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Introduction to Calmm

Calmm is an architecture and a concrete collection of libraries for implementing reactive UIs with JavaScript and React. It was born when we started a project to implement a custom CMS for a customer. To help with writing UI code we wrote a few small libraries (initially a few hundred lines of code total). This document introduces the concepts behind those libraries and explains how those libraries can be used to write concise, reactive UI code.

Historical note

To help with writing UI code we wrote a few small libraries:

And we also used Bacon and Ramda. Later, as an alternative to Bacon, we ported the relevant libraries to Kefir:

Currently Calmm is being revised in the form of a new way to embed observables into VDOM. The relevant libraries are

  • karet, replacing kefir.react.html, and
  • baret, replacing bacon.react.html.

There is also a utility library for karet:

In this document we use the latest incarnation, karet. If you are working on a project using an bacon.react.html or kefir.react.html, then much of this document is still relevant. Only the specific way of embedding observables into VDOM has changed.



Wouldn't you like to able to design and implement UI components in isolation as if they were the root of the UI component hierarchy? Such components would just play on their own and you could just plug them into more complex applications. For real? You are probably skeptic, because you have no doubt seen many UI frameworks promise to give you such plug-and-play, but fail to deliver. Why did they fail? They failed because they were not based on solid means of composition and decomposition.

It is widely appreciated that being able to compose UI components from primitive components, to containers, and all the way to whole apps is part of the solution. However, composition is not enough. In order to make plug-and-play possible, one must also have a truly effective solution to the problem of decomposing state. It must be possible to write individual components without knowledge about the whole application state. To plug such components into an application it is necessary to decompose the application state until it matches the interface of the component.

UI code is by no means trivial, so being able to modularize parts of UI code into truly reusable components that play on their own and can be just plugged in, without writing copious amounts of glue code, is highly desirable. The term plug-and-play was used to refer to the idea that one could, essentially, compose a computer by plugging in hardware modules without having to perform manual configuration. That is very much like what we want for UIs.

In this document we'll introduce the ingredients and the architecture of our approach to reach the age old vision of making plug-and-play UI components.


Before going into the details of our approach, let's articulate some criteria based on which we've developed our approach. Here are some of the things we desire from our solution(s):

  • Eliminate boilerplate and glue
  • Avoid all-or-nothing or lock-in
  • Be declarative where it matters
  • Avoid unnecessary encoding of effects
  • Structural programming
  • Testability
  • Efficiency

Let's open up those a little bit.

Copious amounts of boilerplate and glue code are indicators that there is in fact scope for improvement. More code tends to mean more details and more places to make small oversights and introduce bugs. Furthermore, eliminating boilerplate and glue often leads to the discovery of useful abstractions. So, clearly, we want to avoid boilerplate and glue as long as doing so does not prevent us from using lower level means when necessary.

An approach that has an all-or-nothing or lock-in effect is a hard sell. Most experienced programmers can probably tell a horror story or two based on having to use a particular framework that they had to workaround in the most hideous ways. That is clearly something we want to avoid. We want our approach to be able to work both as subordinate and as a master in conjunction with other approaches.

By declarative programming we refer to the idea of writing referentially transparent descriptions of programs or program components. To use those declarations one has to run or instantiate them. Declarative programming tends to have many desirable properties such as composability and testability, but that is not given.

Fundamentalist declarative programming leads to having to encode all side-effects. This is something that is necessary in a language such as Haskell, because its evaluation model does not admit performing side-effects directly in a reasonable manner. In most other languages we have a choice. We can either go for fundamentalist declarative programming and encode all side-effects or we can choose to embrace side-effects when the benefits (such as potentially improved testability) of full encoding seem smaller than the disadvantages (such as higher-level of indirection and additional implementation and interface complexity (e.g. complex types)).

By structural programming, which is a term that we've invented to describe an idea, we mean that we want to be able to just straightforwardly translate the structure of problems, such as the structure of the desired HTML, into declarations. Note that declarative programming isn't necessarily structural. In some declarative approaches you may need to invent significant amounts of structure that is not unique to the problem, which means that there are many solutions to the problem. This tends to go hand-in-hand with having to write boilerplate or glue code. When possible, it is typically preferable to pick one effective way to do the plumbing and make that free of boilerplate.

Testability is especially important in a language such as JavaScript that is notorious for its YOLO roots. As much as possible, we want parts of our UI logic to be unit-testable in addition to being amenable to other forms of testing. Besides having parts that can be tested, it is also important to avoid having to make testing otherwise difficult. For example, if an approach requires everything to be asynchronous, it means that unit-tests also have to be asynchronous, which tends to complicate things.

We also want our approach to be efficient. But what does that mean? It is important to distinguish between performance and efficiency. We want our approach to be algorithmically efficient, e.g. by avoiding unnecessary work. OTOH, sometimes good performance, especially in simple scenarios, can be achieved using poor algorithms, but optimized code. We, however, generally prefer approaches that lend themselves to algorithmically efficient solutions.

Note that we have not explicitly listed simplicity as a goal. We have yet to see an approach to programming that claims to be complex and therefore desirable. But what is simple? Is an approach based on one Golden Hammer concept simple? Not necessarily. In his talk, Simple made Easy, Rich Hickey makes the point that:

[...] when you simplify things, you often end up with more things.

In our approach, we have identified several parts, all of which are quite simple on their own and solve a particular problem well, but not everything. The selective composition of those parts, while perhaps difficult to understand at first, is what gives the ability to solve a variety of problems in UI programming.

The basic ingredients

The basic ingredients of the Calmm approach can be summarized, in order of importance, as follows:

  1. We specify dependent computations as observables.
  2. We embed observables directly into React VDOM.
  3. We store state in modifiable observable atoms.
  4. We use lenses to selectively decompose state in atoms.

The following subsections go into the details of the above ingredients. However, let's briefly describe how these ingredients relate to our problem and goals.

The use of observables to specify dependent computations is the key ingredient that aims to solve the consistency problem. It simply means that we express the application state as observables. When we need to compute something that depends on that state, we use observable combinators to declare those computations. This means that those dependent computations are essentially always consistent with respect to the state. One could stop right here, because observable combinators solve the consistency problem and are often seen as a Golden Hammer: powerful enough for everything. However, we do not stop here, because we don't want to stop at consistency. We also want to eliminate boilerplate and glue, we want plug-and-play, structural programming (at higher levels) and efficiency. None of these happens simply as a consequence of using observable combinators.

To make the use of observables convenient we extend VDOM to allow observables as direct properties and children. This eliminates a ton of boilerplate and glue and helps to keep the code declarative, because the side-effects of observable life-cycle management can be implemented once and for all by exploiting the React VDOM life-cycle mechanism. This also allows us to obtain an amount of algorithmic efficiency, because we can make it so that VDOM is updated incrementally only when the values produced by observables actually change. Like with so called stateless React components, we only use simple functions and never use createClass—that has been done once and for all for us. The React VDOM itself adheres to the structural programming paradigm, which we preserve by embedding observables directly into VDOM.

Storing state in modifiable observable atoms allows the state to be both observed and modified. Atoms are actually used to store immutable data. To modify an atom means that the immutable data structure stored by the atom is replaced by some new immutable data structure. Modifications are serialized by the Atom implementation. Unlike in fundamentalist declarative approaches, we only partially encode mutation of state. Once a component is instantiated (or mounted) it can directly attach callbacks to VDOM that call operations to modify atoms. This way we do lose a bit of testability. However, this also makes the implementation of components more direct as we don't have to encode it all and implement new mechanisms to execute side-effects.

Lenses are a form of bidirectional programs. In combination with atoms, lenses provide a way to selectively decompose state to be passed to components. A component, that is given a modifiable atom to access state, does not need to know whether that atom actually stores the root state or whether the atom is in fact only a small portion of root state or even a property computed from state. Lenses allow state to be stored as a whole, to reap benefits such as trivial undo-redo, and then selectively decomposed and passed step-by-step all the way trough the component hierarchy to leaf components that are only interested in some specific part of the state. Like VDOM, lenses enable structural programming, but in this case following the structure of the data rather than that of the desired display elements.

The combination of atoms and lenses realizes the plug-and-play vision for components. The passing of state to components becomes concise and effective.

It must be emphasized that all parts of the above are essentially optional. For example, a component that only needs to display state, and doesn't need to modify it, does not need atoms. Such a component would likely still use observables and embed those into VDOM and might even use lenses, because they can be convenient even when one is only reading state.

At the end of the day, the end result of all this is just a set of React components that you can use as parts of React based UIs that otherwise make no use of the ingredients. You can also use other React components as parts to implement more complex components with these ingredients.


As described earlier, atoms are not the most important ingredient of Calmm. The most important ingredient is the use of observable combinators to express dependent computations to solve the consistency problem. However, atoms are a simple way to create root observables, which is what we will need in order to talk about dependent computations. Therefore we will first take a brief look at atoms and later take a another look when we talk about lenses.

Let's start by importing an implementation of Atoms. In this introduction we will be using Kefir as our observable implementation. Therefore we will import the Kefir based Atom implementation:

import Atom from "kefir.atom"

There also exists a Bacon based Atom implementation, which is actually the implementation that our original project uses in production, and it should be possible to port the concept to pretty much any observable framework (e.g. Rx).

Atoms are essentially first-class storage locations or variables. We can create a new atom using the Atom constructor function:

const elems = Atom(["earth", "water", "air", "fire"])

And we can get the value of an atom:

// [ 'earth', 'water', 'air', 'fire' ]

And we can also set the value of an atom:

elems.set(["observables", "embedding", "atoms"])
// [ 'observables', 'embedding', 'atoms' ]

However, as we will learn, getting and, to a lesser degree, setting the values of atoms is generally discouraged, because doing so does not help to keep the state of our program consistent. There are better ways.

We can also modify the value of the atom, by passing it a function, that will be called with the current value of the atom and must return the new value. Here is an example where we use Ramda's append to add an element to the list:

// [ 'observables', 'embedding', 'atoms', 'lenses' ]

The modify method is, in fact, the primitive operation used to modify atoms and set method is just for convenience. Modifications are executed one by one. Each operation to modify an atom therefore gets to see the current value of the atom before deciding what the new value should be. This helps to keep the state of an atom consistent.

The term "atom" perhaps gives the idea that one should only use atoms to store simple primitive values. That is not the case. The term "atom" is borrowed from Clojure and comes from the idea that one only performs "atomic", or race-condition free, operations on individual atoms. For this to work, the value stored by an atom must be treated as an immutable value. We will later see how lenses make it practical to store arbitrarily complex immutable data structures in atoms.

Atoms are the independent variables of our system. They are used to hold the essential state that is being modified by the UI. But there really should be a tax on introducing new atoms to a system. Each time one creates a new atom, one should pause and think for a moment:

  • Is this really an independent variable?
    • If not, it shouldn't be an atom.
  • Is this actually a substate of some existing variable?
    • If true, then extend the state space of that variable instead.
  • Does the value of this variable need to change in response to a change of some other variable?
    • If true, then this should be a dependent computation rather than a new atom.

Overuse of atoms can lead to imperative spaghetti code, which is something that we do not want. One of the most common code review results in our experience has been to notice that a particular atom could be eliminated completely, which has simplified the code.

On the other hand, there are other forms of spaghetti, such as complicated observable expressions. We have, in fact, more than once, initially written components using just observable computations that maintained state, using scan or some other observable combinator, in response to events from UI elements, because we thought it would be simpler. Later we found out that by identifying the essential root state and creating an atom for that state we were able to simplify the logic significantly—typically by a factor of about two.

Dependent computations

Atoms, alone, do not solve the consistency problem. Suppose you store a list of items in an atom and want to display the items. How do you ensure that the view of items is always consistent with respect to the items stored in the atom? The view is essentially formed by a computation that is dependent on the state of the atom and, because atoms are observable, we can express such computations using observable combinators.


Atoms are observable, but there are, in fact, many kinds of observables. The purpose of this document is not to serve as an extensive introduction to programming with observables. There are many introductions, including full length books and entertaining videos, to programming with observables already. However, in order to put things into perspective, we take a brief look at a hierarchy of concepts that we choose to categorize observables into:

The basic pattern behind observables is very old. Basically, and to simplify a bit, an Observable is just an object that an observer can subscribe to in order to get notifications that include a value. The semantics of when exactly you get such notifications is one way to distinguish observables:

  • A Stream gives you notifications only when some discrete event occurs. Streams know nothing about past events and do not have a current value—when you subscribe to a stream, you will not get a notification until some new event occurs.

  • A Property has the concept of a current value. In other words, properties can recall the value that they previously notified their subscribers with. When you subscribe to a property, and assuming the property has a value, you will subsequently get a notification. After that, just like with streams, you will get notifications whenever new events occur.

The concepts Observable, Stream and Property can be directly found in Bacon and Kefir, but many other observable frameworks, such as Rx, which can be considered as a lower level framework, do not identify the concepts of streams and properties. However, in most of those other frameworks it is possible to create observables that have the same or nearly same semantics as streams and properties. Cutting a few corners, in Rx, for example, streams can be obtained by applying share() and properties can be obtained by applying shareReplay(1).

As the above diagram shows, an Atom is also a Property. In addition to having a current value, an atom also just directly allows the current value to be modified using the modify method introduced previously. It turns out that in order to support such modification, it isn't actually necessary to store the value. We can introduce the concept of a LensedAtom that doesn't actually store a value, but, rather, only declares a way to get and modify some part of an actual root Atom. For this reason we also identify the concept of an AbstractMutable, which is actually the concept that most of our code using atoms depends upon: we don't typically care whether we are given an actual root atom or a lensed atom. Once created, the interfaces, and, essentially, the semantics of AbstractMutable, Atom and LensedAtom are the same. To talk more about LensedAtoms we need to introduce the concept of lenses, which are the topic of a later section.

Combining properties

Both streams and properties, as described in the previous section, are relevant to programming in Calmm. However, we mostly make use of properties. One reason for this is that, when we create components that display state obtained from observables, we expect that, when such components are hidden or removed from view and subsequently redisplayed, they will actually display a value immediately rather than only after notifications of new values. So, we typically mostly use observables that have the concept of a current value and those are properties.

Observable frameworks such as Bacon and Kefir provide a large number of combinators for observables. While most of those combinators have uses in conjunction with Calmm, we are frequently only interested in combining, with some function, a bunch of properties, possibly contained in some data structure, into a new property that is kept up-to-date with respect to the latest values of the original properties. We also do a lot of this. Everywhere. That is one of the two main reasons why we have defined a generalized combinator for that use case. Let's just import the Kefir based version of the combinator from the karet.util library:

import K from "karet.util"

The basic semantics of the combinator can be described as

K(x1, ..., xN, fn) === combine([x1, ..., xN], fn).skipDuplicates(equals)

where combine and skipDuplicates come from Kefir and equals from Ramda. We skip duplicates, because that avoids some unnecessary updates. Ramda's equals provides a semantics of equality that works, for immutable data, just the way we like.

Suppose, for example, that we define two atoms representing independent variables:

const x = Atom(1)
const y = Atom(2)

Using K we could specify their sum as a dependent variable as follows:

const x_plus_y = K(x, y, (x, y) => x + y)

To see the value, we can use Kefir's log method:

x_plus_y.log("x + y")
// x + y <value:current> 3

Now, if we modify the variables, we can see that the sum is recomputed:

// x + y <value> 0
// x + y <value> 1

We can, of course, create computations that depend on dependent computations:

const z = Atom(1)
const x_plus_y_minus_z = K(x_plus_y, z, (x_plus_y, z) => x_plus_y - z)
x_plus_y_minus_z.log("(x + y) - z")
// (x + y) - z <value:current> 0
x.modify(x => x + 1)
// x + y <value> 2
// (x + y) - z <value> 1

The K combinator is actually somewhat more powerful, or complex, than what the previous basic semantics claimed. First of all, as we are using K to compute properties to be embedded to VDOM, we don't usually care whether we are really dealing with constants or with observable properties. For this reason any argument of K is allowed to be a constant. For example:

const a = 2
const b = Atom(3)
K(a, b, (a, b) => a * b).log("a * b")
// a * b <value:current> 6

Even further, when all the arguments to K are constants, the value is simply computed immediately:

K("there", who => "Hello, " + who + "!")
// 'Hello, there!'

This reduces the construction of unnecessary observables.

The second special feature of K is that when the constructor of an argument is Array or Object, then that argument is treated as a template that may contain observables. The values from observables found inside the template are substituted into the template to form an instance of the template that is then passed to the combiner function. For example:

K({i: Atom(1), xs: ["a", Atom("b"), Atom("c")]}, r => r.xs[r.i]).log()
// result <value:current> b

In other words, K also includes the functionality of combineTemplate.

Unlike with Kefir's combine, the combiner function is also allowed to be an observable. For example:

const f = Atom(x => x + 1)
K(1, f).log("result")
// result <value:current> 2
f.set(x => x * 2 + 1)
// result <value> 3

Finally, like with Kefir's combine the combiner function is optional. If the combiner is omitted, the result is an array. For example:

// []
K(1, 2, 3)
// [ 1, 2, 3 ]
K({x: Atom(1)}, 2, [Atom(3)]).log("result")
// result <value:current> [ { x: 1 }, 2, [ 3 ] ]

Phew! This might be overwhelming at first, but the K combinator gives us a lot of leverage to reduce boilerplate and also helps by avoiding some unnecessary updates. The Kefir based implementation of K is actually carefully optimized for space. For example, K(x, f), which, assuming x is a property and f is a function, is equivalent to Of those two, K(x, f) takes less space.

It should be mentioned, however, there is nothing magical about K. We use it, because it helps to eliminate boilerplate. We also use other observable combinators when they are needed. There is no requirement in Calmm to use K—all the same functionality can be obtained by using just basic observable combinators. However, avoiding boilerplate isn't the only reason to use K. As we will see shortly, it also helps to keep things easier to understand.

Embedding observables into VDOM

What we ultimately want is to keep the views of our UI consistent with their state and to do that we need to create VDOM that contains values obtained from observables. One could use the ordinary observable combinators for that purpose, but it leaves a lot to be desired. First of all, we would then need to somehow manage the subscriptions of observables and be careful to avoid leaks. Combining observables manually would also add a lot of boilerplate code.

Instead of manually combining observables to form VDOM expressions, we choose to extend VDOM to admit observables as properties and children. Consider the following example:

import React from "react"

const Hello = ({who}) => <div>Hello, {who}!</div>

If we'd create VDOM that specifies an ordinary constant for the Hello class

<Hello who="world"/>

it would render as expected. If, instead, we'd specify an observable

const who = Atom("world")
<Hello who={who}/>

and try to render it, the result would be an error message. Indeed, React's div (pseudo) class knows nothing about observables. What if we could write JSX that would also allow observables as properties and children? We can do that importing React from karet:

import React from "karet"

const Hello = ({who}) => <div>Hello, {who}!</div>

Now both

<Hello who="world"/>


<Hello who={who}/>

would render the same. If we'd assign to who, e.g. who.set("there") the latter element would be rerendered.

From here on we assume that React has been imported from karet.

Now that we have the tools for it, let's create something just a little bit more interesting. Here is a toy React class that converts Celcius to Fahrenheit:

const Converter = ({value = Atom("0")}) =>
    <input onChange={e => value.set(}
           value={value}/>°C is {K(value, c => c * 9/5 + 32)}°F

Using the bind helper from karet.util

import * as U from "karet.util"

we can shorten the Converter further:

const Converter = ({value = Atom("0")}) =>
  <p><input {...U.bind({value})}/>°C is {K(value, c => c * 9/5 + 32)}°F</p>

This latter version using U.bind evaluates to the exact same functionality as the previous version that uses onChange. U.bind({x}) is equivalent to {x, onChange: e => x.set(}.

Dispelling the Magic

There is a very simple reason for why it is at all possible to embed observables into VDOM and why, in fact, it is actually quite simple. The reason is that React classes are observers that support a life-cycle mechanism that allows them to be robustly combined with observables.

React's VDOM itself is just a tree of JavaScript objects. That tree can be traversed and its elements analyzed. This allows us to find the observables from VDOM. Inside the karet library is an implementation of a React class that implements the life-cycle methods:

componentWillReceiveProps(nextProps) {
componentWillMount() {
shouldComponentUpdate(np, ns) {
  return ns.rendered !== this.state.rendered
componentWillUnmount() {
  this.setState( /* empty state */ )
render() {
  return this.state.rendered
doSubcribe( /* ... */ ) {
  // Extracts observables from own VDOM properties and direct children.
  // Combines them into an observable skipping duplicates and producing VDOM.
  // Subscribes to the VDOM observable to setState with the results.
doUnsubscribe( /* ... */ ) {
  // Unsubscribes from the observable created by doSubscribe.

Our initial implementations of this were actually very simple. You can find one version here. We basically just used Bacon's combineTemplate. This turned out to be the wrong idea, however, because it eliminates observables arbitrarily deep inside the VDOM rather than just those that appear as own properties or as direct children. This seemed convenient at first, but it does not work compositionally.

Taking toll

Is this a good idea at all? We believe it is and here are some reasons why:

  • Observables solve the consistency problem quite nicely.
  • Observables with Atoms are powerful enough for managing arbitrary state.
  • Embedding observables into VDOM makes it convenient to use observables.
  • Embedding observables allows VDOM to be updated incrementally and efficiently.

Embedding observables into VDOM practically eliminates the need to write new React classes using createClass or by inheriting from React.Component. It also practically eliminates the need to write specialized shouldComponentUpdate implementations. Our production project has exactly zero examples of those.

Embedding observables allows us to think almost like we were always using stateless components and this actually extends to the beneficial properties of stateless components. For example, React's documentation mentions a gotcha related to the use of inline function expressions as arguments to the ref property:

Also note that when writing refs with inline function expressions as in the examples here, React sees a different function object each time so on every update, ref will be called with null immediately before it's called with the component instance.

If you write a custom render method that returns a VDOM expression containing an inline function expression for ref, React will call those inline functions on every update—Oops! Issues such as these are eliminated by our approach.

Lists of items

We previously mentioned the problem of displaying a list of items. Let's suppose we indeed have a list of items, say names, and we want to create a component that displays such a list. Here is perhaps a straightforward solution:

const ListOfNames = ({names}) =>
    {K(names, =>
       <li key={name}>{name}</li>))}

Note that above we use K when we are dealing with an observable and we use Ramda's map to map over the array of names. Instead of K, one could also use the map method of observables:

const ListOfNames = ({names}) =>
    { =>
       <li key={name}>{name}</li>))}

And one could also just use the built-in map method of arrays:

const ListOfNames = ({names}) =>
    { => =>
       <li key={name}>{name}</li>))}

We actually initially did this, but we found it unnecessarily confusing and limiting. The use of, rather than the map method of arrays, eliminates an odd looking xs => => ... pattern from our code. The use of K, rather than the map method of observables, helps to avoid confusing properties with arrays. It also makes the code more flexible, because it now allows the arguments to be both constants and observables. Finally, it also helps with efficiency, as it also skips duplicates.

Back to the ListOfNames. It already works. We can give List an observable that produces an array of names

const names = Atom(["Markus", "Matti"])
<ListOfNames {...{names}}/>

and if we would modify the list of names


the list would be rerendered.

In many cases this is good enough, but consider what happens when the list changes? The entire list of VDOM is recomputed. In a trivial case like this, it is not much of a problem, but with more complex components per item, it might lead to unacceptable performance.

Fortunately this is not difficult to fix. We just cache the VDOM between changes. karet.util provides the mapCached observable combinator for this purpose:

import * as U from "karet.util"

Using it we can rewrite the ListOfNames component:

const ListOfNames = ({names}) =>
    {U.mapCached(name => <li key={name}>{name}</li>, names)}

This version of ListOfNames works efficiently in the sense that, when names in the list change, VDOM is computed only for new names with respect to the previously displayed list of names. What makes that possible is that the expression

                 name => <li key={name}>{name}</li>

specifies a referentially transparent function, which allows us to use U.mapCached to cache the results.

Our Kefir and Calmm based TodoMVC also just uses U.mapCached and seems to be one of the fastest and one of the most concise TodoMVC implementations around. To test the performance of that TodoMVC implementation, you can run the following script in your browser's console to populate the storage with 2000 todo items:

var store = []
for (var i = 1; i <= 2000; ++i)
  store.push({title: 'Todo' + i, completed: false})
localStorage.setItem('todos-karet', JSON.stringify({value: store}))

It pays off to be declarative where it matters.

There is more to say about lists, especially in conjunction with stateful components, but we defer further discussion until later.


To motivate the introduction of lenses, let's first create a simple text input component. Here is the one-liner using karet and assuming that the value property will be an Atom:

const TextInput = ({value}) => <input {...U.bind({value})}/>

If we now create an atom

const text = Atom("initial")

and give it as the value property to TextInput

<TextInput value={text}/>

it gives us a text input that we can use to edit the value of the text atom.

But could we reuse the TextInput component to change the list of names introduced in the previous section editable? In that case the atom contained a list of names—not just a single name. We somehow need to pass a single name from a list of names to the TextInput in a modifiable form. Using lenses we can do that.

Lenses 101

So, what are lenses? Lenses are a form of composable bidirectional computations. For our purposes it is mostly sufficient to think that lenses allow us to compose a path from the root of some data structure to some element of said data structure and that path can be used to both view and update the element.

Let's see how lenses work in practice. First we import the partial.lenses library:

import * as L from "partial.lenses"

Now, consider the following JSON:

const db = {"classes": [{"id": 101, "level": "Novice"},
                        {"id": 202, "level": "Intermediate"},
                        {"id": 303, "level": "Advanced"}]}

We can specify the lens


to identify the object

{"id": 101, "level": "Novice"}

within db. We can confirm this by using L.get to view through the lens:

// { id: 101, level: 'Novice' }

If viewing elements were the only thing that lenses were good for they would be rather useless, but they also allow us to update elements deep inside data structures. Let's update the level of the first class:

// { classes:
//    [ { level: 'Introduction', id: 101 },
//     { id: 202, level: 'Intermediate' },
//     { id: 303, level: 'Advanced' } ] }

The L.set function on lenses is a referentially transparent function that does not mutate the target value—it merely creates a new value with the specified changes.

Like with observables, we use lenses a lot, which means that there is value in keeping lens definitions concise. For this purpose we abbreviate

Using the abbreviations, the set expression from the previous example can be rewritten as:

L.set(["classes", 0, "level"],

This is really the absolute minimum that we need to know about lenses to go forward. Partial lenses can actually do much more than just view and update elements given a static path. See the documentation of the partial.lenses library for details.

Combining atoms and lenses

Where things get really interesting is that Atoms support lenses. Recall the list of names:

const names = Atom(["Markus", "Matti"])

To create a LensedAtom, that uses lenses to decompose state, we just call the view method with the desired lens:

const firstOfNames = names.view(L.index(0))

Let's take a look at what is going on by using the log method:

// names <value:current> [ 'Markus', 'Matti' ]
firstOfNames.log("first of names")
// first of names <value:current> Markus

If we now modify either firstOfNames or names, the changes are reflected in the other:

names.set(["Vesa", "Matti"])
// names <value> [ 'Vesa', 'Matti' ]
// first of names <value> Vesa
// names <value> [ 'Markus', 'Matti' ]
// first of names <value> Markus

To the astute reader this might actually seem dangerous. Can we unintentionally create infinite loops? Atoms and lensed atoms do not form a constraint system where a change of one variable would cause the system to try to find an assignment of the other variables to satisfy constraints. When an atom or lensed atom is modified, only a single modification to the root atom is performed and then the change is propagated to observers. So, just by using atoms and lensed atoms it is not possible to write loops. However, when you try and combine atoms with other kinds of observables, you can write loops. In our experience this does not seem to happen too easily.

Aside from acyclicity, there are a couple of important properties that we should mention. First of all, the view method of atoms and lensed atoms does not create new mutable state, it merely creates a reference to existing state, namely to the state, represented as an immutable data structure, being referred to by the root atom. This means that we can regard the view method as a referentially transparent function. For example, in

const b1 = a.view(a_to_b)
const b2 = a.view(a_to_b)

we can regard b1 and b2 as equivalent. The other important property is that from the compositionality of lenses and the way lensed atoms are defined, we can derive the equation

a.view(a_to_b).view(b_to_c) = a.view(L.compose(a_to_b, b_to_c))
                            = a.view([a_to_b, b_to_c])

for composable lenses a_to_b and b_to_c and abstract mutable a. This just basically means that everything will work, or compose, as one should expect.

Editable lists

Let's then proceed to make an editable list of names. Here is one way to do it:

const ListOfNames = ({names}) =>
    {U.mapCached(i => <li key={i}><TextInput value={names.view(i)}/></li>,

Aside from putting the TextInput in place, we changed the way elements are identified for U.mapCached. In this case we identity them by their index. The function U.indices from karet.util maps a list [x0, ..., xN] to a list of indices [0, ..., N]. Those indices are then used as the ids.

The architecture

Ingredients are a start, but not enough. To bake a cake, we need a proper recipe. In fact, when we started using the Calmm ingredients, we had some ideas on how we could break down UI logic and combine the ingredients to solve problems, but it wasn't until we had gathered some experience using them that we started to see how to really do that effectively. For example, we didn't initially understand the full potential of combining atoms and lenses. We also unnecessarily complected models with observables.

Model :: JSON

In the Calmm architecture, model refers to the object or state being displayed by and manipulated through the UI. Usually it is just a JSON object or array that adheres to some schema. Most importantly, the model is just simple data. The model knows nothing about observables or anything else about the UI. It just is.

An important point is that we don't generally "normalize" or even expressly "design" the model for the UI components. Rather, we keep model data coming from the external world intact. We then use lenses to decompose the data into the forms that the UI components work with.

Meta :: JSON -> JSON

Meta refers to operations on the model. The term "meta" literally refers to the idea that it is "about the model". The operations are just simple synchronous functions, lenses and other kinds of operations on JSON. The meta is typically represented as either an object or a module containing functions or a combination of both.

The fact that models are just simple data and meta is just simple operations on said data means that meta becomes extremely simple to test. One does not need to worry about asynchronicity or observables. Mocking the model is as simple as writing a JSON expression.

Atom :: Atom m :> AbstractMutable m

Atoms take care of serializing access to their contents. They are created by giving some initial contents. Atoms then allow the contents to be shared, dependent upon and modified. In the context of Calmm, the atoms contain the application state and the contents are modified using operations from meta objects.

Atoms can be created in a variety of ways and with a variety of properties, such as undo-redo capability or local storage persistence or both, and then passed to controls that do not necessarily need to know about the special properties of the atom or about other controls that have been passed the same atom.

LensedAtom :: AbstractMutable w -> PLens w p -> LensedAtom p

Atoms can also be created from existing atoms by specifying a lens through which the contents of the existing atom are to be viewed and mutated. Unlike when creating a new atom with an initial value, an expression to create a lensed atom is referentially transparent.

Control :: [Observable p | AbstractMutable m | d]* -> VDOM

A control is a function from observables, modifiables and constants to VDOM.

We don't actually directly invoke the Control function. Instead we construct VDOM that contains a reference to the function and the actual arguments with which the control is to be called with. In other words, the evaluation of a JSX expression, <Control {...arguments}/>, to create VDOM, does not actually invoke the Control function, but it does evaluate the arguments. The function is invoked if and when the component is actually mounted for display. This latent invocation has the effect that as long as the expressions that we use to compute the arguments are referentially transparent then so is the VDOM expression as a whole.

In Calmm we choose to keep VDOM expressions referentially transparent. Note that basic observable combinators are referentially transparent and so is the act of creating a lensed atom. By keeping VDOM expressions referentially transparent, we gain important benefits such as being able to cache VDOM and being able to compose VDOM and components liberally. However, once a control is mounted, the function is invoked and the control as a whole is allowed to perform side-effects.

Advanced topics


A component is a function that returns React VDOM.

const A = () => <div>I'm a component.</div>

Typically components are named, but a component can also be an anonymous, first-class object.

() => <div>I'm also a component!</div>

When a component is put to use it is mounted, creating an instance of the component. An instance of a component may hold state and perform IO. When an instance is no longer needed, it is unmounted, which (when implemented correctly) tears down any state held by the instance and stops any IO performed by the instance. IOW, state generally only exists within the lifetime of an instance of a component.

const A = ({state = Atom("")}) =>
  <input value={state}
         onChange={e => state.set(}/>

We generally abuse terminology and speak of "components" when we actually refer to instances of components. It is nevertheless very important to distinguish between the two.

Generally the main purpose of any component is to produce output in the form of VDOM that will be rendered to DOM. A component may also produce other kinds of output and perform side-effects.

A component may have any number of parameters. A parameter can serve as an input, an output or both. Components can take components as parameters. Parameters, regardless of kind, can be shared by any number of components, which means that components may communicate with each other via parameters.

A composition of components is a VDOM expression that specifies a tree structure of component instantiations with their parameters.

<div>I am not a <em>component</em>! I'm a free <strong>composition</strong>!</div>

Understanding composition

The way components are expressed using only

  • reactive properties,
  • reactive variables, and
  • functions returning VDOM

and then composed as VDOM expressions in Calmm may seem limiting.

Don't we need some more exposed scaffolding or wiring to make it possible to create composition of components with input-output relationships?

The answer seems to be that it is enough for components to expose their inputs and outputs as parameters. Let's examine what this means.

Connecting components with reactive variables

The simplest case of creating a component that is the composition of two or more components is when nothing is shared by the composed components:

const NothingShared = () =>

Things get more interesting when we have components that take input:

const DisplaysInput = ({input}) =>

and components that produce output:

const ProducesOutput = ({output}) =>
  <input type="text" onChange={e => output.set(}/>

and we wish to create compositions of such components and route the outputs of some components to the inputs other components:

const Composition = ({variable = Atom("")}) =>
    <ProducesOutput output={variable}/>
    <DisplaysInput input={variable}/>

There are several important things to note here. First of all, the ProducesOutput component takes a parameter, a reference to a reactive variable, through which it produces output. The Composition component then uses a variable to connect the output of ProducesOutput to the input of DisplaysInput. The Composition also exposes the variable as a parameter with a default. This allows us to further compose the Composition component with other components:

const FurtherComposition = ({variable = Atom("")}) =>
    <Composition {...{variable}}/>
    <DisplaysInput input={variable}/>

When components expose their inputs and outputs as parameters, we can use reactive variables to flexibly wire the inputs and outputs of components together.

Note that reactive variables used for wiring components do not need to be concrete atoms. It is perfectly possible to connect components together using lensed atoms and make it so that the entire state of the application is ultimately stored in just a single atom.

Split vs atomic state

In the basic Calmm architecture, the model or state is supposed to be represented by a single JSON object transmitted to the component via a single atom. This is, in many ways, an ideal situation, that can often be achieved with relative ease, and is basically the recommended approach to designing components. Sometimes, however, it might be more convenient to split state into multiple atoms or it might be more convenient to design components that access state via multiple separate atoms.

Transactions with split state

In a lot of cases dividing state into multiple atoms poses no problems, because the division of state happens to match the desired state transitions. Sometimes, however, it becomes necessary to perform state transitions that require manipulating multiple atoms. Usually, in such a case, the transition should ideally be atomic so that, for example, side-effects such as HTTP requests performed in response to state changes would only be performed once. For those cases, the kefir.atom library provides the holding operation, which can be used to perform multiple state changes without intermediate event propagation.

Recomposing atoms as Molecules

What if we want to reuse a component that expects state as a single object stored in a single atom, but the application state has actually been divided into multiple objects stored in multiple atoms? For those situations, the kefir.atom library provides for a way to compose, with limitations, multiple separate atoms into a single Molecule.

Related work

Most papers in computer science describe how their author learned what someone else already knew. — Peter Landin

Ideas do not exist in vacuum. In fact, we make absolutely no claim of originality in any way. All of the ingredients of Calmm are actually old news:

  • Observables for dependent computations
  • Embedding observables into VDOM
  • Atoms for storing state
  • Lenses for decomposing state

In fact, much of Calmm was initially shaped by a search of way to make it possible to program in ways similar to what could be done using Reagent and (early versions of) WebSharper UI.Next. Calmm in turn served as inspiration for Focal.

The idea of combining atoms and lenses came from Bacon.Model, which we used initially. Later we learned that WebSharper UI.Next added support for lenses roughly just two months before our project started.

The partial lenses library came about as we wanted to give components not just the ability to update existing data, but also the abilities to insert new data and to remove existing data. To make that both possible and convenient, we simply made the inputs and outputs of all lenses optional. Similar ideas can be found in many other lens libraries.

Great ideas are discovered!

Going further

By intention, this document does not try to compare Calmm to other UI programming approaches. With the intention to gain a deeper understanding, the following documents explore how concepts in Calmm relate to concepts found in other UI programming approaches:

Our production codebase is unfortunately not publicly available. However, we have built some small examples: