There has been a significant shift in recent years towards server-side and network programming using event-driven asynchronous runtime environments and frameworks such as Node.js, Twisted, and Netty/NIO. Asynchronous code allows independent IO operations to run concurrently, resulting in efficient code. However, this improved efficiency comes at a cost — straightforward synchronous code may become a mess of nested callbacks.

Can we do better? Can we combine the simplicity of synchronous code with the efficiency of the asynchronous approach? It turns out we can. Futures are an abstraction that allow us to express the effect of latency in asynchronous computations, encapsulate event-handling code, and use higher-order functions such as map, reduce, and filter, to compose clean and readable asynchronous code.

We will explore this by looking at a web-scraping word count example. First, we’ll write simple synchronous code and consider how this code may look rewritten with a callback-based asynchronous framework such as Node.js or Netty. Then, we’ll use promises to turn callback-based building blocks into functions returning futures, allowing us to compose code using functional programming constructs.

While the examples below are in Scala, the abstractions and patterns described are applicable to most modern languages. Basic familiarity with functional patterns is assumed, but you can catch up by reading Mary’s awesome introduction to functional programming.

To callback hell and back

Consider the following example of single-threaded synchronous code: the countWordOccurrences function takes a List of URLs and a keyword, fetches the HTML behind each URL, parses the HTML into a DOM representation, and counts the number of times the keyword occurs within each page. It returns a List of pairs of URL and count:

def countWordOccurrences ( urls : List [ String ], keyword : String ) : List [( String , Int )] = { urls map { url => val html = fetchUrl ( url ) val dom = parseHtmlToDOM ( html ) val count = countWordOccurrencesInDOM ( dom , keyword ) ( url , count ) } }

This code is easy to reason about — operations are performed one after another, in the specified order. However, this is not very efficient—each fetchUrl call is independent from one another and thus easily parallelized, yet this code executes them serially. Furthermore, each fetchUrl operation involves network IO and will block the execution thread while waiting for IO to complete.

With a callback-based asynchronous approach, the code may look something like this:

def countWordOccurrencesAsync ( urls : List [ String ], keyword : String , successHandler : ( List [( String , Int )]) => Unit , errorHandler : ( Throwable ) => Unit ) : Unit = { // access to these shared variables would need // to be synchronized, adding even more complexity var resultsAccumulator = List [( String , Int )]() var isUrlCompleted = urls . foldLeft ( HashMap [ String , Boolean ]()) { ( acc , url ) => acc . updated ( url , false ) } urls map { url => fetchUrlAsync ( url , successHandler = { html => parseHtmlToDOMAsync ( html , successHandler = { dom => countWordOccurrencesInDOMAsync ( dom , keyword , successHandler = { count => // add this result to the accumulator resultsAccumulator = ( url , count ) :: resultsAccumulator // update state to denote that this url is completed isUrlCompleted = isUrlCompleted . updated ( url , true ) // check if all urls have been completed // if so, invoke the top success handler val allDone = isUrlCompleted . map { case ( key , value ) => value } . reduce { ( a , b ) => a && b } if ( allDone ) { successHandler ( resultsAccumulator ) } }, errorHandler ) }, errorHandler ) }, errorHandler ) } }

Here, each ...Async function returns immediately and execution continues while the network IO or other computation is underway. Callback functions are passed as arguments to handle cases when the operation is successful and when an error occurs. The callbacks are executed once the operation is completed.

This approach allows for more efficient use of system resources, but still has a number of drawbacks:

We need to have shared state variables ( resultsAccumulator and isUrlCompleted ) and synchronize access to them

Each asynchronous processing step requires a nested callback, quickly leading to “callback hell”

The code is much harder to read and understand

Futures and promises

What is particularly nice about the synchronous code in the first example is that a function like fetchUrl returns a String value that other functions in turn use for their computations. This leads to easily readable and composable code.

Futures allow for a similar pattern using asynchronous rather than synchronous code. A future is an object that expresses a result of asynchronous computation—a value that is not available yet but may be available in the future. This allows the asynchronous version of fetchUrl to return a value of type Future[String] which is then used in synchronous-looking code, without worrying about whether a String value is actually available.

Promise objects are utility objects that make it easier to construct futures, like so:

def fetchUrl ( url : String ) : Future [ String ] = { val p = Promise [ String ]() fetchUrlAsync ( url , successHandler = { html => p . success ( html ) }, errorHandler = { error => p . failure ( error ) }) p . future }

In this example, fetchUrl calls the callback-based counterpart fetchUrlAsync with a success handler that completes Promise p with success and a failure handler that completes p with failure. The function then extracts a Future out of Promise p and returns it to the caller. This “wrapping” of callback-based code using promise objects to return a future object is a very common pattern in reactive programming.

The parseHtmlToDOM and countWordOccurrencesInDOM functions would be similarly refactored to return Future[DOM] and Future[Int] respectively.

Map/filter/reduce hell

The Future trait defines standard higher-order functions such as map , flatMap , filter , fold , and reduce . These functions are not exactly the same as ones defined on List , but are similar. For example, flatMap defined on Future[T] applies a function that takes T as an argument and returns Future[S] and flattens the resulting Future[Future[S]] into a single Future[S] . This is analogous to how flatMap on List flattens a List of List into a single List .

The higher-order functions make it possible to compose functions returning futures using familiar functional programming patterns:

import scala.concurrent. { Future , Promise } import scala.concurrent.ExecutionContext.Implicits.global def countWordOccurrences ( urls : List [ String ], keyword : String ) : Future [ List [( String , Int )]] = { // partially apply countWordOccurrencesInDOM val countKeywordOccurrencesInDOM = countWordOccurrencesInDOM ( _: DOM , keyword ) // expression evaluates to List[Future[(String, Int)] val listOfFutures = urls . map { url => fetchUrl ( url ) . flatMap ( parseHtmlToDOM ) . flatMap ( countKeywordOccurrencesInDOM ) . map { count => ( url , count ) } } // transform List[Future[(String, Int)] to Future[List[(String, Int)]] Future . sequence ( listOfFutures ) }

The new version of countWordOccurrences simply performs flatMap on the result of fetchUrl over parseHtmlToDOM and countKeywordOccurrencesInDOM and maps the result to a pair of URL and count, resulting in Future[(String, Int)] .

The call to Future.sequence is necessary to transform the List of Future[(String, Int)] into a Future of List[(String, Int)] .

This code appears much cleaner than the callback example. However, it is important to note that each function in the above example is defined to take exactly output of the preceding function. We may not be so lucky with real-world APIs and we may need to process output before passing it to the next function. So, it is easy to imagine that real-world code could become much more complicated by additional processing steps. It may even feel like we are simply trading callback hell for map/filter/reduce hell.

Finally, reactive code composed with higher-order functions is often not purely functional—it typically involves side effects through network or file system IO.

So, is there an even better way to write asynchronous code?

Macros to the rescue: async/await

The Scala Async library provides async and await macros inspired by the similar constructs originally introduced by C#. The macros make it possible to write efficient asynchronous code in a direct style, very similar to how the synchronous code was written in the first example.

The basic approach is to wrap each block of asynchronous code within an async block and each computation resulting in a future within an await block. The computation within an await block will be “suspended” until the corresponding future is completed, but in a non-blocking fashion and without any performance penalties.

import scala.concurrent. { Future , Promise } import scala.async.Async. { async , await } import scala.concurrent.ExecutionContext.Implicits.global def countWordOccurrences ( urls : List [ String ], keyword : String ) : Future [ List [( String , Int )]] = { // listOfFutures evaluates to type List[Future[(String, Int)] val listOfFutures = urls . map { url => async { // html, dom and numberOfOccurrences values are of type String, // DOM, and Int respectively val html = await { fetchUrl ( url ) } val dom = await { parseHtmlToDOM ( html ) } val count = await { countWordOccurrencesInDOM ( dom , keyword ) } ( url , count ) } } // transform List[Future[(String, Int)] to Future[List[(String, Int)]] Future . sequence ( listOfFutures ) }

The code above looks nearly identical to the synchronous code from the first example, reflects our intent more directly, and feels more natural.

What’s next?

We barely scratched the surface of reactive programming using Future , Promise and async / await constructs. The upcoming Coursera course Principles of Reactive Programming digs deeper into the details, incorporates interesting programming exercises, and covers two more reactive patterns that are particularly useful: observables and actors.

Observables provide a powerful abstraction over event streams, similarly to how futures provide an abstraction over discrete events. The most notable implementation of observables is probably Microsoft’s open-source Reactive Extensions library. This library was used extensively at Netflix to redefine how they approach both back-end and front-end development. Netflix also put together a great interactive tutorial that covers using Reactive Extensions with JavaScript.

Actors provide the building blocks for distributed, fault-tolerant message-passing applications, such as messaging servers, trading systems, and telecom appliances. The most notable implementations of actors include Erlang OTP and TypeSafe Akka.