re-frame/README.md

Derived Values, Flowing

This, milord, is my family's axe. We have owned it for almost nine hundred years, see. Of course, sometimes it needed a new blade. And sometimes it has required a new handle, new designs on the metalwork, a little refreshing of the ornamentation . . . but is this not the nine hundred-year-old axe of my family? And because it has changed gently over time, it is still a pretty good axe, y'know. Pretty good.

-- Terry Pratchett, The Fifth Elephant

Why Should You Care?

Either:

1. You want to develop an SPA in ClojureScript, and you are looking for a framework; or
2. You believe that, by early 2015, React had won the JavaScript framework wars and you are curious about the bigger implications. Is the combination of reactive programming, functional programming and immutable data going to completely change everything? And, if so, what would that look like in a language that embraces those paradigms?
3. You are curious about a modern lisp like Clojure. How could a language THAT old still be so revered? And what are these whispers I hear about "Data Oriented" architectures, and leveragable schemas. I want to expand my mind.

re-frame

re-frame is a pattern for writing SPAs in ClojureScript, using Reagent.

This repo contains both a description of this pattern and a reference implementation.

To quote McCoy: "It's MVC, Jim, but not as we know it".

To build a re-frame app, you:

• design your app's information model (data layer)
• write and register subscription functions (query layer)
• write Reagent view functions (view layer)
• write and register event handler functions (control layer and/or state transition layer)

Features:

1. The functions you write are pure, so the computational pieces of your app can be described, understood and tested independently. Dependency Injection isn't needed.
2. re-frame looks after the conveyance of data - the transport of data between pure functions.
3. One half of this "conveyance" involves a reactive data flow - a dynamic, unidirectional Signal graph.
4. The resulting architecture involves "derived data" flowing in a two-stage, loop. Without realising it, you will be explicitly modelling time.
5. It is fast, straight out of the box.
6. The surprising thing about re-frame is how simple it is. The reference implementation is 350 lines of (ClojureScript) code. Learn it in an afternoon.
7. But it scales up nicely to more complex apps. Frameworks are just pesky overhead at small scale - measure them instead by how they help you tame the complexity of bigger apps.
8. Re-frame is impressively buzzword compliant: it has FRP-nature, unidirectional data flow, pristinely pure functions, interceptors, conveyor belts, statechart-friendliness (FSM) and claims an immaculate hammock conception. It also has a charming xkcd reference (soon) and a hilarious, insiders-joke T-shirt, ideal for conferences (in design). What could possibly go wrong?

Warning: That was the summary. What follows is a long-ish tutorial/explanation.

Tutorial Table of Contents

• What Problem Does It Solve?
• Guiding Philosophy
• FRP Clarifications
• Explaining re-frame
• On Data
• The Big Ratom
• The Benefits Of Data-In-The-One-Place
• Flow
• How Flow Happens In Reagent
• Components
• Truth Interlude
• Components Like Templates?
• React etc.
• Subscribe
• Just A Read-Only Cursor?
• The Signal Graph
• A More Efficient Signal Graph
• The 2nd Flow
• Event Flow
• What are events?
• Dispatching Events
• Event Handlers
• Routing
• Control Via FSM
• As A Reduce
• Derived Data, Everywhere, flowing
• Logging And Debugging
• In Summary
• Where Do I Go Next
• Licence

What Problem Does It Solve?

First, we decided to build our SPA apps with ClojureScript, then we choose Reagent, then we had a problem.

For all its considerable brilliance, Reagent (+ React) delivers only the 'V' part of a traditional MVC framework.

But apps involve much more than V. Where does the control logic go? How is state stored & manipulated? etc.

We read up on Pedestal App, Flux, Hoplon, Om, early Elm, etc and re-frame is the architecture that emerged. Since that time we have kept a close eye on developments like Elm Architecture, Om.Next, BEST, Cycle.js, etc. They have taught us much.

re-frame does have M, V, and C parts but they aren't objects, they are pure functions (or pure data), and they are wired together via data flows. It is sufficiently different in nature from (traditional, Smalltalk) MVC that calling it MVC would be confusing. I'd love an alternative.

Perhaps it is a RACES framework - Reactive-Atom Component Event Subscription framework (I love the smell of acronym in the morning).

Or, if we distill to pure essence, DDATWD - Derived Data All The Way Down.

TODO: get acronym down to 3 chars! Get an image of stacked Turtles for DDATWD insider's joke, conference T-Shirt.

Guiding Philosophy

First, above all we believe in the one true Dan Holmsand, creator of Reagent, and his divine instrument the ratom. We genuflect towards Sweden once a day.

Second, we believe in ClojureScript, immutable data and the process of building a system out of pure functions.

Third, we believe in the primacy of data. Within the Clojure community this view is sometimes captured with aphorisms like "data is the ultimate in late binding" and "data > functions > macros". There is also talk of Data Oriented Architectures.

Fourth, we believe that [Reactive Programming] is one honking great idea. But only up to a point. It is a beautiful solution to one half of the data conveyance problem.

Finally, we believe in command-query separation. As a result, we specifically don't like two way data binding or read/write cursors which promote the two way flow of data. As programs get bigger, we've found that their use seems to encourage control logic into all the wrong places. Sadly, each generation of programmers seems to have to rediscover this principle.

FRP Clarifications

We'll get to the meat in a second, I promise, but first one final, useful diversion ...

Terminology in the FRP world seems to get people hot under the collar. Those who believe in continuous-time semantics might object to me describing re-frame as having FRP-nature. They'd claim that it does something different from pure FRP, which is true.

But, these days, FRP seems to have become a "big tent" (a broad church?). Broad enough perhaps that re-frame can be in the far, top, left paddock of the tent, via a series of qualifications: re-frame has "discrete, dynamic, asynchronous, push FRP-ish-nature" without "glitch free" guarantees. (Surprisingly, "glitch" has specific meaning in FRP).

If you are new to FRP, or reactive programming generally, browse these resources before going further (certainly read the first two):

• Creative Explanation
• Reactive Programming Backgrounder
• presentation (video) by Alan Dipert (co-author of Hoplon)
• serious pants Elm thesis

And for the love of all that is good, please watch this terrific StrangeLoop presentation (40 mins). Watch what happens when you re-imagine a database as a stream!! Look at all the problems that are solved. Think about that: shared mutable state (the root of all evil), re-imagined as a stream!! Blew my socks off.

re-frame tries to be Derived Data everywhere, flowing. Or perhaps, Derived Data All The Way Down (an infinite loop of Derived Data). More explanation on all these claims soon.

Explaining re-frame

To explain re-frame, I'll incrementally develop a diagram, describing each part as it is added.

I'll be using Reagent at an intermediate level, so you will need to have done some introductory Reagent tutorials before going on. Try The Introductory Tutorial or this one or Building Single Page Apps with Reagent.

On Data

Well-formed Data at rest is as close to perfection in programming as it gets. All the crap that had to happen to put it there however...

— Fogus (@fogus) April 11, 2014

The Big Ratom

Our re-frame diagram starts (very modestly) with Fogus' well-formed data at rest bit:

app-db

re-frame says that you put your data into one place which we'll call app-db. You should specify an information model for the data in this one place, giving it a powerful schema.

Now, this advice is not the slightest bit controversial for 'real' databases, right? You'd happily put all your well-formed data into PostgreSQL or MySQL.

But within a running application (in memory), it is different. If you have a background in OO, this data-in-one-place business is a really, really hard one to swallow. You've spent your life breaking systems into pieces, organised around behaviour and trying to hide the data. I still wake up in a sweat some nights thinking about all that Clojure data lying around exposed and passive.

But, as Fogus reminded us, data at rest is the easy bit.

From here on in this document, we'll assume app-db is one of these:

(def app-db  (reagent/atom {}))    ;; a Reagent atom, containing a map

The reference implementation already creates and manages an internal app-db for you, you don't need to declare one yourself.

Although it is a Reagent atom (hereafter ratom), I'd encourage you to think of it as an in-memory database. It will contain structured data. You will need to query that data. You will perform CRUD and other transformations on it. You'll often want to transact on this database atomically, etc. So "in-memory database" seems a more useful paradigm than plain old map-in-atom.

A clarification: app-db doesn't actually have to be a reagent/atom containing a map. It could, for example, be a datascript database. In fact, any database which is reactive (can tell you when it changes) would do. (We'd love! to be using datascript - so damn cool - but we had too much data in our apps. If you were to use it, you'd have to tweak the reference implementation a bit, perhaps using this inspiration).

The Benefits Of Data-In-The-One-Place

Here's the big one: because there is a single source of truth, we do not have to write code which synchronizes state between many different stateful components. I cannot stress too much how significant this is. You end up writing less code and an entire class of bugs is eliminated. The simplicity it brings is breathtaking.

Further benefits also flow:

1. Undo/Redo becomes straight forward to implement. With a central store for state, it is easy to snapshot and restore. This is particularly the case because the data is immutable.
2. The data in app-db has a strong schema so, at any moment, we can validate all the data in the system. All of it. This enables us to catch errors easily and early. It increases confidence in the way that Types increases confidence, only schemas can potentially supply more leverage than types.
3. The ability to genuinely model control via FSMs (discussed later)
4. The ability to do time travel debugging, even in a production setting. More soon.

Flow

Arguments from authority ...

Everything flows, nothing stands still. (Panta rhei)

No man ever steps in the same river twice for it's not the same river and he's not the same man.

Heraclitus 500 BC. Who, being Greek, had never seen a frozen river. alt version.

Think of an experience from your childhood. Something you remember clearly, something you can see, feel, maybe even smell, as if you were really there. After all you really were there at the time, weren’t you? How else could you remember it? But here is the bombshell: you weren’t there. Not a single atom that is in your body today was there when that event took place .... Matter flows from place to place and momentarily comes together to be you. Whatever you are, therefore, you are not the stuff of which you are made. If that does not make the hair stand up on the back of your neck, read it again until it does, because it is important.

Steve Grand

How Flow Happens In Reagent

To implement FRP, Reagent provides a ratom and a reaction. re-frame uses both of these building blocks, so let's now make sure we understand them.

ratoms behave just like normal ClojureScript atoms. You can swap! and reset! them, watch them, etc.

From a ClojureScript perspective, the purpose of an atom is to hold mutable data. From a re-frame perspective, we'll tweak that paradigm slightly and view a ratom as having a value that changes over time. Seems like a subtle distinction, I know, but because of it, re-frame sees a ratom as a Signal. Pause and read this.

The 2nd building block, reaction, acts a bit like a function. It's a macro which wraps some computation (a block of code) and returns a ratom holding the result of that computation.

The magic thing about a reaction is that the computation it wraps will be automatically re-run whenever 'its inputs' change, producing a new output (return) value.

Eh, how?

Well, the computation is just a block of code, and if that code dereferences one or more ratoms, it will be automatically re-run (recomputing a new return value) whenever any of these dereferenced ratoms change.

To put that yet another way, a reaction detects a computation's input Signals (aka input ratoms) and it will watch them, and when, later, it detects a change in one of them, it will re-run that computation, and it will reset! the new result of that computation into the ratom originally returned.

So, the ratom returned by a reaction is itself a Signal. Its value will change over time when the computation is re-run.

So, via the interplay between ratoms and reactions, values 'flow' into computations and out again, and then into further computations, etc. "Values" flow (propagate) through the Signal graph.

But this Signal graph must be without cycles, because cycles cause mayhem! re-frame achieves a unidirectional flow.

While the mechanics are different, reaction has the intent of map in Elm and defc= in Hoplon.

Right, so that was a lot of words. Some code to clarify:

(ns example1
 (:require-macros [reagent.ratom :refer [reaction]])  ;; reaction is a macro
 (:require        [reagent.core  :as    reagent]))

(def app-db  (reagent/atom {:a 1}))           ;; our root ratom  (signal)

(def ratom2  (reaction {:b (:a @app-db)}))    ;; reaction wraps a computation, returns a signal
(def ratom3  (reaction (condp = (:b @ratom2)  ;; reaction wraps another computation
                             0 "World"
                             1 "Hello")))

;; Notice that both computations above involve de-referencing a ratom:
;;   - app-db in one case
;;   - ratom2 in the other
;; Notice that both reactions above return a ratom.
;; Those returned ratoms hold the (time varying) value of the computations.

(println @ratom2)    ;; ==>  {:b 1}       ;; a computed result, involving @app-db
(println @ratom3)    ;; ==> "Hello"       ;; a computed result, involving @ratom2

(reset!  app-db  {:a 0})       ;; this change to app-db, triggers re-computation
                               ;; of ratom2
                               ;; which, in turn, causes a re-computation of ratom3

(println @ratom2)    ;; ==>  {:b 0}    ;; ratom2 is result of {:b (:a @app-db)}
(println @ratom3)    ;; ==> "World"    ;; ratom3 is automatically updated too.

So, in FRP-ish terms, a reaction will produce a "stream" of values over time (it is a Signal), accessible via the ratom it returns.

Okay, that was all important background information for what is to follow. Back to the diagram ...

Components

Extending the diagram, we introduce components:

app-db  -->  components  -->  Hiccup

When using Reagent, your primary job is to write one or more components. This is the view layer.

Think about components as pure functions - data in, Hiccup out. Hiccup is ClojureScript data structures which represent DOM. Here's a trivial component:

(defn greet
  []
  [:div "Hello ratoms and reactions"])

And if we call it:

(greet)
;; ==>  [:div "Hello ratoms and reactions"]

You'll notice that our component is a regular Clojure function, nothing special. In this case, it takes no parameters and it returns a ClojureScript vector (formatted as Hiccup).

Here is a slightly more interesting (parameterised) component (function):

(defn greet                    ;; greet has a parameter now
  [name]                       ;; 'name' is a ratom  holding a string
  [:div "Hello "  @name])      ;; dereference 'name' to extract the contained value

;; create a ratom, containing a string
(def n (reagent/atom "re-frame"))

;; call our `component` function, passing in a ratom
(greet n)
;; ==>  [:div "Hello " "re-frame"]    returns a vector

So components are easy - at core they are a render function which turns data into Hiccup (which will later become DOM).

Now, let's introduce reaction into this mix. On the one hand, I'm complicating things by doing this, because Reagent allows you to be ignorant of the mechanics I'm about to show you. (It invisibly wraps your components in a reaction allowing you to be blissfully ignorant of how the magic happens.)

On the other hand, it is useful to understand exactly how the Reagent Signal graph is wired, because in a minute, when we get to subscriptions, we'll be directly using reaction, so we might as well bite the bullet here and now ... and, anyway, it is pretty easy...

(defn greet                ;; a component - data in, Hiccup out.
  [name]                   ;; name is a ratom
  [:div "Hello "  @name])  ;; dereference name here, to extract the value within

(def n (reagent/atom "re-frame"))

;; The computation '(greet n)' returns Hiccup which is stored into 'hiccup-ratom'
(def hiccup-ratom  (reaction (greet n)))    ;; <-- use of reaction !!!

;; what is the result of the initial computation ?
(println @hiccup-ratom)
;; ==>  [:div "Hello " "re-frame"]    ;; returns hiccup  (a vector of stuff)

;; now change 'n'
;; 'n' is an input Signal for the reaction above.
;; Warning: 'n' is not an input signal because it is a parameter. Rather, it is
;; because 'n' is dereferenced within the execution of the reaction's computation.
;; reaction notices what ratoms are dereferenced in its computation, and watches
;; them for changes.
(reset! n "blah")            ;;    n changes

;; The reaction above will notice the change to 'n' ...
;; ... and will re-run its computation ...
;; ... which will have a new "return value"...
;; ... which will be "reset!" into "hiccup-ratom"
(println @hiccup-ratom)
;; ==>   [:div "Hello " "blah"]    ;; yep, there's the new value

So, as n changes value over time (via a reset!), the output of the computation (greet n) changes, which in turn means that the value in hiccup-ratom changes. Both n and hiccup-ratom are FRP Signals. The Signal graph we created causes data to flow from n into hiccup-ratom.

Derived Data, flowing.

Truth Interlude

I haven't been entirely straight with you:

1. Reagent re-runs reactions (re-computations) via requestAnimationFrame. So a re-computation happens about 16ms after an input Signals change is detected, or after the current thread of processing finishes, whichever is the greater. So if you are in a bREPL and you run the lines of code above one after the other too quickly, you might not see the re-computation done immediately after n gets reset!, because the next animationFrame hasn't run (yet). But you could add a (reagent.core/flush) after the reset! to force re-computation to happen straight away.

2. reaction doesn't actually return a ratom. But it returns something that has ratom-nature, so we'll happily continue believing it is a ratom and no harm will come to us.

On with the rest of my lies and distortions...

Components Like Templates?

A component such as greet is like the templates you'd find in Django, Rails, Handlebars or Mustache -- it maps data to HTML -- except for two massive differences:

1. you have the full power of ClojureScript available to you (generating a Clojure data structure). The downside is that these are not "designer friendly" HTML templates.
2. these templates are reactive. When their input Signals change, they are automatically rerun, producing new DOM. Reagent adroitly shields you from the details, but the renderer of any component is wrapped by a reaction. If any of the the "inputs" to that render change, the render is rerun.

React etc.

Okay, so we have some unidirectional, dynamic, async, discrete FRP-ish data flow happening here.

Question: To which ocean does this river of data flow? Answer: The DOM ocean.

The full picture:

app-db  -->  components  -->  Hiccup  -->  Reagent  -->  VDOM  -->  React  --> DOM

Best to imagine this process as a pipeline of 3 functions. Each function takes data from the previous step, and produces (derived!) data for the next step. In the next diagram, the three functions are marked (f1, f2, f3). The unmarked nodes are derived data, produced by one step, to be input to the following step. Hiccup, VDOM and DOM are all various forms of HTML markup (in our world that's data).

app-db  -->  components  -->  Hiccup  -->  Reagent  -->  VDOM  -->  React  -->  DOM
               f1                           f2                      f3

In abstract ClojureScript syntax terms, you could squint and imagine the process as:

(-> app-db
   components    ;; produces Hiccup
   Reagent       ;; produces VDOM   (virtual DOM that React understands)
   React         ;; produces HTML   (which magically and efficiently appears on the page).
   Browser       ;; produces pixels
   Monitor)      ;; produces photons?

Via the interplay between ratom and reaction, changes to app-db stream into the pipeline, where it undergoes successive transformations, until pixels colour the monitor you to see.

Derived Data, flowing. Every step is acting like a pure function and turning data into new data.

All well and good, and nice to know, but we don't have to bother ourselves with most of the pipeline. We just write the components part and Reagent/React will look after the rest. So back we go to that part of the picture ...

Subscribe

components render the app's state as hiccup.

app-db  -->  components

components (view layer) need to query aspects of app-db (data layer).

But how?

Let's pause to consider our dream solution for this part of the flow. components would:

• obtain data from app-db (their job is to turn this data into hiccup).
• obtain this data via a (possibly parameterised) query over app-db. Think database kind of query.
• automatically recompute their hiccup output, as the data returned by the query changes, over time
• use declarative queries. Components should know as little as possible about the structure of app-db. SQL? Datalog?

re-frame's subscriptions are an attempt to live this dream. As you'll see, they fall short on the declarative query part, but they comfortably meet the other requirements.

As a re-frame app developer, your job will be to write and register one or more "subscription handlers" - functions that do a named query.

Your subscription functions must return a value that changes over time (a Signal). I.e. they'll be returning a reaction or, at least, the ratom produced by a reaction.

Rules:

• components never source data directly from app-db, and instead, they use a subscription.
• subscriptions are only ever used by components (they are never used in, say, event handlers).

Here's a component using a subscription:

(defn greet         ;; outer, setup function, called once
  []
  (let [name-ratom  (subscribe [:name-query])]    ;; <---- subscribing happens here
     (fn []        ;; the inner, render function, potentially called many times.
         [:div "Hello" @name-ratom])))

First, note this is a Form-2 component (there are 3 forms).

Previously in this document, we've used the simplest, Form-1 components (no setup was required, just render). With Form-2 components, there's a function returning a function:

• the returned function is the render function. Behind the scenes, Reagent will wrap this render function in a reaction to make it produce new Hiccup when its input Signals change. In our example above, that means it will rerun every time name-ratom changes.
• the outer function is a setup function, called once for each instance of the component. Notice the use of 'subscribe' with the parameter :name-query. That creates a Signal through which new values are supplied over time; each new value causing the returned function (the actual renderer) to be run.

It is important to distinguish between a new instance of the component versus the same instance of a component reacting to a new value. Simplistically, a new component is returned for every unique value the setup function (i.e. the outer function) is called with. This allows subscriptions based on initialisation values to be created, for example:

  (defn my-cmp [row-id]
    (let [row-state (subscribe [row-id])]
      (fn [row-id]
        [:div (str "Row: " row-id " is " @row-state)])))

In this example, [my-cmp 1][my-cmp 2] will create two instances of my-cmp. Each instance will re-render when its internal row-state signal changes.

subscribe is always called like this:

   (subscribe  [query-id some optional query parameters])

There is only one (global) subscribe function and it takes one parameter, assumed to be a vector.

The first element in the vector (shown as query-id above) identifies/names the query and the other elements are optional query parameters. With a traditional database a query might be:

select * from customers where name="blah"

In re-frame, that would be done as follows: (subscribe [:customer-query "blah"]) which would return a ratom holding the customer state (a value which might change over time!).

So let's now look at how to write and register the subscription handler for :customer-query

(defn customer-query     ;; a query over 'app-db' which returns a customer
   [db, [sid cid]]      ;; query fns are given 'app-db', plus vector given to subscribe
   (assert (= sid :customer-query))   ;; subscription id was the first element in the vector
   (reaction (get-in @db [:path :to :a :map cid])))    ;; re-runs each time db changes

;; register our query handler
(register-sub
   :customer-query       ;; the id (the name of the query)
   customer-query)       ;; the function which will perform the query

Notice how the handler is registered to handle :customer-query subscriptions.

Rules and Notes:

• you'll be writing one or more handlers, and you will need to register each one.
• handlers are functions which take two parameters: the db atom, and the vector given to subscribe.
• components tend to be organised into a hierarchy, often with data flowing from parent to child via parameters. So not every component needs a subscription. Very often the values passed in from a parent component are sufficient.
• subscriptions can only be used in Form-2 components and the subscription must be in the outer setup function and not in the inner render function. So the following is wrong (compare to the correct version above)

(defn greet         ;; a Form-1 component - no inner render function
  []
  (let [name-ratom  (subscribe [:name-query])]    ;; Eek! subscription in renderer
    [:div "Hello" @name-ratom]))

Why is this wrong? Well, this component would be re-rendered every time app-db changed, even if the value in name-ratom (the result of the query) stayed the same. If you were to use a Form-2 component instead, and put the subscription in the outer functions, then there'll be no re-render unless the value queried (i.e. name-ratom) changed.

Just A Read-Only Cursor?

Subscriptions are different to read-only cursors.

Yes, subscriptions abstract away (hide) the data source, like a Cursor, but they also allow for computation. To put that another way, they can create derived data from app-db (a Materialised View of app-db).

Imagine that our app-db contained :items - a vector of maps. And imagine that we wanted to display these items sorted by one of their attributes. And that we only want to display the top 20 items.

This is the sort of "derived data" which a subscription can deliver. (And as we'll see, more efficiently than a Cursor).

The Signal Graph

Let's sketch out the situation described above ...

app-db would be a bit like this (items is a vector of maps):

(def L  [{:name "a" :val 23 :flag "y"}
        {:name "b" :val 81 :flag "n"}
        {:name "c" :val 23 :flag "y"}])

(def  app-db (reagent/atom  {:items L
                            :sort-by :name}))     ;; sorted by the :name attribute

The subscription-handler might be written:

(register-sub
 :sorted-items      ;; the query id  (the name of the query)
 (fn [db [_]]       ;; the handler for the subscription
   (reaction
      (let [items      (get-in @db [:items])     ;; extract items from db
            sort-attr  (get-in @db [:sort-by])]  ;; extract sort key from db
          (sort-by sort-attr items)))))          ;; return them sorted

Subscription handlers are given two parameters:

1. app-db - that's a reagent/atom which holds ALL the app's state. This is the "database" on which we perform the "query".
2. the vector originally supplied to subscribe. In our case, we ignore it.

In the example above, notice that the reaction depends on the input Signal: db. If db changes, the query is re-run.

In a component, we could use this query via subscribe:

(defn items-list         ;; Form-2 component - outer, setup function, called once
  []
  (let [items   (subscribe [:sorted-items])   ;; <--   subscribe called with name
        num     (reaction (count @items))     ;; Woh! a reaction based on the subscription
        top-20  (reaction (take 20 @items))]  ;; Another dependent reaction
     (fn []
       [:div
           (str "there's " @num " of these suckers. Here's top 20")     ;; rookie mistake to leave off the @
           (into [:div ] (map item-render @top-20))])))   ;; item-render is another component, not shown

There's a bit going on in that let, most of it tortuously contrived, just so I can show off chained reactions. Okay, okay, all I wanted really was an excuse to use the phrase "chained reactions".

The calculation of num is done by a reaction which has items as an input Signal. And, as we saw, items is itself a reaction over two other signals (one of them the app-db).

So this is a Signal Graph. Data is flowing through computation into renderer, which produce Hiccup, etc.

A More Efficient Signal Graph

But there is a small problem. The approach above might get inefficient, if :items gets long.

Every time app-db changes, the :sorted-items query is going to be re-run and it's going to re-sort :items. But :items might not have changed. Some other part of app-db may have changed.

We don't want to perform this computationally expensive re-sort each time something unrelated in app-db changes.

Luckily, we can easily fix that up by tweaking our subscription function so that it chains reactions:

(register-sub
 :sorted-items             ;; the query id
 (fn [db [_]]
   (let [items      (reaction (get-in @db [:some :path :to :items]))]  ;; reaction #1
         sort-attr  (reaction (get-in @db [:sort-by]))]                ;; reaction #2
       (reaction (sort-by @sort-attr @items)))))                       ;; reaction #3

The original version had only one reaction which would be re-run completely each time app-db changed. This new version, has chained reactions. The 1st and 2nd reactions just extract from db. They will run each time app-db changes. But they are cheap. The 3rd one does the expensive computation using the result from the first two.

That 3rd, expensive reaction will be re-run when either one of its two input Signals change, right? Not quite. reaction will only re-run the computation when one of the inputs has changed in value.

reaction compares the old input Signal value with the new Signal value using identical?. Because we're using immutable data structures (thank you ClojureScript), reaction can perform near instant checks for change on even deeply nested and complex input Signals. And reaction will then stop unneeded propagation of identical? values through the Signal graph.

In the example above, reaction #3 won't re-run until :items or :sort-by are different (do not test identical? to their previous value), even though app-db itself has changed (presumably somewhere else).

Hideously contrived example, but I hope you get the idea. It is all screamingly efficient.

Summary:

• you can chain reactions.
• a reaction will only be re-run when its input Signals test not identical? to previous value.
• As a result, unnecessary Signal propagation is eliminated using highly efficient checks, even for large, deep nested data structures.

The 2nd Flow

At the top, I said that re-frame had two data flows.

The data flow from app-db to the DOM is the first half of the story. We now need to consider the 2nd part of the story: the flow in the opposite direction.

While the first flow has FRP-nature, the 2nd flow does not. Well, not at first glance anyway.

When I think about these two flows, I imagine one of those school diagrams showing the water cycle.

Rivers taking water down to the oceans, and evaporation/clouds/wind taking water back over the mountains to fall again as rain or snow. Repeat.

There is a cycle, but it is handled by two independent flows.

With re-frame, it is not water that is flowing, it is data.

Event Flow

Events are what flow in the opposite direction.

In response to user interaction, a DOM will generate events like "clicked delete button on item 42" or "unticked the checkbox for 'send me spam'".

These events have to be "handled". The code doing this handling might mutate app state (in app-db), or request more data from the server, or POST somewhere and wait for a response, etc.

In fact, all these actions ultimately result in changes to the app-db.

An application has many handlers, and collectively they represent the control layer of the application.

In re-frame, the backwards data flow of events happens via a conveyor belt:

app-db  -->  components  -->  Hiccup  -->  Reagent  -->  VDOM  -->  React  -->  DOM
 ^                                                                              |
 |                                                                              v
 handlers <-------------------  events  -----------------------------------------
                          a "conveyor belt" takes events
                          from the DOM to the handlers

Generally, when the user manipulates the GUI, the state of the application changes. In our case, that means the app-db will change. After all, it is the state. And the DOM presented to the user is a function of that state.

So that tends to be the cycle:

1. the user clicks something which causes an event to be dispatched
2. a handler manages the event
3. and causes app-db to change (mutation happens here!)
4. which then causes a re-render
5. the user sees something different
6. goto #1

That's our water cycle.

Because handlers are that part of the system which does app-db mutation, you could almost imagine them as a "stored procedures" on a database. Almost. Stretching it? We do like our in-memory database analogies.

What are events?

Events are data. You choose the format.

In our reference implementation we choose a vector format. For example:

[:delete-item 42]

The first item in the vector identifies the event and the rest of the vector is the optional parameters -- in the example above, the id (42) of the item to delete.

Here are some other example events:

   [:yes-button-clicked]
   [:set-spam-wanted false]
   [[:complicated :multi :part :key] "a parameter" "another one"  45.6]

Rule: events are pure data. No dirty tricks like putting callback functions on the wire. You know who you are.

Dispatching Events

Events tend to start in the DOM in response to user actions. They are dispatched.

For example, a button component might be like this:

   (defn yes-button
       []
       [:div  {:class "button-class"
               :on-click  #(dispatch [:yes-button-clicked])}
               "Yes"])

Notice the on-click DOM handler:

   #(dispatch [:yes-button-clicked])

With re-frame, we try to keep the DOM as passive as possible. We do not want our views containing any control logic. That "on-click" is as simple as we can make it.

There's a single dispatch function in the entire framework, and it takes one parameter: the event (vector) to be dispatched (which is pure simple, lovely data, flowing).

Let's update our diagram to show dispatch:

app-db  -->  components  -->  Hiccup  -->  Reagent  -->  VDOM  -->  React  -->  DOM
 ^                                                                              |
 |                                                                              v
 handlers <----------------------------------------  (dispatch [event-id  event params])

Rule: components are as passive and minimal as possible when it comes to handling events. They dispatch pure data and nothing more.

Event Handlers

Collectively, event handlers provide the control logic in a re-frame application.

An event handler is a pure function of two parameters:

1. current value in app-db. Note: that's the map in app-db, not the atom itself.
2. an event (represented as a vector)

It returns the new value which should be reset! into app-db.

An example handler:

(defn handle-delete
   [app-state [_ item-id]]          ;; notice how event vector is destructured -- 2nd parameter
   (dissoc-in app-state [:some :path item-id]))     ;; return a modified version of 'app-state'

Handling an event invariably involves mutating the value in app-db (which is provided as the first parameter). An item is added here, or one is deleted there. So, often simple CRUD, but sometimes much more, and sometimes with async results.

But the app-db mutation is ultimately handled by re-frame (it does the reset!). That leaves your event handlers pure. As a result, they tend to be easy to test and understand. Many are almost trivial.

There's more to event handlers than can be covered here in this introductory tutorial. Read up on issues like Middleware in the Wiki.

Routing

When dispatch is passed an event vector, it just puts that event onto a conveyor belt.

The consumer on the end of the conveyor is a router which will organise for that event to be processed by the right handler.

app-db  -->  components  -->  Hiccup  -->  Reagent  -->  VDOM  -->  React  -->  DOM
 ^                                                                              |
 |                                                                              v
 handlers <-----  router  <-----------------------  (dispatch [event-id  event params])

The router will:

1. inspect the 1st element of the arriving vector
2. look in its registry for the handler which is registered for this kind of event
3. call that handler with two parameters: (1) the current value in app-db and (2) the event vector
4. reset! the returned value back into app-db.

As a re-frame app developer, your job is to write handlers for each kind of event, and then to register those handlers with the router.

Here's how we would register our event handler:

(reg-event-db
  :delete-item 			;; the event id (name)
  (fn [db [_ item]]		;; the handler function for that event
  ...					
  )       

Any arriving event vector which has :delete-item as the first element will now be routed to our handler.

Control Via FSM

Above, I commented that event handlers collectively represent the "control layer" of the application. They contain logic which interprets arriving events and they "step" the application "forward" via mutations to app-db.

Our delete-handler above is trivial, but as an application grows more features, the logic in many handlers will become more complicated, and they will have to query BOTH the current state of the app AND the arriving event vector to determine what action to take.

If the app is in logical State A, and event X arrives, then the handler will move the app to logical state B (by changing values in app-db).

Sound like anything you learned in those Theory Of Computation lectures?

That's right - as an app becomes more complex, the handlers are likely to be collectively implementing a Finite State Machine:

• your app is in a certain logical state (defined by the current values in app-db)
• the arriving event vector represents a trigger.
• the event handler implements "a transition", subject to BOTH the current logical state and the arriving trigger.
• after the handler has run, the transition may have moved the app into a new logical state.
• Repeat.

Not every app has lots of logical states, but many do, and if you are implementing one of them, then formally recognising it and using a technique like state charts will help greatly in getting a clean design and a nice datamodel.

The beauty of re-frame from a FSM point of view is that all the data is in one place - unlike OO systems where the data is distributed (and synchronized) across many objects. So implementing your control logic as a FSM is both possible and natural in re-frame, whereas it is often difficult and contrived to do so in other kinds of architecture (in my experience).

As A Reduce

So here's another way of thinking about what's happening with this data flow - another useful mental model.

First, imagine that all the events ever dispatched by a certain running app were stored in a collection. So, if when the app started, the user clicked on button X then the first item in this collection would be the event generated by that button, and then, if next the user moved a slider, the associated event would be the next item in the collection, and so on and so on. We'd end up with a collection of event vectors.

Second, remind yourself that the combining function of a reduce takes two parameters:

1. the current state of the reduction and
2. the next collection member to fold in.

Then notice that event handlers take two parameters too:

1. the current state of app-db
2. the next item to fold in.

Which is the same as a combining function in a reduce!!

So now we can introduce the new mental model: at any point in time, the value in app-db is the result of performing a reduce over the entire collection of events dispatched in the app up until that time. The combining function for this reduce is the set of handlers.

It is almost like app-db is the temporary place where this imagined perpetual reduce stores its on-going reduction.

Derived Data, Everywhere, flowing

Have you watched that StrangeLoop presentation yet? I hope so. Database as a stream, right?

If you have then, given the explanation above, you might twig to the idea that app-db is really a derived value (of the perpetual reduce).

And yet, it acts as the authoritative source of state in the app. And yet, it isn't, it is simply a piece of derived state. And yet, it is the source.

Hmm. This is an infinite loop of sorts. Derived data is flowing around the loop, reactively, through pure functions. There is a pause in the loop whenever we wait for a new event, but the moment we get it, it's another iteration of the "derived data" FRP loop.

Derived values, all the way down, forever.

Good news. If you've read this far, your insiders T-shirt will be arriving soon - it will feature turtles and xkcd. We're still working on the hilarious caption bit. Open a repo issue with a suggestion.

Back to the more pragmatic world ...

Logging And Debugging

How did that exception happen, you wonder, shaking your head? What did the user do immediately prior to the exception? What state was the app in that this event was so disastrous?

To debug it, you need to know this information:

1. the state of the app immediately before the exception
2. What final event then caused your app to fall in a screaming mess.

Well, with re-frame you need to record (have available):

1. A recent checkpoint of the app state in app-db (perhaps the initial state)
2. all the events dispatched since the last checkpoint, up to the point where the exception occurred.

Note: that's all just data. Pure, lovely loggable data.

If you have that data, then you can reproduce the exception.

re-frame allows you to time travel. Install the "checkpoint" state into app-db and then "play forward" through the collection dispatched events.

The only way the app "moves forwards" is via events. "Replaying events" moves you step by step towards the exception causing problem.

This is utterly, utterly perfect for debugging assuming, of course, you are in a position to capture a checkpoint, and the events since then.

In Summary

re-frame has two distinct flows, and I claim they are BOTH FRP in nature. The first is clearly FRP. The second one is conceptually FRP, but you do have to squint.

All the parts are simple. The parts are easy to understand in isolation. The parts are composed so that derived data flows in a perpetual reactive loop, through pure functions.

To build an app using re-frame, you'll have to:

• design your app's data structure.
• write and register subscription functions (query layer).
• write component functions (view layer).
• write and register event handler functions (control layer and/or state transition layer).

Where Do I Go Next?

Next:

• look at the examples: https://github.com/Day8/re-frame/tree/master/examples
• read the docs: https://github.com/Day8/re-frame/blob/master/docs/README.md

Then:

• use the lein template: https://github.com/Day8/re-frame-template

Also, if you want reusable layout and widget components, consider this sister project: https://github.com/Day8/re-com

Licence

Copyright © 2015 Michael Thompson

Distributed under The MIT License (MIT) - See LICENSE.txt