Speaker 1: Welcome to the deep dive. Today, we are getting into
Speaker 1: something that, well, if you're a developer, it has probably
Speaker 1: kept you up at night at least once. We're talking
Speaker 1: about modern concurrency and sea shark.
Speaker 2: Yeah, it's it's a massive topic.
Speaker 1: It really is. And we are pulling our insights today
Speaker 1: from some excerpts of Stephen Cleary's Concurrency and c Shark cookbook.
Speaker 1: Our mission here is to really just extract the critical
Speaker 1: knowledge you need to master responsive scalable programming, you know,
Speaker 1: without getting bogged down by the absolute nightmares of old
Speaker 1: school multi threading, right.
Speaker 2: Because nobody wants to go back to those nightmares. I
Speaker 2: mean it's just it's.
Speaker 1: Rough, definitely not. But before we get into the code,
Speaker 1: I have to talk about the cover of this book.
Speaker 1: If you haven't seen it, you might expect, I don't know,
Speaker 1: a majestic animal like maybe a falcon or a cheetah,
Speaker 1: something to represent speed and raw power.
Speaker 2: Yeah, that would make sense for a book on concurrency.
Speaker 1: Right, But instead you get a common palms of bit.
Speaker 1: And if you read the author's own preface, he actually
Speaker 1: points out that this animal is widely considered like it
Speaker 1: gets into attics, it poops everywhere, and it just makes
Speaker 1: these incredibly loud, crashing noises at the absolute worst possible times.
Speaker 2: And there's another really funny detail about the civet. It
Speaker 2: eats coffee cherries and well passes the beans through its
Speaker 2: digestive tract, which is just ea yeah, but those excreted
Speaker 2: beans are actually harvested to make Kobe Luwac, which is
Speaker 2: notoriously one of the most expensive coffees in the world.
Speaker 2: Yet and this is the best part. The Specialty Coffee
Speaker 2: Association officially states that it quote just tastes bad.
Speaker 1: Oh wow. So it's expensive but awful exactly.
Speaker 2: And the author chose this mascot for a very specific, deliberate.
Speaker 1: Reason because to anyone who has been in the trenches,
Speaker 1: to the uninitiated, old school multi threading is exactly like
Speaker 1: the civet. I mean, it is a messy pest. It
Speaker 1: causes loud, unpredictable crashes. And this crashes usually happen you know,
Speaker 1: the value moment you.
Speaker 2: Deployed at production yep, always in production.
Speaker 1: Always and after all the time, the money, the sleepless
Speaker 1: nights you spend trying to fix it, it leaves a
Speaker 1: really bad taste in your mouth.
Speaker 2: It really does. And I think the mission of our
Speaker 2: deep dive today is to show you how modern c
Speaker 2: shark has just completely evolved past that mess. We are
Speaker 2: moving away from writing strict, step by step instructions that
Speaker 2: forcefully micromanage your system resources, and we're moving toward well
Speaker 2: choreographing a self balancing digital ecosystem. It is a fundamental
Speaker 2: shift in how we architect applications today.
Speaker 1: Okay, let's unpack this because the author he really doesn't
Speaker 1: pull any punches regarding that shift. There's this brutal, just
Speaker 1: instantly memorable quote early in the text. It simply says,
Speaker 1: as soon as you type new thread, it's over. Your
Speaker 1: project already has legacy code.
Speaker 2: Ouch. Yeah, which is I mean, that's a tough pill
Speaker 2: to swallow. If you grew up manually spinning up threads.
Speaker 1: It feels wrong, It feels like you're losing control right.
Speaker 2: Exactly, But it requires us to really look at what
Speaker 2: the run time is actually doing. Now we have to
Speaker 2: clarify the terminology here. Multi threading is just one form
Speaker 2: of concurrency, the direct manual management of threads, you know,
Speaker 2: allocating them, destroying them, trying to figure out exactly how
Speaker 2: many your specific hardware can handle that is dead.
Speaker 1: But the threads of themselves aren't gone obviously.
Speaker 2: Right right, They're just abstracted. They live on behind the
Speaker 2: thread pool under the hood. And the modern net threadpool
Speaker 2: is this highly optimized dynamic heuristic algorithm.
Speaker 1: So it's making decisions for you.
Speaker 2: Yeah, constantly. It monitors the CPU load, the memory constrains,
Speaker 2: the queue of pending work, and it just continuously adjusts
Speaker 2: the number of active threads on the fly. I mean,
Speaker 2: it is vastly better at managing resource allocation than any
Speaker 2: custom code you or I could ever write.
Speaker 1: It's kind of like, I mean, think about legacy threading
Speaker 1: like manually moving the gears inside your car's transmission by hand,
Speaker 1: just constantly grinding them, whereas modern concurrency is like an
Speaker 1: automatic transmission that dynamically adjusts to the traffic around you.
Speaker 2: That's a really good analogy. Yeah, but just handing over
Speaker 2: thread allocation to the run time, you know, that isn't
Speaker 2: enough to prevent the classic nightmare scenario.
Speaker 1: Like deadlocks and race conditions exactly.
Speaker 2: The source material emphasizes that modern concurrency heavily relies on
Speaker 2: adopting a functional mindset.
Speaker 1: Okay, so what does that actually mean in practice, Well.
Speaker 2: It's the real secret sauce. Modern concurrency strongly leans toward
Speaker 2: writing pure code. That means methods that take inputs, produce outputs,
Speaker 2: and have zero side effects. And you combine that with
Speaker 2: immutable data structures.
Speaker 1: Meaning data that literally cannot be changed in memory once
Speaker 1: it has been created.
Speaker 2: Right, So if the data can't change, multiple threads can
Speaker 2: read it simultaneously without needing complex locks. You inherently bypass
Speaker 2: the synchronization issues entirely because well, there's nothing to synchronize.
Speaker 1: Wow, okay, so you eliminate shared state, and when you
Speaker 1: eliminate shared mutable state, those classic multi threading bugs just sort.
Speaker 2: Of evaporate, they do. They just go away.
Speaker 1: So, if we aren't creating threads manually anymore, how do
Speaker 1: we handle tasks that take time? Like how do we
Speaker 1: wait for external cs stems to respond without locking up
Speaker 1: our entire application?
Speaker 2: That takes us right into the mechanics of asynchronous programming,
Speaker 2: the async and await keywords. Asynchronous programming is fundamentally about
Speaker 2: the art of waiting without blocking. In dot net, we
Speaker 2: use objects like task to represent a future. It's basically
Speaker 2: an operation that will complete at some point. When you
Speaker 2: use the awight keyword on a task, you are telling
Speaker 2: the runtime, Hey, I need the result of this operation,
Speaker 2: but I don't need to hold onto the current thread
Speaker 2: while I wait.
Speaker 1: So the thread is just instantly returned to the threadpool
Speaker 1: to do other useful work.
Speaker 2: Exactly, and on the surface, you should care about this
Speaker 2: because it has huge benefits. On the client side, it
Speaker 2: keeps your UIs totally responsive, the app doesn't freeze. On
Speaker 2: the server side, it massively increases scalability.
Speaker 1: Because an asp net server isn't just burning through its
Speaker 1: finite pool of threads staring blankly at a database connection
Speaker 1: waiting for a response.
Speaker 2: Right, it can serve other users in the meantime.
Speaker 1: But wait, let me push back on this a little bit.
Speaker 1: If a weight pauses a method and completely lets go
Speaker 1: of the threads so it isn't blocked, where does the
Speaker 1: rest of the code actually execute when it wakes up? Like,
Speaker 1: how does the local state survive if the thread is
Speaker 1: just gone?
Speaker 2: That is where the compiler performs some absolute magic. When
Speaker 2: you mark a method as async, the c sharp compiler
Speaker 2: actually rips that method apart. It rewrites it into a
Speaker 2: complex state machine behind the scenes.
Speaker 1: Literally rewrites your code.
Speaker 2: Literally, it takes all your local variables, which would normally
Speaker 2: live on the thread stack and be destroyed when the
Speaker 2: thread is released, and it elevates them into fields on
Speaker 2: a hidden class that's allocated on the heap.
Speaker 1: Okay, so the run time is basically leaving a bookmark
Speaker 1: complete with all the local environment variables saved safely in
Speaker 1: memory before it steps away.
Speaker 2: Yes, and along with that state machine, it captures something crucial.
Speaker 2: It's called the synchronization context. Think of the synchronization context
Speaker 2: as the specific environment the code was running in before
Speaker 2: it paused.
Speaker 1: Give you an example of that.
Speaker 2: So if you click a button in a window interface,
Speaker 2: you are on the UI thread and the context that
Speaker 2: gets captured represents that specific UI environment.
Speaker 1: Okay, I think I have a way to visualize this.
Speaker 1: It's like a restaurant heirstas.
Speaker 2: Okay, let's hear it.
Speaker 1: You have a table, but you need to step outside
Speaker 1: to take a phone call. The hostess doesn't just leave
Speaker 1: your table empty for an hour while a huge line
Speaker 1: forms at the door.
Speaker 2: Right.
Speaker 1: She gives the table to someone else, but she captures
Speaker 1: your context. She remembers exactly what you ordered and exactly
Speaker 1: where you were sitting right right, So when you finish
Speaker 1: your call and come back inside, she uses that context
Speaker 1: to seat you at an appropriate table to resume your
Speaker 1: meal right where you left off.
Speaker 2: That is a brilliant way to look at it. Yeah.
Speaker 2: When the asynchronous operation, like say a database call, finally finishes,
Speaker 2: the state machine uses that captured synchronization context to route
Speaker 2: the execution back to where it belongs. Got it So
Speaker 2: if it captured a UI context, it cues the rest
Speaker 2: of the method to resume specifically on the UI thread.
Speaker 2: And that allows you to safely update a textbox or
Speaker 2: progress bar without getting cross thread exceptions.
Speaker 1: And the text gives some very practical ways to wield
Speaker 1: this right, like task delay. Instead of the legacy approach
Speaker 1: of thread sleep, which let's be honest, violently blocks a
Speaker 1: thread and ruins your scalability, task delay simply sets a timer,
Speaker 1: yields the thread back to the pool, and then uses
Speaker 1: that state machine to resume when the time is up.
Speaker 2: Yeah, and you can orchestrate really complex workflows using combinators
Speaker 2: like task dot went all that takes an entire collection
Speaker 2: of tasks and gives you back a single task that
Speaker 2: completes only when every underlying operation finishes.
Speaker 1: Which is perfect for modern micro services. You might need
Speaker 1: to gather data from an inventory API, a pricing API,
Speaker 1: and a user profile API all at the same time
Speaker 1: before you can render a single checkout page exactly.
Speaker 2: And the flip side of that, which is equally useful
Speaker 2: is task When anything, you give it a bunch of tasks,
Speaker 2: but it completes the moment the first one finishes.
Speaker 1: Oh right. The book uses the example of pinging multiple
Speaker 1: stock quote web service simultaneously. You only care about the
Speaker 1: fastest response to give your user the real time price,
Speaker 1: so you just take the first one that comes back
Speaker 1: and discard the rest.
Speaker 2: Right. Super elegant, but as powerful as this aweight and
Speaker 2: synchronization context mechanism is, the text really emphasizes that we
Speaker 2: are stepping into a very dangerous performance trap if we
Speaker 2: don't understand what's happening under the hood.
Speaker 1: Okay, here's where it gets really interesting because I actually
Speaker 1: struggled with this exact concept when I was reading the source.
Speaker 2: Oh yeah, yeah.
Speaker 1: If capturing the context is what makes a sinc weate
Speaker 1: so seamless, why does the book treat it like a hazard.
Speaker 1: Like if I'm building an application, why wouldn't I always
Speaker 1: want my code to resume right where it left off.
Speaker 1: Jumping around between random background threads seems super confusing.
Speaker 2: It does seem confusing at first. Yeah, you have to
Speaker 2: look at the physical bottleneck of the UI thread. The
Speaker 2: UI thread is just one thungal thread. Capturing and resuming
Speaker 2: on that context requires the run time to package up
Speaker 2: a continuation message and literally shove it into the UI
Speaker 2: thread's message pump. Okay, and the team that built the
Speaker 2: Windows runtime actually quantified this exact.
Speaker 1: Overhead right the paper cuts issue the one hundred versus
Speaker 1: one thousand continuations metric exactly.
Speaker 2: They found that if your application is routing about one
Speaker 2: hundred continuations per second back to the UI thread, it's
Speaker 2: totally fine. The thread can handle drawing the screen and
Speaker 2: processing those resume messages. But if you hit around one
Speaker 2: thousand continuations per second, you completely flood the message pump.
Speaker 2: Oh wow, Yeah, the UI thread becomes so overwhelmed just
Speaker 2: processing resume messages that it actually stops drawing the screen.
Speaker 2: The application freezes. It's literally death by a thousand paper cuts.
Speaker 1: So if I'm writing a core library, say a data
Speaker 1: access layer that loops through thousands of records and makes
Speaker 1: internal asynchronous calls, and I allow every single one of
Speaker 1: those weights to capture the UI context. I'm going to
Speaker 1: accidentally lock up the screens of whoever uses my library.
Speaker 2: Yes, you'll freeze their app. And the fix for this
Speaker 2: is a method called configure weight false. When you pend
Speaker 2: to configure weight false to an awaitable task, you are
Speaker 2: explicitly overwriting the state machine.
Speaker 1: You're telling it to ignore the hostess exactly.
Speaker 2: You are telling the compiler do not bother capturing the context.
Speaker 2: When this external operation finishes, just grab any available background
Speaker 2: thread from the threadpool and continue executing.
Speaker 1: Which entirely skips the UI message pump bottleneck. So the
Speaker 1: rule of thumb is use configure a weight false rigorously
Speaker 1: in all your core library and background code, and only
Speaker 1: rely on the default context capturing behavior in your outermost
Speaker 1: user interface layer. You know where you actually need to
Speaker 1: touch visual elements.
Speaker 2: That's the exact architectural boundary you want to establish. But yeah,
Speaker 2: configure way is really just a performance optimization. There is
Speaker 2: a much bigger trap mentioned in the sources, something that
Speaker 2: will flat out destroy your application. Oh boy, the ultimate
Speaker 2: sin of modern c shark concurrency ACYNC void.
Speaker 1: Yeah, the book practically puts a skull and crossbones next
Speaker 1: to ACYNC void. But let's clarify why it even exists.
Speaker 1: If it's so incredibly dangerous, why did Microsoft put it
Speaker 1: in the language in the first place.
Speaker 2: Well, they had to purely for historical backward compatibility. Think
Speaker 2: about a classic Windows Forms or WPF application. You have
Speaker 2: a button click event handler, right. The signature for that event,
Speaker 2: which was mandated by the framework years before acing programming
Speaker 2: ever existed, is private void button click. It absolutely must
Speaker 2: return void. But what if you want to await a
Speaker 2: database call when the user clicks that button.
Speaker 1: You can't just change the signature to return a task,
Speaker 1: or the framework won't recognize it as an event handler anymore.
Speaker 2: Exactly. So, acinc void was introduced as a compromise just
Speaker 2: to allow developers to use the await keyword inside legacy
Speaker 2: event handlers. But doing so breaks the fundamental air handling
Speaker 2: mechanics of the modern language.
Speaker 1: Because there's no task object. Let's trace the mechanics of
Speaker 1: a failure here, because this is fascinating. When you write
Speaker 1: a proper acinc task method and something goes wrong, say
Speaker 1: the network just drops what happened to the exception okay.
Speaker 2: So normally, the run time catches that exception and neatly
Speaker 2: packages it inside the task object that is returned to
Speaker 2: the collar. The exception just sits there, completely harmless, until
Speaker 2: the calling code actually awaits the task. At that exact moment,
Speaker 2: the state machine unwraps the exception and rethrows it, allowing
Speaker 2: you to catch it gracefully in a standard try catch block.
Speaker 1: But if the method is async void, there is no
Speaker 1: task object to act as that protective wrapper.
Speaker 2: Right, There's nowhere to store the error. So when an
Speaker 2: exception is thrown inside an ACYNC void method, it bypasses
Speaker 2: any triketch block you might have set up. It just
Speaker 2: blows right past it. The exception escapes the method entirely
Speaker 2: and is posted directly to the ambient synchronization context.
Speaker 1: And well that's then.
Speaker 2: Well. In almost every modern dot net environment, an unhandled
Speaker 2: exception hitting the context like that will immediately tear down
Speaker 2: the entire application domain. Your app just vanishes.
Speaker 1: From the screen, which is terrifying, just poof gone. So
Speaker 1: the takeaway is absolute avoid ACYNC void all costs, unless
Speaker 1: you are strictly wiring up a legacy UI event handler.
Speaker 2: Absolutely.
Speaker 1: So we've figured out how to wait for external systems
Speaker 1: to respond without locking up our app and how to
Speaker 1: avoid crashing the context. But what happens when the delay
Speaker 1: isn't external? What happens when our own CPU is the
Speaker 1: bottleneck because we have a massive internal data set to crunch.
Speaker 2: Ah okay, this is where we pivot from a secretous programming,
Speaker 2: which remember, is about ioefficiency, to parallel programming, which is
Speaker 2: about CPU throughput. Parallel programming is about taking a massive
Speaker 2: amount of computational work and partitioning it across multiple cores
Speaker 2: to get it done faster.
Speaker 1: Let's ground this in a modern scenario. Instead of just
Speaker 1: doing work, imagine we have a massive database query that
Speaker 1: takes an hour to run on a single core. We
Speaker 1: don't want to just yield the thread and wait an hour.
Speaker 1: We want to partition that data set, effectively sharding it
Speaker 1: into ten smaller chunks, and hand each chunk to a
Speaker 1: different CPU core to PROCESSI simultaneously.
Speaker 2: Yeah, that's a perfect architectural analogy. It's like a chef, right.
Speaker 2: Acinc is like a chef putting a pianney evident wiping
Speaker 2: down the counters while it bakes. Like that, The parallel
Speaker 2: processing is like hiring ten sous chefs to chop ten
Speaker 2: thousand carrots at the exact same time.
Speaker 1: Wow. Yeah.
Speaker 2: And the task parallel library gives us two main tools
Speaker 2: for this kind of data parallelism, parallel four and pliq
Speaker 2: which stands for parallel linq. Both of these tools handle
Speaker 2: a complex logic of partitioning your data set and destructing
Speaker 2: the chunks to different threads automatically, but.
Speaker 1: The sources highlight a critical behavioral difference between how plionq
Speaker 1: and the parallel class actually interact with the operating system.
Speaker 2: Yes, and it really comes down to aggressiveness. Pli inq
Speaker 2: is incredibly elegant to write, but by default it is greedy.
Speaker 2: Greedy how it assumes your query is the most important
Speaker 2: thing happening on the entire machine, and it will eagerly
Speaker 2: try to spin up enough threads to consume every single
Speaker 2: CPU core available, which.
Speaker 1: Sounds great for speed, but what if this is running
Speaker 1: on a web server that is all so trying to
Speaker 1: serve hundreds of other user requests at the same time.
Speaker 2: Then plimq can actually degrade your overall performance by starving
Speaker 2: the OS of threads. It just hogs everything. That is
Speaker 2: where parallel for each is often the safer choice. The
Speaker 2: parallel class methods are designed to be polite. Yeah. They
Speaker 2: dynamically monitor the current CPU load and adjust their thread
Speaker 2: usage if the server gets busy with other requests, parallel
Speaker 2: for each will actually throttle itself back to play nicely
Speaker 2: with the rest of the ecosystem.
Speaker 1: But regardless of which tool you use, there is a
Speaker 1: golden rule that dictates whether a parallelization will actually speed
Speaker 1: up your code or just grind it to a halt,
Speaker 1: and that rule is the chunks of work must be
Speaker 1: completely independent.
Speaker 2: This goes right back to the functional mindset we discussed
Speaker 2: at the start. If you shard your data set across
Speaker 2: eight cores, but all eight threads are trying to write
Speaker 2: their results into a single shared list in memory, you
Speaker 2: have to introduce locks to prevent data corruption.
Speaker 1: And what happens when eight threads hit a lot at
Speaker 1: the exact same time you.
Speaker 2: Get locked contention, Seven of those threads instantly freeze and
Speaker 2: just stand in line waiting for the first thread to
Speaker 2: finish writing. You've just destroyed your parallel efficiency. In fact,
Speaker 2: because of the overhead of managing those locks and switching
Speaker 2: thread contexts, your code will likely run significantly slower than
Speaker 2: if you had just used a single thread in the
Speaker 2: first place. Independence is the absolute key to scalability here.
Speaker 1: Which perfectly sets up the final evolution the book covers.
Speaker 1: We've solved waiting for external IO, we've solved maximizing the
Speaker 1: CPU for static data sets, but modern applications rarely deal
Speaker 1: entirely in static data. What happens when you aren't iterating
Speaker 1: over a fixed array, but instead you are dealing with
Speaker 1: an infinite stream of events over time or building a
Speaker 1: complex multi step processing pipeline that takes.
Speaker 2: Us into reactive extensions generally known as RX and TPL dataflow.
Speaker 2: Let's start with RX. If you understand LIANQ, you can
Speaker 2: understand RX as basically LANQ two events normal IONQ uses
Speaker 2: a pole model static array at memory, and your code
Speaker 2: proactively pulls the data out one item at a time.
Speaker 1: But RX flips the architecture to a push model.
Speaker 2: Exactly With RX, the data might not even exist yet,
Speaker 2: and said you define a query and as events happen
Speaker 2: in real time, they push themselves through your query logic.
Speaker 1: The source material makes a really vital distinction here for
Speaker 1: anyone architecting event driven systems. The difference between hot and
Speaker 1: cold observables. I found this super helpful.
Speaker 2: It's a crucial concept. Yeah. A hot observable is an
Speaker 2: event stream that is broadcasting regardless of whether anyone is listening.
Speaker 2: Think of mouse movements across a screen or a live
Speaker 2: feed of stock market ticks. If your application isn't actively
Speaker 2: subscribed to that stream, those events are just gone. They
Speaker 2: pass right into the ether.
Speaker 1: Contrast that with a cold observable. The book uses an
Speaker 1: HTTP download as an example. The server has the data,
Speaker 1: but the download stream doesn't actually begin broadcasting until your
Speaker 1: code explicitly subscribes to it. It waits for you, right, and.
Speaker 2: RX gives developers a unified interface to treat both of
Speaker 2: these chaotic streams of events as if they were just
Speaker 2: standard collections of data.
Speaker 1: And then we have TPL data flow, which tackles a
Speaker 1: slightly different architectural problem. Where RX is great for reacting
Speaker 1: to events, data flow is designed for building heavy processing
Speaker 1: pipelines or meshes.
Speaker 2: Yeah. It uses an actor bottle approach. You create distinct
Speaker 2: blocks of logic, like say, a transform block to parse
Speaker 2: incoming data and an action block to save it to
Speaker 2: a database.
Speaker 1: You link these blocks together pour data into the top
Speaker 1: of the funnel, and the run time automatically handles passing
Speaker 1: the data from one block to the next. But as
Speaker 1: I was reading this section, I noticed a massive red
Speaker 1: flag regarding memory management.
Speaker 2: Oh, the unbounded buffers.
Speaker 1: Yes, if I have a fast source block downloading thousands
Speaker 1: of files and it's pushing them into a slow target
Speaker 1: block that has to parse them, won't the slow block
Speaker 1: just get completely overwhelmed?
Speaker 2: You've identified the exact danger of unbounded buffers. By default,
Speaker 2: the input buffer on a data flow block is unbounded,
Speaker 2: meaning it will blindly accep every single piece of data
Speaker 2: pushed into it, hoarding it all in memory. Right. If
Speaker 2: your parsing block is slower than your download block, that
Speaker 2: unbounded buffer will just grow and grow until your application
Speaker 2: crashes with an out of memory exception.
Speaker 1: So how do we implement back pressure? Like, how do
Speaker 1: we tell the fast block to just slow down?
Speaker 2: The solution is actually a simple configuration property called bounded capacity.
Speaker 2: By setting a strict limit on the buffer, say capping
Speaker 2: it at ten items, you force the block to reject
Speaker 2: new data once it's full. Oh nice, Yeah, this creates
Speaker 2: natural back pressure. The system pauses the fast download block
Speaker 2: until the slow parsing block has room to accept more data.
Speaker 2: It turns a fragile memory hogging script into a robust
Speaker 2: self throttling pipeline.
Speaker 1: It creates load balancing right out of thin air, which
Speaker 1: really brings us full circle. We started this deep dive
Speaker 1: looking at the civic ca you know, the chaotic, unpredictable
Speaker 1: world of legacy threading that crashed applications and left a
Speaker 1: bad taste in our mouths. And step by step we've
Speaker 1: seen how c sharp has evolved to solve these problems.
Speaker 2: Yeah, we move from men thread allocation to the dynamic threadpool.
Speaker 1: We explored the magic of the compiler state machine, letting
Speaker 1: us await operations without blocking threads while using configure weight
Speaker 1: false to protect the UI message pump and of course
Speaker 1: avoiding async void to prevent catastrophic crashes.
Speaker 2: We learned how to shatter CPU bottlenecks by partitioning data
Speaker 2: with parallel for each and adhering strictly to the rule
Speaker 2: of independence to avoid lock contention.
Speaker 1: And finally, we looked at how to handle infinite streams
Speaker 1: of time based data with RX and how to build
Speaker 1: robust self throttling pipelines with TPL data flow.
Speaker 2: Really is a profound architectural evolution. I want to leave
Speaker 2: you with this thought, based on everything we've uncovered today,
Speaker 2: look at the trajectory here. We've moved entirely away from
Speaker 2: writing rigid, sequential code that force fully micromanages memory and threads. Instead,
Speaker 2: we are utilizing pure functions, immutable data state machines, and
Speaker 2: reactive pipelines. If our applications are becoming networks of independent,
Speaker 2: self throttling blocks reacting to streams of data, are we
Speaker 2: even writing instructions anymore? Or are we truly choreographing a
Speaker 2: self balancing digital ecosystem. Think about how adopting that specific
Speaker 2: mindset could completely change the resilience and scalability of your
Speaker 2: next big project.
Speaker 1: Because when you stop fighting the system for control and
Speaker 1: start choreographing the ecosystem, you ensure your code never becomes
Speaker 1: a legacy pest. Thank you for joining us on this
Speaker 1: deep dive into the source material. We will see you
Speaker 1: next time.