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ReactiveX
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Description

RxJava – Reactive Extensions for the JVM – a library for composing asynchronous and event-based programs using observable sequences for the Java VM.

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RxJava: Reactive Extensions for the JVM

codecov.io Maven Central

RxJava is a Java VM implementation of Reactive Extensions: a library for composing asynchronous and event-based programs by using observable sequences.

It extends the observer pattern to support sequences of data/events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.

Version 3.x (Javadoc)

  • single dependency: Reactive-Streams
  • Java 8+ (Android desugar friendly)
  • Java 8 lambda-friendly API
  • fixed API mistakes and many limits of RxJava 2
  • intended to be a replacement for RxJava 2 with relatively few binary incompatible changes
  • non-opinionated about the source of concurrency (threads, pools, event loops, fibers, actors, etc.)
  • async or synchronous execution
  • virtual time and schedulers for parameterized concurrency
  • test and diagnostic support via test schedulers, test consumers and plugin hooks

Learn more about RxJava in general on the Wiki Home.

:information_source: Please read the What's different in 3.0 for details on the changes and migration information when upgrading from 2.x.

Version 2.x

The 2.x version is in maintenance mode and will be supported only through bugfixes until February 28, 2021. No new features, behavior changes or documentation adjustments will be accepted or applied to 2.x.

Version 1.x

The 1.x version is end-of-life as of March 31, 2018. No further development, support, maintenance, PRs and updates will happen. The Javadoc of the very last version, 1.3.8, will remain accessible.

Getting started

Setting up the dependency

The first step is to include RxJava 3 into your project, for example, as a Gradle compile dependency:

implementation "io.reactivex.rxjava3:rxjava:3.x.y"

(Please replace

x
and
y
with the latest version numbers: Maven Central )

Hello World

The second is to write the Hello World program:

package rxjava.examples;

import io.reactivex.rxjava3.core.*;

public class HelloWorld { public static void main(String[] args) { Flowable.just("Hello world").subscribe(System.out::println); } }

Note that RxJava 3 components now live under

io.reactivex.rxjava3
and the base classes and interfaces live under
io.reactivex.rxjava3.core
.

Base classes

RxJava 3 features several base classes you can discover operators on:

Some terminology

Upstream, downstream

The dataflows in RxJava consist of a source, zero or more intermediate steps followed by a data consumer or combinator step (where the step is responsible to consume the dataflow by some means):

source.operator1().operator2().operator3().subscribe(consumer);

source.flatMap(value -> source.operator1().operator2().operator3());

Here, if we imagine ourselves on

operator2
, looking to the left towards the source is called the upstream. Looking to the right towards the subscriber/consumer is called the downstream. This is often more apparent when each element is written on a separate line:
source
  .operator1()
  .operator2()
  .operator3()
  .subscribe(consumer)

Objects in motion

In RxJava's documentation, emission, emits, item, event, signal, data and message are considered synonyms and represent the object traveling along the dataflow.

Backpressure

When the dataflow runs through asynchronous steps, each step may perform different things with different speed. To avoid overwhelming such steps, which usually would manifest itself as increased memory usage due to temporary buffering or the need for skipping/dropping data, so-called backpressure is applied, which is a form of flow control where the steps can express how many items are they ready to process. This allows constraining the memory usage of the dataflows in situations where there is generally no way for a step to know how many items the upstream will send to it.

In RxJava, the dedicated

Flowable
class is designated to support backpressure and
Observable
is dedicated to the non-backpressured operations (short sequences, GUI interactions, etc.). The other types,
Single
,
Maybe
and
Completable
don't support backpressure nor should they; there is always room to store one item temporarily.

Assembly time

The preparation of dataflows by applying various intermediate operators happens in the so-called assembly time:

Flowable flow = Flowable.range(1, 5)
.map(v -> v * v)
.filter(v -> v % 3 == 0)
;

At this point, the data is not flowing yet and no side-effects are happening.

Subscription time

This is a temporary state when

subscribe()
is called on a flow that establishes the chain of processing steps internally:
flow.subscribe(System.out::println)

This is when the subscription side-effects are triggered (see

doOnSubscribe
). Some sources block or start emitting items right away in this state.

Runtime

This is the state when the flows are actively emitting items, errors or completion signals:

Observable.create(emitter -> {
     while (!emitter.isDisposed()) {
         long time = System.currentTimeMillis();
         emitter.onNext(time);
         if (time % 2 != 0) {
             emitter.onError(new IllegalStateException("Odd millisecond!"));
             break;
         }
     }
})
.subscribe(System.out::println, Throwable::printStackTrace);

Practically, this is when the body of the given example above executes.

Simple background computation

One of the common use cases for RxJava is to run some computation, network request on a background thread and show the results (or error) on the UI thread:

import io.reactivex.rxjava3.schedulers.Schedulers;

Flowable.fromCallable(() -> { Thread.sleep(1000); // imitate expensive computation return "Done"; }) .subscribeOn(Schedulers.io()) .observeOn(Schedulers.single()) .subscribe(System.out::println, Throwable::printStackTrace);

Thread.sleep(2000); //

This style of chaining methods is called a fluent API which resembles the builder pattern. However, RxJava's reactive types are immutable; each of the method calls returns a new

Flowable
with added behavior. To illustrate, the example can be rewritten as follows:
Flowable source = Flowable.fromCallable(() -> {
    Thread.sleep(1000); //  imitate expensive computation
    return "Done";
});

Flowable runBackground = source.subscribeOn(Schedulers.io());

Flowable showForeground = runBackground.observeOn(Schedulers.single());

showForeground.subscribe(System.out::println, Throwable::printStackTrace);

Thread.sleep(2000);

Typically, you can move computations or blocking IO to some other thread via

subscribeOn
. Once the data is ready, you can make sure they get processed on the foreground or GUI thread via
observeOn
.

Schedulers

RxJava operators don't work with

Thread
s or
ExecutorService
s directly but with so-called
Scheduler
s that abstract away sources of concurrency behind a uniform API. RxJava 3 features several standard schedulers accessible via
Schedulers
utility class.

  • Schedulers.computation()
    : Run computation intensive work on a fixed number of dedicated threads in the background. Most asynchronous operators use this as their default
    Scheduler
    .
  • Schedulers.io()
    : Run I/O-like or blocking operations on a dynamically changing set of threads.
  • Schedulers.single()
    : Run work on a single thread in a sequential and FIFO manner.
  • Schedulers.trampoline()
    : Run work in a sequential and FIFO manner in one of the participating threads, usually for testing purposes.

These are available on all JVM platforms but some specific platforms, such as Android, have their own typical

Scheduler
s defined:
AndroidSchedulers.mainThread()
,
SwingScheduler.instance()
or
JavaFXSchedulers.gui()
.

In addition, there is an option to wrap an existing

Executor
(and its subtypes such as
ExecutorService
) into a
Scheduler
via
Schedulers.from(Executor)
. This can be used, for example, to have a larger but still fixed pool of threads (unlike
computation()
and
io()
respectively).

The

Thread.sleep(2000);
at the end is no accident. In RxJava the default
Scheduler
s run on daemon threads, which means once the Java main thread exits, they all get stopped and background computations may never happen. Sleeping for some time in this example situations lets you see the output of the flow on the console with time to spare.

Concurrency within a flow

Flows in RxJava are sequential in nature split into processing stages that may run concurrently with each other:

Flowable.range(1, 10)
  .observeOn(Schedulers.computation())
  .map(v -> v * v)
  .blockingSubscribe(System.out::println);

This example flow squares the numbers from 1 to 10 on the computation

Scheduler
and consumes the results on the "main" thread (more precisely, the caller thread of
blockingSubscribe
). However, the lambda
v -> v * v
doesn't run in parallel for this flow; it receives the values 1 to 10 on the same computation thread one after the other.

Parallel processing

Processing the numbers 1 to 10 in parallel is a bit more involved:

Flowable.range(1, 10)
  .flatMap(v ->
      Flowable.just(v)
        .subscribeOn(Schedulers.computation())
        .map(w -> w * w)
  )
  .blockingSubscribe(System.out::println);

Practically, parallelism in RxJava means running independent flows and merging their results back into a single flow. The operator

flatMap
does this by first mapping each number from 1 to 10 into its own individual
Flowable
, runs them and merges the computed squares.

Note, however, that

flatMap
doesn't guarantee any order and the items from the inner flows may end up interleaved. There are alternative operators:
  • concatMap
    that maps and runs one inner flow at a time and
  • concatMapEager
    which runs all inner flows "at once" but the output flow will be in the order those inner flows were created.

Alternatively, the

Flowable.parallel()
operator and the
ParallelFlowable
type help achieve the same parallel processing pattern:
Flowable.range(1, 10)
  .parallel()
  .runOn(Schedulers.computation())
  .map(v -> v * v)
  .sequential()
  .blockingSubscribe(System.out::println);

Dependent sub-flows

flatMap
is a powerful operator and helps in a lot of situations. For example, given a service that returns a
Flowable
, we'd like to call another service with values emitted by the first service:
Flowable inventorySource = warehouse.getInventoryAsync();

inventorySource .flatMap(inventoryItem -> erp.getDemandAsync(inventoryItem.getId()) .map(demand -> "Item " + inventoryItem.getName() + " has demand " + demand)) .subscribe(System.out::println);

Continuations

Sometimes, when an item has become available, one would like to perform some dependent computations on it. This is sometimes called continuations and, depending on what should happen and what types are involved, may involve various operators to accomplish.

Dependent

The most typical scenario is to given a value, invoke another service, await and continue with its result:

service.apiCall()
.flatMap(value -> service.anotherApiCall(value))
.flatMap(next -> service.finalCall(next))

It is often the case also that later sequences would require values from earlier mappings. This can be achieved by moving the outer

flatMap
into the inner parts of the previous
flatMap
for example:
service.apiCall()
.flatMap(value ->
    service.anotherApiCall(value)
    .flatMap(next -> service.finalCallBoth(value, next))
)

Here, the original

value
will be available inside the inner
flatMap
, courtesy of lambda variable capture.

Non-dependent

In other scenarios, the result(s) of the first source/dataflow is irrelevant and one would like to continue with a quasi independent another source. Here,

flatMap
works as well:
Observable continued = sourceObservable.flatMapSingle(ignored -> someSingleSource)
continued.map(v -> v.toString())
  .subscribe(System.out::println, Throwable::printStackTrace);

however, the continuation in this case stays

Observable
instead of the likely more appropriate
Single
. (This is understandable because from the perspective of
flatMapSingle
,
sourceObservable
is a multi-valued source and thus the mapping may result in multiple values as well).

Often though there is a way that is somewhat more expressive (and also lower overhead) by using

Completable
as the mediator and its operator
andThen
to resume with something else:
sourceObservable
  .ignoreElements()           // returns Completable
  .andThen(someSingleSource)
  .map(v -> v.toString())

The only dependency between the

sourceObservable
and the
someSingleSource
is that the former should complete normally in order for the latter to be consumed.

Deferred-dependent

Sometimes, there is an implicit data dependency between the previous sequence and the new sequence that, for some reason, was not flowing through the "regular channels". One would be inclined to write such continuations as follows:

AtomicInteger count = new AtomicInteger();

Observable.range(1, 10) .doOnNext(ignored -> count.incrementAndGet()) .ignoreElements() .andThen(Single.just(count.get())) .subscribe(System.out::println);

Unfortunately, this prints

0
because
Single.just(count.get())
is evaluated at assembly time when the dataflow hasn't even run yet. We need something that defers the evaluation of this
Single
source until runtime when the main source completes:
AtomicInteger count = new AtomicInteger();

Observable.range(1, 10) .doOnNext(ignored -> count.incrementAndGet()) .ignoreElements() .andThen(Single.defer(() -> Single.just(count.get()))) .subscribe(System.out::println);

or

AtomicInteger count = new AtomicInteger();

Observable.range(1, 10) .doOnNext(ignored -> count.incrementAndGet()) .ignoreElements() .andThen(Single.fromCallable(() -> count.get())) .subscribe(System.out::println);

Type conversions

Sometimes, a source or service returns a different type than the flow that is supposed to work with it. For example, in the inventory example above,

getDemandAsync
could return a
Single
. If the code example is left unchanged, this will result in a compile-time error (however, often with a misleading error message about lack of overload).

In such situations, there are usually two options to fix the transformation: 1) convert to the desired type or 2) find and use an overload of the specific operator supporting the different type.

Converting to the desired type

Each reactive base class features operators that can perform such conversions, including the protocol conversions, to match some other type. The following matrix shows the available conversion options:

| | Flowable | Observable | Single | Maybe | Completable | |----------|----------|------------|--------|-------|-------------| |Flowable | |

toObservable
|
first
,
firstOrError
,
single
,
singleOrError
,
last
,
lastOrError
1 |
firstElement
,
singleElement
,
lastElement
|
ignoreElements
| |Observable|
toFlowable
2 | |
first
,
firstOrError
,
single
,
singleOrError
,
last
,
lastOrError
1 |
firstElement
,
singleElement
,
lastElement
|
ignoreElements
| |Single |
toFlowable
3 |
toObservable
| |
toMaybe
|
ignoreElement
| |Maybe |
toFlowable
3 |
toObservable
|
toSingle
| |
ignoreElement
| |Completable |
toFlowable
|
toObservable
|
toSingle
|
toMaybe
| |

1: When turning a multi-valued source into a single-valued source, one should decide which of the many source values should be considered as the result.

2: Turning an

Observable
into
Flowable
requires an additional decision: what to do with the potential unconstrained flow of the source
Observable
? There are several strategies available (such as buffering, dropping, keeping the latest) via the
BackpressureStrategy
parameter or via standard
Flowable
operators such as
onBackpressureBuffer
,
onBackpressureDrop
,
onBackpressureLatest
which also allow further customization of the backpressure behavior.

3: When there is only (at most) one source item, there is no problem with backpressure as it can be always stored until the downstream is ready to consume.

Using an overload with the desired type

Many frequently used operator has overloads that can deal with the other types. These are usually named with the suffix of the target type:

| Operator | Overloads | |----------|-----------| |

flatMap
|
flatMapSingle
,
flatMapMaybe
,
flatMapCompletable
,
flatMapIterable
| |
concatMap
|
concatMapSingle
,
concatMapMaybe
,
concatMapCompletable
,
concatMapIterable
| |
switchMap
|
switchMapSingle
,
switchMapMaybe
,
switchMapCompletable
|

The reason these operators have a suffix instead of simply having the same name with different signature is type erasure. Java doesn't consider signatures such as

operator(Function>)
and
operator(Function>)
different (unlike C#) and due to erasure, the two
operator
s would end up as duplicate methods with the same signature.

Operator naming conventions

Naming in programming is one of the hardest things as names are expected to be not long, expressive, capturing and easily memorable. Unfortunately, the target language (and pre-existing conventions) may not give too much help in this regard (unusable keywords, type erasure, type ambiguities, etc.).

Unusable keywords

In the original Rx.NET, the operator that emits a single item and then completes is called

Return(T)
. Since the Java convention is to have a lowercase letter start a method name, this would have been
return(T)
which is a keyword in Java and thus not available. Therefore, RxJava chose to name this operator
just(T)
. The same limitation exists for the operator
Switch
, which had to be named
switchOnNext
. Yet another example is
Catch
which was named
onErrorResumeNext
.

Type erasure

Many operators that expect the user to provide some function returning a reactive type can't be overloaded because the type erasure around a

Function
turns such method signatures into duplicates. RxJava chose to name such operators by appending the type as suffix as well:
Flowable flatMap(Function super T, ? extends Publisher extends R>> mapper)

Flowable flatMapMaybe(Function super T, ? extends MaybeSource extends R>> mapper)

Type ambiguities

Even though certain operators have no problems from type erasure, their signature may turn up being ambiguous, especially if one uses Java 8 and lambdas. For example, there are several overloads of

concatWith
taking the various other reactive base types as arguments (for providing convenience and performance benefits in the underlying implementation):
Flowable concatWith(Publisher extends T> other);

Flowable concatWith(SingleSource extends T> other);

Both

Publisher
and
SingleSource
appear as functional interfaces (types with one abstract method) and may encourage users to try to provide a lambda expression:
someSource.concatWith(s -> Single.just(2))
.subscribe(System.out::println, Throwable::printStackTrace);

Unfortunately, this approach doesn't work and the example does not print

2
at all. In fact, since version 2.1.10, it doesn't even compile because at least 4
concatWith
overloads exist and the compiler finds the code above ambiguous.

The user in such situations probably wanted to defer some computation until the

someSource
has completed, thus the correct unambiguous operator should have been
defer
:
someSource.concatWith(Single.defer(() -> Single.just(2)))
.subscribe(System.out::println, Throwable::printStackTrace);

Sometimes, a suffix is added to avoid logical ambiguities that may compile but produce the wrong type in a flow:

Flowable merge(Publisher extends Publisher extends T>> sources);

Flowable mergeArray(Publisher extends T>... sources);

This can get also ambiguous when functional interface types get involved as the type argument

T
.

Error handling

Dataflows can fail, at which point the error is emitted to the consumer(s). Sometimes though, multiple sources may fail at which point there is a choice whether or not wait for all of them to complete or fail. To indicate this opportunity, many operator names are suffixed with the

DelayError
words (while others feature a
delayError
or
delayErrors
boolean flag in one of their overloads):
Flowable concat(Publisher extends Publisher extends T>> sources);

Flowable concatDelayError(Publisher extends Publisher extends T>> sources);

Of course, suffixes of various kinds may appear together:

Flowable concatArrayEagerDelayError(Publisher extends T>... sources);

Base class vs base type

The base classes can be considered heavy due to the sheer number of static and instance methods on them. RxJava 3's design was heavily influenced by the Reactive Streams specification, therefore, the library features a class and an interface per each reactive type:

| Type | Class | Interface | Consumer | |------|-------|-----------|----------| | 0..N backpressured |

Flowable
|
Publisher
1 |
Subscriber
| | 0..N unbounded |
Observable
|
ObservableSource
2 |
Observer
| | 1 element or error |
Single
|
SingleSource
|
SingleObserver
| | 0..1 element or error |
Maybe
|
MaybeSource
|
MaybeObserver
| | 0 element or error |
Completable
|
CompletableSource
|
CompletableObserver
|

1The

org.reactivestreams.Publisher
is part of the external Reactive Streams library. It is the main type to interact with other reactive libraries through a standardized mechanism governed by the Reactive Streams specification.

2The naming convention of the interface was to append

Source
to the semi-traditional class name. There is no
FlowableSource
since
Publisher
is provided by the Reactive Streams library (and subtyping it wouldn't have helped with interoperation either). These interfaces are, however, not standard in the sense of the Reactive Streams specification and are currently RxJava specific only.

R8 and ProGuard settings

By default, RxJava itself doesn't require any ProGuard/R8 settings and should work without problems. Unfortunately, the Reactive Streams dependency since version 1.0.3 has embedded Java 9 class files in its JAR that can cause warnings with the plain ProGuard:

Warning: org.reactivestreams.FlowAdapters$FlowPublisherFromReactive: can't find superclass or interface java.util.concurrent.Flow$Publisher
Warning: org.reactivestreams.FlowAdapters$FlowToReactiveProcessor: can't find superclass or interface java.util.concurrent.Flow$Processor
Warning: org.reactivestreams.FlowAdapters$FlowToReactiveSubscriber: can't find superclass or interface java.util.concurrent.Flow$Subscriber
Warning: org.reactivestreams.FlowAdapters$FlowToReactiveSubscription: can't find superclass or interface java.util.concurrent.Flow$Subscription
Warning: org.reactivestreams.FlowAdapters: can't find referenced class java.util.concurrent.Flow$Publisher

It is recommended one sets up the following

-dontwarn
entry in the application's
proguard-ruleset
file:
-dontwarn java.util.concurrent.Flow*

For R8, the RxJava jar includes the

META-INF/proguard/rxjava3.pro
with the same no-warning clause and should apply automatically.

Further reading

For further details, consult the wiki.

Communication

Versioning

Version 3.x is in development. Bugfixes will be applied to both 2.x and 3.x branches, but new features will only be added to 3.x.

Minor 3.x increments (such as 3.1, 3.2, etc) will occur when non-trivial new functionality is added or significant enhancements or bug fixes occur that may have behavioral changes that may affect some edge cases (such as dependence on behavior resulting from a bug). An example of an enhancement that would classify as this is adding reactive pull backpressure support to an operator that previously did not support it. This should be backwards compatible but does behave differently.

Patch 3.x.y increments (such as 3.0.0 -> 3.0.1, 3.3.1 -> 3.3.2, etc) will occur for bug fixes and trivial functionality (like adding a method overload). New functionality marked with an

@Beta
or
@Experimental
annotation can also be added in the patch releases to allow rapid exploration and iteration of unstable new functionality.

@Beta

APIs marked with the

@Beta
annotation at the class or method level are subject to change. They can be modified in any way, or even removed, at any time. If your code is a library itself (i.e. it is used on the CLASSPATH of users outside your control), you should not use beta APIs, unless you repackage them (e.g. using ProGuard, shading, etc).

@Experimental

APIs marked with the

@Experimental
annotation at the class or method level will almost certainly change. They can be modified in any way, or even removed, at any time. You should not use or rely on them in any production code. They are purely to allow broad testing and feedback.

@Deprecated

APIs marked with the

@Deprecated
annotation at the class or method level will remain supported until the next major release but it is recommended to stop using them.

io.reactivex.rxjava3.internal.*

All code inside the

io.reactivex.rxjava3.internal.*
packages are considered private API and should not be relied upon at all. It can change at any time.

Full Documentation

Binaries

Binaries and dependency information for Maven, Ivy, Gradle and others can be found at http://search.maven.org.

Example for Gradle:

implementation 'io.reactivex.rxjava3:rxjava:x.y.z'

and for Maven:

    io.reactivex.rxjava3
    rxjava
    x.y.z

and for Ivy:


Snapshots

Snapshots are available via https://oss.jfrog.org/libs-snapshot/io/reactivex/rxjava3/rxjava/

repositories {
    maven { url 'https://oss.jfrog.org/libs-snapshot' }
}

dependencies { compile 'io.reactivex.rxjava3:rxjava:3.0.0-SNAPSHOT' }

JavaDoc snapshots are available at http://reactivex.io/RxJava/3.x/javadoc/snapshot

Build

To build:

$ git clone [email protected]:ReactiveX/RxJava.git
$ cd RxJava/
$ ./gradlew build

Further details on building can be found on the Getting Started page of the wiki.

Bugs and Feedback

For bugs, questions and discussions please use the Github Issues.

LICENSE

Copyright (c) 2016-present, RxJava Contributors.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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