re-frame/docs/WIP/scratch-pad.md

Table Of Contents

• Implements Reactive Data Flows
• Flow
• Reactive Programming
  • How Flow Happens In Reagent
• Components
  • Truth Interlude
  • React etc.
• Subscribe
  • Just A Read-Only Cursor?
• The Signal Graph
• A More Efficient Signal Graph

XXX

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.

Implements Reactive Data Flows

This document describes how re-frame implements the reactive data flows in dominoes 4 and 5 (queries and views).

It explains the low level mechanics of the process which not something you need to know initially. So, you can defer reading and understanding this until later, if you wish. But you should at some point circle back and grok it. It isn't hard at all.

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

Reactive Programming

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

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.

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...

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.

Back to the more pragmatic world ...