: We'll explore why this combination can fundamentally revolutionize your approach
Speaker 1: to building efficient software.
Speaker 2: And we've got some really cool real world examples too.
Speaker 1: Oh yeah, we're going to hit you with some surprising
Speaker 1: ones that might just change how you think about programming
Speaker 1: languages forever. So let's unpack this, let's do it. Python
Speaker 1: as we know is truly amazing. It's the go to
Speaker 1: for rapid prototyping, building complex logic.
Speaker 2: And the libraries, the ecosystems.
Speaker 1: Just vast exactly. It's flexible, object oriented programming is ideal
Speaker 1: for solving real world problems quickly. But here's the unavoidable truth,
Speaker 1: the double edged sword, uh huh, the speed. It's notoriously
Speaker 1: slow and not always the most efficient with system resources.
Speaker 1: This is where, especially with the explosion of big data
Speaker 1: and computationally intensive tasks, the need for faster, more performance
Speaker 1: languages becomes undeniable.
Speaker 2: Precisely, and this is exactly where RUSS steps onto the
Speaker 2: stage and truly shines. It's fundamentally memory safe. It compiles
Speaker 2: directly to machine code, giving you raw, unadulterated.
Speaker 1: Speed right direct compilation.
Speaker 2: But here's the game changer, what makes RUSS utterly unique.
Speaker 2: It achieves all that memory safety without relying on garbage collection.
Speaker 1: Ah. The GC explain that.
Speaker 2: A bit sure for those unfamiliar, garbage collection is like
Speaker 2: a program periodically hitting the pause button to sweep up
Speaker 2: unused variables and free up memory. It's necessary in many languages,
Speaker 2: but it can cause pauses.
Speaker 1: Unpredictable pauses sometimes exactly.
Speaker 2: Rust elegantly sidesteps this, meaning no pauses, no unpredictable spikes
Speaker 2: and latency, just consistent, blazing fast performance.
Speaker 1: That's a huge operational advantage, and we've seen this validated
Speaker 1: in the real world in some pretty compelling ways, haven't we.
Speaker 2: Oh, definitely.
Speaker 1: I immediately think of Discord's twenty twenty blog post why
Speaker 1: Discord is switching from Go to Rust.
Speaker 2: Absolutely. Discord's experience is a perfect illustration. They observe that Go,
Speaker 2: while fast, had spiky performance, meeting inconsistent and unpredictable latency.
Speaker 1: Which is bad for real time chat.
Speaker 2: Exactly, Rust, on the other hand, delivered a flat line
Speaker 2: in their performance graphs, consistently low latency, which is critical
Speaker 2: for a real time communication platform.
Speaker 1: And didn't some people argue about the Go version they used.
Speaker 2: Yeah, there was some pushback, but Discord confirmed they had
Speaker 2: rigorously tested a range of Go versions and they all
Speaker 2: produced similar spiky results. This truly reinforced Rust's consistent performance
Speaker 2: edge for them.
Speaker 1: So if we connect these dots, what does this all
Speaker 1: boil down to for us? As developers. What's the takeaway.
Speaker 2: It's about achieving the best of both worlds. Really, the
Speaker 2: goal isn't to replace Python, not at all. It's about
Speaker 2: augmenting it. Okay, you use Python for what it excels
Speaker 2: at rapp development, complex high level logic, orchestrating your application,
Speaker 2: and then when you hit a performance bottleneck, you strategically reach.
Speaker 1: For Rust like a surgical strike for speed.
Speaker 2: Precisely this way you get the unparalleled efficiency and speed
Speaker 2: without compromising Python's developer friendly experience too much.
Speaker 1: That vision sounds incredibly appealing, speed without the usual headaches,
Speaker 1: but that speed and safety and rust come at a price, right,
Speaker 1: And that the price is strictness.
Speaker 2: Yes, it's definitely stricter.
Speaker 1: As a Python developer, we're used to the incredible ease
Speaker 1: of mixing types, like just adding one plus two point
Speaker 1: two seamlessly. How does Rust approach that fundamental concept?
Speaker 2: This is often the very first hurdle of Python developer encounters.
Speaker 2: Rust enforces what we call aggressive typing.
Speaker 1: GRIFFI is typing.
Speaker 2: Okay, if you tried let result one plus two point
Speaker 2: two in Rust, the compiler would immediately throw an error.
Speaker 2: It won't let you mix an integer and a floating
Speaker 2: point number directly like that.
Speaker 1: No implicit conversion.
Speaker 2: Nope, this isn't just about being pedantic. This aggressive typing
Speaker 2: ensures a level of safety in the long run that
Speaker 2: Python's dynamic nature can't match, because it catches entire classes
Speaker 2: of bugs before your code even run that compile time exactly.
Speaker 2: Another immediate difference is that variables in Rust are automatically
Speaker 2: immutable by default. You have to explicitly declare them with
Speaker 2: MUTT if you intend to change their value.
Speaker 1: So you have to opt into mutability right.
Speaker 2: And this strictness extends to numbers themselves. Rust differentiates between
Speaker 2: signed integers like I eight or I one twenty eight,
Speaker 2: which can hold both positive and negative.
Speaker 1: Values like regular integers and Python.
Speaker 2: Mostly kind of yeah, and unsigned integers you eight or
Speaker 2: U one twenty eight, which only old non negative numbers.
Speaker 2: This impacts their range. For example, you eight can hold
Speaker 2: zero two of fifty five. If you try to let
Speaker 2: braking number you eight equals two to fifty six, the
Speaker 2: compiler will simply tell you it doesn't fit Bam, compile error.
Speaker 1: It stops you right there.
Speaker 2: For floats, you have F thirty two and F sixty four.
Speaker 2: This explicit typing allows Rust to be incredibly efficient with
Speaker 2: memory and execution because it knows exactly what kind of
Speaker 2: data it's dealing with at all times.
Speaker 1: So it's not just about simple variables. How does this
Speaker 1: strictness ripple through to something as fundamental as data structures?
Speaker 1: In Python? Lists are incredibly flexible. We can throw anything
Speaker 1: you want into them.
Speaker 2: Yeah, strings, numbers, objects.
Speaker 1: And they're mutable by default. Tuples are like immutable arrays.
Speaker 1: How does Rust handle this?
Speaker 2: Rust's a roach to data structures like arrays in vectors
Speaker 2: is all about control and consistency. A rasor fixed in size,
Speaker 2: and crucially, all their elements must be of the same
Speaker 2: type or implement a.
Speaker 1: Consistent trait same type only got it.
Speaker 2: Vectors are Rust's equivalent of a dynamic array. They're expandable
Speaker 2: and live on the heap, but just like variables, they're
Speaker 2: immutable by default. You need that mutt keyword to add
Speaker 2: or manipulate.
Speaker 1: Elements still upt in for changes.
Speaker 2: Right. The why behind this consistency is paramount rusts type
Speaker 2: enforcement at compile time prevents runtime errors. Imagine trying to
Speaker 2: perform a mathematical operation on a list in Python that
Speaker 2: unexpectedly contains a string, it would crash.
Speaker 1: Yeah, the dreaded typer runtime.
Speaker 2: RUSS simply won't let you build that in the first place,
Speaker 2: ensuring predictable behavior.
Speaker 1: That makes a lot of sense. So if you do
Speaker 1: need to store multiple different types of data in a
Speaker 1: container and rust, like in a Python dictionary where values
Speaker 1: could be strings or integers, how do you manage that
Speaker 1: without running a foul of these stripped type rules.
Speaker 2: That's where enoms come in, short for enumeration. Think of
Speaker 2: an enom as a way to say this value could
Speaker 2: be one of these specific types.
Speaker 1: Okay, like defining the possibilities exactly.
Speaker 2: So you might define an enom called value that can
Speaker 2: be either value dot string or value dot nti three two.
Speaker 2: Then to safely extract the value, RUSS requires you to
Speaker 2: use a match.
Speaker 1: Statement match like a switch statement, similar but more powerful.
Speaker 1: This match statement is like a supercharged switch, but with
Speaker 1: a critical difference. The compiler forces you to handle every
Speaker 1: single possible outcome that your enom could represent, every single case,
Speaker 1: every single one. If you miss a case, it won't compile.
Speaker 1: This provides an incredibly robust safety net, ensuring you don't
Speaker 1: accidentally overlook a scenario. Wow, you can use the pattern
Speaker 1: like an underscore to catch any unhandled cases or values
Speaker 1: you genuinely don't care about, which keeps the compiler happy.
Speaker 2: Error handling is another crucial aspect of writing Roebus software.
Speaker 2: Pythons try except blocks are pretty straightforward. How does Rust
Speaker 2: approach errors? And what about memory safety? That's like Rust's.
Speaker 1: Superpower, right, yeah, it really is. Rust handles errors with
Speaker 1: a result type. It's a bit like Python's none for
Speaker 1: missing values may be, but much more explicit. How so,
Speaker 1: our result is a container that either holds an OK
Speaker 1: value meaning success and it contains the successful result, or
Speaker 1: an error value indicating that something went wrong, containing the
Speaker 1: error information.
Speaker 2: So you have to deal with the possibility of an
Speaker 2: error exactly. It forces you to explicitly acknowledge and handle
Speaker 2: potential errors. But the real magic, the core of Rust's
Speaker 2: promise lies in its compile time memory protection. Rust's compiler
Speaker 2: is a vigilant guardian the borrow checker, that's the one.
Speaker 2: It aggressively checks your code to prevent a whole host
Speaker 2: of common, notoriously difficult to debug memory errors. We're talking
Speaker 2: about use after freeze.
Speaker 1: Using memory after you said you were done.
Speaker 2: With it, right, dangling pointers where references point to non
Speaker 2: existed data, double freeze, freeing memory twice, segmentation faults accessing
Speaker 2: memory you shouldn't, and buffer overruns reading beyond the bounds
Speaker 2: of an array.
Speaker 1: Nasky bugs, horrible ones.
Speaker 2: RUSS catches these before your program even runs at compile time.
Speaker 1: That's an impressive list of safeguards. So what are the
Speaker 1: fundamental rules that rustin forces to achieve this incredible memory safety?
Speaker 1: How does it actually work?
Speaker 2: There are a few core tenets that simplify Rust's approach,
Speaker 2: though they take getting used to. First, every value in
Speaker 2: Rust has an owner, which is the variable assigned to it.
Speaker 1: One owner per value.
Speaker 2: Yes. Second, when that owner variable goes at a scope,
Speaker 2: like at the end of a function or block, the
Speaker 2: memory it occupies is automatically deallocated. You don't have to
Speaker 2: manage it manually automatic cleanup right, and Third, values can
Speaker 2: be used by other variables, but only if you adhere
Speaker 2: to very specific conventions. Copying moving, immutable borrowing, immutable borrowing.
Speaker 1: Okay, copy move borrow. Let's break that down copying versus moving.
Speaker 2: Let's illustrate with strings. Unlike a simple number like an
Speaker 2: I thirty two, a string and Rust doesn't implement the
Speaker 2: copy trait. Think of a string as a physical book,
Speaker 2: not a photograph. If I give you the book.
Speaker 1: You don't have it anymore, exactly, it's moved.
Speaker 2: If I then try to read from my original book variable,
Speaker 2: the compiler will stop me. It'll say value borrowed here
Speaker 2: after move.
Speaker 1: Why not just copy it?
Speaker 2: Because copying the underlying pointer could be dangerous. You'd have
Speaker 2: multiple pointers to the same mutable data. If one changes that,
Speaker 2: the others might break. So if you truly want a separate,
Speaker 2: independent copy of a string, a deep copy, you explicitly
Speaker 2: us dot on tound.
Speaker 1: Okay, So moving is the default for complex types like strings.
Speaker 1: What about borrowing? That sounds like a key distinction.
Speaker 2: It absolutely is. It's central to rust. Think of it
Speaker 2: like lending that book out instead of giving it away.
Speaker 2: With an immutable borrow, you're lending someone a copy to read.
Speaker 2: Multiple people can have immutable borrows read only access at
Speaker 2: the same.
Speaker 1: Time, with lots of readers no writers.
Speaker 2: Correct the original variable can still be copied or immutably borrowed,
Speaker 2: but it cannot be mutably borrowed while immutable borrows exist.
Speaker 2: Now with a mutable.
Speaker 1: Borrow, like letting someone edit the book exactly.
Speaker 2: You're giving someone exclusive access to that book so they
Speaker 2: can make changes. Only one mutable borrow can exist at
Speaker 2: a time, only one editor, and crucially, the original variable
Speaker 2: cannot be used at all, not read, not borrowed again
Speaker 2: while it's mutably borrowed, because its state might be altered unpredictably.
Speaker 2: This prevents conflicts and ensures data integrity.
Speaker 1: Wow, so either many readers or one writer.
Speaker 2: That's the core rule.
Speaker 1: All these rules around ownership and borrowing seem to tie
Speaker 1: into the concept of scopes and lifetimes, which can be
Speaker 1: a bit of a head scratcher for Python developers, where
Speaker 1: scope is primarily enforced within functions.
Speaker 2: You're right, it's a shift in thinking. Rust has very
Speaker 2: strict scoping rules. Variables are dropped, meaning your memory is
Speaker 2: deallocated as soon as they exit the scope where they
Speaker 2: were created. Like the curly braces. Define scopes very strictly.
Speaker 1: And this leads to the lifetime problem often.
Speaker 2: Yes, it's a classic example. Imagine you declare a variable
Speaker 2: one outside of scope, then inside an interscope you create
Speaker 2: two and make one borrow a reference to two. Okay,
Speaker 2: when that interscope ends, two is dropped. Its memory is gone,
Speaker 2: but one still exists outside holding a reference to nothing,
Speaker 2: a dangling.
Speaker 1: Pointer ah, and Rust catches that catches it.
Speaker 2: At compile time. It sees that two lives for a
Speaker 2: shorter time than one needs the reference, it will refuse
Speaker 2: to compile. This is fundamental to Rust's memory safety guarantee.
Speaker 1: So how do you handle references across scopes? Then?
Speaker 2: Rust allows for explicit lifetime annotations using syntax like a pike.
Speaker 2: These essentially let you tell the compiler, Hey, this reference
Speaker 2: I'm passing around will only be valid as long as
Speaker 2: this other piece of data it points to is valid.
Speaker 2: It helps manage complex borrowing scenarios across different parts of
Speaker 2: your code.
Speaker 1: Okay, so Rust has these powerful, strict foundational principles, but
Speaker 1: how do you actually build something with it? What are
Speaker 1: the tools and architectural blueprints that make this robust, high
Speaker 1: performance code come to life? In Python? We have PIP
Speaker 1: and virtual and PIV. How does Rust handle its projects?
Speaker 2: For Rust, Cargo is your single source of truth. It's
Speaker 2: the all in one tool that handles everything everything pretty
Speaker 2: much running your code, testing, generating documentation, building your binaries,
Speaker 2: managing your dependencies from creates dot io the Rust package registry.
Speaker 2: It's incredibly integrated.
Speaker 1: And project setup.
Speaker 2: Your project configuration is neatly defined in a single Cargo
Speaker 2: dot tomol file like Python's pipeproject dot tomlol or set
Speaker 2: up dot py, but maybe more standardized. It specifies things
Speaker 2: like the package name, version, authors, dependencies.
Speaker 1: What about ignoring build files like Python's pi cash.
Speaker 2: What's wonderfully clean about Rust is its dot get ignore file.
Speaker 2: Typically it just contains target. That's it. Target. Yeah, because
Speaker 2: everything Cargo produces compile binaries, documentation Cach's intermediate files is
Speaker 2: neatly stored in that single directory. It keeps your project
Speaker 2: root very clean compared to Python's often sprawling cash files
Speaker 2: and build artifacts.
Speaker 1: That sounds nice and I understand. Cargo also has integrated
Speaker 1: documentation capabilities.
Speaker 2: Yes, it's fantastic. You just run carbo doc. It generates interactive,
Speaker 2: searchable mark down based documentation directly from your code comments
Speaker 2: using for docs, much like Python.
Speaker 1: Dock strengths, and you view it locally.
Speaker 2: Yep opens right in your web browser. It's a testament
Speaker 2: to Rust's philosophy of encouraging good practices right from the start.
Speaker 2: Documenting public APIs is strongly.
Speaker 1: Encouraged beyond documentation, How do you design your Rust modules
Speaker 1: for scalability while maintaining that strictness. Rust is known for
Speaker 1: effectively enforcing your application's architecture.
Speaker 2: The PUB keyword in Rust is key. By default, everything
Speaker 2: is private to its module. You use pub to make items, functions, structs,
Speaker 2: enoms public for use outside their.
Speaker 1: Module opt invisibility exactly.
Speaker 2: The core principle here is to isolate modules to a
Speaker 2: single cohesive concept. This drastically increases flexibility and makes large
Speaker 2: applications far easier to maintain and understand.
Speaker 1: And Rust helps enforce this.
Speaker 2: Oh yeah, Rust doesn't just suggest this, it enforces it.
Speaker 2: Rust actively won't compile if you try to access non
Speaker 2: public implementation details from outside a module. This forces you
Speaker 2: to define clear well structured public interfaces.
Speaker 1: So you have to think about your module boundaries.
Speaker 2: You really do. For instance, in a module handling, say
Speaker 2: stock orders, you would define public enoms like order type,
Speaker 2: public structs like order, and then provide public functions maybe
Speaker 2: constructors like order dot new and methods like order dot
Speaker 2: current value.
Speaker 1: And keep the internal workings private precisely.
Speaker 2: You can even use option F thirty two for optional
Speaker 2: parameters in your public functions and using self for methods
Speaker 2: that just read data rather than self, which takes ownership,
Speaker 2: is important for usability. This layered approach means internal refactoring
Speaker 2: is safer as long as the public interface stays the same.
Speaker 2: You don't break downstream code.
Speaker 1: So we've got to handle on Rust's building blocks. Now,
Speaker 1: if you take this compiled rustcode and actually combine it
Speaker 1: with Python's existing distribution methods, what's involved in packaging and
Speaker 1: distributing rustcode as a Python pip module.
Speaker 2: Okay, so, for packaging standard Python pitp modules, you typically
Speaker 2: start with a GitHub repository, use a dot godn or
Speaker 2: file again usually much smaller for Rust. Often just target
Speaker 2: within the Rust part and then can figure a setup
Speaker 2: dot pifile or more modernly piproject dot tom l with
Speaker 2: a build back end.
Speaker 1: Right the usual Python packaging stuff.
Speaker 2: Yeah, this defines all your package metadata like the name, version, authors,
Speaker 2: and crucial dependencies via install requires installing. It is then
Speaker 2: as simple as pip installed, get plus https dot GitHub
Speaker 2: dot com, your dash repoy, your dat package at main
Speaker 2: or eventually pick install your package from PIPI.
Speaker 1: Any security things to watch out for.
Speaker 2: There definitely a quick but important note. Traditionally, set up
Speaker 2: dot pi runs arbitrary code as your user potentially root
Speaker 2: during installation. Malicious code injected there can be a serious
Speaker 2: vector for attack. Modern builds with piproject dot com tomol
Speaker 2: can mitigate this somewhat. Also, always be wary of type
Speaker 2: A squadding accidentally installing Request instead of the popular Request
Speaker 2: library for example.
Speaker 1: Good points. And if you want to expose a command
Speaker 1: line airphase from your Python package so users can run
Speaker 1: scripts directly from the terminal like my tool inputfile dot txt.
Speaker 2: You'd use Python's built in argparse module for parsing those
Speaker 2: command line arguments and then define entry points in your
Speaker 2: setup dot PI or piproject dot tomol. This links a
Speaker 2: console command, say fib number, directly to a specific Python
Speaker 2: function within your package.
Speaker 1: Okay, what about quality control like unit testing? How do
Speaker 1: we ensure the Python side of our fused application is robust?
Speaker 2: For Python unit testing, the standard library's unitist module is common,
Speaker 2: or frameworks like bytest. With unit test, you inherit from
Speaker 2: test case your test simply functions prefixed with test.
Speaker 1: And you use ascertain methods.
Speaker 2: YEP assertion methods like assert equal, assert true, assert reasons
Speaker 2: for checks. For more advanced scenarios, especially when dealing with
Speaker 2: external dependencies or complex logic you want to.
Speaker 1: Isolate, like network calls or database access.
Speaker 2: Exactly, patch from unit test dot mock is invaluable. You
Speaker 2: can use it as a decorator to temporarily replace or
Speaker 2: mock dependent functions or objects. This allows you to test
Speaker 2: specific logic in isolation and even inspect like what arguments
Speaker 2: were passed to your mocked functions.
Speaker 1: Very useful for isolating units. How do you run them?
Speaker 2: You can automate running these tests with a simple batchscript
Speaker 2: or use tools like tox or just run Python basem,
Speaker 2: unit test, discover or PI test.
Speaker 1: And for continuous integrasion, automating all this testing and maybe
Speaker 1: deployment on platforms like GitHub.
Speaker 2: Gehub actions are perfect for this. You automate these processes
Speaker 2: defined in dot iml files like run tests, dot aml
Speaker 2: and your dot GitHub workflows directory.
Speaker 1: And trigger them on pushes or poor requests.
Speaker 2: Right. The typical steps involve checking out the code, setting
Speaker 2: up Python, installing dependencies from requirements dot txt or your
Speaker 2: package definition, running your unit tests, and maybe also performing
Speaker 2: type checking with tools like mypie.
Speaker 1: Can you handle optional features and packages like install extra
Speaker 1: stuff only if needed.
Speaker 2: Yes, you can define optional dependencies using extras require and
Speaker 2: setup dot pie or the equivalent in piproject dot com ol.
Speaker 2: This allows users to install, say pip install, flitt and
Speaker 2: fibpie server to get specific server related functionalities without bloating
Speaker 2: the base install for everyone.
Speaker 1: Speak in my pie. How does Python time checking with
Speaker 1: my pie compare to Rust's built in aggressive type enforcement
Speaker 1: we talked about earlier.
Speaker 2: It's a great question. My pie checks type consistency and
Speaker 2: Python coming statically. It mimics some of Rust's compile time checks,
Speaker 2: catching type errors before you run. But here's the crucial difference.
Speaker 2: It's not enforced during run time by the Python interpreter itself, so.
Speaker 1: Python might still run code mypieflags.
Speaker 2: Potentially, Yes, if the specific type inconsistency doesn't cause an
Speaker 2: immediate crash. Mypile provides a fantastic layer of confidence and
Speaker 2: catches mini bugs early. But it's a lner an external
Speaker 2: tool that you runs. Hope system is a fundamental inescapable
Speaker 2: part of its compilation process right.
Speaker 1: Compile versalint And finally, a brief mention on automatic versioning.
Speaker 1: How can that help you?
Speaker 2: Can write Python code, perhaps using the request library to
Speaker 2: interact with the PIPIAPI to fetch the latest version number
Speaker 2: of your own package or dependency. This is incredibly useful
Speaker 2: for automated release processes, generating change logs, and ensuring your
Speaker 2: continuous deployment pipelines are always working with the correct intended versions.
Speaker 1: All these pieces are fitting together. This brings us to
Speaker 1: the actual fusion point. How do Python and Rust truly
Speaker 1: talk to each other? What's the secret? Sauce, the mechanism
Speaker 1: for creating a Rust interface that Python can seamlessly call the.
Speaker 2: PIO three crate is the star here. It's the enabling technology,
Speaker 2: the bridge that allows rustcode to interact directly and quite
Speaker 2: easily with Python.
Speaker 1: Okay, PIO three. How do you set that up? In
Speaker 1: your build?
Speaker 2: You typically use set up tools rust or mature and
Speaker 2: as your build back end in piproject dot tamil. These
Speaker 2: tools manage the compilation of the Rust code within your
Speaker 2: Python package build.
Speaker 1: Process and in the Russ project itself.
Speaker 2: In your RUSS project's cargo dot com l you define
Speaker 2: create type c ADM. This tells Cargo to compile your
Speaker 2: Rust code as a c style dynamic library that Python
Speaker 2: can load and interact with like.
Speaker 1: A DL or dot so file exactly.
Speaker 2: The build tool then links this compiled RUSS library into
Speaker 2: the Python package structure. The most important detail in your
Speaker 2: Rust code is simply adding a special marker an attribute
Speaker 2: macro hashtag PI function on any Rust function you want
Speaker 2: to expose to Python.
Speaker 1: Just hashtag p function pretty much.
Speaker 2: You also need a hashtag PI module function to declare
Speaker 2: the Python module itself and add your halftag PI functions
Speaker 2: to it. PIO three handles all the complex glue code
Speaker 2: type conversions, air handling, mapping in the background. It makes
Speaker 2: it feel remarkably like Python is calling just another Python
Speaker 2: function even though it's running compiled rustcode under the hood.
Speaker 1: So PIO three handles the deep technical plumbing. What about
Speaker 1: the Python side of this bridge. We often hear about
Speaker 1: adapters and software design. How do Python adapters help bridge
Speaker 1: this language gap, especially when bringing in something like Rust.
Speaker 2: That's a great pattern to consider. An adapter in general
Speaker 2: is a design pattern that manages the interface between two
Speaker 2: different modules, applications, or even languages like we're doing here.
Speaker 2: Think of it like a universal travel adapter for your electronics.
Speaker 1: Right, let's your UK plug work in the US exactly.
Speaker 2: It allows your Python code to interact with the Rust
Speaker 2: module to a consistent Python interface, even if the underlying
Speaker 2: Rust implementation details change later. It provides a flexible layer,
Speaker 2: so if your back end computation module switches from say
Speaker 2: pure Python to Rust.
Speaker 1: You just update the adapter, right.
Speaker 2: You only need to update the adapter class to call
Speaker 2: the new Rust functions via PIO three, not every single
Speaker 2: place in your Python toebase that needed that calculation. This
Speaker 2: significantly simplifies maintenance and refactoring.
Speaker 1: That makes sense. What about sharing resources, like if multiple
Speaker 1: parts of your Python app need to use the same
Speaker 1: instance of a Rust backed object.
Speaker 2: Ah yeah. A common pattern often used with adapters, particularly
Speaker 2: for managing a shared resource or state, is the singleton
Speaker 2: design pattern. This ensures that all references to a particular
Speaker 2: class point to one single instance of that class throughout
Speaker 2: your application.
Speaker 1: How do you do that? In Python?
Speaker 2: A typical way is using a metaclass, a class that
Speaker 2: defines how other classes are created. The meta class can
Speaker 2: manage a dictionary of instances, ensuring only one instance of
Speaker 2: the target class is ever created and returned. So every
Speaker 2: time you ask for the adapter, you get the same
Speaker 2: one and.
Speaker 1: When you put it all together, especially for raw performance,
Speaker 1: what's the big reveal? Like if you're calculating something computationally
Speaker 1: intensive like Fibonacci numbers, how does Rust even with PIO
Speaker 1: three bindings compared to pure Python or even number Numba
Speaker 1: tries to speed up Python two right.
Speaker 2: Right Number is a just in time compiler for Python functions,
Speaker 2: often using LLVM. It can give significant speed ups. But
Speaker 2: this is where the jaws drop when you bring in
Speaker 2: Rust via PIO three. We ran a speed test for
Speaker 2: fibonacci calculation across pure Python, number and our new Rust
Speaker 2: module via PIO three bindings, and the results The results
Speaker 2: were astounding. The Rust class, even with the overhead of
Speaker 2: those PIO three bindings, translating between Python and RUSS types
Speaker 2: was a staggering forty three times faster than the pure
Speaker 2: Python class for calculating, say, the thirty fifth Vivonacci number
Speaker 2: forty three times forty three. While Number also provided a
Speaker 2: significant speed up, maybe ten to fifteen times faster than
Speaker 2: pure Python in our test, it comes with its own challenges.
Speaker 2: It can be notoriously difficult to install and get working
Speaker 2: reliably on certain systems. Like say in M one MAC,
Speaker 2: often requiring specific and pilot setups or environments.
Speaker 1: So RUSS was significantly faster even than number and potentially
Speaker 1: easier to integrate across platforms via PIO three.
Speaker 2: In this specific benchmark. Yes, this year, magnitude of Russ's
Speaker 2: performance game, even with the interoperability layer, clearly justifies the
Speaker 2: integration effort for those critical bottlenecks.
Speaker 1: That's a staggering difference. Forty three times faster. Okay, moving
Speaker 1: on from raw sequential speed. When you're dealing with complex systems,
Speaker 1: mastering concurrency with threads and processes becomes crucial. What's the
Speaker 1: fundamental difference between threads and processes? Especially for a Python
Speaker 1: developer dealing with the GIL, that's a key distinction.
Speaker 2: Threads are the smallest unit of computation. They live within
Speaker 2: a single process and share resources like memory and processing power. However,
Speaker 2: in Standard Sea Python, due to the Global Interpreter Lock
Speaker 2: or GIL.
Speaker 1: The infamous GIL.
Speaker 2: Indeed, CPU intensive multi threaded tasks often don't speed up significantly,
Speaker 2: sometimes even slow down. The GIL essentially prevents true parallel
Speaker 2: execution of Python bycode across multiple threads on multiple CPU
Speaker 2: core simultaneously, So.
Speaker 1: Python, threads are better for.
Speaker 2: What they're primarily useful for IO heavy tasks, things like
Speaker 2: waiting for network responses, reading writing files. While one thread
Speaker 2: is waiting, the GIL can be released, allowing another thread
Speaker 2: to run, so you get concurrency but not necessarily.
Speaker 1: CPU parallelism okay, and processes.
Speaker 2: Processes, on the other hand, are more expensive to create
Speaker 2: than three threads because they are independent entities managed by
Speaker 2: the operating system. They do not share memory by default,
Speaker 2: which makes them inherently safer for true parallelism, as each
Speaker 2: process gets its own Python interpreter and its own GIL.
Speaker 1: So they can run on multiple cores properly.
Speaker 2: Yes, a process can host multiple threads itself, but the
Speaker 2: key is that different processes run independently. While context switches
Speaker 2: between processes are more expensive for the OS, they're generally
Speaker 2: better for both CPU bound and ioheavy tasks in Python
Speaker 2: because they bypass the GIL limitation for parallelism.
Speaker 1: So how do you practically spin up these concurrent workers?
Speaker 1: Starting with Python threads. If you're doing iobound work.
Speaker 2: In Python, you use the Standard Library's threading thread class.
Speaker 2: You create a thread, give it a target function, and
Speaker 2: call start. The join method is critical it blocks the
Speaker 2: main program until that specific thread finishes its.
Speaker 1: Execution, and they finish in any order. Right.
Speaker 2: It's important to remember that when you spawn multiple threads,
Speaker 2: they generally finish in determined order whenever their task completes.
Speaker 1: How does Rust handle threads? Can it use multiple cores?
Speaker 2: Yes? Absolutely? In Rust you use std dot thread dot spawn,
Speaker 2: which takes a closure to run. It returns a join handle.
Speaker 2: Unlike Python's gil limited threads, threads in Rust can fully
Speaker 2: utilize multiple CPU cores for true parallel execution if the
Speaker 2: hardware is.
Speaker 1: Available, so real parallelism real parallelism.
Speaker 2: The join function on the joint handle in Rust returns
Speaker 2: a result, specifically something like result a boxed in any
Speaker 2: plus end, where t is the return type of your
Speaker 2: threads closure.
Speaker 1: WHOA break that down result box tin any plus send okay.
Speaker 2: Result is the standard ok or air for error handling.
Speaker 2: If the thread panics crashes. Join returns in air. Box
Speaker 2: means the error is allocated on the heat because its
Speaker 2: size might not be known at compile time. Any means
Speaker 2: the error could be dynamically of almost any type, and
Speaker 2: send is a marker trait indicating the error type is
Speaker 2: safe to transfer across thread boundaries.
Speaker 1: So rust makes thread errors type safe very.
Speaker 2: Much, so it forces you to handle that potential error
Speaker 2: or panic from the join thread in showing robustness.
Speaker 1: And what about managing multiple processes in Python, especially for
Speaker 1: those CPU bound tasks where you want to bypass the GIL.
Speaker 2: Python's multiprocessing pool is an excellent abstraction for this. A
Speaker 2: pool manages a fixed number of worker processes. You can
Speaker 2: easily map a function over a list of inputs, and
Speaker 2: the pool distributes the work across its processes, collecting the
Speaker 2: results back into an array or a list for you.
Speaker 1: So it handles the process creation and communication.
Speaker 2: Yeah, it abstracts away a lot of the boilerplate. This
Speaker 2: approach keeps the expense of multiprocessing context localized and allows
Speaker 2: for easy control over the number of workers, which is
Speaker 2: great for parallelizing heavy computations without managing individual processes manually.
Speaker 1: We often use Fibonacci recursion as a classic example in programming,
Speaker 1: but there's a warning about it in the context of concurrency. Right,
Speaker 1: it's not always a great benchmark.
Speaker 2: Yes, absolutely be careful with recursive Fibonac. It leads to
Speaker 2: an exponential computation tree the same calculations are repeated countless times.
Speaker 2: It's incredibly inefficient.
Speaker 1: So it looks busy, but isn't doing much unique work exactly.
Speaker 2: If you're using it to demonstrate concurrency, be aware that
Speaker 2: the overhead of spinning up processes or even threads can
Speaker 2: easily outweigh any computational gains unless the number you're calculating
Speaker 2: is genuinely large, and you use techniques like memoization or
Speaker 2: caching to avoid recalculating redundant values. It's often a poor
Speaker 2: choice for benchmarking real world performance gains from concurrency.
Speaker 1: But if you want to achieve genuine efficient multi threading
Speaker 1: and Rust for a real CPU bound task, what's a
Speaker 1: good way to do it without all that low level
Speaker 1: manual thread management.
Speaker 2: The Rayon crate is fantastic for this. It's almost magical.
Speaker 2: It simplifies data parallelism in Rust tremendously.
Speaker 1: How does it work?
Speaker 2: By simply calling dot interperator on a collection like a VEC,
Speaker 2: you convert a standard iterator into a parallel iterator. You
Speaker 2: can then apply functions like map, filter, induced, etc. Just
Speaker 2: like normal iterators, but Rayon automatically parallelizes the work across
Speaker 2: a threadpool. Utilizing all available CPU.
Speaker 1: Course, so just change perator basically.
Speaker 2: Often it's close to that simple for basic data parallelism.
Speaker 2: For tasks like parallel FIBONACI calculations on a list of numbers,
Speaker 2: this approach can yield significantly better speed increases. We've seen
Speaker 2: it yield maybe a twenty percent or more speed up
Speaker 2: and RUST for appropriate tasks, compared to Python's multiprocessing, which
Speaker 2: might only give you a five percent gain for the
Speaker 2: same task, partly due to the GIL and pythons overheads.
Speaker 1: Even in multiprocessing, What if you need lower level control
Speaker 1: in Rust without Rayon, Well, if you.
Speaker 2: Wanted to go even lower level in Rust without third
Speaker 2: party creates like Rayon, it involves manually using std dot
Speaker 2: process dot command to spawn new OS processes and explicitly
Speaker 2: managing their student and stood out pipes, perhaps using tools
Speaker 2: like buff reader to send data back and forth. It's
Speaker 2: much more manual.
Speaker 1: Work, sounds complex. It seems like they're powerful tools for concurrency,
Speaker 1: but also some silent killers to watch out for. What
Speaker 1: are some of those common concurrency traps that can completely
Speaker 1: derail your performance gains or even cause your application to
Speaker 1: grind to a Hult.
Speaker 2: Yeah, concurrency is powerful, but tricky. First, what's crucial to
Speaker 2: understand is Ambel's law. It basically states that there are
Speaker 2: diminishing returns when you keep adding more cores or workers
Speaker 2: to a task.
Speaker 1: More isn't always better.
Speaker 2: Not infinitely better. No, if only say fifty percent of
Speaker 2: your task can actually be parallelized, even with infinite cores,
Speaker 2: you'll only ever get a two x speed up max.
Speaker 2: The formula speed up pull one one P plus PS
Speaker 2: illustrates this P is the parallelizable proportion. S is the
Speaker 2: speed up of that part. Performance will inevitably plateau based
Speaker 2: on the sequential portion. So throwing more hardware at a
Speaker 2: problem with inherent sequential bottlenecks won't help indefinitely.
Speaker 1: Okay, Amidl's law sets limits. What else?
Speaker 2: Then? There are deadlocks. These are truly insidious, especially in
Speaker 2: distributed systems. They often occur in applications managing multiprocessing via
Speaker 2: a task broker or a queue like red as or salary.
Speaker 1: How does a deadlock happen there?
Speaker 2: Imagine a task running in your worker pool needs a
Speaker 2: result of another task to finish, so it sends that
Speaker 2: new task dependency to the queue and waits. But what
Speaker 2: if the worker pool is already full and maybe all
Speaker 2: the busy workers are also waiting for their dependencies.
Speaker 1: Ah, so nothing new can start and everything is waiting
Speaker 1: for something else that can't start.
Speaker 2: Gridlock exactly. And the crucial frustrating part is that no
Speaker 2: errors are raised. Usually your system just quietly hangs. Debugging
Speaker 2: deadlocks can be a nightmare.
Speaker 1: Yikes. Okay, what's the third trap?
Speaker 2: Finally? Race conditions. These are probably the most common concurrency bug.
Speaker 2: They occur when multiple threads or processes try to access
Speaker 2: and modify shared data simultaneously without proper coordination. The final
Speaker 2: result depends unpredictably on the exact timing and interleaving of operations.
Speaker 1: Leading to corrupted data or inconsistent state.
Speaker 2: Precisely, locks, mutexes or semaphors can prevent this, but only
Speaker 2: reliably within a single process. For distributed systems or shared
Speaker 2: resources like databases or caches, you often rely on the
Speaker 2: built in atomic operations or transaction mechanisms those systems provide.
Speaker 1: So what's the big message here?
Speaker 2: The big message is concurrency is hard. If it becomes
Speaker 2: overly complex to manage, or you're spending more time debugging
Speaker 2: deadlocks and race conditions than writing features. Then maybe simpler
Speaker 2: sequential programming is safer for that part of the system,
Speaker 2: or it's a strong sign that a fundamental design rethink
Speaker 2: is needed for your architecture. Don't introduce concurrency.
Speaker 1: Lightly wise words. Okay, moving on to putting it all
Speaker 1: together in a real world scenario, how do you structure
Speaker 1: a Python flask gap to seamlessly integrate rust building a
Speaker 1: truly robust and high performance foundation. Let's think about a
Speaker 1: web out.
Speaker 2: Right a common use case, you'd likely build a comprehensive
Speaker 2: flask web application stack that might include an NGI and
Speaker 2: x reverse proxy for handling incoming requests and serving static files,
Speaker 2: a postgressful database for persistence.
Speaker 1: A sellery message bus with you redd Us as a
Speaker 1: broker for background tasks exactly.
Speaker 2: And you'd probably package all of this using Docker containers
Speaker 2: orchestrated by Docker Compose for easy development and deployment.
Speaker 1: What special setup does the flask apps docker container need
Speaker 1: for Rust?
Speaker 2: For the Docker file setup for your main class application service,
Speaker 2: you'll need to make sure you install the necessary build tools.
Speaker 2: That usually means things like Python three dev for Python
Speaker 2: c headers, maybe build essential or specifically GCC and the
Speaker 2: Rust tool chain itself, so you can compile your Rust
Speaker 2: extension module directly within the container image during the Docker build.
Speaker 1: Got it install Rust and build tools in the docker file.
Speaker 1: What about the Python code structure database access.
Speaker 2: For your database access layer in Python, you'd likely use
Speaker 2: squalk me as an arm and a lembic for managing
Speaker 2: database migrations, tracking and applying schema changes reliably over time.
Speaker 2: Psychot two binary is the common driver for connecting squalkam
Speaker 2: to postgress.
Speaker 1: Any tips for managing the database connection itself.
Speaker 2: Yes, Crucially, it's good practice to initialize your database engine
Speaker 2: dB engine using squalkmmuse creen engine only once, perhaps as
Speaker 2: a singleton instance, and import that instance across your application.
Speaker 2: This helps avoid issues like too many dangling sessions or
Speaker 2: connection fool exhaustion.
Speaker 1: And cleaning up sessions right.
Speaker 2: Database sessions must also be properly managed per request. You
Speaker 2: typically create a session at the start of a request
Speaker 2: and insure it's expired, closed and removed using flasks at
Speaker 2: app dot teardown request or at app dot teardown app
Speaker 2: context decorators after each request finishes or fails.
Speaker 1: And integrating sealery for background tasks.
Speaker 2: Celery integrates nicely with the flask app context. You usually
Speaker 2: use a Makecelery factory function. This allows your background Selery
Speaker 2: tasks to access your flask apps configuration and extensions like
Speaker 2: the database session correctly. It lets you offload heavy, long
Speaker 2: running tasks like processing and upload or sending emails to
Speaker 2: background workers, significantly improving your web apps responsiveness for the user.
Speaker 1: So, with that robust foundation in place to flask sucul
Speaker 1: Alchemy Accelery, how do you inject the Rust module directly
Speaker 1: into that flask gap for the seamless, tangible performance boost
Speaker 1: we've been aiming for.
Speaker 2: The integration can be quite elegant. You typically create a
Speaker 2: flexible interface within your Python code, say a function coucfibnum.
Speaker 2: This function could internally decide whether to call the pure
Speaker 2: Python implementation or your compiled Rust implementation.
Speaker 1: How would it decide maybe based on a.
Speaker 2: Configuration setting, an environment variable, or even dynamically based on
Speaker 2: the input size. You could use a simple calculation method
Speaker 2: enum fere g, Piython or rus to control the switch.
Speaker 1: And how does this look in practice in the web?
Speaker 2: Back when you demonstrate this in a live web context,
Speaker 2: you might have two endpoints calling the Rust backed endpoints
Speaker 2: say Rust calculate dot number, you'll likely see it can
Speaker 2: be dramatically faster. We observed it being potentially four times
Speaker 2: faster or more for higher Fibonacci numbers. Compared to a
Speaker 2: Python calculate dot number endpoint, calling the pure Python logic
Speaker 2: for the same calculation, the difference becomes very apparent.
Speaker 1: And for deployment, how does the Rust code get built?
Speaker 2: As we mentioned for the Docker file, you simply modify
Speaker 2: it to first install the Rust tool chain. These caimply
Speaker 2: using the official Rust upscript dot, curl, HTTPS, dot s dot,
Speaker 2: rust up dot r s SSFA, sy profile minimal, modify path,
Speaker 2: and then as part of your Python package installation staff
Speaker 2: like pipinstall dot the build back end set up tools
Speaker 2: Rust mature automatically compiles your Rust extension module within that
Speaker 2: Docker image before your application.
Speaker 1: Starts, so it's built into the container image. Can Rust
Speaker 1: even interact directly with the database in the setup or
Speaker 1: is that strictly Python's domain via squalkamy No.
Speaker 2: Rust can absolutely interact directly with the database and often
Speaker 2: very efficiently too. You can use crates like Diesel, which
Speaker 2: is a popular RM and querybuilder for Rust supporting Postcress School,
Speaker 2: my school, and squite.
Speaker 1: So Rust could bypass Python for database work.
Speaker 2: Yes, this involves configuring a Diesel dot com l file
Speaker 2: using the Diesel Cli tool to manage migrations, migration run
Speaker 2: and to introspect your database schema. Are generating RUSS code
Speaker 2: definitions for your tables into a schema dot r s file.
Speaker 2: There are even tools like Diesel x that can help
Speaker 2: autogenerate your database model strucks models dot rs from the schema.
Speaker 1: Then the rest code talks SQL exactly.
Speaker 2: Your Rust extension module can then directly perform complex queries
Speaker 2: or bulk operations against the database using Diesel, potentially much
Speaker 2: faster than going through Python and skoalcomy for certain tasks,
Speaker 2: and then expose just the final results back to your
Speaker 2: Python Flask application. This opens up even more possibilities for
Speaker 2: optimizing performance critical database interactions.
Speaker 1: Okay, we've covered an incredible amount of ground here. Let's
Speaker 1: try to boil this down to some best practices for
Speaker 1: creating a truly harmonious blend between Python and Rust. What's
Speaker 1: the main guiding principle for a successful integration.
Speaker 2: I think the guiding principle is elegantly simple. Keep it simple,
Speaker 2: or maybe use the right tool for the job.
Speaker 1: Don't just rewrite everything in Rust for the sake of
Speaker 1: it exactly.
Speaker 2: Pythons should remain your go to for high level tasks
Speaker 2: building the web application framework, general data manipulation, scripting, machine learning,
Speaker 2: where rapid development and its rich library ecosystem key. You
Speaker 2: should reserve rest for those specific identified critical performance bottlenecks,
Speaker 2: low level system programming, or areas where memory safety and
Speaker 2: explicit concurrency control are absolutely paramount Be strategic.
Speaker 1: And when you're structuring the interfaces between the two languages,
Speaker 1: how should you approach that? For optimal developer experience, especially
Speaker 1: for the Python developers using the module, you should.
Speaker 2: Aim to maintain Python centric interfaces. Do the heavy lifting,
Speaker 2: the number crunching the low level operations in Rust for speed,
Speaker 2: but keep the data formation, the API design, access patterns,
Speaker 2: and maybe even visualization entirely in Python.
Speaker 1: Can you give an example sure?
Speaker 2: For instance, in a two D physics simulation, Rust could
Speaker 2: efficiently calculate all the coordinates of a trajectory maybe millions
Speaker 2: of points, and return that raw data, perhaps as a
Speaker 2: simple list dictionary, back to Python. Python would then consume
Speaker 2: those coordinates via an intuitive Python class or object, making
Speaker 2: it seamless and familiar for the Python user. They don't
Speaker 2: need to know the gritty details of the Rust.
Speaker 1: Calculation, hide the Rust implementation details behind a nice Python
Speaker 1: facade precisely.
Speaker 2: This leverages each language's strengths. You can even use Python
Speaker 2: meta classes, as we mentioned with singletons, to further enhance
Speaker 2: this by creating sophisticated wrappers around your Rust functionalities that
Speaker 2: feel completely native to Python developers.
Speaker 1: Rust's trait system seems unique and powerful compared to Python's classes.
Speaker 1: How can you leverage that for defining behavior? Maybe even
Speaker 1: across the language boundary?
Speaker 2: Traits and Rust are super powerful. They define shared behavior
Speaker 2: for structs somewhat similar to interfaces in languages like Java
Speaker 2: or Go, or maybe conceptually like Python's abstract based classes ABC's.
Speaker 2: Combined with duct typing, they let you define capabilities like
Speaker 2: what For example, you could create speak, clinical skills, and
Speaker 2: advance medical traits in Rust. Then you implement these specific
Speaker 2: traits for different people's structs like patient maybe only implements speak,
Speaker 2: nurse implement speak and clinical skills or doctor implements.
Speaker 1: All three And this allows for polymorphism.
Speaker 2: Yes, it enables polymorphism functions can accept any struck that
Speaker 2: implements a specific trait, often using syntax like impletratee or
Speaker 2: and in trait. This ensures consistent behavior and contracts across
Speaker 2: different concrete types.
Speaker 1: How does that translate to Python when using pio three.
Speaker 2: Interestingly, when exposing Rust strucks that implement traits to Python
Speaker 2: using PIO three's hashtag et opi class, these trait implementations
Speaker 2: often translate very naturally into direct attributes or methods on
Speaker 2: the resulting Python class, making them immediately usable in a
Speaker 2: Pythonic way. For instance, the Advanced Medical trait might expose diagnose,
Speaker 2: and prescribe methods that become directly callable on the doctor
Speaker 2: Python object.
Speaker 1: And finally, for parallelization, what's the best practice for using
Speaker 1: rayon effectively? We said it was easy, but are there caveats?
Speaker 2: Rayon crate provides an incredibly simple way to achieve data
Speaker 2: parallelism in Rust using parallel iterators comperiator. The crucial consideration, though,
Speaker 2: is overhead.
Speaker 1: There's a cost to setting up the parallysm.
Speaker 2: Exactly, there's a non zero setup cost to coordinating the
Speaker 2: threadpool and distributing the work for very small tasks like
Speaker 2: processing a vector of just five numbers a normal sequential loop,
Speaker 2: and Rust might actually be faster than using Rayon. The
Speaker 2: overhead outweighs the benefit.
Speaker 1: But for larger tasks.
Speaker 2: But as the data size and the computational load per
Speaker 2: item increase, for example calculating Fibonacci numbers up to thirty
Speaker 2: three or higher for a list of inputs, Rayon's benefits
Speaker 2: become immediately clear and substantial. It's about knowing when the
Speaker 2: overhead is worth the game profile your code.
Speaker 1: Wow, what an absolutely incredible journey. We've taken a really
Speaker 1: deep dive through the intricacies of Rust, haven't we. From
Speaker 1: its foundational principles of memory safety and strict typing.
Speaker 2: All the way to its powerful tooling like cargo, and
Speaker 2: it's surprisingly seamless integration with via pio three.
Speaker 1: Yeah, we've seen how you can build truly robust performance systems.
Speaker 1: Whether it's a small, lightning fast calculation module tucked inside
Speaker 1: your Python code or a full fledged flask Web application
Speaker 1: leveraging Rust for critical parts.
Speaker 2: It's a powerful combination when used thoughtfully.
Speaker 1: You listening now hopefully have a much deeper understanding of
Speaker 1: how to leverage Python's rapid development cycle and rich ecosystem
Speaker 1: for your complex application logic, while surgically infusing the raw speed, reliability,
Speaker 1: and concurrency benefits of Rust into your most performance critical.
Speaker 2: Components, getting the best of both.
Speaker 1: So here's the final thought. In a world drowning in data,
Speaker 1: where every millisecond can translate into real world impact or
Speaker 1: user satisfaction, how will this deeper understanding of Python and
Speaker 1: Rust influence your next architectural decision? How might you start
Speaker 1: to identify the hitting performance bottlenecks lurking in your current
Speaker 1: projects and strategically introduce Rust to truly turbocharge them, all
Speaker 1: while maintaining that developer friendly expert orian's Python is so
Speaker 1: famous for.
Speaker 2: It really opens up possibilities.
Speaker 1: Food for thought, isn't it