2 Years of λ Real World FP at REA - YOW! Conferences · input output; no side effects You can tell...

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2 Years of Real World FP at REA @KenScambler Scala Developer at λ

Transcript of 2 Years of λ Real World FP at REA - YOW! Conferences · input output; no side effects You can tell...

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2 Years of Real World FP at REA@KenScambler

Scala Developer at

λ

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Me14 years

5 years

5 years

when possible

when bored

when forced

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@

Jul 13 Jan 14 Jul 14 Jan 15

- 3 teams- 17 codebases- 43K LOC

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Why Functional Programming?

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Compelling, tangible software engineering benefits

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Modularity

Abstraction

Composability

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Modular, abstract, composable programs

are simple programs

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Modularity

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Can you reason about something in isolation?

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Or do you need to fit everything in your head at once?

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A

B C K

Is the cost of replacing Bwith K just writing K?

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A

B CK

Is the cost of replacing Bwith K just writing K?

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AK

B CK

K

…or rewriting half the program?

glue

glue

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A pure function is just input output; no side effects

You can tell what it does without Looking at surrounding context.

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Consider: Let’s parse a string like “Richmond, VIC 3121”

def parseLocation(str: String): Location = {val parts = str.split(“,”)val secondStr = parts(1)val parts2 = secondStr.split(“ “)Location(

parts(0), parts2(0), parts(1).toInt)}

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Could be null

Possible errors: 1

def parseLocation(str: String): Location = {val parts = str.split(“,”)val secondStr = parts(1)val parts2 = secondStr.split(“ “)Location(

parts(0), parts2(0), parts(1).toInt)}

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def parseLocation(str: String): Location = {val parts = str.split(“,”)val secondStr = parts(1)val parts2 = secondStr.split(“ “)Location(

parts(0), parts2(0), parts(1).toInt)}

Might not have enough elements

Possible errors: 2

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def parseLocation(str: String): Location = {val parts = str.split(“,”)val secondStr = parts(1)val parts2 = secondStr.split(“ “)Location(

parts(0), parts2(0), parts(1).toInt)}

Might not have enough elements

Possible errors: 3

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def parseLocation(str: String): Location = {val parts = str.split(“,”)val secondStr = parts(1)val parts2 = secondStr.split(“ “)Location(

parts(0), parts2(0), parts(1).toInt)}

Might not have enough elements

Possible errors: 4

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def parseLocation(str: String): Location = {val parts = str.split(“,”)val secondStr = parts(1)val parts2 = secondStr.split(“ “)Location(

parts(0), parts2(0), parts(1).toInt)}

Might not have enough elements

Possible errors: 5

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def parseLocation(str: String): Location = {val parts = str.split(“,”)val secondStr = parts(1)val parts2 = secondStr.split(“ “)Location(

parts(0), parts2(0), parts(1).toInt)}

Might not be an int

Possible errors: 6

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def doSomethingElse(): Unit = {// ...Do other stuffparseLocation(“Melbourne, VIC 3000”)

}

Possible errors: 6 + 3 = 9

63

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def anotherThing(): Unit = {// ...Do even more stuffdoSomethingElse()

}

Possible errors: 9 + 4 = 13

49

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Code inherits all the errors and side-effects of code it calls.

Local reasoning becomes impossible; modularity is lost.

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def parseLocation(str: String): Option[Location] = {

val parts = str.split(”,")for {

locality <- parts.optGet(0)theRestStr <- parts.optGet(1)theRest = theRestStr.split(" ")subdivision <- theRest.optGet(0)postcodeStr <- theRest.optGet(1)postcode <- postcodeStr.optToInt

} yieldLocation(locality, subdivision, postcode)

}

Possible errors: 0

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All possibilities have been elevated into the type system

Local reasoning is possible!

Local reasoning is possible about things that call it, etc…

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Abstraction

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Know as little as you need

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Know as little as you need

Produce as little as you can

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def sumInts(list: List[Int]): Int = {

var result = 0for (x <- list) {

result = result + x}return result

}

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def flatten[A](list: List[List[A]]): List[A] = {

var result = List()for (x <- list) {

result = result ++ x}return result

}

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def all[A](list: List[Boolean]): Boolean = {

var result = truefor (x <- list) {

result = result && x}return result

}

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def sumInts(list: List[Int]): Int = {

var result = 0for (x <- list) {

result = result + x}return result

}

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def flatten[A](list: List[List[A]]): List[A] = {

var result = List()for (x <- list) {

result = result ++ x}return result

}

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def all[A](list: List[Boolean]): Boolean = {

var result = truefor (x <- list) {

result = result && x}return result

}

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trait Monoid[M] {def zero: Mdef append(m1: M, m2: M): M

}

Extract the essence!

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def fold[M](list: List[M])(implicit m: Monoid[M]): M

= {

var result = m.zerofor (x <- list) {

result = m.append(result,x)}result

}

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def fold[M](list: List[M])(implicit m: Monoid[M]): M

= list.foldLeft(m.zero)(m.append)

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fold(List(1, 2, 3, 4)) 10

fold(List(List("a", "b"), List("c"))) List("a", "b", "c")

fold(List(true, true, false, true)) false

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Abstraction is always a good thing!

Less repetition More reuse

Less decay, because code can’tgrow tumours around unnecessary detail

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Composability

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Functions compose.

A => B B => C

C => D D => E

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A => E

Functions compose.

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Sideways too.

A => E

X => Y

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(A,X) => (E,Y)

Sideways too.

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This works in the large, as well as the small!

Entire systems can be composable like functions…. without side effects

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Truly composablesystems can accrue more and more stuff without getting more complex!

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Simplicity

• Modularity – Reason locally

• Abstraction – say only what you need, hide

everything you don’t

• Composability – scale without accruing complexity

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The human process

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Technical excellence isn’t enough!

Software development is a human process

Coffee Tea

Quiet time

GSD

PoniesReconsider!

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Xi’an offshore team

Team

Team

Team

Team

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Xi’an offshore team

• Many teams are partly based in Xi’an.

• They’re very good, but…

• Communication is hard!

• It works, but requires great investment of time and

money

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Bottom-up tech decisions

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GET /foo/bar

PUT

{"mode":

"banana"}

POST {"partyTime": "5:00"}GET /buzz/5/

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ArchitectMountain

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ArchitectMountain

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ArchitectMountain

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ArchitectMountain

Just needs more Agile

Don’t forget your

velocity

More meetings, but littler

NONo no no no no no no

no no no no no no no

no no no no no no no

no no no no no no no

no no no no not like

this. Wake up it’s a

school day

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Bottom-up tech decisions

You have to win

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Bottom-up tech decisions

You have to win

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Bottom-up tech decisions

You have to win

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Software Paleontology

Everyone’s got a history…

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Scriptozoic era1995 – 2010Mostly Perl

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Monolithocene epoch2010 – 2012Ruby, Java

Scriptozoic era1995 – 2010Mostly Perl

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AWS

Monolithocene epoch2010 – 2012Ruby, Java

Scriptozoic era1995 – 2010Mostly Perl

Microservices2012 –Ruby, Scala, JS

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Adoption

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June, 2013

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λ

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λ

λ

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λ

λ

λ

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λ

λ

λ

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λ

λ

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λ

λ

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λ

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Language choiceObject Oriented

Powerful static types

Functional

JVM

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Object Oriented

Powerful static types

Functional

JVM

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Functional

JVM

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Functional

JVM

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Functional

JVM

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Functional

JVM

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Whatever works for you!

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The journey

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Jul 13 Jan 14

1

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LOC

Web

JSON

DB

Dep Inj

Play2

Play2

6K

Squeryl / Play2

Constructors

Type API#1

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LOC

Web

JSON

DB

Dep Inj

Play2

Play2

6K

Squeryl / Play2

Constructors

Type API

• Mentor• Code reviews

Learning Investment

#1

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LOC

Web

JSON

DB

Dep Inj

Play2

Play2

6K

Squeryl / Play2

Constructors

Type API

• Mentor• Code reviews

Learning Investment Report cardLearning curve

Technical result

Productivity

Sentiment

Steep but ok

OK; slight dip

Great; but FWs too heavy

#1

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Jul 13 Jan 14

1 2

Some infrastructure

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LOC

Web

JSON

DB

Dep Inj

Play2

Argonaut

3K

Squeryl / Play2

Constructors

Type API#2

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LOC

Web

JSON

DB

Dep Inj

Play2

Argonaut

3K

Squeryl / Play2

Constructors

Type API

• Almost noneLearning Investment

#2

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LOC

Web

JSON

DB

Dep Inj

Play2

Argonaut

3K

Squeryl / Play2

Constructors

Type API

• Almost noneLearning Investment Report card

Learning curve

Technical result

Productivity

Sentiment

Learning?

Meh

Needed rework; OK in the end

#2

!!!

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Lesson #1

New tech, mindset requires investment in learning

Invest in your people!

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λ

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Another team!

λ

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“We’ll have some of that!”

λ

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Jul 13 Jan 14

1 2

46

5

New team; new ideas!

3

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LOC

Web

JSON

DB

Dep Inj

Unfinagled

Argonaut

2K, 3K, 4K, 1K

Slick

Constructors

Type Web app, libs, API x 2

• 2 x MentorLearning Investment Report card

Learning curve

Technical result

Productivity

Sentiment

Not bad

Great

Great

#3,4,5,6

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Jul 13 Jan 14

1 2

46

5

3

7

Theft & innovation

Jul 14

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Lesson #2

Having multiple teams is great, because you can steal from each other

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LOC

Web

JSON

DB

Dep Inj

Unfinagled

Argonaut

4K

Slick

Monad Transformers

Type API#7

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LOC

Web

JSON

DB

Dep Inj

Unfinagled

Argonaut

4K

Slick

Monad Transformers

Type API

• 2 x MentorLearning Investment

#7

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LOC

Web

JSON

DB

Dep Inj

Unfinagled

Argonaut

4K

Slick

Monad Transformers

Type API

• 2 x MentorLearning Investment Report card

Learning curve

Technical result

Productivity

Sentiment

Vertical

Great

Great

#7

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Monad Transformers

Good technical benefits, but…

Only 2 people could understand the code

Experienced engineers felt totally helpless

Learning curve way too steep

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Devops

All-rounders

Gurus

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All-rounders

Gurus

JS / CSS / HTML

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AWS

All-rounders

Gurus

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Scala (originally!)

Gurus

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All-rounders

Scala (now)

Gurus

• Smooth learning curve is utterly essential

• We need more all-rounders

• We can’t leave people behind

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Lesson #3Familiar, but technically unsound concepts have limited value.

However… if we can’t make a concept learnable, then we can’t use it.

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λ

A Ruby team considers its options…

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λ

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λ

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Jul 13 Jan 14

1 2

46

5

3

7

A 3rd team dips its toe in the water

8

Jul 14

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LOC

Web

JSON

DB

Dep Inj

Play2

Play2

2K

Anorm

Constructors

Type Web app#8

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LOC

Web

JSON

DB

Dep Inj

Play2

Play2

2K

Anorm

Constructors

Type Web app

• Trial and error• Code Katas

Learning Investment

#8

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LOC

Web

JSON

DB

Dep Inj

Play2

Play2

2K

Anorm

Constructors

Type Web app

• Trial and error• Code Katas

Learning Investment Report cardLearning curve

Technical result

Productivity

Sentiment

Steep

Meh

OK

#8

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Lesson #4

It’s really hard learning from scratch

Be prepared for pain up front

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λ

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Jul 13 Jan 14

1 2

46

5

3

7

Latest iteration

8

Jul 14 Jan 15

17

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Design trends

Inheritance/mixins Static functions

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Design trends

Inheritance/mixins Static functions

Partial functions Total functions

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Design trends

Inheritance/mixins Static functions

Partial functions Total functions

Exceptions Sum types

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Design trends

Inheritance/mixins Static functions

Partial functions Total functions

Strings/primitives Wrapper types

Exceptions Sum types

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FP Guild

Every Thursday, in work hours

7 – 12 people each week

Reading, exercises, talks, live coding

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LOC

Web

JSON

DB

Dep Inj

Unfiltered

Argonaut

3K

Slick

Free Monads

Type API#17

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LOC

Web

JSON

DB

Dep Inj

Unfiltered

Argonaut

3K

Slick

Free Monads

Type API

• 2 x Mentors• Pull Requests• Code reviews• Pairing• FP Guild

Learning Investment

#17

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LOC

Web

JSON

DB

Dep Inj

Unfiltered

Argonaut

3K

Slick

Free Monads

Type API

• 2 x Mentors• Pull Requests• Code reviews• Pairing• FP Guild

Learning Investment Report cardLearning curve

Technical result

Productivity

Sentiment

Smooth

Great

Brilliant

#17

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Pure coreRoutes Controllers Logic Script

Interpreter

Actual DB

“Authenticate”“Use config”“Get from DB”“Update DB”“Log result”

Web Framework

Server

App runtime

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Pure coreRoutes Controllers Logic Script

Pure Interpreter

Pure tests

Input

Output

Assert

“Authenticate”“Use config”“Get from DB”“Update DB”“Log result”

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Pure core

Interpreter

Actual DB

Web Framework

Server

App runtimeWafer thinE2E tests

Input

Output

Assert

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object SearchController {

def getList(uid: UserId): Script[Response]= {

for {searches <- getSearches(uid)cfg <- getConfigresults <- addLinks(searches, cfg)

} yield Ok ~> Response(result.toJson)}

}

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Learning curve:

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Learning curve:

Smooooth

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Mocks

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Stubs

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Flaky pretend servers

just to check

a response code

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Flaky tests

that take

30 min to run

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Side effects

everywhere

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Exceptions

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Intrusive

DI frameworks

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You shouldn’t be dealing with all that complexity and crap!

Most business software isn’t rocket science.

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There is a better way

FP is a tall tree

But there is so much low hanging fruit!

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Thank you!


podobia: < ca pe un viteaz >...1 Ήχος Βου podobia: < ca pe un viteaz > e ni ţi a cum i bi to ri lor de prăz nu i i re ve ni iţi a du na rea or to doc și lor

podobia: < ca pe un viteaz >...1 Ήχος Βου podobia: < ca pe un viteaz > e ni ţi a cum i bi to ri lor de prăz nu i i re ve ni iţi a du na rea or to doc și lor

ZEUS Y LEDA GALLEGO RUIZ, CRISTINA. ÉbaloBatíaTestioEuritemiste Tindáreo CástorHelenaClitemnestra Leda Crono Zeus Rea Polideuco GENEALOGÍA.

ZEUS Y LEDA GALLEGO RUIZ, CRISTINA. ÉbaloBatíaTestioEuritemiste Tindáreo CástorHelenaClitemnestra Leda Crono Zeus Rea Polideuco GENEALOGÍA.

CMSC 330: Organization of Programming Languages · B. S → 0S1| S1 | ε C ... •d, c(a), a+, b**c, etc. CMSC 330 Fall 2018 30. Parse Trees Parse tree shows how a string is produced

CMSC 330: Organization of Programming Languages · B. S → 0S1| S1 | ε C ... •d, c(a), a+, b**c, etc. CMSC 330 Fall 2018 30. Parse Trees Parse tree shows how a string is produced

Matilde Marcolli CS101: Mathematical and Computational ...matilde/ParsingTrees.pdf · Parse trees: from formal to natural languages Matilde Marcolli CS101: Mathematical and Computational

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MIT 6 6.035035 Parse Table Construction Parser is expecting to eventually ntually reduceduce e using sing the production A →αc β • Parser has already yp parsed an ...

MIT 6 6.035035 Parse Table Construction Parser is expecting to eventually ntually reduceduce e using sing the production A →αc β • Parser has already yp parsed an ...

Comunicación entre tareas de tiempo rea - UPMweb.dit.upm.es/.../strl/transparencias/04b_Comunicacion.pdf3/11/09 Planificación de tareas 12 Protocolo del techo de prioridad inmediato

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Comment explorer la microcirculation? Jugurtha ALIANE ( DES anesth-rea, Clermont Ferrand ) DESC 1ere année réanimation médicale nice 2010.

Comment explorer la microcirculation? Jugurtha ALIANE ( DES anesth-rea, Clermont Ferrand ) DESC 1ere année réanimation médicale nice 2010.

Jin Matsumoto - Rikennagataki-lab.riken.jp/workshop/Joint_meeting20140707/matsumoto.pdf · deceleration of the jet due to the mixing between the jet and surrounding medium in the

Jin Matsumoto - Rikennagataki-lab.riken.jp/workshop/Joint_meeting20140707/matsumoto.pdf · deceleration of the jet due to the mixing between the jet and surrounding medium in the

2 Years of Real World FP at REA

2 Years of Real World FP at REA

for main entrance - ALUTEC...Aluminium pergola is the best solution for your surrounding spaces, with an either modern or traditional point of view. Πέργκολα Αλουμινίου

for main entrance - ALUTEC...Aluminium pergola is the best solution for your surrounding spaces, with an either modern or traditional point of view. Πέργκολα Αλουμινίου

General description Our ecosystem includes a mountain creek, the surrounding area as well as the an artificial lake created by a dam. The creek creates.

General description Our ecosystem includes a mountain creek, the surrounding area as well as the an artificial lake created by a dam. The creek creates.

arXiv:1701.02964v1 [math.NT] 11 Jan 2017 · The purposes of this paper are to convince readers why (1.2) is a fascinating formula, to discuss the history of (1.2) and formulas surrounding

arXiv:1701.02964v1 [math.NT] 11 Jan 2017 · The purposes of this paper are to convince readers why (1.2) is a fascinating formula, to discuss the history of (1.2) and formulas surrounding

Anti-acetyl-Histone H2A (Lys5) rabbit pAb ChIP Gradepeptide corresponding to residues surrounding Lys5 of human histone H2A. Antibodies are purified by protein A-conjugated agarose

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TAS5751M Digital Audio Power Amplifier with EQ and AGL ... · (SAP) Sample Rate Auto-Detect ... device and into the surrounding PCB area. ... of the device is performed up to and

TAS5751M Digital Audio Power Amplifier with EQ and AGL ... · (SAP) Sample Rate Auto-Detect ... device and into the surrounding PCB area. ... of the device is performed up to and

ucfstudyunion.files.wordpress.com  · Web view4. What happens to the electric flux of a closed sphere surrounding some value of positive charge when the radius of the sphere is tripled.

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Magnetar candidates: new discoveries open new questions · Nanda Rea CSIC-IEEC Isolated Neutron Stars: P-Pdot diagram € E ˙ rot =− 2 3c3 m ˙ ˙ 2=− 2B2R6Ω4sin2α 3c3 PP ˙

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Plasma processes as advanced methods for cavity cleaning N. Patron, R. Baracco, L. Phillips, M. Rea, C. Roncolato, D. Tonini and V. Palmieri … pushing.

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TUHH · Summary The properties of soft matter in the nanoscopic constriction within the voids of surrounding hard condensed matter are investigated. In this context the focus lies

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DERIVATION S RULES USEDPROBABILITY P(s) = Σ j P(T,S) where t is a parse of s = Σ j P(T) P(T) – The probability of a tree T is the product.

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TOPICS SURROUNDING THE COMBINATORIAL ...motizuki/Combinatorial...TOPICS SURROUNDING THE COMBINATORIAL ANABELIANGEOMETRYOFHYPERBOLICCURVESIII: TRIPODS AND TEMPERED FUNDAMENTAL GROUPS

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