JavaScript minification

April 24, 2019

JavaScript applications are written as a collection of .js files that are usually transformed before being served in a browser. One reason to transform is to improve page load time. There are two primary components to improving page load time: bundling multiple files together, and minifying to make the bundle size smaller.

This post walks through why we do these and what the implications are for doing such a thing, and concludes with a few projects I've been working on to improve this space (spoiler: comparitive benchmarks).

Bundling is the process of taking multiple .js files and producing one. If the .js files are independent, or if they refer to one another via global variables, it's as easy as concatenating the files together. But usually the files will use some sort of module system that involves import or require statements that will need to be transformed to work as a single file, often with the support of a module loader library.

Why do we bundle? Otherwise the browser must request each file separately and that can be slow. (You might have read that HTTP/2 solves this. It can help but it does not eliminate the cost; see for example Khan academy's analysis.)

Minifying or optimizing is the process that transforms a file into an equivalent file that does the same thing using fewer bytes. Minification can be as simple as removing whitespace, or replacing the expression true with the shorter !0. More sophisticated tools will rename variables to use shorter names, delete code that is unused (which is called dead code removal or sometimes tree shaking), or rewrite algorithms.

Why do we minify? File size has a cost when transferred over the network, in which case the compressed size of the file matters. The uncompressed size also matters because that is the quantity of data the browser must parse and keep in memory. Simplifying or deleting unused code reduces the quantity of code the browser must download and evaluate.

These transformations improve JavaScript load performance beyond browsers. Even if the JavaScript files are already available on the user’s local machine, opening and evaluating them has a cost. Large client-side apps like Visual Studio Code use these techniques for the same performance reasons you use them in a browser.

Bundling/minification tools

Though I’ve described them as separate concepts, bundling and minification interact and are not clearly layered. For example, an optimizer that inlines code must be able to see all modules to inline code across modules. Typical programs that vaguely handle bundling include webpack, browserify, and rollup; the typical minifier is uglifyjs/terser. But note that e.g. webpack can inline modules, and rollup can remove some dead code.

The most aggressive tool in this space is Google's Closure Compiler, which covers both of these roles. I wrote before about how it predates many of these other tools and consequently is incompatible with most of them. Due to its age and lineage it's had years of optimization work poured into it.

In practice because these two tasks are intertwined, the remainder of the post just calls both minification.

Transformation is unsafe

I wrote that minification produces an "equivalent" program, but that is not quite accurate. In the limit, any transformation is unsafe because it changes the program, and JavaScript is dynamic enough that almost any program change can break the program.

For a warmup, note that it's unsafe even to just delete whitespace! On any function you can call .toString() to get the source code of the function, so it's legitimate (though strange) for a program to do something like:

assertEqual(myFunction.toString().charAt(34), 'a');

and deleting whitespace will break this code.

For a less pathological example, a program might call and expect a specific name, which breaks if the minifier changes function names (or looks them up via eval, or one of many other techniques). Or code might look up a method by string manipulation:

function onClick(elem) {
  someObject[‘on’ + elem.someProperty]();

Another example: is it safe for a compiler to delete this code, which appears to do nothing? (Such code might be the result of applying other optimizations, such as removing unused variables.)


Again, it can be unsafe: JavaScript getters can have side effects, and in fact the popular Chai testing library relies on them for its (in my opinion needlessly cute) assertion syntax.

One last example: a program might declare a function foobar that appears to never be used, but then at runtime it might fetch more JavaScript via a network request, and that fetched JavaScript might then call foobar(). A minifier that deletes apparently-unused code will break this program. (For this reason, Closure Compiler's support for code splitting requires giving the compiler all the code as compile time, even the code that isn't part of the initial bundle.)

In all, for a given program it’s difficult to predict which properties it relies on. For example, AngularJS sometimes relies on the specific names given to function arguments.

The above examples were meant to demonstrate how hard it is to be sure, but in practice there are many transformations that are almost certainly safe for all programs, such as removing curly braces from one-liner if statements. On the other hand, such transformations only remove a few bytes, while more aggressive optimizations are capable of eliminating whole functions and modules.

Minifiers as language variants

Though it's not usually described in these terms, another way to look at bundlers/minifiers is that they are programs that only operate correctly on a subset of the full JavaScript language. A whitespace-deleting minifier operates correctly on programs that don't rely on specific source text or line numbers, while a function-renaming minifier operates correctly on programs that don't use Rollup’s tree shaking only works with code that uses the ES module subset of JavaScript.

In fact, it's often difficult to enumerate the sorts of programs that satisfy or break the rules of a specific minification. For example uglify has a notion of a “pure” function that it handles specially, but in general nobody is really sure what “pure” means. Because of this, these tools often end up with many configuration options to try to let individual apps “carve out” the specific rules they obey. Closure Compiler in particular is worth calling out because it is is likely the most finicky in this respect, where it's relatively easy to let it "optimize" a program into a form that no longer works.

What you gain by working against a JavaScript subset is even smaller output. Within Google, apps that are carefully written to the Closure dialect of JavaScript use Closure Compiler to reduce literally tens of megabytes of source code JavaScript into tiny bundles.

Whole program optimization

Traditionally a given library/framework will perform its own bundle/minify step and then distribute a single .min.js file for users to include in their apps. But this is optimizing at the wrong layer. A better approach is to feed all code — both libraries and your app — into a single bundler and minifier pass so that it can optimize through all the code. In such a layout, the minifier can then for example delete parts of the library that your app doesn't use.

I believe this approach is taken by Angular2+ and Polymer, which no longer publish minified versions of the libraries under the expectation that the user's build process will do the final minification. Closure compiler, which is very aggressive about deleting dead code, also expects to see all the code used in your program.

Minifiers and types

Consider the problem of determining whether it’s safe to change a function’s name. One idea is that a sufficiently smart compiler could examine the program and determine whether the function’s name is relevant to the program’s execution. (It’s larger than just — consider for example a class method that is then overridden in a dynamically-generated subclass.)

This problem of identifying references feels deeply related to type systems, and Closure Compiler relies on type information for some of its optimizations, but I believe the programmer-oriented type systems currently found in Closure Compiler or TypeScript don't cover all the analysis required. It's also vaguely connected the sorts of alias analysis found in the compiler literature. (There are some interesting papers written about the optimizing the Self programming language that are pretty relevant here.) I have a lot of ideas in this space but no time to look into them.

And ultimately no tool can solve the runtime-loaded invocation of foobar() case described above without the programmer’s assistance.

Comparing minifiers

I have been thinking about all of the above because I had been toying with writing a JavaScript minifier of my own, called j8t. I didn’t expect it to go far, but sometimes the best way to understand a problem space is to try to implement something so you can experience all the corners. It was a lot of fun staring at the JS spec to write my own parser.

But what I discovered is that it’s relatively easy to make a program that transforms JavaScript, and very hard to decide whether the transformation it performed was correct! Ultimately the only way to decide is to say: for a specific program X, if you transform it into some program X’, does the program still work? And then defining whether a program “works” or not is itself tricky. For example, at one point in my experimentation my minifier produced a smaller app that looked like everything was fine until I discovered it had broken one minor feature.

In the meantime, others have started on similar projects (e.g. swc) and I realized they must have the same problem.

What we really need, I decided, was a suite of programs that can be transformed and then retested with end-to-end tests. These programs need to be written using different frameworks and in different styles to understand which tools improve or break which programs. And it needs to be exactly reproducible, so minifier developers can take the suite and attempt to improve their relative scores.

And so I present js-min-bench, a step towards that. In the repo there are specific input programs along with configurations of different tools, and a runner that runs the programs against all the tools, runs test suites against those transformed programs, and finally presents the results in a big table.

Here are some major caveats:

Postscript: code size ultimately requires systemic solutions

All approaches to minification take a large program and attempt to make it smaller. The easiest code to optimize is the code that doesn't exist in the first place. If the program wasn't so large to begin with, then minification wouldn't be as important.

Within Google my experience has been that people have paid a lot of attention to smart tools but not as much attention to smarter choices about dependency size. I have two main problems with smarter tools.

One is that smart tools often don't give the user good insight into whether or why a given optimization worked or didn't. A seemingly innocuous change might end up having a large impact on the output bundle size because the code crossed some arbitrary compiler heuristic, and as a user of the compiler you don't often have good resources for figuring out what happened.

The other problem with smart tools is that no matter how small your final output is, your build time is still proportional to the total size of the inputs you provide. As your tools get smarter, it allows your inputs to get larger, and compilation gets ever slower.

So while I still think it's wise to use the minification tools we have, I am less enthusiastic about making them more sophisticated, and I think a better approach is trying to rein in dependencies. To use some self-serving analogies: it's better to live a healthy lifestyle than to develop fancier medicines, or maybe: it's better to drive less than to develop more fuel-efficient cars. But we can still invest in both.

In the public JavaScript world, the tool source-map-explorer can be useful for tracking down where all the bytes are coming from. (Shameless self promotion: I wrote the treemap widget used there.)