RxJava for Android app development

Sharp Learning Curve, Big Rewards

I was pretty much dragged into RxJava by my coworkers…[RxJava] was a lot like git…when I first learned git, I didn’t really learn it. I just spent three weeks being mad at it…and then something clicked and I was like ‘Oh! I get it! And this is amazing and I love it!’ The same thing happened with RxJava. 

Dan Lew¹

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As Dan Lew, a Google Developer Expert Android Developer, points out in the preceding quotation, RxJava can be very difficult to learn. This is unfortunate because, for reasons I point out in the next chapter, RxJava can make asynchronous data handling in Android apps much cleaner and more flexible. In this chapter, I provide a basic introduction to RxJava.

If you are skeptical that RxJava is worth learning about, given its steep learning curve, skip ahead to the second section of the next chapter. In that section, I go over a situation in which RxJava provides us with advantages over traditional ways of handling asynchronous data in Android applications. Although you won’t understand exactly how the code in that section works, you will be able to see how RxJava makes quick work of tasks that can often become messy and inflexible when handled without RxJava. After seeing how much cleaner RxJava can make your Android code, hopefully you will have the motivation to return here to this introduction. 

Let’s start with the guiding example that will help us get a handle on RxJava. Imagine we are building a HackerNews client, an app that allows users to read HackerNews stories and comments. Our HackerNews client might look a little like Figure 1-1:

Obviously, this app would require us to fetch the HackerNews data over the network, and because we can’t block the UI thread, implementing this app would require us to fetch HackerNews data asynchronously. RxJava will be helpful in implementing this app because it is a library that allows us to represent any operation as an asynchronous data stream that can be created on any thread, declaratively composed, and consumed by multiple objects on any thread.

That last statement about RxJava may not make complete sense to you now, but you should be able to understand it by the time you are finished reading this chapter. The first phrase that is likely to seem vague or unfamiliar in the preceding definition of RxJava is “asynchronous data stream.” Let’s start by unpacking that phrase.

Observables

RxJava’s asynchronous data streams are “emitted” by Observables. The reactive extensions website calls Observables the “asynchronous/push ‘dual’ to the synchronous/pull Iterable.” Although Java’s Iterable is not a perfect dual of RxJava’s Observables, reminding ourselves how Java’s Iterables work can be a helpful way of introducing Observables and asynchronous data streams.

Every time we use the for-each syntax to iterate over a Collection, we are taking advantage of Iterables. If we were building our HackerNews client, we might loop over a list of Storys and log the titles of those Storys:

for (Story story : stories) {
    Log.i(TAG, story.getTitle());
}

This is equivalent to the following:²

for (Iterator<Story> iterator = stories.iterator(); iterator.hasNext();) {
    Story story = iterator.next();
    Log.i(TAG, story.getTitle());
}

As we can see in the preceding code, Iterables expose an Iterator that can be used to access the elements of a Collection and to determine when there are no more unaccessed elements left in the Collection.³ Any object that implements the Iterable interface is, from the perspective of clients interacting with that interface, an object that provides access to a stream of data with a well-defined termination point.

Observables are exactly like Iterables in this respect: they provide objects access to a stream of data with a well-defined termination point.

The key difference between Observables and Iterators is that Observables provide access to asynchronous data streams while Iterables provide access to synchronous ones. Accessing a piece of data from an Iterable‘s Iterator blocks the thread until that element has been returned. Objects that want to consume an Observable‘s data, on the other hand, register with that Observable to receive that data when it is ready.

To make this distinction more concrete, think again about the preceding snippet that logs a HackerNews story’s title within a Collection<Story>. Now imagine that the Storys logged in that snippet were not available in memory, that each story had to be fetched from the network, and that we wanted to log the Storys on the main thread. In this case, we would need the stream of Storys to be an asynchronous stream and using an Iterable to access each element in that stream would be inappropriate.

Instead, in this case, we should use an Observable to access each story as it is returned by the HackerNews API. Now, we know that we can access an element in an Iterable‘s stream of data by calling Iterator.next() on its Iterator. We do not know, however, how to access the elements of an Observable‘s asynchronous data stream. This brings us to the second fundamental concept in RxJava: the Observer.

Observers

Observers are consumers of an Observable‘s asynchronous data stream. Observers can react to the data emitted by the Observable in whatever way they want. For example, here is an Observer that logs the titles of Storys emitted by an Observable:

storiesObservable.subscribe(new Observer<Story>() {

    @Override
    public void onCompleted() {}

    @Override
    public void onNext(Story story) {
        Log.i(TAG, story.getTitle());
    }

    //...
});

Note that this code is very similar to the previous for-each snippet. In both snippets, we are consuming a data stream with a well-defined termination point. When we loop through a Collection using the for-each syntax, the loop terminates when iterator.hasNext() returns false. Similarly, in the preceding code, the Observer knows that there are no more elements left in the asynchronous data stream when onCompleted() is called.

The main difference between these two snippets is that when we loop over a Collection, we’re logging the Story titles synchronously and we when subscribe to the stringsObservable, we’re registering to log the Story titles asynchronously as they become available.

An Observer can also handle any exceptions that may occur while the Observable is emitting its data. Observers handle these errors in their onError() method.

To see why this is a useful feature of RxJava, imagine for a moment that the Story objects emitted by the Observable are objects that are converted from a JSON response to a HackerNews API call. If the HackerNews API returned malformed JSON, which in turn caused an exception in converting the JSON to Story model objects, the Observer would receive a call to onError(), with the exception that was thrown when the malformed JSON was being parsed.

At this point, there are two pieces of the aforementioned definition of RxJava that should be clearer. To see this, let’s take a second look at that definition:

RxJava is a library that allows us to represent any operation as an asynchronous data stream that can be created on any thread, declaratively composed, and consumed by multiple objects on any thread.

We have just seen that Observables are what allow us to represent any operation as an asynchronous data stream. Observables are similar to Iterables in that they both provide access to data streams with well-defined termination points. We also now know an important difference between Observables and Iterables: Observables expose asynchronous data streams while Iterables expose synchronous ones.

Observers are objects that can consume the asynchronous data emitted by an Observable. There can be multiple Observers that are registered to receive the data emitted by an Observable. Observers can handle any errors that might occur while the Observable is emitting its data and Observers know when there are no more items that will be emitted by an Observable.

There are still some things from the preceding definition of RxJava that are unclear. How exactly does RxJava allow us to represent any operation as an asynchronous data stream? In other words, how do Observables emit the items that make up their asynchronous data streams? Where do those items come from? These are questions that we will address in the next section.

Observable Creation and Subscribers

Observables emit asynchronous data streams. The way in which Observables emit their items again has some similarities to how Iterables expose their data streams. To see this, recall that Iterables and Iterators are both pieces of the Iterator pattern, a pattern whose main aim is well captured by the Gang of Four in  Design Patterns: Elements of Reusable Object-Oriented Software:

Provide a way to access the elements of an aggregate object without exposing its underlying representation.⁴

The Iterator pattern allows any object to provide access to its elements without exposing that object’s underlying representation. Similarly, Observables provide access to the elements of an asynchronous data stream in a way that completely hides and is largely independent of the process by which that data stream is created. This allows Observables to represent virtually any operation.

Here is an example that will make the Observable‘s flexibility more concrete. Observables are typically created by passing in a function object that fetches the items of an asynchronous data stream and notifies a Subscriber that those items have become available. A Subscriber is just an Observer that can, among other things, unsubscribe itself from the items emitted by an Observable.

Here is how you would create an Observable that emits some HackerNews Storys that have been fetched from the API:

Observable.create(new Observable.OnSubscribe<Story>() { //1
    @Override
    public void call(Subscriber<? super Story> subscriber) {
        if (!subscriber.isUnsubscribed()) { //2
            try {
                Story topStory = hackerNewsRestAdapter.getTopStory(); //3
                subscriber.onNext(topStory); //4
                Story newestStory = hackerNewsRestAdapter.getNewestStory();
                subscriber.onNext(newestStory);
                subscriber.onCompleted(); //5
            } catch (JsonParseException e) {
                subscriber.onError(e); //6
            }
        }
    }
});

Let’s run through what’s happening here step by step:

1. The name “OnSubscribe” provides us with a clue about when this code is typically executed: when an Observer is registered to receive the items emitted by this Observable through a call to Observable.subscribe().
2. We check to see if the Subscriber is unsubscribed before emitting any items. Remember: a Subscriber is just an Observer that can unsubscribe from the Observable that emits items.
3. We are actually fetching the HackerNews data with this method call. Notice that this is a synchronous method call. The thread will block until the Story has been returned.
4. Here we are notifying the Observer that has subscribed to the Observable that there is a new Story available. The Observer has been wrapped by the Subscriber passed into the call() method. The Subscriber wrapper, in this case, simply forwards its calls to the wrapped Observer.
5. When there are no more Storys left to emit in this Observable‘s stream, we notify the Observer with a call to onCompleted().
6. If there’s an error parsing the JSON response returned by the HackerNews API, we notify the Observer with a call to onError().

As you can see from the preceding snippet, Observables can be created from pretty much any operation. The flexibility with which Observables can be created is another way in which they are like Iterables. Any object can be made to implement the Iterable interface, thereby exposing a stream of synchronous data. Similarly, an Observable‘s data stream can be created out of the work done by any object, as long as that object is passed into the Observable.OnSubscribe that’s used to create an Observable.

At this point, astute readers might wonder whether Observables really do emit streams of asynchronous data. Thinking about the previous example, they might wonder to themselves, “If the call() method on the Observable.OnSubscribe function object is typically called when Observable.subscribe() is invoked and if that method invokes blocking synchronous methods on the hackerNewsRestAdapter, then wouldn’t calling Observable.subscribe() block the main thread until the Observable has finished emitting the Storys returned by the hackerNewsRestAdapter?”

This is a great question. Observable.subscribe() would actually block the main thread in this case. There is, however, another piece of RxJava that can prevent this from happening: a Scheduler. 

Schedulers

Schedulers determine the thread on which Observables emit their asynchronous data streams and the thread on which Observers consume those data streams. Applying the correct Scheduler to the Observable that is created in the preceding snippet will prevent the code that runs in the call() method of Observable.OnSubscribe from running on the main thread:

Observable.create(new Observable.OnSubscribe<Story>() {
    //...
}).subscribeOn(Schedulers.io());

As the name implies, Schedulers.io() returns a Scheduler that schedules the code that runs in the Observable.OnSubscribe object to be run on an I/O thread.

There is another method on Observable that takes a Scheduler: observeOn(). The Scheduler passed into this method will determine the thread on which the Observer consumes the data emitted by the Observable subscribeOn() actually returns an Observable, so you can chain observeOn() onto the Observable that is returned by the call to subscribeOn():

Observable.create(new Observable.OnSubscribe<Story>() {
    //...
})
.subscribeOn(Schedulers.io())
.observeOn(AndroidSchedulers.mainThread());

AndroidSchedulers.mainThread() does not actually belong to the RxJava library, but that is beside the point here.⁵ The main point is that by calling observeOn() with a specific Scheduler, you can modify the thread on which Observers consume the data emitted by the Observable.

The subscribeOn() and observeOn() methods are really instances of a more general way in which you can modify the stream emitted by an Observable: operators. We will talk about operators in the next section. For now, let’s return to the definition of RxJava with which we opened to briefly take stock of what we have just learned:

RxJava is a library that allows us to represent any operation as an asynchronous data stream that can be created on any thread, declaratively composed, and consumed by multiple objects on any thread.

What we have just covered in this section is how RxJava allows us to create and consume asynchronous data streams on any thread. The only piece of this definition that should be unclear at this point is the phrase “declaratively composed.” This phrase, as it turns out, is directly related to operators.

Operators

The Schedulers we discussed in the previous section were passed into both the Observable.subscribeOn() and Observable.observeOn() methods. Both of these methods are operators. Operators allow us to declaratively compose Observables. In order to get a better grip on operators, let’s briefly break down the phrase “declaratively compose.”

To compose an Observable is simply to “make” a complex Observable out of simpler ones. Observable composition with operators is very similar to the composition that occurs in function composition, the building of complex functions out of simpler ones. In function composition, complex functions are built by taking the output of one function and using it as the input of another function.

For example, consider the Math.ceil(int x) function. It simply returns the next integer closest to negative infinity that’s greater than or equal to x . For example, Math.ceil(1.2) returns 2.0. Now, suppose we had takeTwentyPercent(double x), a function that simply returned 20% of the value passed into it. If we wanted to write a function that calculated a generous tip, we could compose Math.ceil() and takeTwentyPercent() to define this function:

double calculateGenerousTip(double bill) {
    return takeTwentyPercent(Math.ceil(bill));
}

The complex function calculateGenerousTip() is composed from the result of passing the output of Math.ceil(bill) as the input of takeTwentyPercent().

Operators allow us to compose Observables in a way that is similar to the way in which calculateGenerousTip() is composed. An operator is applied to a “source” Observable and it returns a new Observable as a result of its application. For example, in the following snippet, the source Observable would be storiesObservable:

Observable<String> ioStoriesObservable = storiesObservable.
        .subscribeOn(Schedulers.io());

ioStoriesObservable, of course, is the Observable that’s returned as a result of applying the subcribeOn operator. After the operator is applied, the returned Observable is more complex: it behaves differently from the source Observable in that it emits its data on an I/O thread.

We can take the Observable returned by the subscribeOn operator and apply another operator to further compose the final Observable whose data we will subscribe to. This is what we did earlier when we chained two operator method calls together to ensure that the asynchronous stream of Story titles was emitted on a background thread and consumed on the main thread:

Observable<String> androidFriendlyStoriesObservable = storiesObservable
    .subscribeOn(Schedulers.io())
    .observeOn(AndroidSchedulers.mainThread());

Here we can see that the composition of the Observable is just like the composition of a function. calculateGenerousTip()  was composed by passing the output of Math.ceil() to the input of takeTwentyPercent(). Similarly, androidFriendlyStoriesObservable is composed by passing the output of applying the subcribeOn operator as the input for applying the observeOn operator. 

Note that the way in which operators allow us to compose Observables is declarative. When we use an operator, we simply specify what we want our composed Observable to do instead of providing an implementation of the behavior we want out of our composed Observable.  When we apply the observeOn and subscribeOn operators, for example, we are not forced to work with Threads, Executors, or Handlers. Instead, we can simply pass a Scheduler into these operators and this Scheduler is responsible for ensuring that our composed Observable behaves the way we want it to. In this way, RxJava allows us to avoid intricate and error-prone transformations of asynchronous data. 

Composing an “android friendly” Observable that emits its items on a background thread and delivers those items to Observers on the main thread is just the beginning of what you can accomplish with operators. Looking at how operators are used in the context of an example can be an effective way of learning how an operator works and how it can be useful in your projects. This is something we will do in detail in the next chapter.

For now, let’s simply introduce one additional operator and work it into our HackerNews stories example code.The map operator creates a new Observable that emits items that have been converted from items emitted by the source Observable. The map operator would allow us, for example, to turn an Observable that emits Storys into an Observable that emits the titles of those Storys. Here’s what that would look like:

Observable.create(new Observable.OnSubscribe<Story>() {
    //Emitting story objects...
})
.map(new Func1<Story, String>() {
    @Override
    public String call(Story story) {
        return story.getTitle();
    }
});

The map operator will return a new Observable<String> that emits the titles of the Story objects emitted by the Observable returned by Observable.create().

At this point, we know enough about RxJava to get a glimpse into how it allows us to handle asynchronous data neatly and declaratively. Because of the power of operators, we can start with an Observable that emits HackerNews Storys that are created and consumed on the UI thread, apply a series of operators, and wind up with an Observable that emits HackerNews Storys on an I/O thread but delivers the titles of those stories to Observers on the UI thread.

Here’s what that would look like:

Observable.create(new Observable.OnSubscribe<Story>() {
    //Emitting story objects...
})
.map(new Func1<Story, String>() { 
    @Override 
    public String call(Story story) {
        return story.getTitle();
    }
})
.subscribeOn(Schedulers.io())
.observeOn(AndroidSchedulers.mainThread());

Conclusion

At the beginning of this chapter, I gave a general definition of RxJava:

RxJava is a library that allows us to represent any operation as an asynchronous data stream that can be created on any thread, declaratively composed, and consumed by multiple objects on any thread.

At this point you should have a good grasp of this definition and you should be able to map pieces of the definition onto certain concepts/objects within the RxJava library. RxJava lets us represent any operation as an asynchronous data stream by allowing us to create Observables with an Observable.OnSubscribe function object that fetches data and notifies any registered Observers of new elements in a data stream, errors, or the completion of the data stream by calling onNext(), onError(), and onCompleted(), respectively. RxJava Schedulers allow us to change the threads on which the asynchronous data streams emitted by Observables are created and consumed. These Schedulers are applied to Observables through the use of operators, which allows us to declaratively compose complex Observables from simpler ones.