From first principles: Why I bet on Scala.js

Three years ago, I downloaded the nascent Scala.js compiler and tried to use it on a toy project.

Since then, it has matured greatly: the compiler itself is rock-solid. It has a huge ecosystem of libraries. It has a vibrant community, been adopted by some of the largest commercial users of the Scala language, and is playing a key role in shaping evolution of the language. By any measure, it is a success, and I was one of the key people who evangelized it and built foundations for the open-source community and ecosystem that now exists.

However, three years ago in late 2013, when I first got involved in the project, things were different. The compiler was unstable, buggy, slow, and generated incredibly bloated, inefficient Javascript. No ecosystem, no libraries, no tools, nobody using it, no adoption in enterprise. Clearly, a lot of the things people now see as reasons to use Scala.js did not apply back then. So why did I put in the effort I did? This post will explore the reasons why.

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy the Author's book Hands-on Scala Programming

There are many compile-to-Javascript languages in the world: virtually every widely-used language has a compile-to-Javascript version of some level of quality (Google Web Toolkit, PyJS, Opal Ruby, ...), and then there are those languages that were designed specifically with Javascript as a target (Coffeescript, TypeScript, ...). Scala.js is one of them, probably one of the most robust and production-ready of the compile-to-JS languages. Over the last three years I have played a large role investing time and effort evangelizing Scala.js and building out the ecosystem and community to what it is today.

However, Scala.js was not always the solid, production-ready platform that it is today. When I first got involved, it was a sketchy new project with so many bugs and problems that using it for a "real world" use case would have been unheard of. While I have spent considerable time giving talks telling people why "Scala.js is good", I have never talked about the core reasons why I started working on Scala.js long-before it was "good" by any measure. Why Scala.js, and not one of the many alternatives? This post will tell that story.

My Involvement with Scala.js

Although many people in the Scala community think of me as the "Scala.js person", I actually have never contributed seriously to the core compiler in https://github.com/scala-js/scala-js. Rather, my contribution has been mostly community, ecosystem and evangelism:

In a way, Living Coding Scala.js and Cross-platform development with Scala.js were Scala.js' two big "break-out" presentations: they reached about 10000 views on-line, and were the first time the broader Scala community had a chance to see that this "Scala.js" thing existed and how to use it. Most of the early community came from people who had watched those talks, and most of them got started with Scala.js via my Hands-on Scala.js e-book.

By this point, the ecosystem is mostly self sustaining: even though people continue to think of me as the "Scala.js person", I haven't been actively using, contributing or evangelizing Scala.js for over a year. Other people have taken on building libraries and frameworks, writing documentation, or evangelizing it at meetups and conferences. We even have a new Scala Fiddle being built to replace the existing one which is showing its age.

I am probably still the individual who has spent the most time telling people why they should use Scala.js, but I have never really told people why I started using Scala.js, especially early on when things were rough, performance in all aspects were poor and many of the niceties that exist today do not exist. What was in it for me?

The More Things Change

I first got involved with Scala.js in Sep 2013. At the time I wanted to make a 2D physics-platformer game (what would eventually become Roll), and had just prototyped an implementation using C#/F# to be played on the new-fangled Windows touch-screen computers. Here's an early screenshot:

Not satisfied with a possible market of people-with-windows-touchscreen-computers, I decided to try and port the game to the web so it could run on more platforms. I had done enough Javascript to know I didn't really enjoy it, was working actively with our Python/Coffeescript website at work, and knew Scala from some freelance projects I had done while in college. I came across Sebastien's talk at Scaladays 2013, decided that this side-project platform-game was small enough that the risk of using unknown technologies was fine. It was September, 2013.

The Scala.js of Sep 2013 was very different from the Scala.js that exists right now, in Aug 2016. The easiest way to compare the Scala.js of Aug 2016 to the Scala.js of Sep 2013 is to look at the numbers:

Date Aug 2016 Sep 2013 Version 0.6.11 0.1.0-SNAPSHOT Edit-Refresh time 1-2s 20-30s Hello-World Code Size ~100kb prod/~1mb dev >1mb prod/28.6mb dev Performance Slowdown ~1-2x >10x Libraries Dozens (Js & Scala) None Frameworks React, Angular, Vue... None Users Thousands 0-1 Test Suite Scala Compiler Suite YOLO "Enterprise Use" Yes No Core Maintainers 3 1

If you are a front-end engineer looking for a compile-to-JS language in Aug 2016, or a back-end Scala engineer looking for a way to re-use your Scala skills working on the web, the Aug 2016 version of Scala.js paints a pretty attractive picture. In contrast, the Sep 2013 version of Scala.js looks no where near as nice. In fact, if you were an engineer looking at making something "for real" in Sep 2013 (rather than just messing around as I was) you would have to be insane to choose Scala.js at the time.

However, some things don't change. It turns out that there are some properties of Scala.js that, even back in 2013, made it an attractive platform for a developer to experiment with...

The More They Stay The Same

Not every characteristic of a language or platform is created equal: some characteristics are easy to change. Others are hard to change but, but the path to change them is straightforward if you're willing to put in the (possibly months) of grunt work. Still other characteristics are basically frozen: no matter how many engineers or how much money you throw at trying to change them, it's not clear you'll make any progress at all.

The way Scala.js has changed from Sep 2013 to Aug 2016 falls mostly in the first two buckets. Some are relatively easy:

No test suite? Well, write one! Or leverage existing tests that others use (The Scala.js team ended up re-using the "partest" Scala compiler parallel testing suite)

Edit-Refresh time taking too long? Cache the things you don't need re-compute every single time: instant 10x speedup

While others are difficult but straightforward:

No libraries? Write them. Need a unit test library? HTML generation library? Serialization library? Bindings for Javascript libraries? It's a lot of work, but not so much work you can't sit down, crank out a (simple version of a) library in a week, and in a few months you have quite a collection

No users? Well, if the product itself is in good shape, and the only problem is nobody knows about it, go tell people! Post on mailing lists, forums, twitter. Show it off at conferences and meetups. Obviously if the product you're trying to sell isn't ready due to other issues, this won't work. But if it really is in good shape, and you just need users, you can spend the time and effort to get them.

However, the characteristics that have stayed the same in Scala.js over the last three years fall into the last bucket: not going to change any time soon. None of the competitors I saw to Scala.js really get them right, and since these are basically frozen, it's unlikely they will "suddenly" be able to get them right in the near future

Despite how poorly the Sep 2013 version of Scala.js compares to that of Aug 2016, these things are entirely unchanged throughout all that time:

I'll go through each one in turn.

Perfect IDE Support

I live by my IDE. When I start working on a codebase, I will happily sacrifice multiple days trying to get an IDE set up so I can be comfortable working on that codebase. Even if it involves figuring out how to set up an Samba/SSHFS-share from an Ubuntu VM onto a Windows host because the rest of the team/company/world all uses Vim or Emacs over SSH, I will do it. The time invested in setting up pays for itself really quickly in time saved having the IDE find things for me.

The first thing I noticed when I first started using Scala.js, back in Sep 2013, was how perfect the IDE support was. You get from your IDE everything you'd expect working in a language like Java or Scala or C#, such as immediate error-highlighting:

Inline documentation:

And autocomplete, to help you explore unfamiliar APIs and learn how to use them:

You didn't just get IDE support for writing Scala code interacting with the Scala standard library or other Scala code, but you got IDE support for interacting with the Javascript DOM APIs and other Javascript libraries!

This was IDE support on the level of dedicated Web-IDEs like WebStorm, but better: Scala.js knows the types of the various functions involved, and knows what types they return. When code starts become less trivial: with loops, chained attribute accesses and funtion calls, Javascript IDEs tend to fail at providing help because they cannot guess the types at any particular spot, and so don't know e.g. what methods to show in the autocomplete box.

With Scala.js, the way type-inference works is well known and standardized, so your code. even in more knotty Scala.js code, any IDE would be able to provide perfect assistance:

Building a production-ready IDE is a massive undertaking; even providing a good plugin for an existing IDE is perhaps one person-year of work. If you want support for Vim, Emacs, Sublime, Atom, Eclipse and IntelliJ, you're signing up for a lot of work!

This is an issue for every compile-to-JS language:

Languages like Dart and TypeScript probably have more people working full-time on their various custom IDE integrations than there are people working on Scala.js, in total! But they have corporate backing and can afford to.

Other less-well-financed for-the-web languages such as PureScript or the various Python-to-Javascript converters (Brython, RapydScript, ...) are just entirely unable to allocate the necessary resources to provide decent IDE support. That doesn't stop you from using them, but hopefully you really like Vim/Emacs/Sublime Text without any code-intelligence.

Lastly, more dynamic languages like Javascript itself, or popular variants like (at the time) Coffeescript, have tremendous amounts of effort going into IDEs like WebStorm, but due to the dynamic nature of the language how much it can help (with autocomplete, error-highlighting, etc.) is limited.

In the case of Scala.js, they (or at the time, he, since it was just one person...) was also unable to allocate time and resources to provide custom IDE support for the project; but we didn't need to! As far as the editor is concerned, Scala.js code is just Scala code like any other. Any editor which provides Scala support:

IntelliJ

Eclipse

Vim, Emacs, Sublime, Atom...

Provides just-as-good support for Scala.js out-of-the-box.

There is no "Scala.js IntelliJ Plugin". There is no "Scala.js IDE". There are almost no "Scala.js tools" in general! All the standard-Scala IDEs and tools work just fine.

Tooling and IDE support is always a major issue when creating a new language - whether compile-to-JS or not. It's not just an incredible upfront cost for a young language without a community or corporate backing, but also a heavy ongoing maintenance cost over time. With Scala.js, I saw that right-out-of-the-box it was born with perfect IDE and tool support, multiple existing, well-maintained IDEs. This was entirely for free, and with no added maintenance cost. And that's a great bargain!

Seamless Javascript Interop

In the section above, I used a simple code example to show off how the IDE helps when working with Scala.js code. However, one thing to notice is that the code isn't really Scala-Scala code at all: it's Scala code interacting with the Javascript DOM APIs, using the normal functions and attributes! Furthermore, it looks more or less exactly the same as the equivalent Javascript code! Here it is in Scala:

import dom._ val paragraph = document.createElement("p") paragraph.innerHTML = "<strong>It works!</strong>" document.getElementById("playground").appendChild(paragraph)

And here it is in Javascript:

var paragraph = document.createElement("p") paragraph.innerHTML = "<strong>It works!</strong>" document.getElementById("playground").appendChild(paragraph)

Other than the fact that Scala has val s where in Javascript you'd use var , and an import at the top, the two snippets are entirely identical!

The Scala and Javascript languages are, superficially, similar: . for attribute access, () for calling functions, () => to define anonymous inline functions, etc.. Not only is Scala.js code normal, idiomatic Scala code, it also looks almost exactly the same as normal, idiomatic Javascript code.

Obviously, this does not hold as you get more advanced, and start comparing "more advanced" Scala (with implicits, traits, typeclasses...) to "more advanced" Javascript (dealing with prototypes, using libraries like React.js, ...). However, even this base-level similarity is already great to have when trying to figure out how to inter-operate between them.

Clunky Interop

Many other languages are not as fortunate. For example, here's how you create an element in ClojureScript, compared with Javascript

(let [paragraph (.createElement js/document "p")] (set! (. paragraph -innerHTML) "<b>Bold!</b>"))

var paragraph = document.createElement("p") paragraph.innerHTML = "<strong>It works!</strong>"

Now, the conversion between these two snippets is mechanical, so it's not something you need a PhD to perform. Nevertheless, it is clear that using Javascript APIs in ClojureScript, while looking like ClojureScript, looks almost nothing like the Javascript it represents.

This mapping is something that everyone who wishes to learn Clojurescript will have to internalize.

Google Web Toolkit's Java-to-Javascript interop is also clunky, but for a different reason. The traditional way this has happened has been through the Javascript Native Interface system, which lets you define the way your Java methods call your Javascript code as... magic comments within your Java source files:

package mypackage; public MyUtilityClass { public static int computeLoanInterest(int amt, float interestRate, int term) { ... } public static native void exportStaticMethod() /*-{ $wnd.computeLoanInterest = $entry(@mypackage.MyUtilityClass::computeLoanInterest(IFI)); }-*/; }

public native void doSomething() /*-{ this.@com.company.app.client.MyClass::doSomethingElse(Ljava/lang/String;)("immediate"); someObj.onclick = function() { this.@com.company.app.client.MyClass::doSomethingElse(Ljava/lang/String;)("on click"); } }-*/;

These comments have their own non-Java, non-Javascript syntax that looks like an odd hybrid of Java, Javascript and C++. It's not all bad: they're heavily documented, well specified, with lots of notes and FAQs about how to use them and what to look out for. Nevertheless, I couldn't help but feel that even in Scala.js 0.1.0-SNAPSHOT, the interoperability with the Javascript world was far smoother.

Heavy, Smooth Interop

For a more meaty example, we can look at the logic from my Roll physics-platform game, for animating the moving clouds in the background:

To begin with, we are defining a Scala class , which takes a Javascript cp.Vect object coming from the popular ChipmunkJS library:

class Clouds(widest: cp.Vect) {

We call a Scala function that creates a Javascript Image object, using data loaded from our Scala resource bundle object:

val cloudImg = dom.extensions .Image .createBase64Svg(scala.js.bundle.apply("sprites/Cloud.svg").base64)

We model a Cloud as a pair of a Scala Double representing its velocity, and a Javascript cp.Vect representing its position, and initialize them with random doubles from our Scala math.random function`:

class Cloud(var pos: cp.Vect, val vel: Double) val clouds = Seq.fill((widest.x * widest.y / 100000).toInt){ new Cloud( widest * (math.random, math.random) * 2, math.random ) }

On update, we have a Scala for-loop (which translates into a .foreach method call) which does some arithmetic and updates the Javascript attributes .x and .y on our cp.Vect objects:

def update() = { for(cloud <- clouds){ cloud.pos.x += cloud.vel cloud.pos.x = (cloud.pos.x + widest.x/2) % (widest.x * 2) - widest.x/2 } }

And lastly, we have a Scala function draw that uses a Scala for-loop to draw everything using our Javascript CanvasRenderingContext2D object, and the .x and .y from our Javascript cp.Vect objects:

def draw(ctx: dom.CanvasRenderingContext2D) = { for(cloud <- clouds){ ctx.drawImage( cloudImg, cloud.pos.x - cloudImg.width/2, cloud.pos.y - cloudImg.height/2 ) } }

As you can see, you can freely mix Scala-Scala code and Scala-Javascript interop code, and not only does it work as intended, it looks perfectly natural. Not only can you call function back and forth, you can also freely pass around Scala objects or Javascript objects, read/write their attributes or call their methods, and everything just works as expected. And this is not restricted to "Standard Library" Javascript APIs: the above example makes heavy use of classes, attributes and functions defined in the third-party ChipmunkJS library, and they feel just as natural to use in Scala as any other Scala code.

While you do not see any indications of what's a Javascript object/function/attribute and what's a Scala object/function/attribute in the source code, the compiler knows, and your IDE knows, and will do the right thing in both cases. If you wish to know, it's simply a matter of jumping to the definition of the thing you're unsure of ( Ctrl B in IntelliJ) and seeing for yourself! For example, jumping to cp.Vect above brings us to its definition:

@JSName("cp.Vect") class Vect(var x: Double, var y: Double) extends js.Object

Where the extends js.Object is a clear, unambiguous marker that this is a Javascript class, represented by cp.Vect in Javascript-land, and its x and y attributes are mutable Javascript attributes.

This seamless interop is rare in the world of compile-to-JS languages, especially those not specifically designed for it such as Coffeescript or Typescript. With Scala.js, your Javascript-interop code fits in perfectly with your Scala-Scala code, and also fits in almost-perfectly with any Javascript code you find on StackOverflow. You can mix and match your Scala objects/functions/attributes with javascript objects/functions/attributes, and there's no awkwardness going from one to another.

While it's always better to have "easier interop", the level of interop that Scala.js has results in two surprising benefits:

You can easily make use of libraries from the Javascript ecosystem . Using a new library can be done without writing any bindings, and if you want to write bindings (which provide type-checking and IDE autocomplete) they can be done yourself, incrementally as-needed, in just a few minutes, and as a result many people have published bindings that you can use. Thus Scala.js does not need a "replacement" for widely-used libraries like jQuery, React.js, Angular or Vue.js: you can use the real thing, written in Javascript. Want to leverage the mature ChipmunkJS physics engine instead of writing your own? Go ahead!

Dealing with Javascript APIs is often more pleasant in Scala.js than it is in raw Javascript! This is due to the seamless interop combined with the Perfect IDE Support you get working with Javascript types in Scala.js. Not sure what the difference between .children and .childNodes is? You can read the type signature ( HTMLCollection vs NodeList ) and a description right in the autocomplete menu, without having to google across multiple Stackoverflow/MDN pages to figure out which one you want.

All this has been the case since I started working with it in Sep 2013: even when "Hello World" resulted in >1mb of really-slow Javascript, when there were no tests, no users, no libraries, already the Javascript-interop experience was already best-in-class in the compile-to-JS world. And it was already clear that the knock-on advantages of this best-in-class Javascript interop would be many!

Perfect Scala Compatibility

Another thing that surprised me when I started working with Scala.js, is just how much Scala works in it. That is to say, Scala.js isn't "a language that looks like Scala" or "Scala, sorta". Rather, it is basically full-fledged Scala. While it has edge cases and differences from Scala-on-JVM, these are rare and inconsequential enough that you can go a long time without bumping into or even noticing any difference. It's real Scala.

The Compatibility Status Quo

A language has many features, some used more and some used less. When you try to convert the language to Javascript, it is natural that not every feature will be supported to the same amount. Some are just harder to support, others are easy to support but less important. Yet other features are difficult to support, but important enough the you'd put in the effort to make it happen. Since implementing each feature requires effort, it's only natural the author of a compile-to-JS language would prioritize some features over others.

When dealing with these compile-to-JS languages, the "degree of compatibility" for each feature of the language can be roughly broken down into four categories.

Not implemented at all: you can't even try to use it; it doesn't exist. e.g. in Scala.js, there is no runtime reflection. Never was, probably never will be. That's just the way things are. Implemented but behaves strangely: it's there, but might not work as you would hope. e.g. in Scala.js, accessing an out-of-bounds array is possible, but it just returns undefined rather than throwing an exception as it does in the JVM. Probably not what you'd expect, probably not what you want, but in the grand scheme of things it's just an annoyance when debugging: you can work around it and life goes on. Implemented and behaves mostly correct: not perfect, but probably what you want. e.g. in Scala.js, Float s behave as Double s by default. This is a behavioral difference: the results your float arithmetic on Scala.js will be 0.000000001% different from the results on normal Scala-JVM! But you are unlikely to ever notice. Implemented and behaves perfectly: an example of this would be Scala.js' implementation of Int s. You may know that Javascript, which Scala.js compiles to, simply does not have integers: it has a single number type that represents a Double -precision floating point value. Nevertheless, there are ways to make them behave like integers, and Scala.js jumps through all the hoops and ticks all the boxes to make sure that you, as a programmer using Scala.js, can just treat them as integers and forget about them.

In general, most other languages which have some kind of compile-to-JS functionality added on have

Most of their features falling into bucket 1.,

With some falling into bucket 2.

And enough falling into bucket 3. to make a few cute demos.

This looks fine for the demos, but when actually try to use the compile-to-JS functionality you very quickly bump into all the things which are missing (1.) or broken (2.).

For example, I had tried to use the PyJs Python-to-Javascript compiler to cross-compile some trivial string mangling routines (if a string is too long, cut out the middle and replace it with ... ), and immediately bumped into the fact that PyJS's str s behave differently from Python str s and the unicode function (common in almost any serious program that needs to deal with non-english words) didn't exist at all. Nevermind trying to do "more advanced" Python things like decorators, multiple-inheritance, etc. when even the basics don't work.

Apart from language compatibility, there is also the standard library. While a Python-to-Javascript compiler may work with Python-the-language, it's almost certainly not going to work with all the C extension modules that make up the Python standard library: these will all have to be re-written in Javascript or in Python for the compiler to deal with! Given how many C extension modules live in the standard libraries of Python, Ruby, or many other common "dynamic" languages, it is no surprise that none of the to-Javascript compilers really support more than a tiny fraction of them.

Scala.js' Compatibility

With Scala.js, basically every feature of the Scala language is in bucket 4., with a small list of things scattered throughout buckets 1., 2. and 3., short enough to list on a single page. Many of these have switches you can use to move them to bucket 4. in exchange for a performance penalty. Rather than listing what works, Scala.js lists what doesn't work: the vast majority of Scala works perfectly. Multiple inheritance? Extension-methods? Typeclasses? Macros? They all work perfectly!

Furthermore, the bulk of the Scala standard library is written in Scala. While there are some Java or native library dependencies (which won't work in Scala.js, i.e. bucket 1. above), empirically these tend to be few and far between. Empirically, re-implementing all the Java packages that a typical Scala program depends on (which has been done) is much, much easier than re-implementing all the C modules that a typical Python program depends on.

Practically, what does this mean?

This means that if you are using Scala.js, you are writing Scala. Full Scala, not some narrow subset of the language. All the language features you are used to, almost all of the standard library, many third-party libraries: you can use them in Scala.js just as you would in Scala-on-the-JVM.

This means that if you are writing your own library or application code, there's usually no reason why it can't work on Scala.js, unless it touches processes/filesystem/networking or uses runtime reflection. It turns out that most libraries, and large portions of most applications, do not, and your code often runs on Scala.js automatically, without needing to lift a finger. This applies to not just toy examples, but to large real-world codebases like the Katai Struct Parser-Generator or the Netlogo Compiler.

This means that you do not need to "know Scala.js". Scala.js is Scala. If you can write Scala you can write Scala.js. Sure if you want to deal with the Javascript DOM APIs, or inter-operate with React.js you have to learn the interface of those libraries, but they're just libraries: the Scala you are writing to deal with them is the same Scala that you run on the JVM that deals with JVM libraries. Want to do something odd, like cross-compile some codebase against different Scala versions and have version-specific third-party dependencies and version-specific compatibility shims? It's the same in Scala.js as it is in traditional Scala.

This means there are no books like the Purescript Book or The Dart Programming Language: books that walk you through the semantics of the language. While there are tutorials like Hands-on Scala.js to gently introduce you to using Scala.js, there is no "Scala.js Book" that explains how objects work, how functions work, how the type system works, what the control-flow structures are, all that. This is because you can learn this from any old Scala book or tutorial. Apart from saving time writing books, this also greatly reduces the on-boarding cost for the non-trivial number of Scala-JVM developers out there: they already know how everything works.

Even from the start, Scala.js' compatibility with "normal" Scala-on-the-JVM has been top-notch. Scala.js has always been truly just Scala. Even when I started using the pre-0.1.0 project in Sep 2013, the compatibility with "normal" Scala far exceeded what any other language's compile-to-JS version provided.

In fact, you do not even need to know about Scala.js to write Scala code that runs in the browser! Any code you write that does not deal with JVM-specific things like the filesystem should in theory automatically be valid Scala.js. And it turns out that in practice, this is indeed the case...

Cross-Platform Ecosystem

One big selling point of Scala.js has always been the ability to write cross-platform code that runs on both browser and server, on both the Javascript runtime and on the JVM.

This is a feature that most other "normal" languages with added compile-to-JS functionality lack.

I personally find that incredibly surprising: if you're already going through the effort to compile your language X to Javascript, and are running X on the server, isn't it the natural next step to stop copy-pasting your algorithms and business-logic between your server-codebase and your browser-codebase? That's what I would do. However, for whatever reason, this hasn't happened for the vast majority of existing compile-to-JS languages of the past.

Even languages that are specifically meant for cross-compilation have the same problem, from a different angle. Ur-Web, for example, does not have any libraries at all, cross-platform or not, despite having been worked on for years. If you try to make your own cross-platform language "FooLang" that runs in the browser, you'd be in the same state: building out a cross-platform ecosystem of libraries and tools is hard.

With Scala.js, cross-platform code that runs on both browser/JS and server/JVM is a fact of life, so boring that it's easy to forget how rare it is in the broader web-development community. Whether we're talking about entire applications, libraries for functional programming or binary serialization or other things, or even just snippets, constants and helpers within a larger application, they all can be cross-compiled. In fact, they all have been cross-compiled! Thus we have a massive ecosystem of libraries to use in Scala.js, many of them cribbed from the Scala-JVM or Javascript ecosystems, and it all Just Works.

Anemic Ecosystems of the Past

If you look at other compile-to-JS languages like:

You'll notice one thing in common: none of them have a standard way of writing code that runs on both their "original" runtime (JVM, MRI, CPython, CLI, ...) and on Javascript. And the reason why is straightforward: none of them are compatible enough, complete enough to really let you write "full" Java/Ruby/Python/C#/etc..

As described above, they all have most of their functionality within the "doesn't work at all" or "works but not really" buckets. While some parts of each particular language works well, not enough of it works perfectly in order to take "arbitrary" code and cross-compile it to run on both their host runtime and Javascript. You have to take special care to write "GWT Java" or "Opal Ruby" or "Brython Python" if you want it to work in the browser. For example if you take "plain old Java" from some random Java project, you can be totally sure it won't work at all...

Case Study: Google Web Toolkit's Ecosystem

Google Web Toolkit probably has the best compatibility out of any of those listed, and although Guava seems to have GWT support, searching for GWT Libraries on google returns only a handful, and those purpose-made for GWT. If you want to use "common" Java libraries like Apache Commons or TestNG or Joda Time or Google Gson, you are out of luck.

Why?

It's hard to say concretely why this is the case, especially as someone who hasn't been deeply involved in GWT development in the past, but I can speculate.

My personal theory is that GWT simply isn't compatible enough with "normal Java" for cross-compiling libraries to be a reality.

"Normal Java", as it turns out, is pretty different from the Java you learn in school. It makes use of features like:

Runtime reflection (For serialization, dependency injection, ...)

Filesystem-based external configuration or DSL files (e.g. XML config, file-based HTML templates, ...)

Classloader manipulation (e.g. for loading config files, SL4J logger detection, ...)

Bytecode manipulation (AspectJ, AOP, ...)

You probably did not learn this stuff in school; you probably aren't even going to use this stuff line-by-line in your Java code. But when working in a non-trivial Java codebase you do end up using these things day-by-day. And these are things that GWT doesn't support. How then, could you port your day-by-day work to run on GWT? You cannot.

While this describes GWT, I'd guess that the same reasoning applies to Opal Ruby, or the dozens of Python-to-JS compilers: they're just not compatible enough to take "normal", "real world" Ruby or Python and have it run in JS. Sure you can run "programming 101" Ruby or Python, but doing real-world work requires running real-world code.

In the end, it comes down to compatibility: for a vibrant cross-platform ecosystem of libraries to exist, the compile-to-JS version of a language Foo must be very compatible with how Foo is written, in the wild.

And so a cross-platform ecosystem of Java, Python or Ruby modules that runs in the browser hasn't materialized. Not because there's no desire to run all this code in the browser - there is plenty - but because all that code just doesn't work in it.

Bootstrapping the Scala.js Ecosystem

While Scala.js was not born with a rich ecosystem of libraries, it's Perfect Scala Compatibility meant that it had the potential to support many of the libraries that are common in the Scala ecosystem. While Scala.js and GWT technically support more-or-less the same subsets of their language (no reflection, no file-based config/DSLs, etc.), the way code is written In the Wild means that Scala.js supports much more Scala code (which tends not to use reflection, file-based config/DSLs, etc.) than GWT supports Java code (which tends to use all of it).

The first library that that was ported to Scala.js was the Scalatags HTML templating library, by me, in Dec 2013. Scalatags does basic string-operations to stitch together HTML strings while properly escaping things, and there's no reason why we shouldn't be able to stitch together HTML strings in the browser as well as we could stitch together HTML blobs on the JVM! The actual change to make it happen turned out to be relatively straightforward: A few hours of effort, and we had a widely-used, non-trivial real-world Scala library working with Scala.js.

Porting Scalatags demonstrates the advantage of Scala.js being Scala: there are plenty of HTML templating libraries, both for Java and Scala. However, Java-based libraries (e.g. Mustache.Java) tend to have their HTML templates as a separate file loaded at runtime, and they tend to use reflection when interpolating variables or attributes within their templates. Scala templating engines like Scalatags (and others like Twirl) tend to do all their magic at compile-time. Thus even though Scala.js and GWT have the same restrictions around reflection and loading files, Scala libraries work with Scala.js and the equivalent Java libraries don't work with GWT.

A month after Scalatags was ported, it was followed by my uTest cross Scala-JVM/JS test framework that lets you simultaneously test code on both platforms.

Then a cross-compiled version of my Scala.Rx change-propagation library appeared.

And then a new uPickle cross Scala-JVM/JS serialization library.

While uTest and uPickle were written specifically for Scala.js compatibility (because most traditional test-frameworks and serialization libraries use reflection, which doesn't work in Scala.js) Scala.Rx was an existing JVM library that just happened to work basically-unchanged once a few of its dependencies ( Future s and Atomics) were ported.

These were then followed by a wealth of cross-platform test frameworks, serialization libraries and other things built by members of the community.

Some started porting existing Scala libraries: the famed (or feared?) Scalaz functional-programming library was ported by David Barri in June 2014 with a few lines of build config and no code changes (commit), and in July 2014 Alexander Myltsev similarly ported the Shapeless (commit) type-level programming library.

By Dec 2014, an incomplete listing of the fledgling "ecosystem" looked like this:

This diagram taken from a talk I gave Bootstrapping the Scala.js Ecosystem, at the Scala Exchange 2014 in London. Since then, the ecosystem has grown much larger, but even then the ability of Scala.js to sustain a broad and deep ecosystem was obvious.

Take-aways from the Bootstrapping

While this doesn't quite show a rich and thriving ecosystem, it does demonstrate one thing: that writing cross-platform libraries in Scala.js, which can run both in a browser as Javascript or on a server with a JVM, is not only possible, but really easy. And it is easy in a way that is unique to Scala: trying to port Java libraries to Google Web Toolkit or Ruby libraries to Opal Ruby simply does not work as easily because those compile-to-JS systems are not compatible enough with the way the languages are written in the wild.

This listing has libraries of every sort:

uTest or BooPickle that were designed from the start to be cross-platform,

Scalatags or Scala.Rx which were originally written for the JVM and later cross-compiled to Scala.js by their author, and

Shapeless or Scalaz which were also originally written for the JVM and later cross-compiled to Scala.js. But not by the author, instead by some random third-party who needed to use them.

scalajs-react or scalajs-angular, which are bindings to third-party Javascript libraries.

Most other compile-to-JS languages have few libraries supporting their Javascript side, and often none at all that can run on both Javascript and their original runtime (JVM, MRI Ruby, CPython, ...), due to compatibility and other reasons that make it a difficult task. Scala.js has many, of all sorts.

In fact, many of them were basically "free": huge, complex Scala libraries like Shapeless or Scalaz, ported to Scala.js with a few lines of config. Equally huge, battle-tested Javascript libraries like React.js or Angular, made available to Scala.js via it's Sealess Javascript Interop. Rather than having to build out its own cross-platform ecosystem from scratch, Scala.js is able to leverage both the existing Scala and Javascript ecosystems, benefiting from both while paying the cost of neither.

Although there were no libraries when I just started working with Scala.js, pretty early libraries of all sorts started appearing and it was clear that it was different from earlier compile-to-JS languages.

Building cross-platform libraries was so easy everyone could do it. In fact everyone did do it!

Now, in Aug 2016, it is obvious that Scala.js' library ecosystem is doing great. The libraries listing on the Scala.js website lists no less than:

6 cross-platform "Functional Programming" libraries

10 cross-platform Serialization libraries

9 cross-platform Testing libraries

This is in addition to various uncategorized utility libraries, libraries such a wix/accord which are not listed on the main site, and the dozens of Scala.js specific libraries and bindings to existing Javascript libraries. In 2016, new Scala libraries are adding Scala.js support as a matter of course. However, even back in Dec 2013 and early 2014, even before this rich ecosystem has materialized, the unique potential of Scala.js to allow such a rich cross-platform ecosystem to exist was already obvious.

Static Optimizability

One well-known fact is that statically-typed languages are far easier on "traditional" compilers: by using the types, you have more knowledge about what the variables within your program contain, what the functions you define can do, and thus are better able to generate code that respects their behavior.

Javascript, however, is not a "traditional" compilation target. And many modern runtimes use Just-In-Time "JIT" compilers, rather than "traditional" ahead-of-time compilers. Nonetheless, it turns out unintuitively that "traditional" static ahead-of-time optimizeability is incredibly valuable for languages trying to compile-to-JS, despite Javascript not being statically typed, and Scala.js benefits greatly as a result.

Compiling Dynamic Languages

If you try to compile dynamically-typed languages with a traditional ahead-of-time compiler, you end up with generated code with tons of checks, tons of branches, tons of hashtable-lookups. All this is to make sure the behavior of the generated code matches exactly the behavior of the interpreted version, even if the user does funky dynamic things like monkey-patching classes at runtime.

If the language's semantics are defined as:

foo.bar() calls whatever method bar that is defined by the type of the foo object

there's not much you can do except look up bar every time, in case someone passes in a differently-typed foo object since you last saw it. Whether you have an interpreter looking it up, or generate native code to look it up, it's still slow. There are some tricks you can do to try and speed things up, but overall it's an uphill battle: you simply don't know what foo is until runtime! So if you are trying to generate code at compile-time, you simply don't have the information necessary to make it fast.

It turns out many "modern" languages are no longer just using traditional compilers. Many, including:

Java (on OpenJDK/Hotspot)

Javascript (on Chrome/V8, Firefox/IonMonkey, IE10/Chakra) or

C# (on the .NET CLR)

Are using Just-In-Time (JIT) compilers. These basically defer much of the "meat" of the compilation until runtime, when in theory you have more information about real usage patterns and can make better profile-guided optimizations.

This is really important in dynamic languages like Javascript, since you often don't know you can e.g. inline a method call until you see what method is actually being called, which you only really know at runtime. However, it turns out that even traditional "static" languages like Java and C# have enough dynamism, with their runtime-classloading and virtual method dispatch, that they still see large benefits from JIT compilation and its runtime optimizations.

These languages tend to be used on the server, or in the case of Javascript, shipped as a heavyweight installation that needs to be installed on each computer. Thus, while these JIT compilers are complex, huge pieces of software, their size doesn't matter for what they're used for. However, that's not the niche that Scala.js is targeting...

The world of compile-to-JS languages is different.

With a compile-to-JS language, you realistically cannot ship a runtime: any heavyweight runtime you ship hurts the download/page-load time the first time a user downloads it, and even if it gets cached it will often get cleared and need to get downloaded again.

That's in theory.

In practice, there are projects like PyPyJS, impressive feats of engineering in their own right, that do ship a heavyweight runtime. It ends up taking 10s of seconds to download and initialize 10s of megabytes of code on a reasonable internet connection, probably longer for users on mobile or patchy coffee-shop wifi.

This is enough of a problem that although there is a huge Python community that would love to re-use their existing Python knowledge to run Python in the browser, nobody is using PyPyJS to do so. It's just not practical.

Even a massive company like Google or Apple or Microsoft has trouble forcing their heavyweight language-runtime into every browser. Adobe Flash worked this way, and so did Microsoft's Silverlight, and Google's Native Client. However, these efforts are clearly on the decline. For example, in the world of the mobile-web on smartphones, these technologies are long dead already. On desktop-web it's only a matter of time.

Hence, if you want a compile-to-JS language which results in a good user experience, you have no choice but to have your compiler eliminate as much unnecessary code as possible, ahead-of-time, before it gets sent down the the user over the air. While JIT compilers are great on the server and for Javascript itself, they just won't do here. This requirement really calls for a "traditional", ahead-of-time, optimizing compiler, and a language that works well with such a compiler.

For an "ideal" compile-to-JS language, you want a language where the compiler knows as-much-as-possible about the code it is compiling, before it starts running.

It turns out, this idea is not particularly groundbreaking or revolutionary. Many existing compile-to-JS language communities already know this:

In the Dart community, the documention tells you to avoid runtime reflection, avoid calling Function.apply , and avoid overriding noSuchMethod : all of which are "dynamic" features that make it hard for the optimizer ("tree-shaker" they call it) to eliminate un-used code, and results in heavy executables.

In the Google Closure world (not to be confused with Clojure, which is something else entirely), you have to write your Javascript in a certain restricted subset of Javascript (documented here) in order for the optimizer to know what your code is doing and how it can be optimized. Going outside this subset results in the optimizer breaking your code.

In the Clojurescript community, the differences from JVM Clojure include things like not having reified var s, which reduces the dynamism possible in the code compared to Clojure on the JVM

The Google Web Toolkit Java-to-JS compiler does not support runtime reflection, instead providing a Deferred Binding mechanism that provides a subset of the functionality while still preserving the static analyze-ability (and optimize-ability) of the codebase

Unintuitively, while there is a code-size benefit for having your compile-to-JS source language being similar to Javascript itself, an equally important factor is you want your language to be static, and thus optimizeable. This is a well-established fact that can be seen in all existing compile-to-JS languages out there. The more static, the better.

Static types and Scala

At first glance, Scala looks very similar to common dynamic languages, such as Ruby or Python. In particular:

Ruby/Python support declaring local variables without type annotations, Scala does too

Ruby/Python support operator overloading, Scala does too

Ruby/Python support monkey patching classes to add additional methods, Scala supports "extending" classes via implicit extension methods

Ruby/Python support multiple inheritance of classes with a specified method resolution order in case of diamond inheritance, Scala supports multiple inheritance of traits with a specified linearization order

Ruby/Python support runtime reflection e.g. to inspect the class hierarchy when pickling objects, Scala supports compile-time reflection to e.g. inspect the class hirarchy when generating a pickler in a macro

Superficially, Scala code often looks like Python code with added curly braces, or Ruby code with all the do and end s converted to curly braces.

However, there is one major difference: in the case of Ruby/Python all this magic happens at runtime, while in Scala, the magic - with implicits, macros, traits, etc. - happens at compile time, ahead of time, before the code is run. Importantly, once the relevant phases in the compilation have happened, there is no more magic to worry about!

If you were trying to compile a language like Python to Javascript, you would have to account for the fact that, at runtime,

Someone somewhere may monkey-patch your class, and the method you thought you were calling just changed out from underneath you

Even though a field/method does not appear used anywhere in your code, somewhere some JSON serializer is using it via getattr , or via method_missing

That a + b you see somewhere in your code may not actually be two numbers, but someone may pass in two Point2D objects with __add__ over-ridden, or two Matrix3by3 objects

These kinds of considerations make it impossible to optimize things: how can you inline a method if you're not sure what its implementation is? How can you optimize integer arithmetic operations if you're not sure you're even dealing with integers in the first place?

Case Study: Opal

To take an example, let's see what the Opal Ruby compiler does when it compiles a small snippet of code, and compare it to what the Scala.js compiler does. This is a tiny function that adds two numbers, in Ruby:

def foo(a, b) a + b end

If I was to write this in Javascript, I may write something like:

function foo(a, b){ return a + b }

However, that is not the code Opal generates! Instead, Opal generates the following:

/* Generated by Opal 0.10.1 */ function $rb_plus(lhs, rhs) { return (typeof(lhs) === 'number' && typeof(rhs) === 'number') ? lhs + rhs : lhs['$+'](rhs); } Opal.add_stubs(['$+']); return (Opal.defn(Opal.Object, '$foo', TMP_1 = function ːfoo(a, b) { var self = this; return $rb_plus(a, b); }, TMP_1.$$arity = 2), nil) && 'foo'

This is obviously a lot more code than 1 + 1 , but it is necessary: Opal doesn't know at compile time whether a or b are numbers, or (x, y) points, or something else. Thus rather than just generating the Javascript code a + b , it needs to generate a call to a function that, at runtime, performs a check for whether a and b are numbers, and falling back to a lhs['$+'](rhs) function call if they're not.

On the other hand, with Scala.js, if you write

def foo(a: Int, b: Int) = a + b

This gets compiled into the (approximate) code

foo__I__I__I = function(a, b) { return ((a + b) | 0) };

This is because Scala.js knows that a and b are Int s, and not anything else! Thus it is able to generate just a + b (With an extra | 0 to make sure they overflow as Int s and not as Double s), without any additional checks or guards.

To see this for yourself, go to this fiddle, hit Ctrl J to show the generated Javascript, and Cmd F to search for foo_ to find our function within the mass of the generated standard library code

Scala, and thus Scala.js, allows operator overloading just as Ruby does. What happens if we pass in something that's not an Int to foo , say a Point object with x and y attributes?

First we need to define the thing:

case class Point(x: Int, y: Int){ def +(other: Point) = Point(x + other.x, y + other.y) }

Then we'd need to change our function signature to take Point s rather than Int s:

def foo(a: Point, b: Point) = a + b

Now, compiling the modified function results in the following generated Javascript:

foo__LPoint__LPoint__O = function(a, b) { return a.$$plus__LPoint__LPoint(b) };

(fiddle, again hit Ctrl J to show code and Cmd F for foo_ to find the code for our function among the standard library Javascript).

Here, we have no checks, no if -statements that do one thing on Int s and another thing on Point s. If Scala.js knows it's taking Int s it generates the simple a + b Javascript code, but if it's taking something more complex it generates the a.$$plus__LPoint__LPoint(b) code.

This is similar to Opal's

function $rb_plus(lhs, rhs) { return (typeof(lhs) === 'number' && typeof(rhs) === 'number') ? lhs + rhs : lhs['$+'](rhs); }

Function, but with one key difference: Opal always has to check whether the two things being added are numbers before it can choose what to do. Scala.js, knowing what things are, can write code to always perform the correct operation without any checks.

Revenge of the Static Types

Scala.js is able to generate different, optimized code depending on what it knows of the types of x and y . It can do this, not because of some obscure property of the Scala language, but from the most basic one: Scala knows what x and y are - whether they are Int s, Point s, or something else - and can generate the correct code accordingly. Dynamic languages don't know what x and y are, and cannot.

Thus, these optimizations are available to Scala that are not available in dynamic languages like Ruby or Python, or even static-languages-with-dynamic-parts such as Java-with-runtime-reflection.

Does this matter? It turns out, it does: Opal's usage of $rb_plus and its associated overhead results in a 100x slowdown for performing arithmetic operations in Opal, vs raw Javascript. That's a huge loss! In comparison, arithmetic in Scala.js is compiled to the same code you would write by hand in raw Javascript, and its performance is identical. "Normal" code doesn't show as drastic a difference as integer-arithmetic, but all the dynamic checks still end up being a significant slowdown.

Even though they're both compiling to dynamically-typed Javascript, statically-typed Scala still ends up having a massive performance advantage over dynamically-typed ruby!

It's not that the Opal Ruby community wants their code to be 100x slower than Javascript - there are plenty of issues raised on the tracker and elsewhere complaining about arithmetic performance - it's that they have no choice: given the constraints of a dynamic language with advanced features, that is simply the best solution possible.

This is but one of the many places where Scala's "compile-time magic" turns out to be a major advantage over other language's "run-time magic". The same "language feature" - operator overloading - turns out to be 100x faster when implemented at compile-time than when implemented at run-time.

Apart from the obvious slowdown, Scala.js also the code-size advantage:

(a+b)|0

Is less code than

$rb_plus(a, b)

This is but one of many, many optimizations that a statically-typed language lets a compiler do, that is not available in dynamic languages. Other optimizations such as

Inlining

Constant-folding

Escape-analysis and allocation-sinking

Renaming classes/functions/variables to shorter names

Dead-code-removal

All similarly depend on the static analyze-ability of the code being written.

Overall, these sorts of static optimization is the main driver that reduced the hello-world code size from the >1mb in Sep 2013 to the ~100kb in Aug 2016, and the speed penalty from >10x to 1-2x in the same period of time.

This revelation - that static types help all compile-to-JS languages, even though Javascript isn't statically typed - was one big reason why I was optimistic about Scala.js. Compared to alternatives like Ruby or Python, Scala really does provide any potential optimizer with a lot more information that it can use to speed-up and compress-down your code before it gets sent over-the-wire to someone's browser.

Lastly, this static-ness is a feature that is difficult to "graft on" to an existing language:

You can try to perform all the advanced code-analysis you want on Python/Ruby code, but when you bump into the crazy-dynamic parts of the language there's really not much to analyze.

You can try to tell people to not use the crazy-dynamic parts of the language, e.g. avoiding runtime-reflection in Java, but fundamentally the Python community is full of people using getattr , the Ruby community full of people using monkey-patching, and the Java community full of people using java.reflect . Good luck getting everyone to change!

In theory, you could write a clever analyzer to do type-inference for Ruby/Python code to try and figure out what's going on. In practice, you would need to start annotating some types to help it along, restricting some of the more dynamic things you can do, properly specifying the type-system to it behaves predictably.

In theory, you could try to provide more compile-time features to Java, to try and wean people off the dependency on runtime reflection. Instead of using reflection for serialization, provide macros people can use. Instead of using external files for HTML templates and other DSLs, make the language a bit more flexible so these DSLs could be embedded within the Java code and compiled together.

In both cases, you'd end up with a very different Ruby/Python/Java language. In fact, it would look a lot like Scala!

Thus, even from the start, I felt that Scala.js had an advantage here. Scala-JVM was a platform that was much less dynamic than Python/Ruby, and the Scala community was much less fond of using dynamic runtime-reflection than the Java community was, even though it is technically still available.

I thought that when the time came to really crank down on these dynamic features and crank up the optimization effort, Scala.js would be in a unique spot no compile-to-JS language before has ever been. The incredible optimizer that the core Scala.js team has put together over the last three years has shown that to be true: every release the generated Javascript gets just a bit smaller and faster. e.g. the last release speeding up operations on Long s by 15x and making them probably the fastest Long numbers of any language running in the browser. This would not be possible if not for Scala's static optimizability.

Even in Sep 2013, at a time no "real" optimizer existed, Scala.js was already able to generate fast, efficient code for the example above, 100x faster than the two-year-old Opal Ruby-to-JS compiler. Not bad for a pre-release, version 0.1.0-SNAPSHOT project made by one person in a few months!

Solving Real Problems

This section is less about the individual features that make Scala.js attractive on its own, and more about the features that make Scala.js attractive as a replacement for Javascript.

We all know Javascript has it's problems. It's big and complex, but the good parts of it make up a small portion of the overall language

Even with so few good parts, wise programmers are finding themselves using less and less of the language.

It has a lot of well-defined but terribly unintuitive behaviors:

> ["10", "10", "10", "10"].map(parseInt) [10, NaN, 2, 3]

Bashing Javascript is easy. Bashing Javascript is fun. And bashing Javascript is cathartic. However, once you're done with bashing Javascript, what comes next?

If we're picking a compile-to-JS language, ideally we'd want to pick a language that - apart from not being Javascript - really solves the deep problems we face with web-development as a field. What are those problems?

My Problems with Web Development

Everyone's list of gripes with web development are different, and that's fine. Having spent a few years working on random websites on-the-side, then almost two years working on a large Python/Coffee web codebase professionally, these are mine:

In the following sections, I'll discuss exactly what each of these problems entails, as well as how Scala.js provides real solutions to help solve them.

Refactoring is painful

Refactoring a large, sprawling Javascript codebase is incredibly difficult. The few main failure modes are:

You deleted something that isn't actually un-used: oops, now something is broken and I hope you have test coverage to catch it You renamed something, renamed some use sites, missed one, and now some important feature is broken You didn't delete something that was actually un-used, because you were burned by (1.) before and was scared it would happen again: now you have dead code lying around your codebase to confuse newcomers and veterans alike.

In the first two cases, something breaks immediately. In the third case, you save up more and more technical debt to increase the likelihood of something breaking later. No case is really great. These are all silly mistakes, but the number of times I've seen such silly mistakes cause emergencies at work is ridiculous. And you can't really blame the engineer: they're doing the best they can, optimizing for the blend of risk and progress that they think will bring the most value to the company.

All of these cases would be helped by having something - anything - to perform basic checks on your code to validate you are doing something sane: that the function you are calling exists, and has the name you expect, and is meant to take the arguments you are giving it. That the variable you are referring to really exists. While it's possible to implement basic versions of this for Javascript (and I have done so before) it becomes difficult-to-impossible to check the properties or methods of an object. After all, that would require the object's type! And in Javascript you don't have that.

To solve that problem, you really want a type-checker.

It turns out that almost any compile-to-JS language with a type-checker solves this problem. It doesn't matter if you're writing Typescript, or Elm, or Scala.js: the feedback is always the same, that refactoring is just so easy compared to raw Javascript.

Scala.js, however, goes even further than most other typed compile-to-JS languages, providing...

Type-checked HTML

The Scalatags library lets you generate HTML both in Scala.js running on the browser, and in Scala-JVM running on the server. The same code runs on both platforms, e.g. this Scalatags template:

div( float.left, p("I am cow"), p("Hear me moo") )

Generates this HTML

<div style="float: left"> <p>I am cow</p> <p>Hear me moo</p> </div>

Having the templating library be "Isomorphic" or "Universal" is kind of cool, as described above. However, what's also cool is that typos in your HTML get caught, at compile time

div( float.elft, p("I am cow"), p("Hear me moo") )

value elft is not a member of object float float.elft, ^ Compilation failed

dvi( float.left, p("I am cow"), p("Hear me moo") )

Not found: value dvi dvi( ^ Compilation failed

While you can still make "high-level" mistakes - putting the wrong text in a template, or using the wrong CSS class or HTML tag, trivial mistakes like the above are guaranteed to be caught by the compiler. This frees up your mind to spend more time thinking of more important, high-level things.

Note that this Scalatags library isn't "baked into" Scalajs; Scalatags is simply a library written in Scala. If you think there are better ways to do HTML templating - a better syntax to use, a faster implementation, a way to catch more bugs at compile-time - go ahead and implement it!

Type-checked Ajax Calls

One common problem that everyone faces is that their Ajax routes/calls are a mess: the routes are defined in some config file on the server, the calls are done via string urls such as:

// Javascript $j.ajax("/api/list", { data: inputBox.value, onComplete: function(res){ ... } })

However, we don't actually know if "/api/list" is a valid route! Did someone rename it to "/api/list-all" ? Is passing in a single string as data the right thing to do, or did someone make it take an array of strings since you last saw the code? You don't know! Rather, you don't know until you push your code to production and customers start complaining that some button broke.

Scala.js libraries like Autowire allow you to really easily define type-checked Ajax calls. With Autowire, you define your Ajax controllers as methods in the Scala-JVM/server code and call them "directly" in the Scala.js/browser code:

Ajax[Api].list(inputBox.value).call()

Not only is serialization/deserialization of arguments and return types all handled for you, if you mess up the call in any way (non-existent controller method, wrong number of arguments, wrong argument types, ...) you get a compilation error.

While everyone's experiences differ, my own experience is that working with Ajax calls in a large web application is one of the most fragile, error-prone part. Renaming, deleting or refactoring an ajax route is a terrifying experience, that you avoid doing due to the non-trivial likelihood of breakage.

With Scala.js and the Autowire library, suddenly working with Ajax routes and controllers is as safe and simple as any other method call in your codebase! And all thanks to a tiny, third-party Scala library. Again, if you think there's better ways to do this, Autowire is just another library, and you are free to implement your own!

Re-implementing code in two different languages sucks

Sometimes, you just need to have the same functionality in the browser and on the server. You may want some input-validation logic to be performed client-side and then re-checked server-side. Perhaps you want to pre-render the HTML page on the server, but have the ability to re-render it in the browser without making unnecessary round trips. Or maybe you want to share your library of common helper functions for manipulating whatever domain-specific data you're dealing with.

For example, let's say you find some Javascript browser code that you need to move to your Python server:

function charwidth(s) { return 1.5 // dummy } function truncateAtWidth(string, desiredWidth) { var widths = {} widths[-1] = 0 for (var i = 0; i < string.length; i++) { widths[i] = widths[i - 1] + charwidth(string[i]) } var start = 0 var end = string.length while (start <= end) { var mid = parseInt(Math.floor(start / 2 + end / 2)) var actualWidth = widths[mid - 1] if (actualWidth > desiredWidth) end = mid - 1 else if (actualWidth < desiredWidth) start = mid + 1 else return string.substring(0, parseInt(mid)) } // we overshot but it's probably close enough if (start > mid) return string.substring(0, parseInt(start)) else return string.substring(0, parseInt(mid)) }

The purpose of this code is to try and cut off a string after a certain desiredWidth , taking into account that different characters have different widths on screen (here represented by our charwidth dummy function). Perhaps we were previously using it to truncate text in our Javascript UI, and now we want to truncate text on the server before embedding it in the emails we send to people.

If our server is written in Python, we could run truncateAtWidth as a separate Node.js microservice (with all the attendant deployment, monitoring, exception reporting, etc. infrastructure). Or we may decide that microservices aren't a good fit here and just try re-implementing the code in Python...

import math def charwidth(s): return 1.5 # dummy def truncateAtWidth(string, desiredWidth): widths = {} widths[-1] = 0 for i in range(len(string)): widths[i] = widths[i - 1] + charwidth(string[i]) start = 0 end = len(string) while start <= end: mid = int(math.floor(start / 2 + end / 2)) actualWidth = widths[mid - 1] if actualWidth > desiredWidth: end = mid - 1 elif actualWidth < desiredWidth: start = mid + 1 else: return string[:int(mid)] # we overshot but it's probably close enough if start > mid: return string[:int(start)] else: return string[:int(mid)]

Seems easy enough. You run a few manual tests, and it works! Perhaps you write some unit tests to put into CI and they work too.

You push it, and find that for some inputs, your python truncateAtWidth function is infinite-looping on some inputs and has taken down the production cluster!

If you are feeling adventurous, take the two snippets of code above and try to figure out how they are behaving differently, and why. When you're done (or if you can't be bothered) skip past to the spoilers below.

After debugging it, you'd find that the problem is that Python 2 treats numbers as integers by default, while Javascript treats numbers as double-precision floating point. The fix, of course, is something like this:

@@ -9,10 +9,10 @@ def truncateAtWidth(string, desiredWidth): for i in range(len(string)): widths[i] = widths[i - 1] + charwidth(string[i]) - start = 0 - end = len(string) + start = 0.0 + end = float(len(string)) while start <= end: - mid = int(math.floor(start / 2 + end / 2)) + mid = float(int(math.floor(start / 2 + end / 2))) actualWidth = widths[mid - 1] if actualWidth > desiredWidth: end = mid - 1

Looks like we've solved the problem. Hooray! And now that you're done re-implementing your code in a different language, debugging your code a second time in a different language, you now get the privilege of maintaining the exact same code in two languages, keeping them in sync as bug-fixes and improvements happen. Oh joy!

Here we have a tiny, 30-line function dealing with not much at all (a bit of math, a bit of string stuff) and already we are bumping into subtle differences between the two languages. Any larger piece of code we need to re-implement would have orders-of-magnitude more bugs to fix and subtle-incompatibilities to discover.

More often, it just doesn't happen: Javascript code written by the front-end people is totally separate from Python code written by the back-end people, and never shall they meet. Implementing features on one platform already-implemented on the other is just the way life is. The idea that you could, somehow, "re-use" code written on one platform on the other never even crosses anyone's mind. This wasteful implementation and re-implementation is just part of the background-noise of web-development, a chronic pain that's part of life ever since the dawn of the internet.

This particular pain point that is solved by running a Node.js server, if you actually don't mind working in Javascript full-stack, though to me that just brings all of my front-end Javascript problems to my back-end as well. Notably, using one of the new dedicated compile-to-JS languages like Elm, Purescript or similar would not solve this, unless you're willing to run that same language on a Node.js server, which is possible but I don't know of many people actually doing.

With it's great compatibility to the original Scala language, and ability to easily write cross-platform code that runs on both client and server, Scala.js solves this chronic pain point once and for all. You have your server-side language, you have your client-side language. You can write code that runs on both - re-use whatever helpers or functionality you need - and none of the code is written in Javascript! Pure bliss.

It's too difficult to declare "non-trivial" abstractions

When you are dealing with a non-trivial codebase, you often want to abstract away common patterns so you can easily re-use them.

This could be as simple as a procedure that performs some action, or a function computing some result, or a class that you can instantiate and do things with.

You should never abstract too much: abstractions should pay for themselves in usages, preferably each abstraction should be used 3-4 times at least to be worthwhile defining. Nevertheless, as the size of the codebase grows, you find yourself using each abstraction more and more, and more intricate or detailed patterns end up being used enough to be worth abstracting.

The following is an example of an abstraction that I've helped create in the the front-end codebase we maintain:

// Represents the abstract model of a string-tokenizing text input, which // allows the user to configure how to render the tokens or render the inputs, // as well as how to generate the potential autocompletes, and wires them // together. var TokenizerInput = React.createClass({ "propTypes": { ... "placeholder": React.PropTypes.string, "tokenList": React.PropTypes.array, "renderToken": React.PropTypes.func, "onAdd": React.PropTypes.func, "onRemove": React.PropTypes.func, "onChange": React.PropTypes.func, "logChange": React.PropTypes.func, ... }, ... })

Essentially, it is the "skeleton" of a string-tokenizing autocompleting select-input-box, which is parametrized on the kind of "things" it is selecting for, and allows you plug in various callbacks to happen when a user performs different interactions.

It turns out, the various kinds of functions and arguments you pass in all need to deal with the same kind of "things". More than that, they need to fit together in a very specific way: tokenList is an array of "things", renderToken takes a single "thing" and returns some kind of UI element, onAdd takes an array of "things" and returns nothing (it's just for side effects), etc. etc.. what "things" are can vary between instances of TokenizerInput , but the relationship between these parameters must follow this exact model.

The code as-written worked (obviously not all shown above), and we had one or two use-sites working great. Nevertheless, it was still pretty mysterious to us exactly what the requirements of this class was! In a typical dynamic-language fashion, we had simply beat our call-sites with the debugging/ console.log stick until we could click around without it crashing. That may have been fine for now, but probably wouldn't do for future maintenance.

It took multiple people (including the original author) quite a lot of time to figure out exactly the relationship between the various parameters, and get it down in writing in a way that we, and hopefully future users of the class, could understand. We ended up with something like:

// Represents the abstract model of a string-tokenizing text input, which // allows the user to configure how to render the tokens or render the inputs, // as well as how to generate the potential autocompletes, and wires them // together. // // Each tokenizer you create operates on a single kind of data, which we will // refer to as `Data`. The `renderToken`, `renderOption` and other functions // labeled with this type must all operate on the same kind of `Data` for each // instance of `Tokenizer`. `populatedTokenData` takes an array of `Data` var TokenizerInput = React.createClass({ "propTypes": { ... "placeholder": React.PropTypes.string, // String "tokenList": React.PropTypes.array, // [Data] "renderToken": React.PropTypes.func, // Data => ReactElement "onAdd": React.PropTypes.func, // [Data] => void "onRemove": React.PropTypes.func, // Data => void "onChange": React.PropTypes.func, // ([Data], String) => void "logChange": React.PropTypes.func, // (Data, boolean, any) => void ... }, ... })

It turns out that, just in the act of specifying what the damn thing does, we've come up with our own type-annotation syntax! Furthermore, this is a syntax that others reading the code found very useful in figuring out what it does, even those who have spent all their time working in untyped Python/Coffeescript. And it basically maps precisely to the Scala type system:

/** * Represents the abstract model of a string-tokenizing text input, which * allows the user to configure how to render the tokens or render the inputs, * as well as how to generate the potential autocompletes, and wires them * together. */ class TokenizerInput[Data]( placeholder: String, tokenList: Seq[Data], renderToken: Data => ReactElement, onAdd: Seq[Data] => Unit, onRemove: Data => Unit, onChange: (Seq[Data], String) => Unit, logChange: (Data, Boolean, Any) => Unit, ... ){ ... }

It turns out, that semi-rigorous system of type-comments that we came up with in Javascript is exactly what any type-system with generics would support! In any language with generic types, the above formulation would have happened by default, rather than needing hours of intricate code-analysis in order to achieve.

From the point of view of a Scala or even Java programmer, the above type signature is not complex at all: a generic class with some arguments based on the generic type. On the other hand, in a language without type signatures, the interplay between the various arguments, callbacks, callback arguments and how they all fit together is entirely non-obvious!

Reading the implementation code is the common way you understand code in dynamic languages, and I can tell you trying to derive these relationships from the implementation code and use-sites of each argument is very difficult. In this case we ended up successfully documenting it with faux-type-signatures in comments, but in most cases you won't be so lucky.

In general, being able to specify abstractions precisely turns dangerous, confusing abstractions into boring, well-defined building blocks that anyone can understand.

More insidious than the difficulty of pinning down this particular abstraction, though, is the follow-on effect: when abstractions are difficult to describe, and impossible to check for consistency, people tend to favor copy-paste code as it's safer than trying to work with poorly-described and potentially-unsafe abstractions. Like the hesistance of refactoring described above, this is totally reasonable behavior, but in the long run results in a codebase that is sprawling and much more difficult to keep consistent.

For this particular pain-point, it doesn't matter whether the type-system we're talking about is Scala, Java, or C#, or Typescript, or something else. All of them would help us define our interfaces and abstractions in a precise way.

Notably, this is a pain-point that a Python-to-Javascript or Ruby-to-Javascript compiler would help not-at-all: even assuming we had the most perfect, performant Ruby-to-Javascript compiler possible, having our sprawling Coffeescript codebase converted to a sprawling Ruby codebase won't make defining rigorous abstractions any less difficult.

We did not actually end up re-writing the class in Scala.js - at the time it was very immature, and probably not worth the additional build-complexity for just one class - but nevertheless it was clear to me that this was a problem that Scala.js would have solved.

Scala.js Solves Real Problems

Hopefully this section has convinced you that the problems I see in web development are real. Many of them are chronic pains - annoyances that web developers have lived with for so long they're just part of life and no longer register as a "thing" that's painful - but they're there. And, I think, they can be solved, if only we had the right tool to solve them.

While something like Coffeescript is a perfectly respectable compile-to-JS language - and allows shared client/server code with Node.js - it does not solve any of the problems I have with web development. Worse, it brings all your client-side problems to your server-side codebase!

In a way, Scala.js provides something that many people have long dreamed of:

A programming environment that lets you write your entire client-server web application, from the most back-end database queries, to the ajax-layer and browser code, as a coherent codebase in a single high-level language, sharing code freely between client and server and everything compiler-checked throughout for correctness.

Other projects like Ur-Web have the same dream, but face seemingly insurmountable problems with ecosystem, tooling, and adoption. With Scala.js, those problems are solved from the start.

Even when Scala.js was 0.1.0-SNAPSHOT, the potential was obvious: many people have tried the same thing, but this time it might actually work!

Conclusion

I bet on Scala.js back in Sep 2013 was because I thought it stood the best chance of solving, for good, many of the chronic problems that have plagued web development from the dawn of the internet.

It wasn't the "purest" attempt at doing so (that honor may go to projects like Ur-Web) nor was it the "most pragmatic" (perhaps Coffeescript or Typescript?) or the "most mature" (probably Google Web Toolkit). But I judged that it was well positioned to succeed where "purer" attempts had failed, to succeed in helping more where "more pragmatic" projects had succeeded but helped less, and make rapid progress where "more mature" projects had stagnated.

To recap, this table summarizes how much the Scala.js project has improved in the last three years since I first got involved in it:

Date Aug 2016 Sep 2013 Version 0.6.11 0.1.0-SNAPSHOT Edit-Refresh time 1-2s 20-30s Hello-World Code Size ~100kb-prod/~1mb-dev >1mb-prod/28.6mb-dev Performance Slowdown ~1-2x >10x Libraries Dozens (Js & Scala) None Frameworks React, Angular, Vue... None Users Thousands 0-1 Test Suite Scala Compiler Suite None (YOLO) "Enterprise Use" Yes No Core Maintainers 3 1

These are all real, meaningful, orders-of-magnitude improvements to every metric under the sun: >10x faster, >10x smaller code, >10x quicker development cycle, >100x more libraries, >1000x more users. If you are a developer who is considering using Scala.js to do Serious Business™, these are statistics that matter and matter a lot. Aug 2016 Scala.js is a far better tool than Sep 2013 Scala.js is, in every way possible.

However, not every developer values tools based on the stability and functionality they provide right now: maybe you are a hobbyist, or a researcher, or perhaps are just willing to play long-game investments in potential future capabilities. In that case, you may be willing to tolerate current-weakness in the hope of future gains, perhaps due to some underlying characteristics that are unique to this tool.

That was the position I was in, as a hobbyist, when I first looked at Scala.js. And the list of unique characteristics that I saw in it were those already discussed above:

These are characteristics unique to Scala.js among compile-to-JS languages, even among potential languages that could possible compile-to-JS. In this post, I have discussed why I think each of these characteristics is so important to a compile-to-JS language.

None of the existing compile-to-JS languages I am aware of has all these characteristics, nor do I think any of them are likely to pick them up any time soon:

You can't "just" graft static types onto Python or Ruby to make it suitable as a compile-to-JS language. And even if you did, it wouldn't solve many of the real problems that I have with web development.

You can't "just" make the Java ecosystem (and developers!) use less runtime reflection or the Ruby ecosystem (and developers!) use less monkey-patching.

You can't "just" create multiple IDEs and a tooling ecosystem for your custom dialect of Haskell-on-Javascript, Ur-Web, or some other custom language.

Each of these is certainly doable, but definitely falls in the most difficult category of work items:

"no matter how many engineers or how much money you throw at trying to change them, it's not clear you'll make any progress at all"

Even when Scala.js was still version 0.1.0-SNAPSHOT , when the code was bloated, compiler slow, ecosystem non-existent, these characteristics of Scala.js were already clear to me trying to use it. Furthermore, while many of the "top-line" metrics can be improved with time and effort, these characteristics are deep, fundamental to the language, and not something you can change overnight, over even with months or years of effort.

These are the the advantages I saw in Scala.js, over all the other compile-to-JS languages, and why I thought it had a good shot at solving, at long last, many of the chronic pains that have plagued web development since the dawn of time. Many people have the same dream, and have strived for the same goal in the past. But this time was different.

The last three years of its evolution has shown that these perceived-advantages were real, and Scala.js would not have gotten to where it is today without these factors. While it has by no means revolutionized the industry, it's steady growth has left me optimistic that it's on the right path to move web development field forward, slowly, one codebase at a time.

Have you used Scala.js? Or have you had a good or bad experience with other compile-to-JS languages, or frameworks trying to re-invent web development? Let us know in the comments below!

About the Author: Haoyi is a software engineer, and the author of many open-source Scala tools such as the Ammonite REPL and the Mill Build Tool. If you enjoyed the contents on this blog, you may also enjoy the Author's book Hands-on Scala Programming