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Functional Reactive Programming / Compositional Event Systems

Leonardo Borges
Leonardo Borges

This document provides an overview of functional reactive programming (FRP) and compositional event systems (CES). It discusses how FRP approaches handling time-varying values like regular values. It presents an example of modeling game movements reactively using key press events. It also demonstrates how CES can be used to handle asynchronous workflows by turning network responses into observable streams. The document compares CES to other approaches like core.async and discusses benefits of CES like supporting multiple subscribers.

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Taming Asynchronous Workflows with Functional Reactive Programming EuroClojure - Kraków, 2014 Leonardo Borges @leonardo_borges www.leonardoborges.com www.thoughtworks.com
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About ‣ ThoughtWorker ‣ Functional Programming & Clojure advocate ‣ Founder of the Sydney Clojure User Group ‣ Currently writing “Clojure Reactive Programming”
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Taming Asynchronous Workflows with Functional Reactive Programming
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Taming Asynchronous Workflows with Functional Reactive Programming Compositional Event Systems
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http://bit.ly/conal-ces
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http://bit.ly/rx-commit
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http://bit.ly/reactive-cocoa-commit
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There are only two hard things in Computer Science: cache invalidation and naming things. - Phil Karlton :)
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Ok, so what’s the difference?
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‣ Created in 1997 by Conal Elliott for the reactive animations framework Fran, in Haskell ‣ Since then other implementations have appeared: reactive-banana, NetWire, Sodium (all in Haskell) ‣ And then FRP-inspired ones: Rx[.NET | Java | JS], Baconjs, reagi (Clojurescript) ‣ Main abstractions: Behaviors e Events More about FRP
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‣ Created in 1997 by Conal Elliott for the reactive animations framework Fran, in Haskell ‣ Since then other implementations have appeared: reactive-banana, NetWire, Sodium (all in Haskell) ‣ And then FRP-inspired ones: Rx[.NET | Java | JS], Baconjs, reagi (Clojure[script]) ‣ Main abstractions: Behaviors e Events ‣ Traditionally defined as: type Behavior a = [Time] -> [a]! type Event a = [Time] -> [Maybe a] More about FRP
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We’ll be focusing on Compositional Event Systems
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Prelude
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Imperative programming describes computations as a series of actions which modify program state var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; Requires a variable to store state
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var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; We iterate over the array Imperative programming describes computations as a series of actions which modify program state
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var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; And then we filter the items… Imperative programming describes computations as a series of actions which modify program state
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var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; …and perform the multiplication in the same function Imperative programming describes computations as a series of actions which modify program state
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(def numbers [1 2 3 4 5])! ! (def result! (->> numbers! (filter even?)! (reduce *)))! ! (prn result)! ! ;; 8 In functional programming, we describe what we want to do but not how we want it done
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That is, there are no variables with local state and we get better re-use from single purpose functions
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Compositional Event Systems brings the same principle to values we work with daily: DOM events (clicks, key presses, mouse movement), Ajax calls…
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Let’s look at an example
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Game movements in Javascript var JUMP = 38, CROUCH = 40,! LEFT = 37, RIGHT = 39,! FIRE = 32;! ! function goRight (){! $(‘h1').html("Going right...");! }! ! function goLeft (){! $(‘h1').html("Going left...");! }! ! function jump (){! $('h1').html("Jumping...");! }! ! function crouch (){! $('h1').html("Crouching...");! }! ! function fire (){! $('h1').html("Firing...");! }
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Game movements in Javascript $(window.document).keyup(function(event){! switch(event.keyCode){! case JUMP :! jump();! break;! case CROUCH:! crouch();! break;! case LEFT :! goLeft();! break;! case RIGHT :! goRight();! break;! case FIRE :! fire();! break;! };! });
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We now have limitations similar to the multiplication example
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Let us think key presses as a list of keys over a period of time
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This leads to the following solution
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Reactive game movements (def UP 38) (def RIGHT 39)! (def DOWN 40) (def LEFT 37)! (def FIRE 32) (def PAUSE 80)! ! ! (def source (.fromEvent js/Rx.Observable js/window "keyup"));! ! (-> source (.filter #(= UP %)) (.subscribe jump))! (-> source (.filter #(= DOWN %)) (.subscribe crouch))! (-> source (.filter #(= RIGHT %)) (.subscribe go-right))! (-> source (.filter #(= LEFT %)) (.subscribe go-left))! (-> source (.filter #(= FIRE %)) (.subscribe fire))! http://bit.ly/rxjava-github http://bit.ly/rxjs-github
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Reactive game movements (def UP 38) (def RIGHT 39)! (def DOWN 40) (def LEFT 37)! (def FIRE 32) (def PAUSE 80)! ! ! (def source (.fromEvent js/Rx.Observable js/window "keyup"));! ! (-> source (.filter #(= UP %)) (.subscribe jump))! (-> source (.filter #(= DOWN %)) (.subscribe crouch))! (-> source (.filter #(= RIGHT %)) (.subscribe go-right))! (-> source (.filter #(= LEFT %)) (.subscribe go-left))! (-> source (.filter #(= FIRE %)) (.subscribe fire))! http://bit.ly/rxjava-github http://bit.ly/rxjs-github
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Behaviours ;; behavior examples! (def time-b (r/behavior (System/currentTimeMillis)))! @time-b! ;; 1403691712988! @time-b! ;; 1403691714156! http://bit.ly/reagi
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Behaviours to Event Streams http://bit.ly/reagi (def time-e (r/sample 1000 time-b))! ! (->> time-e (r/map println))! ;; t + 1 sec! ;; 1403692132586! ;; t + 2 sec:! ;; 1403692133587!
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Combinators (-> (Observable/return 42)! (.map #(* % 2))! (.subscribe println))! ! ;; 84! ! (-> (Observable/from [10 20 30])! (.map #(* % 2))! (.reduce +)! (.subscribe println))! ! ;; 120!
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Combinators: flatMap / selectMany (defn project-range [n]! (Rx.Observable/return (range n)))! ! (-> (Observable/from [1 2 3])! (.selectMany project-range)! (.subscribe (rx/fn* println)))! ! ;; 0! ;; 0! ;; 1! ;; 0! ;; 1! ;; 2!
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?
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(Observable/from [1 2 3]) 1 2 3
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(-> (Observable/from [1 2 3])! (.selectMany project-range)! …) (project-range 2) 0 1 (project-range 1) 0 (project-range 3) 0 1 2 0 0 1 0 1 2
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What about network IO? ‣ Callback hell :( ‣ Clojure promises don’t compose ‣ Promises in JS are slightly better but have limitations ‣ They work well for a single level of values ‣ However are a poor composition mechanism ‣ What if we have a series of values that changes over time?
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Demo: Simple polling app
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This is what the server gives us {:id 7! :question "Which is the best music style?"! :results {:a 10! :b 47! :c 17}}!
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And this is what we want ‣ Render results ‣ Continuously poll server every 2 secs ‣ If current question is the same as the previous one update results; Otherwise: ‣ Stop polling; ‣ Display countdown message; ‣ Render new question and results; ‣ Restart polling;
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The core idea
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First, we need to turn the results into a stream 4 3 3 2 1 1
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So we duplicate the stream, skipping one element 4 3 3 2 1 1 5 4 3 3 2 1 (skip 1)
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Finally, we zip the streams 4 3 3 2 1 1 5 4 3 3 2 1 zip [5,4] [4,3] [3,3] [3,2] [2,1] [1,1]
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The core idea, in code (defn results-observable! "Returns an Observable that yields server-side questions/results"! []! (.create js/Rx.Observable! (fn [observer]! (srm/rpc! (poll-results) [resp]! (.onNext observer resp))! (fn [] (.log js/console "Disposed")))))
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The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.publish)! (.refCount))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Turn results into a stream
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The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.publish)! (.refCount))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Clone stream, skip one
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The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Zip them together
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Can we do better?
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Buffering (def results-buffer! "Returns an Observable with results buffered into a 2-element vector"! (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.bufferWithCount 2)))!
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Live Demo
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"FRP is about handling time-varying values like they were regular values" - Haskell Wiki
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"FRP is about handling time-varying values like they were regular values" - Haskell Wiki It also applies to FRP-inspired systems
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Why not use core.async?
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Previously, with CES (-> (Observable/from [10 20 30])! (.map (rx/fn [v] (* v 2)))! (.reduce (rx/fn* +)! (.subscribe (rx/fn* println)))))
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With core.async (defn from-array [coll]! (let [stream-c (chan)]! (go (doseq [n coll]! (>! stream-c n))! (close! stream-c))! stream-c))! ! (def c (->> (async-from [10 20 30])! (a/map< #(* % 2))! (a/reduce + 0)))! ! (go-loop []! (when-let [v (<! c)]! (println v)! (recur)))!
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Multiple subscribers with CES (def sum-of-squares (-> (Observable/from [10 20 30])! (.map (rx/fn [v] (* v 2)))! (.reduce (rx/fn* +))))! ! ! ! (.subscribe sum-of-squares (rx/action* println)) ;; 120! (.subscribe sum-of-squares (rx/action* println)) ;; 120
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Multiple subscribers with core.async [1/3] (def in (chan))! (def sum-of-squares (->> in! (a/map< #(* % 2))! (a/reduce + 0)))!
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Multiple subscribers with core.async [2/3] (def publication (pub sum-of-squares (fn [_] :n)))! ! (def sub-1 (chan))! (def sub-2 (chan))! ! (sub publication :n sub-1)! (sub publication :n sub-2)
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Multiple subscribers with core.async [3/3] (go (doseq [n [10 20 30]]! (>! in n))! (close! in))! ! (go-loop []! (when-let [v (<! sub-1)]! (prn v)! (recur))) ;; 120! ! (go-loop []! (when-let [v (<! sub-2)]! (prn v)! (recur))) ;; 120
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core.async operates at a lower level of abstraction
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it is however a great foundation for a FRP-inspired framework
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Reagi - shown earlier - is built on top of core.async http://bit.ly/reagi
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Bonus example: a reactive API to AWS
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Bonus example: a reactive API to AWS ‣ Retrieve list of resources from a stack (CloudFormation.describeStackResources) ‣ For each EC2 Instance, call EC2.describeInstances to retrieve status ‣ For each RDS Instance, call RDS.describeDBInstances to retrieve status ‣ Merge results and display
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Step 1: turn api calls into streams (defn resources-stream [stack-name]! (.create js/Rx.Observable! (fn [observer]! (.describeStackResources js/cloudFormation #js {"StackName" : stackName}! (fn [err data]! (if err! (.onError observer err)! (doseq [resource data]! (.onNext observer resource))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))
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Step 1: turn api calls into streams (defn ec2-instance-stream [resource-ids]! (.create js/Rx.Observable! (fn [observer]! (.describeInstaces js/ec2 #js {"InstanceIds" resource-ids}! (fn [err data]! (if err! (.onError observer err)! (doseq [instance data]! (.onNext observer instance)))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))!
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Step 1: turn api calls into streams (defn rds-instance-stream [resource-id]! (.create js/Rx.Observable! (fn [observer]! (.describeDBInstances js/rds #js {"DBInstanceIdentifier" resource-id}! (fn [err data]! (if err! (.onError observer err)! (.onNext observer data))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))
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Step 2: transform the different API responses into a common output (def resources (resourcesStream "my-stack"))! ! (def ec2-data (-> resources! (.filter ec2?)! (.map :resource-id)! (.flatMap ec2-instance-stream)! (.map (fn [data] {:instance-id ...! :status ...}))))! ! (def rds-data (-> resources! (.filter rds?)! (.map :resource-id)! (.flatMap rds-instance-stream)! (.map (fn [data] {:instance-id ...! :status ...}))))!
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Step 3: merge results and update UI (-> ec2-data! (.merge rds-data)! (.reduce conj [])! (.subscribe (fn [data] (update-interface ...))))
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Easy to reason about, maintain and test
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References ! ‣ Conal Elliott “Functional Reactive Animation” paper: http://bit.ly/conal-frp ! FRP-inspired frameworks: ‣ Reagi: http://bit.ly/reagi ‣ RxJS: http://bit.ly/rxjs-github ‣ RxJava: http://bit.ly/rxjava-github ‣ Bacon.js: https://github.com/baconjs/bacon.js ! FRP implementations: ‣ Reactive Banana: http://www.haskell.org/haskellwiki/Reactive-banana ‣ Elm: http://elm-lang.org/ ‣ NetWire: http://www.haskell.org/haskellwiki/Netwire
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Thanks! Questions? Leonardo Borges @leonardo_borges www.leonardoborges.com www.thoughtworks.com
Taming Asynchronous Workflows with Functional Reactive Programming EuroClojure - Kraków, 2014 Leonardo Borges @leonardo_borges www.leonardoborges.com www.thoughtworks.com
About ‣ ThoughtWorker ‣ Functional Programming & Clojure advocate ‣ Founder of the Sydney Clojure User Group ‣ Currently writing “Clojure Reactive Programming”
Taming Asynchronous Workflows with Functional Reactive Programming
Taming Asynchronous Workflows with Functional Reactive Programming Compositional Event Systems
http://bit.ly/conal-ces
http://bit.ly/rx-commit
http://bit.ly/reactive-cocoa-commit
There are only two hard things in Computer Science: cache invalidation and naming things. - Phil Karlton :)
Ok, so what’s the difference?
‣ Created in 1997 by Conal Elliott for the reactive animations framework Fran, in Haskell ‣ Since then other implementations have appeared: reactive-banana, NetWire, Sodium (all in Haskell) ‣ And then FRP-inspired ones: Rx[.NET | Java | JS], Baconjs, reagi (Clojurescript) ‣ Main abstractions: Behaviors e Events More about FRP
‣ Created in 1997 by Conal Elliott for the reactive animations framework Fran, in Haskell ‣ Since then other implementations have appeared: reactive-banana, NetWire, Sodium (all in Haskell) ‣ And then FRP-inspired ones: Rx[.NET | Java | JS], Baconjs, reagi (Clojure[script]) ‣ Main abstractions: Behaviors e Events ‣ Traditionally defined as: type Behavior a = [Time] -> [a]! type Event a = [Time] -> [Maybe a] More about FRP
We’ll be focusing on Compositional Event Systems
Prelude
Imperative programming describes computations as a series of actions which modify program state var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; Requires a variable to store state
var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; We iterate over the array Imperative programming describes computations as a series of actions which modify program state
var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; And then we filter the items… Imperative programming describes computations as a series of actions which modify program state
var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; …and perform the multiplication in the same function Imperative programming describes computations as a series of actions which modify program state
(def numbers [1 2 3 4 5])! ! (def result! (->> numbers! (filter even?)! (reduce *)))! ! (prn result)! ! ;; 8 In functional programming, we describe what we want to do but not how we want it done
That is, there are no variables with local state and we get better re-use from single purpose functions
Compositional Event Systems brings the same principle to values we work with daily: DOM events (clicks, key presses, mouse movement), Ajax calls…
Let’s look at an example
Game movements in Javascript var JUMP = 38, CROUCH = 40,! LEFT = 37, RIGHT = 39,! FIRE = 32;! ! function goRight (){! $(‘h1').html("Going right...");! }! ! function goLeft (){! $(‘h1').html("Going left...");! }! ! function jump (){! $('h1').html("Jumping...");! }! ! function crouch (){! $('h1').html("Crouching...");! }! ! function fire (){! $('h1').html("Firing...");! }
Game movements in Javascript $(window.document).keyup(function(event){! switch(event.keyCode){! case JUMP :! jump();! break;! case CROUCH:! crouch();! break;! case LEFT :! goLeft();! break;! case RIGHT :! goRight();! break;! case FIRE :! fire();! break;! };! });
We now have limitations similar to the multiplication example
Let us think key presses as a list of keys over a period of time
This leads to the following solution
Reactive game movements (def UP 38) (def RIGHT 39)! (def DOWN 40) (def LEFT 37)! (def FIRE 32) (def PAUSE 80)! ! ! (def source (.fromEvent js/Rx.Observable js/window "keyup"));! ! (-> source (.filter #(= UP %)) (.subscribe jump))! (-> source (.filter #(= DOWN %)) (.subscribe crouch))! (-> source (.filter #(= RIGHT %)) (.subscribe go-right))! (-> source (.filter #(= LEFT %)) (.subscribe go-left))! (-> source (.filter #(= FIRE %)) (.subscribe fire))! http://bit.ly/rxjava-github http://bit.ly/rxjs-github
Reactive game movements (def UP 38) (def RIGHT 39)! (def DOWN 40) (def LEFT 37)! (def FIRE 32) (def PAUSE 80)! ! ! (def source (.fromEvent js/Rx.Observable js/window "keyup"));! ! (-> source (.filter #(= UP %)) (.subscribe jump))! (-> source (.filter #(= DOWN %)) (.subscribe crouch))! (-> source (.filter #(= RIGHT %)) (.subscribe go-right))! (-> source (.filter #(= LEFT %)) (.subscribe go-left))! (-> source (.filter #(= FIRE %)) (.subscribe fire))! http://bit.ly/rxjava-github http://bit.ly/rxjs-github
Behaviours ;; behavior examples! (def time-b (r/behavior (System/currentTimeMillis)))! @time-b! ;; 1403691712988! @time-b! ;; 1403691714156! http://bit.ly/reagi
Behaviours to Event Streams http://bit.ly/reagi (def time-e (r/sample 1000 time-b))! ! (->> time-e (r/map println))! ;; t + 1 sec! ;; 1403692132586! ;; t + 2 sec:! ;; 1403692133587!
Combinators (-> (Observable/return 42)! (.map #(* % 2))! (.subscribe println))! ! ;; 84! ! (-> (Observable/from [10 20 30])! (.map #(* % 2))! (.reduce +)! (.subscribe println))! ! ;; 120!
Combinators: flatMap / selectMany (defn project-range [n]! (Rx.Observable/return (range n)))! ! (-> (Observable/from [1 2 3])! (.selectMany project-range)! (.subscribe (rx/fn* println)))! ! ;; 0! ;; 0! ;; 1! ;; 0! ;; 1! ;; 2!
?
(Observable/from [1 2 3]) 1 2 3
(-> (Observable/from [1 2 3])! (.selectMany project-range)! …) (project-range 2) 0 1 (project-range 1) 0 (project-range 3) 0 1 2 0 0 1 0 1 2
What about network IO? ‣ Callback hell :( ‣ Clojure promises don’t compose ‣ Promises in JS are slightly better but have limitations ‣ They work well for a single level of values ‣ However are a poor composition mechanism ‣ What if we have a series of values that changes over time?
Demo: Simple polling app
This is what the server gives us {:id 7! :question "Which is the best music style?"! :results {:a 10! :b 47! :c 17}}!
And this is what we want ‣ Render results ‣ Continuously poll server every 2 secs ‣ If current question is the same as the previous one update results; Otherwise: ‣ Stop polling; ‣ Display countdown message; ‣ Render new question and results; ‣ Restart polling;
The core idea
First, we need to turn the results into a stream 4 3 3 2 1 1
So we duplicate the stream, skipping one element 4 3 3 2 1 1 5 4 3 3 2 1 (skip 1)
Finally, we zip the streams 4 3 3 2 1 1 5 4 3 3 2 1 zip [5,4] [4,3] [3,3] [3,2] [2,1] [1,1]
The core idea, in code (defn results-observable! "Returns an Observable that yields server-side questions/results"! []! (.create js/Rx.Observable! (fn [observer]! (srm/rpc! (poll-results) [resp]! (.onNext observer resp))! (fn [] (.log js/console "Disposed")))))
The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.publish)! (.refCount))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Turn results into a stream
The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.publish)! (.refCount))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Clone stream, skip one
The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Zip them together
Can we do better?
Buffering (def results-buffer! "Returns an Observable with results buffered into a 2-element vector"! (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.bufferWithCount 2)))!
Live Demo
"FRP is about handling time-varying values like they were regular values" - Haskell Wiki
"FRP is about handling time-varying values like they were regular values" - Haskell Wiki It also applies to FRP-inspired systems
Why not use core.async?
Previously, with CES (-> (Observable/from [10 20 30])! (.map (rx/fn [v] (* v 2)))! (.reduce (rx/fn* +)! (.subscribe (rx/fn* println)))))
With core.async (defn from-array [coll]! (let [stream-c (chan)]! (go (doseq [n coll]! (>! stream-c n))! (close! stream-c))! stream-c))! ! (def c (->> (async-from [10 20 30])! (a/map< #(* % 2))! (a/reduce + 0)))! ! (go-loop []! (when-let [v (<! c)]! (println v)! (recur)))!
Multiple subscribers with CES (def sum-of-squares (-> (Observable/from [10 20 30])! (.map (rx/fn [v] (* v 2)))! (.reduce (rx/fn* +))))! ! ! ! (.subscribe sum-of-squares (rx/action* println)) ;; 120! (.subscribe sum-of-squares (rx/action* println)) ;; 120
Multiple subscribers with core.async [1/3] (def in (chan))! (def sum-of-squares (->> in! (a/map< #(* % 2))! (a/reduce + 0)))!
Multiple subscribers with core.async [2/3] (def publication (pub sum-of-squares (fn [_] :n)))! ! (def sub-1 (chan))! (def sub-2 (chan))! ! (sub publication :n sub-1)! (sub publication :n sub-2)
Multiple subscribers with core.async [3/3] (go (doseq [n [10 20 30]]! (>! in n))! (close! in))! ! (go-loop []! (when-let [v (<! sub-1)]! (prn v)! (recur))) ;; 120! ! (go-loop []! (when-let [v (<! sub-2)]! (prn v)! (recur))) ;; 120
core.async operates at a lower level of abstraction
it is however a great foundation for a FRP-inspired framework
Reagi - shown earlier - is built on top of core.async http://bit.ly/reagi
Bonus example: a reactive API to AWS
Bonus example: a reactive API to AWS ‣ Retrieve list of resources from a stack (CloudFormation.describeStackResources) ‣ For each EC2 Instance, call EC2.describeInstances to retrieve status ‣ For each RDS Instance, call RDS.describeDBInstances to retrieve status ‣ Merge results and display
Step 1: turn api calls into streams (defn resources-stream [stack-name]! (.create js/Rx.Observable! (fn [observer]! (.describeStackResources js/cloudFormation #js {"StackName" : stackName}! (fn [err data]! (if err! (.onError observer err)! (doseq [resource data]! (.onNext observer resource))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))
Step 1: turn api calls into streams (defn ec2-instance-stream [resource-ids]! (.create js/Rx.Observable! (fn [observer]! (.describeInstaces js/ec2 #js {"InstanceIds" resource-ids}! (fn [err data]! (if err! (.onError observer err)! (doseq [instance data]! (.onNext observer instance)))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))!
Step 1: turn api calls into streams (defn rds-instance-stream [resource-id]! (.create js/Rx.Observable! (fn [observer]! (.describeDBInstances js/rds #js {"DBInstanceIdentifier" resource-id}! (fn [err data]! (if err! (.onError observer err)! (.onNext observer data))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))
Step 2: transform the different API responses into a common output (def resources (resourcesStream "my-stack"))! ! (def ec2-data (-> resources! (.filter ec2?)! (.map :resource-id)! (.flatMap ec2-instance-stream)! (.map (fn [data] {:instance-id ...! :status ...}))))! ! (def rds-data (-> resources! (.filter rds?)! (.map :resource-id)! (.flatMap rds-instance-stream)! (.map (fn [data] {:instance-id ...! :status ...}))))!
Step 3: merge results and update UI (-> ec2-data! (.merge rds-data)! (.reduce conj [])! (.subscribe (fn [data] (update-interface ...))))
Easy to reason about, maintain and test
References ! ‣ Conal Elliott “Functional Reactive Animation” paper: http://bit.ly/conal-frp ! FRP-inspired frameworks: ‣ Reagi: http://bit.ly/reagi ‣ RxJS: http://bit.ly/rxjs-github ‣ RxJava: http://bit.ly/rxjava-github ‣ Bacon.js: https://github.com/baconjs/bacon.js ! FRP implementations: ‣ Reactive Banana: http://www.haskell.org/haskellwiki/Reactive-banana ‣ Elm: http://elm-lang.org/ ‣ NetWire: http://www.haskell.org/haskellwiki/Netwire
Thanks! Questions? Leonardo Borges @leonardo_borges www.leonardoborges.com www.thoughtworks.com
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Functional Reactive Programming / Compositional Event Systems

  • 1. Taming Asynchronous Workflows with Functional Reactive Programming EuroClojure - Kraków, 2014 Leonardo Borges @leonardo_borges www.leonardoborges.com www.thoughtworks.com
  • 2. About ‣ ThoughtWorker ‣ Functional Programming & Clojure advocate ‣ Founder of the Sydney Clojure User Group ‣ Currently writing “Clojure Reactive Programming”
  • 3. Taming Asynchronous Workflows with Functional Reactive Programming
  • 4. Taming Asynchronous Workflows with Functional Reactive Programming Compositional Event Systems
  • 8. There are only two hard things in Computer Science: cache invalidation and naming things. - Phil Karlton :)
  • 9. Ok, so what’s the difference?
  • 10. ‣ Created in 1997 by Conal Elliott for the reactive animations framework Fran, in Haskell ‣ Since then other implementations have appeared: reactive-banana, NetWire, Sodium (all in Haskell) ‣ And then FRP-inspired ones: Rx[.NET | Java | JS], Baconjs, reagi (Clojurescript) ‣ Main abstractions: Behaviors e Events More about FRP
  • 11. ‣ Created in 1997 by Conal Elliott for the reactive animations framework Fran, in Haskell ‣ Since then other implementations have appeared: reactive-banana, NetWire, Sodium (all in Haskell) ‣ And then FRP-inspired ones: Rx[.NET | Java | JS], Baconjs, reagi (Clojure[script]) ‣ Main abstractions: Behaviors e Events ‣ Traditionally defined as: type Behavior a = [Time] -> [a]! type Event a = [Time] -> [Maybe a] More about FRP
  • 12. We’ll be focusing on Compositional Event Systems
  • 14. Imperative programming describes computations as a series of actions which modify program state var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; Requires a variable to store state
  • 15. var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; We iterate over the array Imperative programming describes computations as a series of actions which modify program state
  • 16. var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; And then we filter the items… Imperative programming describes computations as a series of actions which modify program state
  • 17. var result = 1;! numbers.forEach(function(n){! if(n % 2 === 0) {! result *= n;! }! });! console.log( result );! // 8! var numbers = [1,2,3,4,5]; …and perform the multiplication in the same function Imperative programming describes computations as a series of actions which modify program state
  • 18. (def numbers [1 2 3 4 5])! ! (def result! (->> numbers! (filter even?)! (reduce *)))! ! (prn result)! ! ;; 8 In functional programming, we describe what we want to do but not how we want it done
  • 19. That is, there are no variables with local state and we get better re-use from single purpose functions
  • 20. Compositional Event Systems brings the same principle to values we work with daily: DOM events (clicks, key presses, mouse movement), Ajax calls…
  • 21. Let’s look at an example
  • 22. Game movements in Javascript var JUMP = 38, CROUCH = 40,! LEFT = 37, RIGHT = 39,! FIRE = 32;! ! function goRight (){! $(‘h1').html("Going right...");! }! ! function goLeft (){! $(‘h1').html("Going left...");! }! ! function jump (){! $('h1').html("Jumping...");! }! ! function crouch (){! $('h1').html("Crouching...");! }! ! function fire (){! $('h1').html("Firing...");! }
  • 23. Game movements in Javascript $(window.document).keyup(function(event){! switch(event.keyCode){! case JUMP :! jump();! break;! case CROUCH:! crouch();! break;! case LEFT :! goLeft();! break;! case RIGHT :! goRight();! break;! case FIRE :! fire();! break;! };! });
  • 24. We now have limitations similar to the multiplication example
  • 25. Let us think key presses as a list of keys over a period of time
  • 26. This leads to the following solution
  • 27. Reactive game movements (def UP 38) (def RIGHT 39)! (def DOWN 40) (def LEFT 37)! (def FIRE 32) (def PAUSE 80)! ! ! (def source (.fromEvent js/Rx.Observable js/window "keyup"));! ! (-> source (.filter #(= UP %)) (.subscribe jump))! (-> source (.filter #(= DOWN %)) (.subscribe crouch))! (-> source (.filter #(= RIGHT %)) (.subscribe go-right))! (-> source (.filter #(= LEFT %)) (.subscribe go-left))! (-> source (.filter #(= FIRE %)) (.subscribe fire))! http://bit.ly/rxjava-github http://bit.ly/rxjs-github
  • 28. Reactive game movements (def UP 38) (def RIGHT 39)! (def DOWN 40) (def LEFT 37)! (def FIRE 32) (def PAUSE 80)! ! ! (def source (.fromEvent js/Rx.Observable js/window "keyup"));! ! (-> source (.filter #(= UP %)) (.subscribe jump))! (-> source (.filter #(= DOWN %)) (.subscribe crouch))! (-> source (.filter #(= RIGHT %)) (.subscribe go-right))! (-> source (.filter #(= LEFT %)) (.subscribe go-left))! (-> source (.filter #(= FIRE %)) (.subscribe fire))! http://bit.ly/rxjava-github http://bit.ly/rxjs-github
  • 29. Behaviours ;; behavior examples! (def time-b (r/behavior (System/currentTimeMillis)))! @time-b! ;; 1403691712988! @time-b! ;; 1403691714156! http://bit.ly/reagi
  • 30. Behaviours to Event Streams http://bit.ly/reagi (def time-e (r/sample 1000 time-b))! ! (->> time-e (r/map println))! ;; t + 1 sec! ;; 1403692132586! ;; t + 2 sec:! ;; 1403692133587!
  • 31. Combinators (-> (Observable/return 42)! (.map #(* % 2))! (.subscribe println))! ! ;; 84! ! (-> (Observable/from [10 20 30])! (.map #(* % 2))! (.reduce +)! (.subscribe println))! ! ;; 120!
  • 32. Combinators: flatMap / selectMany (defn project-range [n]! (Rx.Observable/return (range n)))! ! (-> (Observable/from [1 2 3])! (.selectMany project-range)! (.subscribe (rx/fn* println)))! ! ;; 0! ;; 0! ;; 1! ;; 0! ;; 1! ;; 2!
  • 33. ?
  • 35. (-> (Observable/from [1 2 3])! (.selectMany project-range)! …) (project-range 2) 0 1 (project-range 1) 0 (project-range 3) 0 1 2 0 0 1 0 1 2
  • 36. What about network IO? ‣ Callback hell :( ‣ Clojure promises don’t compose ‣ Promises in JS are slightly better but have limitations ‣ They work well for a single level of values ‣ However are a poor composition mechanism ‣ What if we have a series of values that changes over time?
  • 38. This is what the server gives us {:id 7! :question "Which is the best music style?"! :results {:a 10! :b 47! :c 17}}!
  • 39. And this is what we want ‣ Render results ‣ Continuously poll server every 2 secs ‣ If current question is the same as the previous one update results; Otherwise: ‣ Stop polling; ‣ Display countdown message; ‣ Render new question and results; ‣ Restart polling;
  • 41. First, we need to turn the results into a stream 4 3 3 2 1 1
  • 42. So we duplicate the stream, skipping one element 4 3 3 2 1 1 5 4 3 3 2 1 (skip 1)
  • 43. Finally, we zip the streams 4 3 3 2 1 1 5 4 3 3 2 1 zip [5,4] [4,3] [3,3] [3,2] [2,1] [1,1]
  • 44. The core idea, in code (defn results-observable! "Returns an Observable that yields server-side questions/results"! []! (.create js/Rx.Observable! (fn [observer]! (srm/rpc! (poll-results) [resp]! (.onNext observer resp))! (fn [] (.log js/console "Disposed")))))
  • 45. The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.publish)! (.refCount))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Turn results into a stream
  • 46. The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.publish)! (.refCount))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Clone stream, skip one
  • 47. The core idea, in code (def results-connectable! "Zips results-observable with itself, but shifted by 1.! This simulates a 'buffer' or 'window' of results"! (let [obs (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable))! obs-1 (.skip obs 1)]! (.zip obs obs-1 (fn [prev curr]! {:prev prev! :curr curr}))))! Zip them together
  • 48. Can we do better?
  • 49. Buffering (def results-buffer! "Returns an Observable with results buffered into a 2-element vector"! (-> js/Rx.Observable! (.interval 2000)! (.selectMany results-observable)! (.bufferWithCount 2)))!
  • 51. "FRP is about handling time-varying values like they were regular values" - Haskell Wiki
  • 52. "FRP is about handling time-varying values like they were regular values" - Haskell Wiki It also applies to FRP-inspired systems
  • 53. Why not use core.async?
  • 54. Previously, with CES (-> (Observable/from [10 20 30])! (.map (rx/fn [v] (* v 2)))! (.reduce (rx/fn* +)! (.subscribe (rx/fn* println)))))
  • 55. With core.async (defn from-array [coll]! (let [stream-c (chan)]! (go (doseq [n coll]! (>! stream-c n))! (close! stream-c))! stream-c))! ! (def c (->> (async-from [10 20 30])! (a/map< #(* % 2))! (a/reduce + 0)))! ! (go-loop []! (when-let [v (<! c)]! (println v)! (recur)))!
  • 56. Multiple subscribers with CES (def sum-of-squares (-> (Observable/from [10 20 30])! (.map (rx/fn [v] (* v 2)))! (.reduce (rx/fn* +))))! ! ! ! (.subscribe sum-of-squares (rx/action* println)) ;; 120! (.subscribe sum-of-squares (rx/action* println)) ;; 120
  • 57. Multiple subscribers with core.async [1/3] (def in (chan))! (def sum-of-squares (->> in! (a/map< #(* % 2))! (a/reduce + 0)))!
  • 58. Multiple subscribers with core.async [2/3] (def publication (pub sum-of-squares (fn [_] :n)))! ! (def sub-1 (chan))! (def sub-2 (chan))! ! (sub publication :n sub-1)! (sub publication :n sub-2)
  • 59. Multiple subscribers with core.async [3/3] (go (doseq [n [10 20 30]]! (>! in n))! (close! in))! ! (go-loop []! (when-let [v (<! sub-1)]! (prn v)! (recur))) ;; 120! ! (go-loop []! (when-let [v (<! sub-2)]! (prn v)! (recur))) ;; 120
  • 60. core.async operates at a lower level of abstraction
  • 61. it is however a great foundation for a FRP-inspired framework
  • 62. Reagi - shown earlier - is built on top of core.async http://bit.ly/reagi
  • 63. Bonus example: a reactive API to AWS
  • 64. Bonus example: a reactive API to AWS ‣ Retrieve list of resources from a stack (CloudFormation.describeStackResources) ‣ For each EC2 Instance, call EC2.describeInstances to retrieve status ‣ For each RDS Instance, call RDS.describeDBInstances to retrieve status ‣ Merge results and display
  • 65. Step 1: turn api calls into streams (defn resources-stream [stack-name]! (.create js/Rx.Observable! (fn [observer]! (.describeStackResources js/cloudFormation #js {"StackName" : stackName}! (fn [err data]! (if err! (.onError observer err)! (doseq [resource data]! (.onNext observer resource))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))
  • 66. Step 1: turn api calls into streams (defn ec2-instance-stream [resource-ids]! (.create js/Rx.Observable! (fn [observer]! (.describeInstaces js/ec2 #js {"InstanceIds" resource-ids}! (fn [err data]! (if err! (.onError observer err)! (doseq [instance data]! (.onNext observer instance)))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))!
  • 67. Step 1: turn api calls into streams (defn rds-instance-stream [resource-id]! (.create js/Rx.Observable! (fn [observer]! (.describeDBInstances js/rds #js {"DBInstanceIdentifier" resource-id}! (fn [err data]! (if err! (.onError observer err)! (.onNext observer data))! (.onCompleted observer)))! (fn [] (.log js/console "Disposed")))))
  • 68. Step 2: transform the different API responses into a common output (def resources (resourcesStream "my-stack"))! ! (def ec2-data (-> resources! (.filter ec2?)! (.map :resource-id)! (.flatMap ec2-instance-stream)! (.map (fn [data] {:instance-id ...! :status ...}))))! ! (def rds-data (-> resources! (.filter rds?)! (.map :resource-id)! (.flatMap rds-instance-stream)! (.map (fn [data] {:instance-id ...! :status ...}))))!
  • 69. Step 3: merge results and update UI (-> ec2-data! (.merge rds-data)! (.reduce conj [])! (.subscribe (fn [data] (update-interface ...))))
  • 70. Easy to reason about, maintain and test
  • 71. References ! ‣ Conal Elliott “Functional Reactive Animation” paper: http://bit.ly/conal-frp ! FRP-inspired frameworks: ‣ Reagi: http://bit.ly/reagi ‣ RxJS: http://bit.ly/rxjs-github ‣ RxJava: http://bit.ly/rxjava-github ‣ Bacon.js: https://github.com/baconjs/bacon.js ! FRP implementations: ‣ Reactive Banana: http://www.haskell.org/haskellwiki/Reactive-banana ‣ Elm: http://elm-lang.org/ ‣ NetWire: http://www.haskell.org/haskellwiki/Netwire