Lesson 28 of 40 Advanced Programming Advanced 60 min

Reactive Programming with Rx.NET

In this lesson, you will learn how reactive programming models streams of events over time, and how Rx.NET helps you compose, transform, and react to asynchronous data sources.

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What you will learn

How reactive programming differs from ordinary imperative code
What observables and observers are
How operators transform event streams
When Rx.NET is a good fit
How to avoid making reactive code unnecessarily hard to understand

Why this matters: Some problems are naturally event-driven. Rx.NET gives you tools to express those flows more clearly than manual callback logic.

Part 1: Observable sequences

In Rx.NET, an observable sequence represents a stream of values arriving over time. This could be UI input, timer events, sensor readings, stock updates, or messages.

IObservable ticks = Observable.Interval(TimeSpan.FromSeconds(1));

IDisposable subscription = ticks.Subscribe(x =>
    Console.WriteLine($"Tick: {x}"));
    

Instead of polling manually, your code reacts when new values arrive.

Part 2: Transforming streams

Rx provides operators such as Select, Where, Throttle, and Merge that let you express event processing declaratively.

var evenTicks = Observable.Interval(TimeSpan.FromMilliseconds(500))
    .Where(x => x % 2 == 0)
    .Select(x => $"Even tick: {x}");
    

This can produce much cleaner code than manual state tracking when event logic becomes more complex.

Part 3: Common reactive use cases

User input streams in desktop or UI apps
Event aggregation and filtering
Timers and scheduled sequences
Sensor or telemetry feeds
Composing multiple async event sources together

Good fit: Rx is strongest when the main complexity is in time, events, and combinations of changing signals.

Part 4: Subscriptions and disposal

Observables can continue producing values until they complete or are unsubscribed. This means lifecycle management matters.

If you subscribe and forget to dispose when appropriate, you can create leaks or unexpected continuing behavior.

using var subscription = evenTicks.Subscribe(Console.WriteLine);

Part 5: Trade-offs

Strength	Trade-off
Expressive event composition	Can be hard to read if overused
Powerful operator model	Requires learning a new mental model
Great for asynchronous event streams	Not needed for every async problem

A practical Rx workflow

Step 1: Identify the event source

Step 2: Model it as an observable sequence

Step 3: Apply transformation and filtering operators carefully

Step 4: Subscribe close to where the result is actually needed

Step 5: Dispose subscriptions properly

Step 6: Keep the reactive pipeline readable and well named

Summary

In this lesson, you learned how Rx.NET models streams of events, how operators transform those streams, and why reactive programming is most useful when time and events are central to the problem.

In the next lesson, you will explore memory management and spans.