Computed Properties are Awesome…

…and should be used as little as possible

One of the things I’ve seen happen multiple times in my career is a language which didn’t have computed properties get them, and then watch as mediocre coders continue to cargo-cult old behaviors that are now counter-productive and obsolete.

The Problem

Things change. In particular, data-structures change. So one day you might decide that a Person looks like:

{
  name,
  address,
  email
}

and another day you decide it really should be:

{
  firstName,
  lastName,
  streetAddress,
  city,
  state,
  postcode,
  country,
  contacts[]
}

In fact, you’re likely to move from the first model to the second model in increments. Meanwhile you’re writing other code that assumes the current model, whatever it happens to be. There’ll always be “perfectly good” stuff that works with a given representation at a given time, and breaking stuff that’s “perfectly good” because other stuff is changing is bad.

The Solution

The solution is to use “getters” and “setters”. So you tell people never to expose underlying representations of data and instead write methods that can remain graven in stone that don’t change. So your initial representation is:

{
  getName,
  setName,
  …,
  _name,
  _address,
  …,
} 

and now you can split _name into _firstName and _lastName, rewrite getName, figure out how to implement setName and/or find all occurrences of setName and fix them, and so on.

Problem solved!

Problems with the Solution

Before, when you started out with only three fields, your Person object had exactly three members and no code. The solution immediately changed that to nine members (3 times as many) and six of those are functions. Now, every time you access any property, it’s a function call. If you’re using a framework that transparently wraps function calls for various reasons you’re maybe calling 3 functions to do a simple property lookup.

But aside from that, it works, and the fact that it prevents unnecessary breakage is a win, even if it makes for more code, less readable code, experimentation, and costs performance and memory.

A better solution

A better solution is immediately obvious to anyone who has used a language with computed properties. In this case you go back to your original object to start with, and when things change, you replace the changed properties with getters and setters so that for legacy code (and everything is legacy code eventually) nothing changes.

E.g. when name becomes firstName and lastName, you implement name as a computed property (implemented in the obvious way) and code that expects a name property to exist just keeps on working (with a slight performance cost that is the same performance cost you would have starting out with the previous solution.

The Antipattern

This exact thing happened when Objective-C added computed properties. All the folks who told you to write getters and setters for your Objective-C properties told you to ignore computed properties and keep doing things the old way or, perhaps worse, use computed properties to write getters and setters, so now you start with:

{
  get name() { return this._name },
  set name(x) { this._name = x },
  _name,
  /* and so on */
}

Is there are good reason for this?

There can be arguments at the margins in some cases that computed properties will be less performant than getters and setters (although in Obj-C that’s a huge stretch, since method dispatch is, by default, not lightning fast in Obj-C — indeed it’s a known performance issue that has a standard workaround (method calls in Obj-C involve looking up methods by name, the optimization is that you essentially grab a function pointer and hang on to it).

There’s absolutely no way that writing computed property wrappers around concrete properties just for the sake of it has any benefit whatsoever.

The short answer is “no”.

Aside: there’s one more marginal argument for getters and setters, that if you’re deprecrating a property (e.g. name) and you’d rather people didn’t call a hacky name setter that tries to compute firstName and lastName from a name string, it’s easier to codemod or grep calls to setName than calls to, say, .name =. I’ll reference this argument here even though I find it about as solid as arguments about how to write Javascript based on how well it plays with obfuscation.

I’m pretty sure cargo cult coders are still writing getters and setters in Swift, which has had computed properties since first release. (By the way, lazy computed properties are even more awesome.)

Data-Table Part 2: literate programming, testing, optimization

I’ve been trying to build the ideas of literate programming into b8r (and its predecessor) for some time now. Of course, I haven’t actually read Knuth’s book or anything. I did play with Oberon for a couple of hours once, and I tried to use Light Table, and I have had people tell me what they think Knuth meant, but what I’ve tried to implement is the thing that goes off in my head in response to the term.

One of the things I’ve grown to hate is being forced to use a specific IDE. Rather than implement my own IDE (which would (a) be a lot of work and (b) at best produce Yet Another IDE people would hate being forced to use) I’ve tried to implement stuff that complements whatever editor, or whatever, you use.

Of course, I have implemented it for bindinator, and of course that’s pretty specific and idiosyncratic itself, but it’s a lot more real-world and less specific or idiosyncratic than — say — forcing folks to use a programming language no-one uses on an operating system no-one uses.

Anyway this weekend’s exploit consisted of writing tests for my debounce, throttle, and — new — throttleAndDebounce utility functions. The first two functions have been around for a long time and used extensively. I’m pretty sure they worked — but the problem is the failure modes of such functions are pretty subtle.

Since all that they really do is make sure that if you call some function a lot of times it will not get called ALL of those times, and — in the case of debounce — that the function will be called at least once after the last time you call it, I thought it would be nice to make sure that they actually worked exactly as expected.

Spoiler alert: the functions did work, but writing solid tests around them was surprisingly gnarly.

The reason I suddenly decided to do this was that I had been trying to optimize my data-table (which is now virtual), and also show off how awesome it is, by adding a benchmark for it to my performance tools. This is a small set of benchmarks I use to check for performance regressions. In a nutshell it compares b8r’s ability to handle largish tables with vanilla js (the latter also cheating, e.g. by knowing exactly which DOM element to manipulate), the goal being for b8r to come as close to matching vanilla js in performance as possible.

I should note that these timings include the time spent building the data source, which turns out to be a mistake for the larger row counts.

So, on my laptop:

  • render table with 10k rows using vanilla js — ~900ms
  • render table with 10k rows using b8r — ~1300ms
  • render table with 10k rows using b8r’s data-table — ~40ms

Note: this is the latest code. On Friday it was a fair bit worse — maybe ~100ms.

Anyway, this is pretty damn cool. (What’s the magic? b8r is only rendering the visible rows of the table. It’s how Mac and iOS applications render tables. The lovely thing is that it “just works” — it even handles momentum scrolling — like iOS.)

Virtual javascript tables are nothing new. Indeed, I’ve had two implementations in b8r’s component examples for three years. But data-table is simple, powerful, and flexible in use.

So I added a 100k row button. Still pretty fast.

A little profiling revealed that it was wasting quite a bit of time on recomputing the slice and type-checking (yes, the b8r benchmarks include dynamically type-checking all of the rows in the table against a heterogeneous array type where the matching type is the second type checked). So, I optimized the list computations and had the dynamic type-checking only check a subsample of arrays > 110 rows.

  • render table with 100k rows using b8r’s data-table — ~100ms

So I added a 1M row button. Not so fast. (Worse, scrolling was pretty bad.)

That was Thursday. And so in my spare time I’ve been hunting down the speed problems because (after all) displaying 40 rows of a 1M row table shouldn’t be that slow, should it?

Turns out that slicing a really big array can be a little slow (>1/60 of a second), and the scroll event that triggers it gets triggered every screen refresh. Ouchy. It seemed to me that throttling the array slices would solve this problem, but throttle was originally written to prevent a function from being called again within some interval of the last call starting (versus finishing). So I modified throttle to delay the next call based on when the function finishes executing.

Also, you don’t want to use throttle, because then the last call to the throttled function (the array slice) won’t be called) which could leave you with a missing array row on the screen. You also don’t want to use debounce because then then the list won’t update until it stops scrolling, which is worse. What you want is a function that is throttled AND debounced. (And composing the two does not work — either way you end up debounced.)

So I needed to write throttleAndDebounce and convince myself it worked. I also wanted to convince myself that the new subtly different throttle worked as expected. And since the former was going to be used in data-table, which I think is going to be a very important component, I really want it to be sure it’s solid.

  • render table with 1M rows using b8r’s data-table — ~1100ms

Incidentally, ~600ms of that is rendering the list, the rest is just building the array.

At this point, scrolling is smooth but the row refreshes are not (i.e. the list scrolls nicely but the visible row rendering can fall behind, and live filtering the array is acceptably fast). Not shabby but perhaps meriting further work.

Profiling now shows the last remaining bottleneck I have any control over is buildIdPathValueMap — this itself is a function that optimizes id-path lookup deep in the bowels of b8r. (b8r implements the concept of a data-path which lets you uniquely identify any part of a javascript object much as you would in javascript, e.g. foo.bar.baz[17].lurman, except that inside of square brackets you can put an id-path such as app.messages[uuid=0c44293e-a768-4463-9218-15d486c46291].body and b8r will look up that exact element in the array for you.

The thing is, buildIdPathValueMap is itself an optimization specifically targeting big lists that was added when I first added the benchmark. b8r was choking on big lists and it was the id-path lookups that were the culprit. So, when you use an id-path on a list, b8r builds a hash based on the id-path once so you don’t need to scan the list each time. The first time the hash fails, it gets regenerated (for a big, static list the hash only needs to be generated once. For my 1M row array, building that hash is ~300ms. Thing is, it’s regenerating that hash.

So, eventually I discover a subtle flaw in biggrid (the thing that powers the virtual slicing of rows for data-table. b8r uses id-paths to optimize list-bindings so that when a specific item in an array changes, only the exact elements bound to values in that list item get updated. And, to save developers the trouble of creating unique list keys (something ReactJS developers will know something about) b8r allows you to use _auto_ as your id-path and guarantees it will efficiently generate unique keys for bound lists.

b8r generates _auto_ keys in bindList, but bindList only generates keys for the list it sees. biggrid slices the list, exposing only visible items, which means _auto_ is missing for hidden elements. When the user scrolls, new elements are exposed which b8r then detects and treats as evidence that the lookup hash for the id-path (_auto_) is invalid. A 300ms thunk later, we have a new hash.

So, the fix (which impacts all filtered lists where the _auto_ id-path is used and the list is not initially displayed without filtering, not just biggrid) is to force _auto_ keys to be generated for the entire list during the creation of the hash. As an added bonus, since this only applies to one specific property but a common case, in the common case we can avoid using getByPath to evaluate the value of each list item at the id-path and just write item._auto_ instead.

  • render table with 1M rows using b8r’s data-table — ~800ms

And now scrolling does not trigger Chrome’s warning about scroll event handlers taking more than a few ms to complete. Scrolling is now wicked fast.

Must all data-tables kind of suck?

b8r's data-table in action
b8r now has a data-table component. It’s implemented in a little over 300loc, supports custom header and fixed headers, a correctly-positioned scrollbar, content cells, sorting, resizing, showing/hiding of columns.

Bindinator, up until a few weeks ago, did not have a data-table component. It has some table examples, but they’ve not seen much use in production because b8r is a rewrite of a rewrite of a library named bindomatic that I wrote for the USPTO that was implemented to do all the data-binding outside of data-tables in a large, complex project and then replaced all the data-tables.

It turns out that almost any complex table ends up being horrifically special-case and bindomatic, by being simpler, made it easier to build complex things bespoke. (I should note that I also wrote the original data-table component that bindomatic replaced.)

Ever since I first wrote a simple file-browser using b8r-native I’ve been wishing to make the list work as well as Finder’s windows. It would also benefit my RAW file manager if that ever turns into a real product.

The result is b8r’s data-table component, which is intended to scratch all my itches concerning data-tables (and tables in general) — e.g. fixed headers, resizable columns, showing/hiding columns, sorting, affording customization, etc. — while not being complex or bloated.

At a little over 300 loc I think I’ve avoided complexity and bloat, at any rate.

As it is, it can probably meet 80% of needs with trivial configuration (which columns do you want to display?), another 15% with complex customization. The remaining 5% — write your own!

Implementation Details

The cutest trick in the implementation is using a precisely scoped css-variable to control a css-grid to conform all the column contents to desired size. In order to change the entire table column layout, exactly one property of one DOM element gets changed. When you resize a column, it rewrites the css variable (if the number of rows is below a configurable threshold, it live updates the whole table, otherwise it only updates the header row until you’re done).

Another cute trick is to display the column borders (and the resizing affordance) using a pseudo-element that’s 100vh tall and clipped to the table component. (A similar trick could be used to hilite columns on mouse-over.)

Handling the actual drag-resizing of columns would be tricky, but I wrote b8r’s track-drag library some time ago to manage dragging once-and-for-all (it also deals with touch interfaces).

Next Steps…

There’s a to-do list in the documentation. None of it’s really a priority, although allowing columns to be reordered should be super easy and implementing virtualization should also be a breeze (I scratched that itch some time back).

The selection column shown in the animation is actually implemented using a custom headerCell and custom contentCell (there’s no selection support built in to data-table). I’ll probably improve the example and provide it as an helper library that provides a “custom column” for data-table and also serves as an example of adding custom functionality to the table.

I’d like to try out the new component with some hard-core use-cases, e.g. the example RAW file manager (which can easily generate lists of over 10,000 items), and the file-browser (I have an idea for a Spotlight-centric file browser), and — especially — my galaxy generator (since I have ideas for a web-based MMOG that will leverage the galaxy generator and use Google Firebase as its backend).

Undo

If there’s one feature almost every non-trivial application should have which is overlooked, left as an exercise, or put aside as “too hard” by web frameworks, it’s undo.

I do not have much (any) love for Redux, but whenever I think about it, I wonder “why isn’t undo support built in out of the box”?

To its credit, the Redux folks do actually address Undo in their documentation. “It’s a breeze” the document says at the top, and it’s a mere 14 pages later that it concludes “This is it!”.

I’ve pretty much never seen a React/Redux application that implements undo. Since it’s such a breeze, I wonder why. My guesses:

  1. The words “service” and “back-end” do not appear in the document.
  2. It doesn’t look like a breeze to me. A 14 page document describing implementation of a core concept in a toy application sounds more like a tornado.
  3. Finally, every non-trivial Redux application I’ve ever seen is an incomprehensible mess. (Once multiple Reduxes get composed together, you’re left with multidimensional spaghetti code of the ‘come-from’ variety.) Adding undo into this mix as an afterthought strikes me as a Kafkaesque nightmare.

It’s since been pointed out to me that there are libraries, such as redux-undo, around that wrap the implementation described in the Redux documentation (or variants thereof) in libraries. The example application is a counter, I don’t know of nor have I seen any non-trivial examples.

“Come-from”? Come again?

Programmers by now fall into two groups. Folks who have either read or know by reputation the article Goto considered harmful — possibly the most famous rant in the history of computer science — or folks who have grown up in a world where the entire concept of goto has been hushed up as a dark family secret.

come-from means code that is triggered by a foreign entity, you don’t know when. You can think of it as an uncharitable description of the Observer Pattern. It’s the dual of goto. There are two common examples that are employed widely in user-interfaces — event-handling and pub-sub.

Event-handling is necessitated by the way human-computer interaction works — you’re talking to the analog world, it’s not going to be pretty. The user will do something, it’s generally unrelated to what, if anything, your code was doing. You need to deal with it.

Pub-sub can be self-inflicted (i.e. used when there’s no reason for it), but is often necessitated by the realities of asynchronous and unreliable communication between different entities. Again, that’s not going to be pretty. You make a request, you make other requests. Time passes. Things change. Suddenly you get a response…

Redux is a state management system, so it is natural that it implements the Observer Pattern, but it adds the additional garnish that the mapping between action names and what parts of your application’s state they modify can be — and often is — completely arbitrary.

implementing undo in b8r should be a breeze!?

If you stand back a bit, b8r‘s registry is a similar to redux except instead of your state being a quasi-immutable object which is swapped out by “actions” (with each action being a name linked to a function that creates a new object from the old object) with b8r your state is the registry (or some subset thereof) and your actions are paths.

In my opinion, b8r is wins here because you generally don’t need to implement your actions, when you do they still act on the registry in the expected way, and instead of having an arbitrary map of action names to underlying functions the path tells you exactly which bit of state you’re messing with.

For purposes of implementing Undo, however, Redux seems to have an advantage out of the gate in that it is (partially) cloning state every time something changes. So all you need to do is maintain a list of previous states, and you’re golden, right?

What should it require to implement Undo (in b8r)?

To my mind, it should be something like this:

import {UndoManager} from 'perfectUndoLib'
new UndoManager('path.to.foo', {
    path: 'foo-history',
    onUndo: ...
    unRedo: ...
    onUndoRedo: ...
    onHandleChange: ...
});
b8r.get('foo-history.undoQueue.length') // number of undoables
b8r.get('foo-history.redoQueue.length') // number of redoables
b8r.call('foo-history.undo') // undoes the last thing
b8r.call('foo-history.redo') // redoes the last thing
b8r.call('foo-history.reset') // clears queues (e.g. after saving a file)

The first parameter could allow a list of paths to include in the undo manager’s purview, while also allowing multiple independent undo chains using multiple undo managers (e.g. some advanced graphics applications separate out changes in viewport from changes in the underlying scene).

The path option would probably default to lastThing-history and so foo-path would be superfluous.

Ideally, the callback options would allow async functions to do things like perform a service call to keep the store consistent with the interface, curate the undo and redo queues (e.g. TextMate used to do single keystroke undo which was infuriating when going back through a large edit history, addressing this appears to have paralyzed development and sidelined what had been the darling text editor of the hipter screencast brogrammer crowd), handle errors, and so on.

Implementation Details

At this point in our story, I hadn’t written a single line of code.

I have, however, been thinking about this for several years, which is more important. I’ve implemented several different non-trivial applications (RiddleMeThis, two word-processors, and a non-destructive image editor) with undo support. I think it’s worthwhile when saying something like “I wrote this code in 2h, am I not a true 100x coder!?” to acknowledge, at least to oneself, that it crystallized thoughts collected over a longer period (in this case several years), or whatever.

E.g. I wrote the core of b8r in less than two weeks, but it was a rewrite from scratch of a library I had written over a period of several months while at Facebook which was itself a rewrite of a rewrite of code I had written at USPTO.

b8r — or rather its registry — already has a robust observe method that informs you when anything happens to a particular path (or its subpaths), so something like:

import b8r from '../source/b8r.js'

Of course we’re going to need to deep clone state because — unlike with Redux — we can’t just grab the current state object.

On the plus side, we can just keep a copy of the subtree that changed. Keeping a clone of the entire state in memory every time something changes can get hairy.

Aside: two of the three serious implementations of undo I’ve been responsible for have been (a) a long form word-processor that handled both annotations and multi-user-edit-history and (b) a non-destructive image-editor). And, in fact two things I’ve been thinking about putting together as side projects are a long-form book-publishing system (which aims to publish on web and ePub vs. print/pdf/etc.) and a photography-workflow application, neither of which are going to play nicely with naive “every undo step is the state of your app” approaches.

Here is the code actually committed to the repository in the course of writing this post. Or you can see it in the inline documentation system.

After a quick visit to StackOverflow and Google, it looks at first approximation like the most robust option is “the dumb thing”:

const deepClone = (thing) => JSON.parse(JSON.stringify(thing));

OK so now for the meat!

export class UndoManager {
  constructor (watchPaths, options={}) {
    watchPaths = [watchPaths].flat()
    const {
      historyPath,
      onUndo,
      onRedo,
      onUndoRedo,
      onHandleChange,
    } = {
      historyPath: '_' + watchPaths[0].split('.').pop() + '_history',
      ...options
    }

We expose the buffers and methods in the registry, which makes binding controls to undo and redo super simple. The observer is how we get notified state we care about changes.

    b8r.register(historyPath, {
      undoBuffer: [],
      redoBuffer: [],
      undo: this.undo,
      redo: this.redo,
      observer: this.handleChange.bind(this)
    });

    Object.assign(this, {
      watchPaths,
      historyPath,
      onUndo: onUndo || onUndoRedo,
      onRedo: onRedo || onUndoRedo,
      onHandleChange
    })

    this.reset()
    this.observe()
  }

  reset () {
    this.undoBuffer.splice(0)

    this.undoBuffer.push(this.watchPaths.reduce(
      (state, watchPath) => {
        state[watchPath] = deepClone(b8r.get(watchPath))
        return state;
      },
      {}
    ));

    b8r.touch(this.historyPath)
  }

b8r.observe informs you of state-changes after they happen. (Because, for perf reasons, changes to the registry are queued and handled asynchronously, so if one or more registry entries are changed rapidly, each relevant observer will only be fired once.) So if we wait to collect state until the first user action, we’re not going to be able to restore our original state.

  observe () {
    this.watchPaths.forEach(path => b8r.observe(path, `${this.historyPath}.observer`))
  }

  unobserve () {
    this.watchPaths.forEach(path => b8r.unobserve(path, `${this.historyPath}.observer`))
  }

  get undoBuffer () {
    return b8r.get(`${this.historyPath}.undoBuffer`)
  }

  get redoBuffer () {
    return b8r.get(`${this.historyPath}.redoBuffer`)
  }

restore doesn’t care where state is coming from, its job is just to restore it without triggering handleChange and notify anything bound to the the instance that its state has changed.

  async restore () {
    this.unobserve()
    // const state = b8r.last(this.undoBuffer)
    // b8r.forEachKey(state, (value, key) => b8r.set(key, value))
    this.undoBuffer.forEach(state => {
      b8r.forEachKey(state, (value, key) => b8r.replace(key, deepClone(value)))
    })
    this.observe()
    b8r.touch(this.historyPath)
  }

It turns out there’s a couple of subtle bugs in restore both of which become obvious if the UndoManager (now called HistoryManager) is handling changes on more than one path. So restore() now replays all changes in the undo queue.

I’ve commended out the bad lines of code — the loop below fixes both bugs. There are obvious optimizations here which I have not yet implemented.

When the state of the world changes owing to user action, we need to put the new state on the undoBuffer and clear the redoBuffer (which is now obsolete).

  async handleChange (pathChanged) {
    if (this.onHandleChange && ! await this.onHandleChange()) return
    this.undoBuffer.push({
      [pathChanged]: deepClone(b8r.get(pathChanged))
    })
    this.redoBuffer.splice(0)
    b8r.touch(this.historyPath);
  }

  undo = async () => {
    if (this.undoBuffer.length === 1) return
    if (this.onUndo && ! await this.onUndo()) return false
    this.redoBuffer.push(this.undoBuffer.pop())
    await this.restore()
    return true
  }

  redo = async () => {
    if (this.onUndo && ! await this.onRedo()) return false
    this.undoBuffer.push(this.redoBuffer.pop())
    await this.restore()
    return true
  }

  destroy() {
    this.unobserve()
    b8r.remove(this.historyPath)
  }
}

And undo/redo are now super simple to implement. E.g. this example is now in the documentation of undo.js:

<button 
  data-event="click:_simple-undo-example_history.undo"
  data-bind="enabled_if=_simple-undo-example_history.undoBuffer.length"
>undo</button>
<button 
  data-event="click:_simple-undo-example_history.redo"
  data-bind="enabled_if=_simple-undo-example_history.redoBuffer.length"
>redo</button><br>
<textarea data-bind="value=simple-undo-example.text"></textarea>
<script>
  const {UndoManager} = await import('../lib/undo.js');
  b8r.register('simple-undo-example', {
    text: 'hello undo'
  })
  const undoManager = new UndoManager('simple-undo-example')
  set('destroy', () => {
    b8r.remove('simple-undo-example');
    undoManager.destroy()
  })
</script>

And that’s it!

I’ve walked through the general-purpose implementation of undo in b8r and it looks to me like you can use this to implement undo in a typical b8r application in a single line of code (plus whatever extra lines it takes to put in and add the widgets to your view and bind them). I’d say that qualifies as a breeze — at least until you get to dealing with services.

b8r goes pure(ish) Javascript

b8r now allows components to be implemented entirely in Javascript, with no eval

In an effort to make b8r work in the universe of “everything must be javascript” web development tools, I’ve finally added provision for pure javascript components in b8r.

Now, there’s four really good reasons for doing this.

First of all, the way b8r’s HTML-based components work intrinsically involves using eval (it actually uses the AsyncFunction constructor, but same difference). Whether this represents a real security threat or not depends a lot on how those components are in fact loaded, but it’s definitely an argument.

Second — and this has proven a small but persistent pain point with b8r development — it makes linters happy. To begin with, the <script> tag inside a component was actually the body of an async function with a ton of parameters. Using these parameters without annoying the lint gods involved /* global … */ to be added at the top of the scripts, while the fact that — for convenience — the code was async meant that linters would scream about use of import() and async/await.

Third — with HTML components, defining a single global controller for a family of components involved some jumping through hoops as did, for example, using computed bindings or lists where the method was registered in the component body (this works fine, but generates error messages if you don’t programmatically add the bindings after the necessary methods are registered). Now you can just do the necessary work outside the component definition (but in the same file!)

Fourth — while b8r has moved over to the world of ES6 Modules, it is distributed in cjs form for those still living with require, webpack, etc. The problem is that HTML components don’t play nicely with javascript bundlers, whereas javascript components are just javascript so if you really want to force a giant slab of javascript down your users’ throats you can. Yay.

So, these issues are fixed if you use pure javascript components.

You are still writing CSS as CSS and HTML as HTML, but now it’s likely going to be inside back-ticks.

In addition to the four big arguments for building components this way, there are some further implications (mostly good).

First of all, if and when you need to use import() inside a component, you can use relative paths without wondering “relative to what” (it used to be the location of b8r.js, because that’s where that code ends up executing).

Next, there was no convenient way to create multiple components in one source file. This is a mixed blessing — making it easy to define lots of small components in source files with no relationship between file name and component name presents the real risk of name-clashes. It may become necessary to introduce lint rules that prevent you from creating a component whose name does not match its source file in some way.

Finally, a really nice side-effect of this is that the boilerplate for a component is a lot more self-explanatory than it used to be. Yay!