play framework: async i/o with java and scala

Asynchronous I/Owith Java and Scala

LinkedIn uses a service oriented architecture (SOA)

Hundreds of different types of services, thousands of instances in multiple data centers.

Internet Load Balancer

Profile frontend

Company frontend

Recruiter frontend

Profile backend

Search backend

Company backend

Recruiter backend

Ads backend

Data Store

Data Store

Data Store

Data Store

Services communicate with each other via remote calls

Profile frontend Profile backend/profile/123

HTTP request

Profile frontend Profile backend

JSON response

{ "id": 123, "first": "Yevgeniy", "last": "Brikman"}

Most people are used to synchronous I/O when making requests between servers

The most popular frameworks typically use one-thread-per-request and blocking I/O

executeMethod blocks the thread until the response comes back

void doGet(HttpServletRequest req, HttpServletResponse res) { // Apache HttpClient HttpClient client = new HttpClient(); GetMethod method = new GetMethod("www.example.com"); // executeMethod is a blocking, synchronous call int statusCode = client.executeMethod(method); System.out.println("Response " + statusCode);}

MyServlet.java

Evented servers have one thread/process per CPU core and use non-blocking I/O

http.request is a non-blocking call: the next line executes before the response comes back

MyNodeApp.js

var callback = function(data) { console.log("Response: " + data);};

var options = { hostname: 'www.google.com', path: '/upload'}; // Non-blocking HTTP callhttp.request(options, callback); console.log('This line may execute before the callback!');

Why threaded vs. evented matters for LinkedIn

void doGet(HttpServletRequest req, HttpServletResponse res) { // Call a number of backend services to get data Profile profile = profileSvc.getProfile(); Company company = companySvc.getCompany(); Skills skills = skillsSvc.getSkills();}

MyServlet.java

Our services spend most of their time waiting for data from other services and data stores

I/O is very expensivehttp://www.eecs.berkeley.edu/~rcs/research/interactive_latency.html

http://localhost:9000/proxy?url=http://www.example.com


In a threaded server, threads spend most of the time idle, waiting on I/O

Threading dilemma

1. Creating new threads on the fly is expensive: a. Use a thread pool

2. Too many threads in the thread pool: a. Memory overheadb. Context switching overhead

3. Too few threads in the thread pool: a. Run out of threads, latency goes upb. Sensitive to downstream latency!


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

Let's say latency goes up a little here


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

Causes threads to get backed up here


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

Latency goes up


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

Now threads get backed up here


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

And here


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

Here too


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

And there


Frontend Server

Frontend Server

Frontend Server

Backend Server

Backend Server

Backend Server

Backend Server

Backend Server

Data Store

Data Store

Data Store

Data Store

And... the site is down.

This is thread pool hell

Play is built on top of Netty, so it supports non-blocking I/O

NIO benefits

1. No sensitivity to downstream slowness

2. Easy to parallelize I/O

3. Supports many concurrent and long-running connections, enabling:a. WebSocketsb. Cometc. Server-Sent Events

This talk is a brief introduction to writing asynchronous code with the Play Framework.

For each section, I will include simplified examples: first in Java and then Scala.

The world’s largest professional network

at

We've been using Play in production for more than 6 months

A few apps built on Play

Channels (frontend)

Premium Subscriptions (frontend)

Polls (frontend + backend)

REST search (internal tool)

About me

Leading the Play project as part of LinkedIn's Service Infrastructure Team. Also: hackdays, engineering blog, incubator, open source.

Outline1. A quick intro to Play

2. Basic async code

3. map and flatMap

4. Parallel and sequential

5. Errors and timeouts

6. Coming soon

Outline1. A quick intro to Play2. Basic async code

3. map and flatMap



6. Coming soon

Download and install Play fromhttp://www.playframework.com

http://www.playframework.com

http://www.playframework.com

> play new my-app

> play idea> play eclipse

> play run

http://localhost:9000



Application layout

app → Application sources └ assets → Compiled asset sources └ controllers → Application controllers └ models → Application business layer └ views → Templatesconf → Configurations files └ application.conf → Main configuration file └ routes → Routes definitionpublic → Public assets └ stylesheets → CSS files └ javascripts → Javascript files └ images → Image filesproject → sbt configuration files └ Build.scala → Application build script └ plugins.sbt → sbt pluginslib → Unmanaged libraries dependencieslogs → Standard logs foldertarget → Generated stufftest → Unit or functional tests

Let's get a feel for Play by creating a Java Controller

public class HelloWorld extends Controller {

public static Result index() { return ok("Hello World"); } }

Controllers are Java classes with methods that return a Result, such as a 200 OK

app/controllers/HelloWorld.java

Don't worry about the use of static. Yes, Play supports IOC. Using static (and other shortcuts) lets me keep the examples simple.

GET /hello controllers.HelloWorld.index()

Expose the controller/action at a URL

conf/routes

http://localhost:9000/hello



Woohoo, hot reload!


public static Result index(String name) { return ok("Hello " + name); } }

Add a parameter


GET /hello controllers.HelloWorld.index( name)

Read the parameter from the query string

conf/routes

http://localhost:9000/hello?name=Jim



GET /hello/:name controllers.HelloWorld.index(name)

Read the parameter from the URL instead

conf/routes

http://localhost:9000/hello/Jim




public static Result index(String name, int age) { return ok("Hello " + name + " you are " + age + " years old"); } }

Add another parameter, this time an int


GET /hello/:name/:age controllers.HelloWorld.index(name: String, age: Int)

Add the parameter. Note the type checking!

conf/routes

http://localhost:9000/hello/Jim/28



@(name: String, age: Int)

<html> <head></head> <body> <img src="/assets/images/play-logo.png"/> Hello @name, you are @age years old </body></html>

Add a view

app/views/hello.scala.html


public static Result index(String name, int age) { return ok(views.html.hello.render(name, age)); } }

Render the view from the controller


http://localhost:9000/hello/Jim/28



Play also natively supports Scala

app/controllers/HelloWorldScala.scala

Just add a .scala file under /app and Play will compile it

object HelloWorldScala extends Controller {

def index = Action { Ok("Hello World Scala") }}

GET /scala controllers.HelloWorldScala.index()

Add it to the routes file as usual

conf/routes

http://localhost:9000/scala




2. Basic async code3. map and flatMap



6. Coming soon

Let's use Play's Web Services library (WS) to make some non-blocking HTTP calls

public class Proxy extends Controller {

public static Result index(String url) { // Non blocking HTTP call Promise<Response> responsePromise = WS.url(url).get(); // How do we turn a Promise into a Play Result? }}

app/controllers/Proxy.java

Create a new controller and use WS to make an HTTP GET

A Promise<T> will eventually contain the value T (or an error)

(Play Framework source code)

Play has a built-in subclass of Result called AsyncResult that takes a Promise<Result>

public static class AsyncResult implements Result { private final Promise<Result> promise; public AsyncResult(Promise<Result> promise) { this.promise = promise; }}

public class Proxy extends Controller { public static Result index(String url) { Promise<Response> response = WS.url(url).get();

// Transform asynchronously into a Play Result Promise<Result> result = response.map(toResult);

return async(result); }

// A function that can transform a Response into a Result private static Function<Response, Result> toResult = new Function<Response, Result>() { public Result apply(Response response) { return ok(response.getBody()).as(("text/html"); } };}


We can use the map method to turn a Promise<Response> into a Promise<Result>

GET /proxy controllers.Proxy.index(url)

Add this endpoint to the routes file

conf/routes

http://localhost:9000/proxy?url=http://example.com



We just built a completely non-blocking proxy!

public class Proxy extends Controller { public static Result index(String url) { Logger.info("Before the HTTP call"); Promise<Response> response = WS.url(url).get(); Promise<Result> result = response.map(toResult); Logger.info("After the HTTP call");

return async(result); }

private static Function<Response, Result> toResult = new Function<Response, Result>() { public Result apply(Response response) { Logger.info("Inside the toResult function"); return ok(response.getBody()).as("text/html"); } };}


To see that it's non-blocking, add logging

Refresh the page and the logs show the HTTP call really is non-blocking

Let's create the same Proxy in Scala

object ProxyScala extends Controller {

def index(url: String) = Action {

val future: Future[Response] = WS.url(url).get()

// How do we turn a Future into a Play Result?

}

}

app/controllers/ProxyScala.scala

Create a new controller and use WS to make an HTTP GET

A Future[T] will eventually contain the value T (or an error)

(Play Framework source code)

Play has a built-in subclass of Result called AsyncResult that takes a Future<Result>

case class AsyncResult(result: Future[Result]) extends

Result

// Convenience function to create an AsyncResult

def Async(promise: Promise[Result]) = AsyncResult(promise)

object ProxyScala extends Controller {



Async {

future.map { response =>

Ok(response.body).as("text/html")

}

}

}

}

app/controllers/ProxyScala.scala

We can use the map method to turn a Future[Response] into a Future[Result]

GET /scala/proxy controllers.ProxyScala.index(url)

Add this endpoint to the routes file

conf/routes

http://localhost:9000/scala/proxy?url=http://example.com




2. Basic async code

3. map and flatMap4. Parallel and sequential


6. Coming soon

What is this "map" thing all about?

It's easiest to think about map with Lists.

val names = List("Jim", "Dean", "Kunal")

Let's start with a List of Strings


def lower(str: String): String = str.toLowerCase

And a simple function that converts a String to lowercase

List.map(f) will return a new List where each element in the new List is the result of

calling f on each element of the original List


def lower(str: String): String = str.toLowerCase

names.map(lower)

// Output: List("jim", "dean", "kunal")

Mapping the lower function over names gives us a new List where each name is lowercase

We saw map transform a List[String] into a new List[String]. Can we transform a List[X] into some other type List[Y]?


def strlen(str: String): Int = str.length

Start with the same List, but now a new function strlen that returns the length of a String


def strlen(str: String): Int = str.length

names.map(strlen)

// Output: List(3, 4, 5)

Mapping strlen over names returns a new List with the length of each String in names

Now we see that map can transform a List[String] into a new List[Int].

class List[A] {

def map[B](f: A => B): List[B]

}

More generally, this is map's signature

Let's look at one more example


def explode(str: String): List[Char] = str.toCharArray.

toList

Same List, but now a new function explode that returns a List of Characters in a String



toList

names.map(explode)

// Output:

// List(List(J, i, m), List(D, e, a, n), List(K, u, n, a,

l))

If we map explode over names, we get nested Lists. But what if we want just one, flat List?

class List[A] {

def flatMap[B](f: A => List[B]): List[B]

}

We can use flatMap , which will combine (flatten) any nested Lists



toList

names.flatMap(explode)

// Output: List(J, i, m, D, e, a, n, K, u, n, a, l)

Using flatMap gives us a single List with each individual character

map and flatMap are defined on any "collection" or "container": List, Set, Map, etc

val namesSet = Set("Jim", "Dean", "Kunal")


toList

namesSet.flatMap(explode)

// Output: Set(e, n, J, u, a, m, i, l, K, D)

Using flatMap on a Set

Futures and Promises are also "containers": they just happen to contain 1 item.


val future: Future[Result] = future.map { response =>

Ok(response.body).as("text/html")

}

This is why it makes sense to use map to turn a Future[Response] into a Future[Result]

The "container" class controls when the function passed to map or flatMap actually gets

applied!


2. Basic async code

3. map and flatMap

4. Parallel and sequential5. Errors and timeouts

6. Coming soon

Making I/O requests in parallel is essential for performance in a Service Oriented Architecture

With non-blocking I/O, parallel is the default

// These 3 HTTP requests will execute in parallel

Promise<Response> yahoo = WS.url("http://yahoo.com").get();

Promise<Response> google = WS.url("http://google.com").get();

Promise<Response> bing = WS.url("http://bing.com").get();

Let's fetch 3 websites in parallel and time them

First, define a function that makes makes an HTTP GET and returns a Promise with timing info

public Promise<Timing> timed(final String url) {

final long start = System.currentTimeMillis();

return WS.url(url).get().map(new Function<Response, Timing>() {

public Timing apply(Response response) throws Throwable {

return new Timing(url, System.currentTimeMillis() - start);

}

});

}

public class Timing {

public String url;

public long latency;

}

Next, make a controller that fires 3 requests in parallel using the timed function we just created

public class Parallel extends Controller {

public static Result index() {

final Promise<Timing> yahoo = timed("http://www.yahoo.com");

final Promise<Timing> google = timed("http://www.google.com");

final Promise<Timing> bing = timed("http://www.bing.com");

}

}

Compose the 3 Promises into a single Promise that will redeem when all 3 are done



final Promise<Long> yahoo = timed("http://www.yahoo.com");

final Promise<Long> google = timed("http://www.google.com");

final Promise<Long> bing = timed("http://www.bing.com");

Promise<List<Timing>> all = Promise.waitAll(yahoo, google, bing);

}

}

Render the results as JSON



final Promise<Timing> yahoo = timed("http://www.yahoo.com");

final Promise<Timing> google = timed("http://www.google.com");

final Promise<Timing> bing = timed("http://www.bing.com");

Promise<List<Timing>> all = Promise.waitAll(yahoo, google, bing);

return async(all.map(new Function<List<Timing>, Result>() {

public Result apply(List<Timing> timings) throws Throwable {

return ok(Json.toJson(timings));

}

}));

}

}

GET /parallel controllers.Parallel.index()

Add it to the routes file

conf/routes

http://localhost:9000/parallel



How about parallel requests in Scala?

Once again, define a function that makes an HTTP GET and returns a Future with timing info

def timed(url: String): Future[Timing] = {

val start = System.currentTimeMillis()

WS.url(url).get().map(_ =>

Timing(url, System.currentTimeMillis() - start)

)

}

case class Timing(url: String, latency: Long)

Next, make a controller that fires 3 requests in parallel using the timed function we just created

object ParallelScala extends Controller {

def index = Action {

val yahoo = timed("http://www.yahoo.com")

val google = timed("http://www.google.com")

val bing = timed("http://www.bing.com")

}

}

Compose the 3 Futures into a single Future that will redeem when all 3 are done






val all = Future.sequence(Seq(yahoo, google, bing))

}

}

Render the results as JSON






val all = Future.sequence(Seq(yahoo, google, bing))

Async {

all.map(timings => Ok(Json.toJson(timings)))

}

}

}

GET /scala/parallel controllers.ParallelScala.index()


conf/routes

http://localhost:9000/scala/parallel



If parallel is the default, how do you do sequential steps that depend on each other?

Example: make a request to duckduckgo's instant answer API (step 1) and proxy an image

from the response (step 2)

First, call make a non-blocking call to duckduckgo

public class LuckyImage extends Controller {

public static Result index(String query) {

Promise<Response> duck = WS.url("http://www.duckduckgo.com")

.setQueryParameter("q", query)

.setQueryParameter("format", "json")

.get();

}

}

As a first step, we'll just proxy the response

public class LuckyImage extends Controller {

public static Result index(String query) {

Promise<Response> duck = WS.url("http://www.duckduckgo.com")

.setQueryParameter("q", query)

.setQueryParameter("format", "json")

.get();

return async(duck.map(new Function<Response, Result>() {

public Result apply(Response response) throws Throwable {

return ok(response.getBodyAsStream())

.as(response.getHeader("Content-Type"));

}

}));

}

}

GET /lucky controllers.LuckyImage.index(url)


conf/routes

http://localhost:9000/lucky?query=linkedin



As the second step, get the image URL from the response and proxy just that.

Promise<Response> duck = // ... (same request as before)

return async(duck.flatMap(new Function<Response, Promise<Result>>() {

public Promise<Result> apply(Response response) {

String url = getImageUrlFromResponse(response);

return WS.url(url).get().map(new Function<Response, Result>() {

public Result apply(Response response) {

return ok(response.getBodyAsStream())

.as(response.getHeader("Content-Type"));

}

});

}

}));

http://localhost:9000/lucky?query=linkedin



Ok, let's try the same example in Scala

First, make the request

object LuckyImageScala extends Controller {

def index(query: String) = Action {

val duck = WS.url("http://www.duckduckgo.com")

.withQueryString("q" -> query, "format" -> "json").get()

}

}

Then extract the image URL and proxy it

object LuckyImageScala extends Controller {

def index(query: String) = Action {

val duck = WS.url("http://www.duckduckgo.com")

.withQueryString("q" -> query, "format" -> "json").get()

Async {

duck.flatMap { response =>

val url = getImageUrlFromResponse(response)

WS.url(url).get().map { r =>

Ok(r.getAHCResponse.getResponseBodyAsBytes)

.as(r.getAHCResponse.getHeader("Content-Type"))

}

}

}

}

}

GET /scala/lucky controllers.LuckyImageScala.index(url)


conf/routes

http://localhost:9000/lucky?query=play+framework



In both Java and Scala, you order async actions sequentially by nesting map and flatMap calls.

Many sequential steps will lead to lots of nesting.

step1.flatMap(new Function<Response, Promise<Result>>() {

public Promise<Result> apply(Response response1) {





// etc

}

}

}

});

}

});

This is "callback hell"

ParSeq

https://github.com/linkedin/parseq



ParSeq is a framework that makes it easier to write asynchronous code in Java

Wrap asynchronous work in ParSeq Task objects,which are similar to Promises and Futures

Task<Response> yahoo = Tasks.wrap(WS.url("http://www.yahoo.com"))

Task<Response> google = Tasks.wrap(WS.url("http://www.google.com"))

Task<Response> bing = Tasks.wrap(WS.url("http://www.bing.com"))

Use Tasks.par to compose tasks in parallel




// Create a new Task that will run all 3 tasks above at the same time

// and redeem when they are all done

Task<?> parallel = Tasks.par(yahoo, google, bing)

Use Tasks.seq to compose tasks sequentially

Task<Response> step1 = new Task() { ... }



// Create a new Task that will run the tasks above one at a time,

// in the order specified, and complete with the return value of the

// last one

Task<Response> sequential = Tasks.seq(task1, task2, task3)

ParSeq makes async code declarative and easier to reason about: you can read it top to bottom!

Task<Result> complex = Tasks.seq(

Tasks.par(profileTask, companyTask, skillsTask),

Tasks.par(recommendedJobsTask, wvmxTask),

Tasks.par(hydrateImagesTask, hydrateJobsTask, hydrateSkillsTask)

Tasks.seq(assemblePageTask, fireTrackingTask)

)

We'll soon be open sourcing a plugin soon to make it easy to use ParSeq in Play.

Sequence Comprehensions

Syntactic sugar built into Scala. Translates into map and flatMap calls without the nesting.

Instead of this...

aFuture.flatMap { a =>

bFuture.flatMap { b =>

cFuture.flatMap { c =>

dFuture.map { d =>

// Build a result using a, b, c, d

}

}

}

}

Use this. Note that this syntax works for any object with map and flatMap methods.

for {

a <- aFuture

b <- bFuture

c <- cFuture

d <- dFuture

} yield {


}

Sequential async I/O example. Note that each step can refer to previous ones.

for {

a <- WS.url(...).get()

b <- WS.url(a).get()

c <- WS.url(a + b).get()

d <- WS.url(a + b + c).get()

} yield {


}

Parallel async I/O example. Only difference is that the async calls are outside the for statement.

val futureA = WS.url(...)

val futureB = WS.url(...)

val futureC = WS.url(...)

val futureD = WS.url(...)

for {

a <- futureA

b <- futureB

c <- futureC

d <- futureD

} yield {


}

Sequence comprehensions provide a clean and consistent API for async code: you can read it

top to bottom!


2. Basic async code

3. map and flatMap


5. Errors and timeouts6. Coming soon

With a single server, the odds of hitting an error are relatively low

In a distributed system with thousands of servers, the odds that you hit an error are very high


Profile frontend

Company frontend

Recruiter frontend

Profile backend

Search backend

Company backend

Recruiter backend

Ads backend

Data Store

Data Store

Data Store

Data Store

Even if a single server is up 99.999% of the time, with 1000 servers, the odds that one is

down are 1 - 0.999991000 = ~1%

Here is how to make your async code more resilient to errors and slow performance

We can use the recover method on a Promise to specify how to handle errors

public class Errors extends Controller {

public static Result index(String url) {

F.Promise<WS.Response> promise = WS.url(url).get();

return async(promise.map(new F.Function<WS.Response, Result>() {

public Result apply(WS.Response response) throws Throwable {

return ok("Got a response!");

}

}).recover(new F.Function<Throwable, Result>() {

public Result apply(Throwable throwable) throws Throwable {

return internalServerError("Got an exception: " + throwable);

}

}));

}

}

There is an analogous recover method on Scala Futures as well

object ErrorsScala extends Controller {


Async {

WS.url(url).get().map { response =>

Ok("Got a response: " + response.status)

}.recover { case t: Throwable =>

InternalServerError("Got an exception: " + t)

}

}

}

}

GET /errors controllers.Errors.index(url)GET /scala/errors controllers.ErrorsScala.index(url)

Add to the routes file

conf/routes

http://localhost:9000/errors?url=http://www.example.com



http://localhost:9000/errors?url=http://www.not-a-real-url.com



If some of the data you fetch isn't required to render the page, you can use an Option

pattern

Create a helper method: on success, return Some<T>. On failure, log the error, return None.

public static <T> Promise<Option<T>> optional(Promise<T> promise) {

return promise.map(new Function<T, Option<T>>() {

public Option<T> apply(T value) throws Throwable {

if (value == null) {

return Option.None();

}

return Option.Some(value);

}

}).recover(new Function<Throwable, Option<T>>() {

public Option<T> apply(Throwable t) throws Throwable {

Logger.error("Hit an error", t);

return Option.None();

}

});

}

Wrap Promises with optional and inside of map, use isDefined to see if you have a value

public static Result index(String url) {

Promise<Option<Response>> promise = optional(WS.url(url).get());

return async(promise.map(new Function<Option<Response>, Result>() {

public Result apply(Option<Response> responseOpt){

if (responseOpt.isDefined()) {

Response response = responseOpt.get();

// Use the response to build the page

} else {

// Build the page without the response

}

}

}));

}

The same pattern is even prettier in Scala, as Option is a first-class citizen of the language

Reusable helper method to convert Future[T] to Future[Option[T]]

def optional[T](future: Future[T]): Future[Option[T]] = {

future.map(Option.apply).recover { case t: Throwable =>

Logger.error("Hit an error", t)

None

}

}

Wrap Futures with optional and use pattern matching, comprehensions, etc within map

object OptionExampleScala extends Controller {


Async {

optional(WS.url(url).get()).map {

case Some(response) => // Use the response to build the page

case _ => // Build the page without the response

}

}

}

}

Sometimes, waiting too long for data is worse than not showing that data at all

You can create a Promise that will be redeemed with someValue after the specified timeout

Promise.timeout(someValue, 500, TimeUnit.MILLISECONDS)

Compose two Promises using or: the result takes on the value of the first one to complete

Promise<Response> response = WS.url(url).get();

Promise<Either<Object, Response>> withTimeout =

Promise.timeout(null, timeout, TimeUnit.MILLISECONDS).or(response);

If right is defined, you got a value in time; otherwise, it must have timed out first.

Promise<Response> response = WS.url(url).get();

Promise<Either<Object, Response>> withTimeout =

Promise.timeout(null, timeout, TimeUnit.MILLISECONDS).or(response);

withTimeout.map(new Function<Either<Object, Response>, Result>() {

public Result apply(Either<Object, Response> either) {

if (either.right.isDefined()) {

Response response = either.right.get();

// Use the response to build the page

} else {

// Hit a timeout, build the page without the response

}

}

});

The Scala version

import play.api.libs.concurrent._

object TimeoutExampleScala extends Controller {

def index(url: String, timeout: Long) = Action {

val timeout = Promise.timeout(null, timeout, MILLISECONDS)

val future = timeout.either(WS.url(url).get())

future.map {

case Right(response) => // Use the response to build the page

case _ => // Hit a timeout, build the page without the response

}

}

}


2. Basic async code

3. map and flatMap



6. Coming soon

play-async-plugin

We'll be open sourcing this plugin soon

It is a collection of async utilities, including...

ParSeq integration for Play to make asynchronous Java code easier




// Create a new Task that will run the tasks above one at a time,

// in the order specified, and complete with the return value of the

// last one

Task<Response> sequential = Tasks.seq(yahoo, google, bing)

Config-driven SLAs (timeouts) for any async I/O

sla.plugin.slas = [

{

resources: ["/profile", "/companies"]

timeout: "2s"

},

{

resources: ["/pymk", "/skills/*"],

timeout: "500ms"

}

]

In-browser visualization of all async requests for a page, including timing, responses, and errors

We're just getting started with Play! We'll be sharing more as we go.

LinkedIn Engineering Bloghttp://engineering.linkedin.com



@LinkedInEng on Twitterhttps://twitter.com/LinkedInEng



Thank you!

play framework: async i/o with java and scala

Technology