Downloaded 60 times
![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
} yield
Location(locality, subdivision, postcode)
}
Possible errors: 0](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-25-2048.jpg)
![def sumInts(
list: List[Int]): Int = {
var result = 0
for (x <- list) {
result = result + x
}
return result
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-30-2048.jpg)
: List[A] = {
var result = List()
for (x <- list) {
result = result ++ x
}
return result
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-31-2048.jpg)
: Boolean = {
var result = true
for (x <- list) {
result = result && x
}
return result
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-32-2048.jpg)
![def sumInts(
list: List[Int]): Int = {
var result = 0
for (x <- list) {
result = result + x
}
return result
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-33-2048.jpg)
: List[A] = {
var result = List()
for (x <- list) {
result = result ++ x
}
return result
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-34-2048.jpg)
: Boolean = {
var result = true
for (x <- list) {
result = result && x
}
return result
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-35-2048.jpg)
![trait Monoid[M] {
def zero: M
def append(m1: M, m2: M): M
}
Extract the essence!](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-36-2048.jpg)
(implicit m: Monoid[M]): M
= {
var result = m.zero
for (x <- list) {
result = m.append(result,x)
}
result
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-37-2048.jpg)
(implicit m: Monoid[M]): M
= list.foldLeft(m.zero)(m.append)](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-38-2048.jpg)
![object SearchController {
def getList(uid: UserId): Script[Response]
= {
for {
searches <- getSearches(uid)
cfg <- getConfig
results <- addLinks(searches, cfg)
} yield Ok ~> Response(result.toJson)
}
}](https://image.slidesharecdn.com/yowrealworldfp-150413084725-conversion-gate01/75/2-Years-of-Real-World-FP-at-REA-148-2048.jpg)
More Related Content
Similar to 2 Years of Real World FP at REA
2 Years of Real World FP at REA
- 1. 2 Years of RealWorld FP at REA @KenScambler Scala Developer at λ
- 2. Me 14 years 5 years 5years when possible when bored when forced
- 3. @ Jul 13 Jan14 Jul 14 Jan 15 - 3 teams - 17 codebases - 43K LOC
- 4.
- 5.
- 6.
- 7. Modular, abstract, composable programs aresimple programs
- 8.
- 9. Can you reasonabout something in isolation?
- 10. Or do youneed to fit everything in your head at once?
- 11. A B C K Isthe cost of replacing B with K just writing K?
- 12. A B CK Is thecost of replacing B with K just writing K?
- 13. AK B CKK …or rewritinghalf the program? glue glue
- 14. A pure functionis just input output; no side effects You can tell what it does without Looking at surrounding context.
- 15. Consider: Let’s parse astring 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) }
- 16. Could be null Possibleerrors: 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) }
- 17. 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
- 18. 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
- 19. 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
- 20. 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
- 21. 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
- 22. def doSomethingElse(): Unit= { // ...Do other stuff parseLocation(“Melbourne, VIC 3000”) } Possible errors: 6 + 3 = 9 6 3
- 23. def anotherThing(): Unit= { // ...Do even more stuff doSomethingElse() } Possible errors: 9 + 4 = 13 4 9
- 24. Code inherits allthe errors and side-effects of code it calls. Local reasoning becomes impossible; modularity is lost.
- 25. 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 } yield Location(locality, subdivision, postcode) } Possible errors: 0
- 26. All possibilities have beenelevated into the type system Local reasoning is possible! Local reasoning is possible about things that call it, etc…
- 27.
- 28. Know as littleas you need
- 29. Know as littleas you need Produce as little as you can
- 30. def sumInts( list: List[Int]):Int = { var result = 0 for (x <- list) { result = result + x } return result }
- 31. def flatten[A]( list: List[List[A]]):List[A] = { var result = List() for (x <- list) { result = result ++ x } return result }
- 32. def all[A]( list: List[Boolean]):Boolean = { var result = true for (x <- list) { result = result && x } return result }
- 33. def sumInts( list: List[Int]):Int = { var result = 0 for (x <- list) { result = result + x } return result }
- 34. def flatten[A]( list: List[List[A]]):List[A] = { var result = List() for (x <- list) { result = result ++ x } return result }
- 35. def all[A]( list: List[Boolean]):Boolean = { var result = true for (x <- list) { result = result && x } return result }
- 36. trait Monoid[M] { defzero: M def append(m1: M, m2: M): M } Extract the essence!
- 37. def fold[M](list: List[M]) (implicitm: Monoid[M]): M = { var result = m.zero for (x <- list) { result = m.append(result,x) } result }
- 38. def fold[M](list: List[M]) (implicitm: Monoid[M]): M = list.foldLeft(m.zero)(m.append)
- 39. 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
- 40. Abstraction is alwaysa good thing! Less repetition More reuse Less decay, because code can’t grow tumours around unnecessary detail
- 41.
- 42. Functions compose. A =>B B => C C => D D => E
- 43. A => E Functionscompose.
- 44. Sideways too. A =>E X => Y
- 45.
- 46. This works inthe large, as well as the small! Entire systems can be composable like functions…. without side effects
- 47. Truly composable systems canaccrue more and more stuff without getting more complex!
- 48. Simplicity • Modularity –Reason locally • Abstraction – say only what you need, hide everything you don’t • Composability – scale without accruing complexity
- 49.
- 50. Technical excellence isn’t enough! Software developmentis a human process Coffee Tea Quiet time GSD Ponies Reconsider!
- 55.
- 56. 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
- 57.
- 68. GET /foo/bar PUT {"mode": "banana"} POST {"partyTime":"5:00"} GET /buzz/5/
- 69.
- 70.
- 71.
- 72. Architect Mountain Just needs more Agile Don’tforget your velocity More meetings, but littler 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 no no not like this. Wake up it’s a school day
- 73. Bottom-up tech decisions Youhave to win
- 74. Bottom-up tech decisions Youhave to win
- 75. Bottom-up tech decisions Youhave to win
- 76.
- 77. Scriptozoic era 1995 –2010 Mostly Perl
- 78. Monolithocene epoch 2010 –2012 Ruby, Java Scriptozoic era 1995 – 2010 Mostly Perl
- 79. AWS Monolithocene epoch 2010 –2012 Ruby, Java Scriptozoic era 1995 – 2010 Mostly Perl Microservices 2012 – Ruby, Scala, JS
- 80.
- 81.
- 85.
- 87.
- 88.
- 89.
- 90.
- 91.
- 92.
- 93. Language choice Object Oriented Powerfulstatic types Functional JVM
- 94. Object Oriented Powerful statictypes Functional JVM
- 95.
- 96.
- 97.
- 98.
- 99.
- 100.
- 101. Jul 13 Jan14 1
- 102. LOC Web JSON DB Dep Inj Play2 Play2 6K Squeryl /Play2 Constructors Type API #1
- 103. LOC Web JSON DB Dep Inj Play2 Play2 6K Squeryl /Play2 Constructors Type API • Mentor • Code reviews Learning Investment #1
- 104. LOC Web JSON DB Dep Inj Play2 Play2 6K Squeryl /Play2 Constructors Type API • Mentor • Code reviews Learning Investment Report card Learning curve Technical result Productivity Sentiment Steep but ok OK; slight dip Great; but FWs too heavy #1
- 105. Jul 13 Jan14 1 2 Some infrastructure
- 106. LOC Web JSON DB Dep Inj Play2 Argonaut 3K Squeryl /Play2 Constructors Type API #2
- 107. LOC Web JSON DB Dep Inj Play2 Argonaut 3K Squeryl /Play2 Constructors Type API • Almost none Learning Investment #2
- 108. LOC Web JSON DB Dep Inj Play2 Argonaut 3K Squeryl /Play2 Constructors Type API • Almost none Learning Investment Report card Learning curve Technical result Productivity Sentiment Learning? Meh Needed rework; OK in the end #2 !!!
- 109. Lesson #1 New tech,mindset requires investment in learning Invest in your people!
- 110.
- 111.
- 112. “We’ll have someof that!” λ
- 113. Jul 13 Jan14 1 2 4 6 5 New team; new ideas! 3
- 114. LOC Web JSON DB Dep Inj Unfinagled Argonaut 2K, 3K,4K, 1K Slick Constructors Type Web app, libs, API x 2 • 2 x Mentor Learning Investment Report card Learning curve Technical result Productivity Sentiment Not bad Great Great #3,4, 5,6
- 115. Jul 13 Jan14 1 2 4 6 5 3 7 Theft & innovation Jul 14
- 116. Lesson #2 Having multipleteams is great, because you can steal from each other
- 117.
- 118. LOC Web JSON DB Dep Inj Unfinagled Argonaut 4K Slick Monad Transformers TypeAPI • 2 x Mentor Learning Investment #7
- 119. LOC Web JSON DB Dep Inj Unfinagled Argonaut 4K Slick Monad Transformers TypeAPI • 2 x Mentor Learning Investment Report card Learning curve Technical result Productivity Sentiment Vertical Great Great #7
- 120. Monad Transformers Good technicalbenefits, but… Only 2 people could understand the code Experienced engineers felt totally helpless Learning curve way too steep
- 121.
- 122.
- 123.
- 124.
- 125. All-rounders Scala (now) Gurus • Smoothlearning curve is utterly essential • We need more all- rounders • We can’t leave people behind
- 126. Lesson #3 Familiar, buttechnically unsound concepts have limited value. However… if we can’t make a concept learnable, then we can’t use it.
- 127. λ A Ruby teamconsiders its options…
- 128.
- 129.
- 130. Jul 13 Jan14 1 2 4 6 5 3 7 A 3rd team dips its toe in the water 8 Jul 14
- 131.
- 132. LOC Web JSON DB Dep Inj Play2 Play2 2K Anorm Constructors Type Webapp • Trial and error • Code Katas Learning Investment #8
- 133. LOC Web JSON DB Dep Inj Play2 Play2 2K Anorm Constructors Type Webapp • Trial and error • Code Katas Learning Investment Report card Learning curve Technical result Productivity Sentiment Steep Meh OK #8
- 134. Lesson #4 It’s reallyhard learning from scratch Be prepared for pain up front
- 135.
- 136. Jul 13 Jan14 1 2 4 6 5 3 7 Latest iteration 8 Jul 14 Jan 15 17
- 137.
- 138. Design trends Inheritance/mixins Staticfunctions Partial functions Total functions
- 139. Design trends Inheritance/mixins Staticfunctions Partial functions Total functions Exceptions Sum types
- 140. Design trends Inheritance/mixins Staticfunctions Partial functions Total functions Strings/primitives Wrapper types Exceptions Sum types
- 141. FP Guild Every Thursday,in work hours 7 – 12 people each week Reading, exercises, talks, live coding
- 142.
- 143. LOC Web JSON DB Dep Inj Unfiltered Argonaut 3K Slick Free Monads TypeAPI • 2 x Mentors • Pull Requests • Code reviews • Pairing • FP Guild Learning Investment #17
- 144. LOC Web JSON DB Dep Inj Unfiltered Argonaut 3K Slick Free Monads TypeAPI • 2 x Mentors • Pull Requests • Code reviews • Pairing • FP Guild Learning Investment Report card Learning curve Technical result Productivity Sentiment Smooth Great Brilliant #17
- 145. Pure core Routes ControllersLogic Script Interpreter Actual DB “Authenticate” “Use config” “Get from DB” “Update DB” “Log result” Web Framework Server App runtime
- 146. Pure core Routes ControllersLogic Script Pure Interpreter Pure tests Input Output Assert “Authenticate” “Use config” “Get from DB” “Update DB” “Log result”
- 147. Pure core Interpreter Actual DB WebFramework Server App runtimeWafer thin E2E tests Input Output Assert
- 148. object SearchController { defgetList(uid: UserId): Script[Response] = { for { searches <- getSearches(uid) cfg <- getConfig results <- addLinks(searches, cfg) } yield Ok ~> Response(result.toJson) } }
- 149.
- 150.
- 151.
- 152.
- 153. Flaky pretend servers justto check a response code
- 154. Flaky tests that take 30min to run
- 155.
- 156.
- 157.
- 158. You shouldn’t be dealingwith all that complexity and crap! Most business software isn’t rocket science.
- 159. There is a betterway FP is a tall tree But there is so much low hanging fruit!
- 160.