RxAndroid and Kotlin: The Ultimate Guide (Part 1)

As a long-time Android developer and lead architect at a top Silicon Valley startup, I‘ve found reactive programming to be an indispensable paradigm for mobile development. After using many languages over the past decade, I‘ve found Kotlin to be the perfect match for unlocking the full potential of RxAndroid.

In this comprehensive guide, we‘ll cover everything you need to know to leverage reactive programming for building smooth, resilient Android applications.

The Evolution of Asynchronous Programming

Over the past decade, mobile app complexity has increased exponentially. From intricate user interfaces to complex background handling, crafting a high-quality user experience requires managing an enormous amount of asynchronous operations.

Traditionally Android has relied on callbacks, AsyncTask, listeners and other ad-hoc patterns. But these approaches are clunky and make code difficult to manage. They also lack robust support for handling multiple streams of data, propagation of errors or thread coordination.

To overcome these challenges, a number of clean architecture approaches have emerged including MVP, MVVM and unidirectional patterns. These do help in modularizing app logic and enabling separation of concerns.

However, they still rely on traditional asynchronous primitives under the hood. This forces developers to manually juggle threads, share state across scopes and handle multiple event loop lifecycles.

Reactive programming aims to solve these issues by providing native concurrency handling through declarative data stream composition. Instead of dealing with callbacks, threads and synchronization ourselves, we declaratively define event streams and data pipelines that encapsulate all of this behavior implicitly.

This makes reactive programming a perfect fit for Android‘s inherent async nature. By leveraging Kotlin with the RxJava library, we can implement extremely elegant and resilient mobile apps.

Key Concepts

The fundamental unit of reactive programming is the observable – a stream of data events distributed over time. For example, user clicks, geolocation updates or network responses.

We can register observers (consumers) to these observables. They react whenever observables emit new data events through callback handlers for onNext, onError, and onComplete:

Observables emit data sequences over time that observers can react to.

The real power comes from using operators – pure functions that transform, filter, combine or process event streams:

Operators allow complex processing of reactive streams.

By chaining these together, we can declaratively express event stream transformations, without having to deal with underlying async handling ourselves:

userClicks
    .debounce(300, TimeUnit.MILLISECONDS)
    .filter { validate(it) }
    .flatMapLatest { apiCall(it) }  
    .retry()
    .subscribe(::updateUi)  

Much cleaner compared to traditional nested async callbacks!

Let‘s analyze some key advantages this provides:

Asynchronous by Default

By abstracting away thread handling behind streams, we don‘t have to juggle callbacks, handlers or continuity issues – async is baked into the paradigm itself.

Parallel Dataflows

Observable sequences can interleave events making it easy to model concurrent dataflows. This helps reduce state complexity.

Declarative

Composable operator pipelines allow us to focus on business logic declaratively instead of execution orchestration.

Stateless

Operators are purely functional allowing easy composition. No need to manage async lifecycles manually.

Error Handling

The reactive stream automatically propagates errors downstream for centralized handling instead of littered try/catch blocks.

Backpressure Support

Strategies like buffer, window and sample help manage different processing speeds between producers and consumers.

These properties make Rx a very elegant fit for Android‘s async environment.

Why Kotlin is the Perfect Match

Kotlin enhances all the advantages mentioned above by providing an extremely concise language syntax that closely aligns with RxJava semantics.

Functional Style

First-class support for lambdas, higher order functions makes Kotlin a breeze for declarative streams.

Extension Functions

We can directly extend existing types like Observable with custom operators.

Null Safety

Type system eliminates billion dollar mistakes like NullPointerException.

Interoperability

Kotlin provides direct bi-directional interop with existing Java Rx codebases.

This makes Kotlin a fantastic choice for writing reactive Android applications. Let‘s see some code examples.

Creating Observables in Kotlin

At the core of reactive programming are Observables – stream sources that emit events over time. For example, user actions, network data etc.

There are many ways of creating observables:

Just: Emits a specified item

val just = Observable.just("Hello")  

From: Emits items from an array, list or iterable

val from = Observable.from(listOf("A", "B", "C"))   

Interval: Emits integers at a particular time interval

val second = Observable.interval(1, TimeUnit.SECONDS) 

Timers: Emit single item after delay

val timer = Observable.timer(5, TimeUnit.SECONDS)

Create: Manually invoke emissions via onNext() / onComplete()

val create = Observable.create<String> { emitter ->
    emitter.onNext("Hello")  
    emitter.onComplete()
}

This covers some basic ways of declaring observables. But in Android apps, they are usually tied to async operations:

UI Events

val clicks = button.clicks() //emits clicks  

Network Calls

fun getUser(): Observable<User>  

Database Queries

fun getUsers(): Flowable<List<User>>

By leveraging these existing async sources instead of managing manually, we get robust observable streams out-of-the-box.

Now let‘s look at ways to subscribe and react to these sequences.

Subscribing to Observables

To receive events emitted by an Observable, we need to subscribe to it. This lets us define observer callbacks:

observable
    .subscribe(  
        { /* onNext */ },  
        { /* onError */ },
        { /* onComplete */ } 
    ) 

• onNext – Called on each emitted item
• onError – Notification for errors
• onComplete – Called on successful sequence completion

By subscribing, we basically say:

"Notify me whenever new data, errors or completion events occur".

Simple Example:

helloObservable
    .subscribe { println(it) } 

The lambda provides handle to onNext data while onComplete gets called automatically afterwards.

In addition to method references or lambdas, we can pass full Observer implementations for maximum control:

observable.subscribe(object: Observer<String> {

    override fun onSubscribe(d: Disposable) {
        // Called on successful subscription 
    }

    override fun onNext(t: String) {
        // Handle emitted items
    }

    override fun onError(e: Throwable) {
        // Handle errors 
    }

    override fun onComplete() {
        // Handle sequence complete
    }

})

This allows custom handling for stream lifecycle events.

Now let‘s explore some powerful operators that enable sophisticated processing of observables.

Transforming Streams with Operators

While creating and subscribing to basic streams is useful, the real advantage comes from composing pipeline of operators that transform the emitted items.

For example:

clicks
    .throttleFirst(500, TimeUnit.MILLISECONDS)
    .map { handleClick(it) } 
    .retry(3)
    .filter { validate(it) }
    .subscribe { updateUi(it) }

Operators allow us to:

✅ Declaratively transform streams

✅ Compose async business logic pipelines

✅ Abstract underlying complexity

✅ React to events easily

There are over 150 built-in operators for transforming observables.

Here are some of the most useful ones:

map(): Transform emitted items

clicks.map { "Clicked" }  

We can also map observables:

val observables = apiCalls.map { networkCall(it) }

filter(): Only emit items matching predicate

clicks.filter { it.isLongPress }

take(): Emit only first n items

clicks.take(3) 

debounce(): Emit item after quiet period

searchInput.debounce(500, MILLISECONDS) 

delay(): Delay emitted items

clicks.delay(2, SECONDS)

retry(): Retry failed emissions

networkCall.retry(3)

zip(): Combine multiple streams

val stream = Observable.zip(    
    userClicks,    
    apiResults,        
    { click, result ->       
        // Combine data  
    }
)

And many more…

Chaining these operators allow declaration of sophisticated event stream processing logic easily.

Now let‘s look at strategies for threading control when using RxJava.

RxAndroid Thread Handling

Carefully controlling execution context of operations is crucial for smooth reactive Android apps.

By default, RxJava subscriptions occur synchronously on the same thread. However, Android has specific threading requirements:

• Network requests must run on background thread
• Database writes should occur asynchronously
• UI updates must happen back on main thread

Thankfully, RxAndroid provides elegant control over this behavior through:

Schedulers: Specify thread for particular operation

subscribeOn(): Specify upstream source execution thread

observeOn(): Downstream notifications on given thread

For example:

getUser() 
    .subscribeOn(io())
    .map { /* process */ }
    .observeOn(mainThread()) 
    .retry()
    .subscribe(::updateUi)    

Here subscribeOn() declares API call on IO thread, while observeOn() switches back to main thread when emitting results to update UI correctly.

RxAndroid includes schedulers for:

• IO intensive work
• Computation tasks
• Android main thread
• Trampoline scheduler
• New thread scheduler
• Single scheduler

Leveraging these APIs declaratively abstracts away all complex thread handling logic in reactive flows.

Testing RxJava Code

The compositional nature of Rx Observables and Operators lends itself very well to stream based unit testing.

We can easily test reactive chains using TestObserver which provides full control over lifecycle events and assertions:

@Test 
fun inputValidator_ValidData() {

    val inputValidator = 
        textChanges     
            .map { /*...*/ }
            .filter { /*...*/ } 

    val testObserver = inputValidator.test()

    testObserver.onNext("foo")

    testObserver.assertValueCount(1)
    testObserver.assertValue("foo")

}

By manually pushing events and asserting output, we can test stream behavior in isolation avoiding side-effects.

This approach helps test both simple and complex Rx pipelines with ease.

For deeper integration testing, strategies like using virtual time schedulers or mocking network calls are useful for end-to-end reactive flow validation.

Key Benefits of Reactive Programming on Android

Now that we‘ve had an overview of core concepts, let‘s analyize some tangible benefits adopting reactive programming provides for Android development:

Reduced Complexity

By using declarative operator pipelines instead of nested callbacks, app logic becomes easier to reason about. Stream transformations directly convey the essence of business functionality.

Code Concurrency

Asynchronous code Requires far less plumbing boilerplate compared to classics listeners, handlers and callbacks. Streams auto-propagate values, errors and completion lifecycles.

Robustness

Built-in backpressure support manages uneven dataflow between producers and consumers gracefully. Error handling avoids pesky crashes through centralized stream error channels.

Modularity

Pure stream transformations make components reusable, testable and interchangeable. This aligns perfectly with clean architecture principles.

Scalability

Operational behavior abstracted from concurrency mechanics allows code reuse. Adding handling for more concurrent workflows requires no refactors unlike callbacks.

Developer Velocity

Studies have shown developers report up to a 2-3x boost in application logic development speed by moving to reactive architectures reducing maintenance overhead down the line.

Beyond qualitative benefits, reactive style also demonstrates tangible performance, storage, traffic and power efficiency wins especially for I/O intensive mobile applications.

By adopting an RxJava+Kotlin driven design, we can accelerate innovation while crafting robust production quality apps.

Ui Event Handling Example

Let‘s look at a real example demonstrating the elegance of RxJava for Android UI event handling:

Reactive search box with debounce & throttling

Here we use a simple search box that makes API calls on text change events.

To optimize network traffic & response rates, additional debounce() & throttleFirst() operators are chained.

searchEdt
    .textChangeEvents()  
    .debounce(300, MILLISECONDS)
    .throttleFirst(500, MILLISECONDS))
    .flatMapLatest { searchApi(it) }
    .retry(3)  
    .subscribeOn(io())
    .observeOn(mainThread())
    .subscribe(::updateUi) 

This provides a smooth user experience by:

• Reactively handling text changes
• Debouncing intervals
• Throttling frequency
• Retrying failed network calls
• Updating UI safely on main thread

By declaratively chaining asynchronous events and data streams, we abstract away enormous complexity under the hood while retaining clean architecture.

The full source code is available on GitHub.

Key Takeaways

The reactive paradigm made possible by RxJava and Kotlin provides an immensely effective way for mobile development by abstracting async handling from business logic.

Some key highlights:

✅ Declarative data streams eliminate callback chaos

✅ Operators enable sophisticated event processing

✅ Kotlin‘s expressiveness accelerates development

✅ Automatic propagation of state/errors simplifies flows

✅ Backpressure & scheduling handles threading overhead

✅ Composable and testable reactive chains

✅ Concurrency modeling through observable sequences

By leveraging reactive architecture, we can massively cut down complexity and boost stability – critical qualities for production grade Android applications.

Conclusion

In this guide, we had an in-depth exploration of reactive programming concepts including Rx Observables, Operators and Schedulers along with Kotlin interop examples.

In the next part, we will tackle more advanced topics like hot vs cold observables, Subjects for communications between chains, testing/debugging strategies and sample real-world architecture blueprints.

To learn more, I highly recommend exploring channels like the Awesome RxJava list for additional quality resources on mastering reactive techniques for JVM and Android.

Let me know if you have any other topics you would like me to cover or apps built with RxKotlin worth featuring!