The Invasion of Small Cubes

The Invasion of Small Cubes

Notes About Reactive Programming With RxJava

Central to RxJava is the Observable type that represents a stream of data or events.

The entire point of RxJava being reactive, is to support push. To support push Observable and Observer connect via subscription.

interface Observable<T> {
  Subscription subscribe(Observer s);

Once subscription is made, streams of data are handled using the following interface

interface Observer<T> {
  void onNext(T t);
  void onError(Throwable t);
  void onCompleted();

onError and onCompleted are terminal events and only one of them can happen (if the stream is not infinite).

An Observable, by default, is synchronous. Although it's bad to use an Observable with synchronous blocking I/O. It's generally not always wrong to use a synchronous Observable. For example, if we retrieve data from a cache, it doesn't make sense to add complexity to the code with asynchronous behavior because a cache has a lookup time of micro/nano seconds.

The actual criteria to decide about how we should implement our Observable, is whether the event production is blocking or not blocking.

That being said, most Observable functions pipeline are synchronous (map, filter, ...) because we want to produce the event stream but then we want to run computation on them. This will guarantee us efficiency and will avoid nondeterministic behavior due to scheduling, context switching and so on.

Parallelism is simultaneous execution of tasks, tipically on different CPUs or machines. Concurrency, on the other hand, is the composition or interleaving of multiple tasks.

The contract of Observable is that events (onNext(), onCompleted(),onError()) can never be emitted concurrently. Allowing concurrent Observable streams (with concurrent onNext()) would limit the types of events that can be processed and required thread-safe data structures. Is also slower to do generic fine-grained parallelism.

It is far more efficient to synchronously execute on a single thread and take advantage of the many memory and CPU optimizations for sequential computation. On a list is quite is to reason in this way but a stream does not know the work ahead of time, it just receives data via onNext() and therefore cannot automatically chunk the work.

The Observable type is lazy, meaning it does nothing until it is subscribed to. With lazyiness is possible to compose Observable together. Creating one does not actually cause any work to happen. The work will happen once subscribed. Being lazy also allows that a particular instance can be invoked more than once.

An Observable is the dual of an Iterable. It push instead of pull. Besides that, the same programming model can be applied by both:

getDataFromMemory(). // return Stream<String>
// OR
getDataAsynch(). // Observable<String>
.map(s -> s + "_transformed")

rx.Observable<T> is the abstraction is going to be used all the time. An Observable can produce an arbitrary number of events. It can produce:

  • Values of type T, as declared by Observable
  • Completion events
  • Error event

An instance of Observable does not emit any events until someone is actually interested in receiving them (this are called cold Observable).

    (Tweet tweet) -> { System.out.println(tweet); },
    (Throwable t) -> { t.printStackTrace(); }

It's guaranteed that no other Tweet will be emitted after the exception.

By default Observable runs on current thread if not otherwise specified:

Observable<Integer> ints = Observable.create(
        subscriber -> {
ints.subscribe( i -> log("Element"+ i) );

This will print out:

main: CREATE
main: Element5
main: Element6
main: Element7

We can implement others Observable constructors as create:

static <T> Observable<T> just(T t) {
    return Observable.create(
            s -> {

static <T> Observable<T> never() {
    return Observable.create(
            s -> {}

static <T> Observable<T> empty() {
    return Observable.create(
            s -> {

static Observable<Integer> range(Integer from, Integer to) {
    return Observable.create(
            s -> {
                IntStream.range(from, to).forEach( i -> s.onNext(i));

Every time we call subscribe(), the subscrition handler inside create is invoked. If you want to avoid call create every time, you can use cache() that will give events already computed. Of course if stream is infinite, using cache() will kill a kitten.

To produce infinite streams it's not really convenient calling the computation inside the client thread, but rather start a new thread that will handle the computation. For how long should we keep computing stuff?

It is advised to use the method isUnsubscribed() to check if we have subscribers listening:

static Observable<BigInteger> naturalNumbers() {
    return Observable.create(
            s -> {
                Runnable r = () -> {
                    BigInteger i = BigInteger.ZERO;
                    while(!s.isUnsubscribed()) {
                        i = i.add(BigInteger.ONE);
                Thread thread = new Thread(r);
Subscription subscribe = naturalNumbers()
          e -> System.out.println(e.getMessage())); // not triggered

// do something else [...]


Between the check of isUnsubscribed and the actual computation we could have also seconds to wait because the computation could be really big.

Passing thread::interrupt to the subscription allows you to quit as soon as possible without wasting time. Be Aware that this will not trigger the onError. Subscription will end gracefully.

You cannot call the onNext from multiple threads. Don't do it, it violates Rx principles.

The class Observable has two useful methods: timer and interval.

The first one is like the method sleep, the latter instread emits at a fixed rate some element.

A cold Observable is lazy and doesn't emits until someone subscribes. This implies that every subscriber has its own copy of the stream because for every subscription the method create as mentioned above. Generally speaking a cold Observable involves some side effect like connecting to a database.

Hot Observable are independent from consumers, they emits even if there is no subscriber. At a certain point, when a subscriber subscribes, it will receive events that are being currently being emitted. An example of hot Observable is for example mouse events.

An example of hot subscriber can be seen via the method publish from Observable. An example of what (to me seems) an almost cold subscriber can be seen via share().


private static void refCount(Observable<Status> observable) {
    Observable<Status> observable1 = observable.share(); // or observable.publish().refCount();

    Subscription subscribe1 = observable1.subscribe();

    Subscription subscribe2 = observable1.subscribe();




private static void publish(Observable<Status> observable) {
    ConnectableObservable<Status> publish = observable.publish();

    Subscription subscribe1 = publish.subscribe();

    Subscription subscribe2 = publish.subscribe();

    Subscription connect = publish.connect();



    System.out.println("Disposing connection");


System.out.println("with Share:");
System.out.println("with Publish:");

You get this output:

with Share:

with Publish:
Disposing connection

There are many operators.

One interesting thing is:

Observable.just(1,2,3).scan((total, chunk) -> total+chunk).subscribe(out::println);
Observable.just(1,2,3).reduce((total, chunk) -> total+chunk).subscribe(out::println);

and the output is:


Keep in mind that in case of infinite stream scan will keep emitting whereas reduce will never emit. If we use distinct, this will cause problems because it caches every event generated.

You can do this: concat(fromCache, fromDb).first() and just call fromDb only when element is not present in cache!! (concat is lazy!)

Observable<String> fromCacheEmpty = Observable.empty();
Observable<String> fromCacheOneElem = Observable.just("1");
Observable<String> fromCacheNElem = Observable.just("1", "3");
Observable<String> fromDb = Observable.just("2");

System.out.println("EMPTY -> DB");
Observable.concat(fromCacheEmpty, fromDb).first().subscribe(out::println);
System.out.println("CACHE1 -> NODB");
Observable.concat(fromCacheOneElem, fromDb).first().subscribe(out::println);
System.out.println("CACHE2 -> DB");
Observable.concat(fromCacheNElem, fromDb).first().subscribe(out::println);

with output:


Read on the chapter about concat, merge and switchOnNext starting on page 97. It's so cool.

Want to do event sourcing with RxJava? Done!

.subscribe( uuid -> uuid.subscribe(this::updateProjection));

You can use observeOn and subScribeOn to move on different thread the computation and the subscription of your flow of data. subscribeOn allows you to tell on which thread you want to push events down to your stream. A common use case is to process the stream on the the background but emit it on another thread (common example is to do heavy computation on the backend and observe on the UI thread). In this case you can use the observeOn method.

You can also achieve parallelism with this feature but keep in mind that you need to take care of how much parallelism your system can handle. flatMap method has an overloaded method that allows you to control parallelism. You have many Schedulers from which you can get threads. Remember that io() will spawn new threads if existing ones are being used whereas computation() has CPU-bound threads.

To recap:

  • Observable without subscribeOn will work like a single threaded program with blocking calls
  • Observable with subscribeOn will start a thread on background where the work is done (inside the thread still sequential calls)
  • Observable using flatMap and inside it subscribeOn will start a new thread for each Observable

Another concept really important with reactive programming is backpressure. In every system based on message passing, the problem of the consumer not consuming fast enough can be present. If the consumer is able to give a feedback to the producer, the producer now can control how much it's producing (although if you have a hot producer, that could be not true).

Many operators have backpressure built-in so there is no need to worry about it. If you have to produce from scratch a producer, you can use SyncOnSubscribe.createStatless|createStateful (there is also the Async version). Don't use create from Observable because you won't have backpressure on it.

To know about testing, you can read about it here