Our previous talk "Intro to Reactive Programming" defined reactive programming and provided details around key initiatives such as Reactive Streams and ReactiveX. In this talk we'll focus on where we are today with building reactive web applications. We'll take a look at the choice of runtimes, how Reactive Streams may be applied to network I/O, and what the programming model may look like. While this is a forward looking talk, we'll spend plenty of time demoing code built with with back-pressure ready libraries available today.

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WASHINGTON, DC
Unless otherwise indicated, these slides are © 2013-2014 Pivotal Software, Inc. and licensed under a Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Reactive Web Applications
Stephane Maldini
Rossen Stoyanchev

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About the Speakers
• Stephane Maldini
• Reactor Project Lead
• Reactive Streams Spec contributor
• Rossen Stoyanchev
• Spring Framework committer
• Spring MVC
• WebSocket messaging (STOMP, SockJS)
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Why are you here ?

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What is Reactive ?

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Why Non Blocking Matters ?

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Non Blocking = Faster ?

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Non Blocking = Internet Scale ?

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Reactive Web App Stack
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Servlet 3.0 Async Requests
• Async HTTP requests have been possible since Servlet 3.0
• Essentially: the ability to get off the Servlet container thread
• originated from Jetty continuations, CometD, etc.
• “Live” data in web apps
• e.g. HTTP streaming, long polling
• Reduce burden on server thread pool
• scatter-gather, external REST APIs, etc
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• A bit quirky, Servlet API wasn’t built for async
• Complex to grasp in its entirety + comes with some caveats
• e.g. lack of notification when client goes away
• Spring MVC provides full support for Servlet 3 async requests
• alleviates most concerns
• Effectively async can be introduced into existing applications
• … and ecosystem of libraries !
Servlet 3.0 Async Requests: the bottom line
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Servlet 3.1 Non-Blocking I/O
• Read and write without blocking
• Register ReadListener and WriteListener callbacks
• switches to non-blocking mode
• ServletInputStream and ServletOutputStream change behavior
• Must call isReady first before read() or write()
• if connection not ready (slow client), callback is scheduled
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Servlet 3.1 Non-Blocking: the bottom line
• Carefully designed + it does provide non-blocking capability
• However not easy to use correctly, a number of pitfalls
• Many “innocent” looking parts of Servlet API may lead to blocking
• e.g. request.getParameters()
• For a good overview and perspective see Greg Wilkins’ talk
• ”Into the Wild with Servlet 3.1 Async I/O”
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Reactive Streams Spec for the JVM
• Specification for non-blocking library interop
• governs exchange of data across async boundaries
• Collaborative effort
• Kaazing, Netflix, Pivotal, RedHat, Twiiter, Typesafe
• Momentum and growing ecosystem of libraries
• network, database, etc.
• On path to adoption in JDK 9 java.util.concurrent
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onSubscribe
onNext*
(onError|onComplete)?
Publisher
Subscriber
Subscription
Reactive Streams API
request(n)
cancel()

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Reactive Back-Pressure
• A key part of Reactive Streams is back-pressure
• It’s a type of flow control
• Subscribers make requests for additional data
• Publishers abide and produce what has been requested
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Reactive Streams for Network I/O
public interface ServerHttpRequest extends HttpRequest {
Publisher<byte[]> getBody();
}
public interface ServerHttpResponse extends HttpMessage {
Publisher<Void> writeWith(Publisher<byte[]> publisher);
}
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Instead of...
public interface ServerHttpRequest extends HttpRequest {
InputStream getBody();
}
public interface ServerHttpResponse extends HttpMessage {
OutputStream getBody();
}
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Reactive Streams over Servlet Runtime ?
• Servlet 3.1 provides the non-blocking capability
• Reactive Streams provides a non-blocking API with broad industry support
• A single Reactive Streams pipeline
• back-pressure through the layers (from DB to Web server)
• Jetty is actively investigating this very option
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Reactive Streams and Servlet 4.0 ???
• It is under discussion in the expert group
• It would be great if it did happen
• Potential obstacle is the Java EE 8 - JDK 8 association
• Hopefully not a show-stopper since Reactive Streams is here today
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• Netty was designed from the ground up to be non-blocking
• Easy to translate to Reactive Streams semantics
• Two ongoing experiments
• RxNetty
• Reactor Net
Reactive Streams Over Netty
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Reactive Streams Network I/O
Akka HTTP, Vert.x, Ratpack, Reactor Net, RxNetty*
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*(via RxJava-ReactiveStreams bridge)

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Netty, Servlet 3.1
Reactive Streams
Reactive
Web App Stack

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Web Framework
Netty, Servlet 3.1
Reactive Streams
Reactive
Web App Stack

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Web Framework
Application
Netty, Servlet 3.1
Reactive Streams
Reactive
Web App Stack

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Web Framework
Application
Netty, Servlet 3.1
Reactive Streams
Reactive
Streams
HTTP
Data
Broker
Reactive
Web App Stack

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?
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Web Framework
Application
Netty, Servlet 3.1
Reactive Streams
Reactive
Streams
HTTP
Data
Broker
?
API to compose
asynchronous
streams?

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Working with Streams
• Non-blocking services return Publisher<T> instead of <T>
• How do you attach further processing?
• Reactive Streams is a callback-based API
• becomes very nested quickly
• Need something more declarative
• it’s beyond the scope of Reactive Streams
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• Publisher represents a stream of data
• It’s natural to apply operations functional-style
• like the Java 8 Stream
• Need API for composing async logic
• rise above callbacks
Stream Operations
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Reactor Stream
• Project Reactor provides a Stream API
• Reactive Streams Publisher + composable operations
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Streams.just('a' 'b' 'c')
.take(2)
.map(Character::toUpperCase)
.consume(System.out::println);

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Reactive Extensions (Rx)
• Stream composition API based on Observer pattern
• originated at Microsoft, work by Erik Meijer
• Implemented for different languages -- RxJava, RxJS, Rx.NET, …
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Observable.just('a', 'b', 'c')
.take(2)
.map(Character::toUpperCase)
.subscribe(System.out::println);

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RxJava and Reactive Streams
• RxJava 1.x predates Reactive Streams and doesn’t implement it directly
• Very similar concepts, different names
• Observable-Observer vs Publisher-Subscriber
• RxJava supports “reactive pull” back-pressure
• RxJava 1.x - Reactive Streams bridge
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Demo:
HeadFirst
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RxNetty
• RxJava as the reactive bridge to Netty IO operations
• Immediate and embedded client/server for TCP and HTTP
• Decorating IO Read with rx.Observable and applying dynamic pull/push
• Decorating IO Write with rx.Subscriber and requesting more on flush
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RxNetty … what’s up
• RxNetty has introduced Backpressure support in 0.5
• Powered by the backpressure protocol from RxJava
• Overall the API has been heavily lifted too
https://github.com/ReactiveX/RxNetty/wiki/0.5.x-FAQs
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Reactor Net
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• Reactive Streams as the reactive bridge to Netty and ZeroMQ IO operations
• Immediate and embedded client/server for TCP, UDP and HTTP
• Decorating IO Read with Publisher and applying dynamic pull/push
• Decorating IO Write with Subscriber and requesting more on flush

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Member of the Reactor initiative
• Reactor is a maturing Reactive Streams suite of libraries
• As with a lot of the reactive libraries, observing a rapidly evolving set of API
• Precluded Reactive Streams specification work
• Now evolving and getting ready for Spring 5 Reactive story
projectreactor.io , github.com/reactor
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Demo:
Back-Pressure
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Async I/O with Reactive Streams
• Never block on Applications Queues :
Max(request) == Queue.capacity()
• Keep Reading : Any asynchronous handoff is non-blocking
• Make Writes Quicker : IO operation signals demand on availability
• Optimize hardware resources : Claim bounded memory and cpu
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Reactive Read
One read implementation : Publisher stop-read
• Consume NIO Buffers only when requested
• request size is != bytes read
• Application stopping reads propagates back-pressure
• server buffers fill
• client buffers fill
• eventually throttles client
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Reactive Write
One write implementation : Subscriber request after flush
• Request after flush indicates availability in write buffer
• Request size is != bytes written
• Write buffers filling naturally propagates back-pressure
• buffers fill
• no requests
• eventually throttles application
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Serialization Concerns
• Subscription.request(n)is not absolute ( != bytes number )
• Decoding operations might produce one to many objects
• must match subscriber capacity
• Encoding operations might consume one to many byte chunks
• must match buffering capacity
• Available serialization libraries not built for chunked streams
• Jackson, Kryo, Protobuf, etc.
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Demo:
Scatter-gather
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Spring Reactive
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Spring Reactive
• Experimental work for Spring Framework 5
• Non-blocking runtimes -- Netty, Jetty, Tomcat
• Reactive Streams over network I/O -- RxNetty, Reactor Net
• Spring web processing
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@RequestMapping("/capitalize")
@ResponseBody
public Publisher<Person> capitalize(@RequestBody Publisher<Person> persons) {
// …
}
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Reactive Streams Publisher

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@RequestMapping("/capitalize")
@ResponseBody
public Stream<Person> capitalize(@RequestBody Stream<Person> persons) {
return persons.map(person -> {
person.setName(person.getName().toUpperCase());
return person;
});
}
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Reactor Stream

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@RequestMapping("/capitalize")
@ResponseBody
public Observable<Person> capitalize(@RequestBody Observable<Person> persons) {
return persons.map(person -> {
person.setName(person.getName().toUpperCase());
return person;
});
}
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RxJava Observable

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ReactiveX On the Client Side
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RxJS
function() {
StreamService.connect("stream")
.flatMap(unpackCsv)
.subscribe(updateUI, logError, logComplete);
}
var unpackCsv = function (ev) {
return Rx.Observable.from(ev.data ? ev.data.split("n") : [])
.filter(discardEmpty)
.map(csvToJson)
}
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Demo:
RxJS and Ratpack
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