SlideShare a Scribd company logo
1 of 47
Download to read offline
A Scala Corrections Library
Paul Phillips	

paulp@improving.org

Source: xkcd, of course.
“When I'm working on a problem, I never think
about beauty. I think only how to solve the problem.”
!

“But when I have finished, if the solution is not
beautiful, I know it is wrong.”
– R. Buckminster Fuller
(syntax highlighting donated by paulp)
“When I'm working on a problem, I never
think about beauty. I think only how to
solve the problem.”

!

“But when I have finished, if the solution
is not beautiful, I know it is wrong.”

– R. Buckminster Fuller

trait ParSeqViewLike[
+T,
+Coll <: Parallel,
+CollSeq,
+This <: ParSeqView[T, Coll, CollSeq]
with ParSeqViewLike[T, Coll, CollSeq, This, ThisSeq],
+ThisSeq <: SeqView[T, CollSeq]
with SeqViewLike[T, CollSeq, ThisSeq]
] extends GenSeqView[T, Coll]
with GenSeqViewLike[T, Coll, This]
with ParIterableView[T, Coll, CollSeq]
with ParIterableViewLike[T, Coll, CollSeq, This, ThisSeq]
with ParSeq[T]
with ParSeqLike[T, This, ThisSeq]
The Winding Stairway
• Five years on scala	

• Rooting for scala/typesafe	

• But I quit a dream job...	

• ...because I lost faith
Credentials
Credentials, cont.
Should you care?
•

I offer my credentials only to bear witness to my
credibility

•

I suspect I have written more scala code than
anyone else, ever.

•

What’s visible in compiler/library represents only
a small fraction of it
Caveats
•

I ran out of time. Slides are rushed. Forgive me.

•

Error messages and repl transcripts have been
heavily trimmed for clarity on a slide

•

This works counter to message when the point
involves complexity or incomprehensibility

•

So verbosify all compiler messages by a factor
of three for a more accurate feel
My axe is dull
•

I have been pulling my punches

•

This has left some thinking that I quit over
technical esoterica: java compatibility, jvm
limitations, intractable compiler challenges

•

This is not accurate
Subtext, people
•

Prevailing programmer culture frowns upon
criticism of named individuals

•

In this case that doesn’t leave much room for
additional specificity

•

All the relevant facts are available in the googles
Is Scala too complex?
•

I’ll field this one: YES

•

Is anyone fooled by specious comparisons
of language grammar size? Who cares?

•

Half the time when someone hits a bug they
can’t tell whether it is a bug in scala or the
expected behavior

•

That definitely includes me
Perceived Problem

C

•

A meme is going around that scala is too complex

•

Option A: Own it

•

Option B: Address it

•

Option C: Obscure it

p
O

i
t

n
o
Thus is born the “use case”
// A fictional idealized version of the genuine method
def map[B](f: (A)
B): Map[B]
!

// The laughably labeled "full" signature
def map[B, That](f: ((A, B))
B)
(implicit bf: CanBuildFrom[Map[A, B], B, That]): That

neither has any basis in reality!
the true name of map
// markers to distinguish Map's class type parameters
scala> class K ; class V
defined class K, V
!
scala> val host = typeOf[Map[K, V]]
host: Type = Map[K,V]
!
scala> val method = host member TermName("map")
method: Symbol = method map
!
// Correct signature for map has FOUR distinct identifiers
scala> method defStringSeenAs (host memberType method)
res0: String = 
def map[B, That](f: ((K, V)) => B)
(implicit bf: CBF[Map[K,V],B,That]): That
•

Now you’re thinking “use case thing is a bug, big deal,
bugs get fixed.” Do they?

•

Surely as soon as it is known the documentation spins
these fabrications, it will be addressed? If not fixed, at
least it’ll be marked as inaccurate? Something?

•

Nope! To this day it’s the same. Your time is worthless.
Slightly Caricatured
map
def map[B](f: A => B): F[B]

Signature

Elegance

Advantages

Spokespicture

“map”
def map[B, That](f: A => B)
(implicit bf:
CanBuildFrom[Repr, B,
That]): That

Among the purest and
most reusable
<—- Not this.
abstractions known to
computing science
Can reason abstractly
about code

Can map a BitSet to a
BitSet without typing
“toBitSet”
The Bitset Gimmick
// Fancy, we get a Bitset back!
scala> BitSet(1, 2, 3) map (_.toString.toInt)
res0: BitSet = BitSet(1, 2, 3)

!
// Except…
scala> BitSet(1, 2, 3) map (_.toString) map (_.toInt)
res1: SortedSet[Int] = TreeSet(1, 2, 3)

!
// Um…
scala> (BitSet(1, 2, 3) map identity)(1)
<console>:21: error: type mismatch;
found
: Int(1)
required:
scala.collection.generic.CanBuildFrom[scala.collection.imm
utable.BitSet,Int,?]
(BitSet(1, 2, 3) map identity)(1)
^
similarly
scala> def f[T](x: T) = (x, new Object)
f: [T](x: T)(T, Object)
!
scala> SortedSet(1 to 10: _*)
res0: SortedSet[Int] = TreeSet(1, 2, 3,
!
scala> SortedSet(1 to 10: _*) map (x =>
res1: SortedSet[Int] = TreeSet(1, 2, 3,
!
scala> SortedSet(1 to 10: _*) map f map
res2: Set[Int] = Set(5, 10, 1, 6, 9, 2,

4, 5, 6, 7, 8, 9, 10)
f(x)._1)
4, 5, 6, 7, 8, 9, 10)
(_._1)
7, 3, 8, 4)
and in a similar vein
scala> val f: Int => Int = _ % 3
f: Int => Int = <function1>
!
scala> val g: Int => Int = _ => System.nanoTime % 1000000 toInt
g: Int => Int = <function1>
!
scala> Set(3, 6, 9) map f map g
res0: Set[Int] = Set(633000)
!
scala> Set(3, 6, 9) map (f andThen g)
res1: Set[Int] = Set(305000, 307000, 308000)
Java Interop: the cruelest joke
•

It’s impossible to call scala’s map from java!

•

See all the grotesque details at SI-4389

IX
F
T
ON

“I played with it until it got too tedious. I think the signatures work fine.
What does not work is that the variances of CanBuildFrom cannot be
modelled in Java, so types do not match. And it seems Java does not
even let me override with a cast. So short answer: You can't call these
things from Java because instead of declaration side variance you
have only a broken wildcard system.”
!
— Martin Odersky

W
Lightning Round
•

My time is running out and I can hear you saying…

•

“Just give us a laundry list of collections issues”

•

Okay, you asked for it (in my mind)
•

Implementation details infest everything

•

And every detail is implementation-defined

•

Capabilities should be designed around the laws
of variance; instead variance checks are
suppressed and key method contains is untyped

•

Specificity rules render contravariance useless

•

Implicit selection and type inference inextricably
bound - so type inference is largely frozen
because any change will break existing code
•

Extreme pollution of base objects - all collections
have “size: Int”, all Seqs have “apply”, etc.

•

Bundling of concerns (e.g. invariant Set)

•

Inheritance of implementation is the hammer for
every nail…

•

…yet “final” and “private”, critical for a hope of
correctness under inheritance, are almost
unknown

•

Semantics discovered instead of designed
assume the worst
In Set(x) ++ Set(x), which x wins?
!

Can xs filter (_ => true) return xs?
!

Are defaults preserved across
operations? Which operations? Is
sortedness? Will views and Streams
retain laziness when zipped?
xs map identity
scala> val m = Map(1 -> 2) withDefaultValue 10
m: Map[Int,Int] = Map(1 -> 2)
!
scala> m(1000)
res0: Int = 10
!
scala> (m map identity)(1000)
<console>:9: error: type mismatch;
found
: Int(1000)
required: CanBuildFrom[Map[Int,Int],(Int, Int),?]
(m map identity)(1000)
^
!
scala> m map identity apply 1000
java.util.NoSuchElementException: key not found: 1000
at MapLike$class.default(MapLike.scala:228)
types are for suckers
% find collection -name ‘*.scala’ |
xargs egrep asInstanceOf | wc -l

556
How could 556 casts ever go wrong
scala> val xs: Set[Int] =
(1 to 3).view.map(x => x)(breakOut)
!

java.lang.ClassCastException: SeqViewLike$$anon$3
cannot be cast to immutable.Set
get and apply
trivially fall into disagreement
!
scala> Map[Int,Int]() withDefaultValue 123
res0: Map[Int,Int] = Map()
!
scala> res0 contains 55
res1: Boolean = false
!
scala> res0 get 55
res2: Option[Int] = None
!
scala> res0 apply 55
res3: Int = 123
Why is covariance such an object of worship?
Types exist so we don’t have to live like this!
// WHY infer this utterly useless type?
scala> List(1, 2) ::: List(3, 4.0)
res0: List[AnyVal] = List(1, 2, 3.0, 4.0)
!

scala> PspList(1, 2) ::: PspList(3, 4.0)
<console>:23: error: type mismatch;
found
: PspList[Int]
required: PspList[Double]
Type Inference
+
Variance
——————————
Abstracting over mutability
•
•
•

An inherited implementation is ALWAYS wrong somewhere!!

•

Half the overrides in collections exist to stave off the
incorrectness which looms above. This is nuts.!

•

Not to mention “Map”, “Set”, etc. in three namespaces

Example: how do you write "drop" so it's reusable?!
In a mutable class, drop MUST NOT share, but in an
immutable class, drop MUST share!
How many ways are there to write ‘slice’ ?
% ack --no-filename 'def slice(' src/library/

!

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

override def slice(from: Int, until: Int): Iterator[A] =
def slice(from: Int, until: Int): Iterator[A] = {
def slice(from: Int, until: Int): Repr =
def slice(from: Int, until: Int): Repr = {
def slice(from: Int, until: Int): Repr = {
def slice(start: Int): PagedSeq[T] = slice(start, UndeterminedEnd)
def slice(unc_from: Int, unc_until: Int): Repr
override /*IterableLike*/ def slice(from: Int, until: Int): Vector[A] =
override /*TraversableLike*/ def slice(from: Int, until: Int): Repr = {
override def slice(_start: Int, _end: Int): PagedSeq[T] = {
override def slice(from1: Int, until1: Int): IterableSplitter[T] =
override def slice(from1: Int, until1: Int): SeqSplitter[T] =
override def slice(from: Int, until: Int) = {
override def slice(from: Int, until: Int) = {
override def slice(from: Int, until: Int): List[A] = {
override def slice(from: Int, until: Int): Repr = self.slice(from, until)
override def slice(from: Int, until: Int): Repr = {
override def slice(from: Int, until: Int): Stream[A] = {
override def slice(from: Int, until: Int): String = {
override def slice(from: Int, until: Int): This =
override def slice(from: Int, until: Int): This =
override def slice(from: Int, until: Int): Traversable[A]
override def slice(from: Int, until: Int): WrappedString = {
override def slice(unc_from: Int, unc_until: Int): Repr = {
scala.conflation
•

Every collection must have size

•

Every sequence must have apply

•

Every call to map includes a "builder factory"

•

Every set must be invariant

•

Everything must suffer universal equality
predictability
One of these expressions returns 2 and one returns
never. Feeling lucky?	

!

scala> (Stream from 1) zip (Stream from 1)
map { case (x, y) => x + y } head
!

scala> (Stream from 1, Stream from 1).zipped
map (_ + _) head
sets
Two complementary ways to define Set[A].
Complementary - and NOT the same thing!
Intensional

Extensional

Specification

Membership test

Members

Variance

Set[-A]

Set[+A]

Defining Signature

A => Boolean

Iterable[A]

Size

Unknowable

Known

Duplicates(*)

Meaningless

Disallowed
What's going on here?
scala> class xs[A] extends Set[A]
error: class xs has 4 unimplemented members.
!

// Intensional/extensional, conflated.
// Any possibility of variance eliminated.
def iterator: Iterator[A]
def contains(elem: A): Boolean
// What are these doing in the interface?
// Why can I define a Seq without them?
def -(elem: A): Set[A]
def +(elem: A): Set[A]
todo: also add all other methods
% git grep 'todo: also add' 607cb4250d
SynchronizedMap.scala: // !!! todo: also add all other methods

!
% git grep 'todo: also add' origin/master
SynchronizedMap.scala: // !!! todo: also add all other methods

!
commit 607cb4250d
Author: Martin Odersky <odersky@gmail.com>
Date:
Mon May 25 15:18:48 2009 (4 years, 8 months ago)

!
added SynchronizedMap; changed Set.put to Set.add, implemented
LinkedHashMap/Set more efficiently.
tyranny of the interface
•

Mandating "def size: Int" for all collections is the fast
track to Glacialville!

•

Countless times have I fixed xs.size != 0

•

Collections are both worlds: all performance/
termination trap, no exploiting of size information!

•

A universal size method must be SAFE and CHEAP
Psp Collections
•

So here is a little of what I would do differently

•

I realized since agreeing to this talk that I may
have to go cold turkey to escape scala’s orbit.
It’s just too frustrating to use.

•

Which means this may never go anywhere

•

But you can have whatever gets done
Conceptual Integrity
trait Collections {
type CC[+X]
type Min[+X]
type Opt[+X]
type CCPair[+X]
type ~>[-V1, +V2]

!

!

}

type
type
type
type
type
type
type
type

//
//
//
//
//

Iso[A]
Map[-A, +B]
FlatMap[-A, +B]
Grouped[A, DD[X]]
Fold[-A, +R]
Flatten[A]
Build[A]
Pure[A]

the overarching container type (in scala: any covariant collection, e.g. List, Vector)
least type constructor which can be reconstituted to CC[X] (scala: GenTraversableOnce)
the container type for optional results (in scala: Option)
some representation of a divided CC[A] (at simplest, (CC[A], CC[A]))
some means of composing operations (at simplest, Function1)
=
=
=
=
=
=
=
=

CC[A] ~> CC[A]
CC[A] ~> CC[B]
CC[A] ~> Min[B]
CC[A] ~> CC[DD[A]]
CC[A] ~> R
CC[Min[A]] ~> CC[A]
Min[A] ~> CC[A]
A ~> CC[A]

trait Relations[A] {
type MapTo[+B] = Map[A, B]
type FoldTo[+R] = Fold[A, R]
type This
= CC[A]
type Twosome
= CCPair[A]
type Self
= Iso[A]
type Select
= FoldTo[A]
type Find
= FoldTo[Opt[A]]
type Split
= FoldTo[Twosome]
}

//
//
//
//
//
//
//
//

//
//
//
//
//
//
//
//

e.g. filter, take, drop, reverse, etc.
e.g. map, collect
e.g. flatMap
e.g. sliding
e.g. fold, but also subsumes all operations on CC[A]
e.g. flatten
for use in e.g. sliding, flatMap
we may not need

an alias incorporating the known A
another one
the CC[A] under consideration
a (CC[A], CC[A]) representation
a.k.a. CC[A] => CC[A], e.g. tail, filter, reverse
a.k.a. CC[A] => A, e.g. head, reduce, max
a.k.a. CC[A] => Opt[A], e.g. find
a.k.a. CC[A] => (CC[A], CC[A]), e.g. partition, span
“Do not multiply entities
unnecessarily”
•

mutable / immutable

•

Seq / Set / Map

•

parallel / sequential

•

view / regular

24 Combinations!
Surface Area Reduced 96%
•

A Set is a Seq without duplicates.

•

A Map is a Set paired with a function K => V.

•

A mutable collection has nothing useful in common with an
immutable collection. Write your own mutable collections.

•

If we can’t get sequential collections right, we have no hope of
parallel collections. Write your own parallel collections.

•

“Views” should be how it always works.
predictability: size matters
scala> def f(xs: Iterable[Int]) = xs.size
f: (xs: Seq[Int])Int
!

// O(1)
scala> f(Set(1))
res0: Int = 1
!

// O(n)
scala> f(List(1))
res1: Int = 1
!

// O(NOES)
scala> f(Stream continually 1)
<ctrl-C>
Asking the right question
SizeInfo
/
Atomic
/
Infinite


Precise


Bounded
Don’t ask unanswerable questions
(Unless you enjoy hearing lies)
scala> val xs = Foreach from BigInt(1)
xs: psp.core.Foreach[BigInt] = unfold(1)(<function1>)

!
scala> xs.size
<console>:22: error: value size is not a member of
psp.core.Foreach[BigInt]
xs.size
^

!
scala> xs.sizeInfo
res0: psp.core.SizeInfo = <inf>
the joy of the invariant leaf
scala> List(1, 2, 3) contains "1"
res0: Boolean = false
!

scala> PspList(1, 2, 3) contains "1"
<console>:23: error: type mismatch;
found
: String("1")
required: Int
PspList(1, 2, 3) contains "1"
^
HEY! MAP NEED NOT BE RUINED!
scala> "abc" map (_.toInt.toChar)
res1: String = abc
!
scala> "abc" map (_.toInt) map (_.toChar)
res2: IndexedSeq[Char] = Vector(a, b, c)
!
// psp to the rescue
scala> "abc".m map (_.toInt) map (_.toChar)
res3: psp.core.View[String,Char] = view of abc
!
scala> res3.force
res4: String = abc

More Related Content

What's hot

Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsBest Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsCloudera, Inc.
 
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...confluent
 
Hive 입문 발표 자료
Hive 입문 발표 자료Hive 입문 발표 자료
Hive 입문 발표 자료beom kyun choi
 
Modern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time APIModern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time APIGanesh Samarthyam
 
Morel, a Functional Query Language
Morel, a Functional Query LanguageMorel, a Functional Query Language
Morel, a Functional Query LanguageJulian Hyde
 
My Top 5 APEX JavaScript API's
My Top 5 APEX JavaScript API'sMy Top 5 APEX JavaScript API's
My Top 5 APEX JavaScript API'sRoel Hartman
 
Tanel Poder - Scripts and Tools short
Tanel Poder - Scripts and Tools shortTanel Poder - Scripts and Tools short
Tanel Poder - Scripts and Tools shortTanel Poder
 
Koalas: Pandas on Apache Spark
Koalas: Pandas on Apache SparkKoalas: Pandas on Apache Spark
Koalas: Pandas on Apache SparkDatabricks
 
REST services and IBM Domino/XWork - DanNotes 19-20. november 2014
REST services and IBM Domino/XWork - DanNotes 19-20. november 2014REST services and IBM Domino/XWork - DanNotes 19-20. november 2014
REST services and IBM Domino/XWork - DanNotes 19-20. november 2014John Dalsgaard
 
Functor, Apply, Applicative And Monad
Functor, Apply, Applicative And MonadFunctor, Apply, Applicative And Monad
Functor, Apply, Applicative And MonadOliver Daff
 
Tweaking the interactive grid
Tweaking the interactive gridTweaking the interactive grid
Tweaking the interactive gridRoel Hartman
 
Programación Funcional 101 con Scala y ZIO 2.0
Programación Funcional 101 con Scala y ZIO 2.0Programación Funcional 101 con Scala y ZIO 2.0
Programación Funcional 101 con Scala y ZIO 2.0Jorge Vásquez
 
Oracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data Streaming
Oracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data StreamingOracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data Streaming
Oracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data StreamingMichael Rainey
 
Property Based Testing in PHP
Property Based Testing in PHPProperty Based Testing in PHP
Property Based Testing in PHPvinaikopp
 
[Pgday.Seoul 2020] SQL Tuning
[Pgday.Seoul 2020] SQL Tuning[Pgday.Seoul 2020] SQL Tuning
[Pgday.Seoul 2020] SQL TuningPgDay.Seoul
 
React Native Firebase Realtime Database + Authentication
React Native Firebase Realtime Database + AuthenticationReact Native Firebase Realtime Database + Authentication
React Native Firebase Realtime Database + AuthenticationKobkrit Viriyayudhakorn
 
Structuring Spark: DataFrames, Datasets, and Streaming by Michael Armbrust
Structuring Spark: DataFrames, Datasets, and Streaming by Michael ArmbrustStructuring Spark: DataFrames, Datasets, and Streaming by Michael Armbrust
Structuring Spark: DataFrames, Datasets, and Streaming by Michael ArmbrustSpark Summit
 

What's hot (20)

Defensive Apex Programming
Defensive Apex ProgrammingDefensive Apex Programming
Defensive Apex Programming
 
SQL Tuning 101
SQL Tuning 101SQL Tuning 101
SQL Tuning 101
 
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsBest Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop Professionals
 
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...
 
Hive 입문 발표 자료
Hive 입문 발표 자료Hive 입문 발표 자료
Hive 입문 발표 자료
 
Modern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time APIModern Programming in Java 8 - Lambdas, Streams and Date Time API
Modern Programming in Java 8 - Lambdas, Streams and Date Time API
 
Morel, a Functional Query Language
Morel, a Functional Query LanguageMorel, a Functional Query Language
Morel, a Functional Query Language
 
My Top 5 APEX JavaScript API's
My Top 5 APEX JavaScript API'sMy Top 5 APEX JavaScript API's
My Top 5 APEX JavaScript API's
 
Tanel Poder - Scripts and Tools short
Tanel Poder - Scripts and Tools shortTanel Poder - Scripts and Tools short
Tanel Poder - Scripts and Tools short
 
Koalas: Pandas on Apache Spark
Koalas: Pandas on Apache SparkKoalas: Pandas on Apache Spark
Koalas: Pandas on Apache Spark
 
REST services and IBM Domino/XWork - DanNotes 19-20. november 2014
REST services and IBM Domino/XWork - DanNotes 19-20. november 2014REST services and IBM Domino/XWork - DanNotes 19-20. november 2014
REST services and IBM Domino/XWork - DanNotes 19-20. november 2014
 
Functor, Apply, Applicative And Monad
Functor, Apply, Applicative And MonadFunctor, Apply, Applicative And Monad
Functor, Apply, Applicative And Monad
 
Tweaking the interactive grid
Tweaking the interactive gridTweaking the interactive grid
Tweaking the interactive grid
 
Monads do not Compose
Monads do not ComposeMonads do not Compose
Monads do not Compose
 
Programación Funcional 101 con Scala y ZIO 2.0
Programación Funcional 101 con Scala y ZIO 2.0Programación Funcional 101 con Scala y ZIO 2.0
Programación Funcional 101 con Scala y ZIO 2.0
 
Oracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data Streaming
Oracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data StreamingOracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data Streaming
Oracle GoldenGate and Apache Kafka: A Deep Dive Into Real-Time Data Streaming
 
Property Based Testing in PHP
Property Based Testing in PHPProperty Based Testing in PHP
Property Based Testing in PHP
 
[Pgday.Seoul 2020] SQL Tuning
[Pgday.Seoul 2020] SQL Tuning[Pgday.Seoul 2020] SQL Tuning
[Pgday.Seoul 2020] SQL Tuning
 
React Native Firebase Realtime Database + Authentication
React Native Firebase Realtime Database + AuthenticationReact Native Firebase Realtime Database + Authentication
React Native Firebase Realtime Database + Authentication
 
Structuring Spark: DataFrames, Datasets, and Streaming by Michael Armbrust
Structuring Spark: DataFrames, Datasets, and Streaming by Michael ArmbrustStructuring Spark: DataFrames, Datasets, and Streaming by Michael Armbrust
Structuring Spark: DataFrames, Datasets, and Streaming by Michael Armbrust
 

Viewers also liked

Naming Things and Finding Cothings
Naming Things and Finding CothingsNaming Things and Finding Cothings
Naming Things and Finding CothingsPaul Phillips
 
Brief tour of psp-std
Brief tour of psp-stdBrief tour of psp-std
Brief tour of psp-stdPaul Phillips
 
Keynote, PNW Scala 2013
Keynote, PNW Scala 2013Keynote, PNW Scala 2013
Keynote, PNW Scala 2013Paul Phillips
 
Keynote, LambdaConf 2014 - The Silent Productivity Killer
Keynote, LambdaConf 2014 - The Silent Productivity KillerKeynote, LambdaConf 2014 - The Silent Productivity Killer
Keynote, LambdaConf 2014 - The Silent Productivity KillerPaul Phillips
 
Keynote, Lambdaconf 2016 - Equality is Hard
Keynote, Lambdaconf 2016 - Equality is HardKeynote, Lambdaconf 2016 - Equality is Hard
Keynote, Lambdaconf 2016 - Equality is HardPaul Phillips
 
Composing Project Archetyps with SBT AutoPlugins
Composing Project Archetyps with SBT AutoPluginsComposing Project Archetyps with SBT AutoPlugins
Composing Project Archetyps with SBT AutoPluginsMark Schaake
 
Transformative Git Practices
Transformative Git PracticesTransformative Git Practices
Transformative Git PracticesNicola Paolucci
 
Keynote, LambdaConf 2015 - Ipecac for the Ouroboros
Keynote, LambdaConf 2015 - Ipecac for the OuroborosKeynote, LambdaConf 2015 - Ipecac for the Ouroboros
Keynote, LambdaConf 2015 - Ipecac for the OuroborosPaul Phillips
 
Age is not an int
Age is not an intAge is not an int
Age is not an intoxbow_lakes
 
Practical scalaz
Practical scalazPractical scalaz
Practical scalazoxbow_lakes
 
Lightning Talk: Running MongoDB on Docker for High Performance Deployments
Lightning Talk: Running MongoDB on Docker for High Performance DeploymentsLightning Talk: Running MongoDB on Docker for High Performance Deployments
Lightning Talk: Running MongoDB on Docker for High Performance DeploymentsMongoDB
 
Future of ai on the jvm
Future of ai on the jvmFuture of ai on the jvm
Future of ai on the jvmAdam Gibson
 
Effective Actors
Effective ActorsEffective Actors
Effective Actorsshinolajla
 
RESTful API using scalaz (3)
RESTful API using scalaz (3)RESTful API using scalaz (3)
RESTful API using scalaz (3)Yeshwanth Kumar
 
Scala Json Features and Performance
Scala Json Features and PerformanceScala Json Features and Performance
Scala Json Features and PerformanceJohn Nestor
 
Stateful Distributed Stream Processing
Stateful Distributed Stream ProcessingStateful Distributed Stream Processing
Stateful Distributed Stream ProcessingGyula Fóra
 
What We (Don't) Know About the Beginning of the Universe
What We (Don't) Know About the Beginning of the UniverseWhat We (Don't) Know About the Beginning of the Universe
What We (Don't) Know About the Beginning of the UniverseSean Carroll
 
Introduction to ScalaZ
Introduction to ScalaZIntroduction to ScalaZ
Introduction to ScalaZKnoldus Inc.
 
Gifford Lecture One: Cosmos, Time, Memory
Gifford Lecture One: Cosmos, Time, MemoryGifford Lecture One: Cosmos, Time, Memory
Gifford Lecture One: Cosmos, Time, MemorySean Carroll
 

Viewers also liked (20)

Naming Things and Finding Cothings
Naming Things and Finding CothingsNaming Things and Finding Cothings
Naming Things and Finding Cothings
 
Brief tour of psp-std
Brief tour of psp-stdBrief tour of psp-std
Brief tour of psp-std
 
Keynote, PNW Scala 2013
Keynote, PNW Scala 2013Keynote, PNW Scala 2013
Keynote, PNW Scala 2013
 
Keynote, LambdaConf 2014 - The Silent Productivity Killer
Keynote, LambdaConf 2014 - The Silent Productivity KillerKeynote, LambdaConf 2014 - The Silent Productivity Killer
Keynote, LambdaConf 2014 - The Silent Productivity Killer
 
Keynote, Lambdaconf 2016 - Equality is Hard
Keynote, Lambdaconf 2016 - Equality is HardKeynote, Lambdaconf 2016 - Equality is Hard
Keynote, Lambdaconf 2016 - Equality is Hard
 
Scalaz
ScalazScalaz
Scalaz
 
Composing Project Archetyps with SBT AutoPlugins
Composing Project Archetyps with SBT AutoPluginsComposing Project Archetyps with SBT AutoPlugins
Composing Project Archetyps with SBT AutoPlugins
 
Transformative Git Practices
Transformative Git PracticesTransformative Git Practices
Transformative Git Practices
 
Keynote, LambdaConf 2015 - Ipecac for the Ouroboros
Keynote, LambdaConf 2015 - Ipecac for the OuroborosKeynote, LambdaConf 2015 - Ipecac for the Ouroboros
Keynote, LambdaConf 2015 - Ipecac for the Ouroboros
 
Age is not an int
Age is not an intAge is not an int
Age is not an int
 
Practical scalaz
Practical scalazPractical scalaz
Practical scalaz
 
Lightning Talk: Running MongoDB on Docker for High Performance Deployments
Lightning Talk: Running MongoDB on Docker for High Performance DeploymentsLightning Talk: Running MongoDB on Docker for High Performance Deployments
Lightning Talk: Running MongoDB on Docker for High Performance Deployments
 
Future of ai on the jvm
Future of ai on the jvmFuture of ai on the jvm
Future of ai on the jvm
 
Effective Actors
Effective ActorsEffective Actors
Effective Actors
 
RESTful API using scalaz (3)
RESTful API using scalaz (3)RESTful API using scalaz (3)
RESTful API using scalaz (3)
 
Scala Json Features and Performance
Scala Json Features and PerformanceScala Json Features and Performance
Scala Json Features and Performance
 
Stateful Distributed Stream Processing
Stateful Distributed Stream ProcessingStateful Distributed Stream Processing
Stateful Distributed Stream Processing
 
What We (Don't) Know About the Beginning of the Universe
What We (Don't) Know About the Beginning of the UniverseWhat We (Don't) Know About the Beginning of the Universe
What We (Don't) Know About the Beginning of the Universe
 
Introduction to ScalaZ
Introduction to ScalaZIntroduction to ScalaZ
Introduction to ScalaZ
 
Gifford Lecture One: Cosmos, Time, Memory
Gifford Lecture One: Cosmos, Time, MemoryGifford Lecture One: Cosmos, Time, Memory
Gifford Lecture One: Cosmos, Time, Memory
 

Similar to Scala Corrections Library Simplifies Complex Code

BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...
BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...
BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...Andrew Phillips
 
Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...
Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...
Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...Andrew Phillips
 
Speaking Scala: Refactoring for Fun and Profit (Workshop)
Speaking Scala: Refactoring for Fun and Profit (Workshop)Speaking Scala: Refactoring for Fun and Profit (Workshop)
Speaking Scala: Refactoring for Fun and Profit (Workshop)Tomer Gabel
 
42: Rise of the dependent types
42: Rise of the dependent types42: Rise of the dependent types
42: Rise of the dependent typesGeorge Leontiev
 
Slides chapter3part1 ruby-forjavaprogrammers
Slides chapter3part1 ruby-forjavaprogrammersSlides chapter3part1 ruby-forjavaprogrammers
Slides chapter3part1 ruby-forjavaprogrammersGiovanni924
 
Scalding - the not-so-basics @ ScalaDays 2014
Scalding - the not-so-basics @ ScalaDays 2014Scalding - the not-so-basics @ ScalaDays 2014
Scalding - the not-so-basics @ ScalaDays 2014Konrad Malawski
 
Taxonomy of Scala
Taxonomy of ScalaTaxonomy of Scala
Taxonomy of Scalashinolajla
 
The Fuss about || Haskell | Scala | F# ||
The Fuss about || Haskell | Scala | F# ||The Fuss about || Haskell | Scala | F# ||
The Fuss about || Haskell | Scala | F# ||Ashwin Rao
 
Scala Refactoring for Fun and Profit
Scala Refactoring for Fun and ProfitScala Refactoring for Fun and Profit
Scala Refactoring for Fun and ProfitTomer Gabel
 
Is Haskell an acceptable Perl?
Is Haskell an acceptable Perl?Is Haskell an acceptable Perl?
Is Haskell an acceptable Perl?osfameron
 
C Interview Questions for Fresher
C Interview Questions for FresherC Interview Questions for Fresher
C Interview Questions for FresherJaved Ahmad
 
C interview-questions-techpreparation
C interview-questions-techpreparationC interview-questions-techpreparation
C interview-questions-techpreparationsonu sharma
 
C interview-questions-techpreparation
C interview-questions-techpreparationC interview-questions-techpreparation
C interview-questions-techpreparationKgr Sushmitha
 
楽々Scalaプログラミング
楽々Scalaプログラミング楽々Scalaプログラミング
楽々ScalaプログラミングTomoharu ASAMI
 
Compass, Sass, and the Enlightened CSS Developer
Compass, Sass, and the Enlightened CSS DeveloperCompass, Sass, and the Enlightened CSS Developer
Compass, Sass, and the Enlightened CSS DeveloperWynn Netherland
 
Haskell retrospective
Haskell retrospectiveHaskell retrospective
Haskell retrospectivechenge2k
 
Code Fast, Die Young, Throw Structured Exceptions
Code Fast, Die Young, Throw Structured ExceptionsCode Fast, Die Young, Throw Structured Exceptions
Code Fast, Die Young, Throw Structured ExceptionsJohn Anderson
 
Getting Started With Scala
Getting Started With ScalaGetting Started With Scala
Getting Started With ScalaMeetu Maltiar
 

Similar to Scala Corrections Library Simplifies Complex Code (20)

BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...
BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...
BASE Meetup: "Analysing Scala Puzzlers: Essential and Accidental Complexity i...
 
Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...
Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...
Scala Up North: "Analysing Scala Puzzlers: Essential and Accidental Complexit...
 
Speaking Scala: Refactoring for Fun and Profit (Workshop)
Speaking Scala: Refactoring for Fun and Profit (Workshop)Speaking Scala: Refactoring for Fun and Profit (Workshop)
Speaking Scala: Refactoring for Fun and Profit (Workshop)
 
42: Rise of the dependent types
42: Rise of the dependent types42: Rise of the dependent types
42: Rise of the dependent types
 
Slides chapter3part1 ruby-forjavaprogrammers
Slides chapter3part1 ruby-forjavaprogrammersSlides chapter3part1 ruby-forjavaprogrammers
Slides chapter3part1 ruby-forjavaprogrammers
 
Scalding - the not-so-basics @ ScalaDays 2014
Scalding - the not-so-basics @ ScalaDays 2014Scalding - the not-so-basics @ ScalaDays 2014
Scalding - the not-so-basics @ ScalaDays 2014
 
Taxonomy of Scala
Taxonomy of ScalaTaxonomy of Scala
Taxonomy of Scala
 
Introduction to Scala
Introduction to ScalaIntroduction to Scala
Introduction to Scala
 
The Fuss about || Haskell | Scala | F# ||
The Fuss about || Haskell | Scala | F# ||The Fuss about || Haskell | Scala | F# ||
The Fuss about || Haskell | Scala | F# ||
 
Scala Refactoring for Fun and Profit
Scala Refactoring for Fun and ProfitScala Refactoring for Fun and Profit
Scala Refactoring for Fun and Profit
 
Is Haskell an acceptable Perl?
Is Haskell an acceptable Perl?Is Haskell an acceptable Perl?
Is Haskell an acceptable Perl?
 
C Interview Questions for Fresher
C Interview Questions for FresherC Interview Questions for Fresher
C Interview Questions for Fresher
 
C interview-questions-techpreparation
C interview-questions-techpreparationC interview-questions-techpreparation
C interview-questions-techpreparation
 
C interview-questions-techpreparation
C interview-questions-techpreparationC interview-questions-techpreparation
C interview-questions-techpreparation
 
C interview Question and Answer
C interview Question and AnswerC interview Question and Answer
C interview Question and Answer
 
楽々Scalaプログラミング
楽々Scalaプログラミング楽々Scalaプログラミング
楽々Scalaプログラミング
 
Compass, Sass, and the Enlightened CSS Developer
Compass, Sass, and the Enlightened CSS DeveloperCompass, Sass, and the Enlightened CSS Developer
Compass, Sass, and the Enlightened CSS Developer
 
Haskell retrospective
Haskell retrospectiveHaskell retrospective
Haskell retrospective
 
Code Fast, Die Young, Throw Structured Exceptions
Code Fast, Die Young, Throw Structured ExceptionsCode Fast, Die Young, Throw Structured Exceptions
Code Fast, Die Young, Throw Structured Exceptions
 
Getting Started With Scala
Getting Started With ScalaGetting Started With Scala
Getting Started With Scala
 

Recently uploaded

IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Allon Mureinik
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...Martijn de Jong
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...apidays
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Paola De la Torre
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationRadu Cotescu
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 

Recently uploaded (20)

IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)Injustice - Developers Among Us (SciFiDevCon 2024)
Injustice - Developers Among Us (SciFiDevCon 2024)
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
 
Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101Salesforce Community Group Quito, Salesforce 101
Salesforce Community Group Quito, Salesforce 101
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 

Scala Corrections Library Simplifies Complex Code

  • 1. A Scala Corrections Library Paul Phillips paulp@improving.org Source: xkcd, of course.
  • 2. “When I'm working on a problem, I never think about beauty. I think only how to solve the problem.” ! “But when I have finished, if the solution is not beautiful, I know it is wrong.” – R. Buckminster Fuller (syntax highlighting donated by paulp)
  • 3. “When I'm working on a problem, I never think about beauty. I think only how to solve the problem.” ! “But when I have finished, if the solution is not beautiful, I know it is wrong.” – R. Buckminster Fuller trait ParSeqViewLike[ +T, +Coll <: Parallel, +CollSeq, +This <: ParSeqView[T, Coll, CollSeq] with ParSeqViewLike[T, Coll, CollSeq, This, ThisSeq], +ThisSeq <: SeqView[T, CollSeq] with SeqViewLike[T, CollSeq, ThisSeq] ] extends GenSeqView[T, Coll] with GenSeqViewLike[T, Coll, This] with ParIterableView[T, Coll, CollSeq] with ParIterableViewLike[T, Coll, CollSeq, This, ThisSeq] with ParSeq[T] with ParSeqLike[T, This, ThisSeq]
  • 4. The Winding Stairway • Five years on scala • Rooting for scala/typesafe • But I quit a dream job... • ...because I lost faith
  • 7. Should you care? • I offer my credentials only to bear witness to my credibility • I suspect I have written more scala code than anyone else, ever. • What’s visible in compiler/library represents only a small fraction of it
  • 8. Caveats • I ran out of time. Slides are rushed. Forgive me. • Error messages and repl transcripts have been heavily trimmed for clarity on a slide • This works counter to message when the point involves complexity or incomprehensibility • So verbosify all compiler messages by a factor of three for a more accurate feel
  • 9. My axe is dull • I have been pulling my punches • This has left some thinking that I quit over technical esoterica: java compatibility, jvm limitations, intractable compiler challenges • This is not accurate
  • 10. Subtext, people • Prevailing programmer culture frowns upon criticism of named individuals • In this case that doesn’t leave much room for additional specificity • All the relevant facts are available in the googles
  • 11. Is Scala too complex? • I’ll field this one: YES • Is anyone fooled by specious comparisons of language grammar size? Who cares? • Half the time when someone hits a bug they can’t tell whether it is a bug in scala or the expected behavior • That definitely includes me
  • 12. Perceived Problem C • A meme is going around that scala is too complex • Option A: Own it • Option B: Address it • Option C: Obscure it p O i t n o
  • 13. Thus is born the “use case” // A fictional idealized version of the genuine method def map[B](f: (A) B): Map[B] ! // The laughably labeled "full" signature def map[B, That](f: ((A, B)) B) (implicit bf: CanBuildFrom[Map[A, B], B, That]): That neither has any basis in reality!
  • 14. the true name of map // markers to distinguish Map's class type parameters scala> class K ; class V defined class K, V ! scala> val host = typeOf[Map[K, V]] host: Type = Map[K,V] ! scala> val method = host member TermName("map") method: Symbol = method map ! // Correct signature for map has FOUR distinct identifiers scala> method defStringSeenAs (host memberType method) res0: String = def map[B, That](f: ((K, V)) => B) (implicit bf: CBF[Map[K,V],B,That]): That
  • 15. • Now you’re thinking “use case thing is a bug, big deal, bugs get fixed.” Do they? • Surely as soon as it is known the documentation spins these fabrications, it will be addressed? If not fixed, at least it’ll be marked as inaccurate? Something? • Nope! To this day it’s the same. Your time is worthless.
  • 16. Slightly Caricatured map def map[B](f: A => B): F[B] Signature Elegance Advantages Spokespicture “map” def map[B, That](f: A => B) (implicit bf: CanBuildFrom[Repr, B, That]): That Among the purest and most reusable <—- Not this. abstractions known to computing science Can reason abstractly about code Can map a BitSet to a BitSet without typing “toBitSet”
  • 17. The Bitset Gimmick // Fancy, we get a Bitset back! scala> BitSet(1, 2, 3) map (_.toString.toInt) res0: BitSet = BitSet(1, 2, 3) ! // Except… scala> BitSet(1, 2, 3) map (_.toString) map (_.toInt) res1: SortedSet[Int] = TreeSet(1, 2, 3) ! // Um… scala> (BitSet(1, 2, 3) map identity)(1) <console>:21: error: type mismatch; found : Int(1) required: scala.collection.generic.CanBuildFrom[scala.collection.imm utable.BitSet,Int,?] (BitSet(1, 2, 3) map identity)(1) ^
  • 18. similarly scala> def f[T](x: T) = (x, new Object) f: [T](x: T)(T, Object) ! scala> SortedSet(1 to 10: _*) res0: SortedSet[Int] = TreeSet(1, 2, 3, ! scala> SortedSet(1 to 10: _*) map (x => res1: SortedSet[Int] = TreeSet(1, 2, 3, ! scala> SortedSet(1 to 10: _*) map f map res2: Set[Int] = Set(5, 10, 1, 6, 9, 2, 4, 5, 6, 7, 8, 9, 10) f(x)._1) 4, 5, 6, 7, 8, 9, 10) (_._1) 7, 3, 8, 4)
  • 19. and in a similar vein scala> val f: Int => Int = _ % 3 f: Int => Int = <function1> ! scala> val g: Int => Int = _ => System.nanoTime % 1000000 toInt g: Int => Int = <function1> ! scala> Set(3, 6, 9) map f map g res0: Set[Int] = Set(633000) ! scala> Set(3, 6, 9) map (f andThen g) res1: Set[Int] = Set(305000, 307000, 308000)
  • 20. Java Interop: the cruelest joke • It’s impossible to call scala’s map from java! • See all the grotesque details at SI-4389 IX F T ON “I played with it until it got too tedious. I think the signatures work fine. What does not work is that the variances of CanBuildFrom cannot be modelled in Java, so types do not match. And it seems Java does not even let me override with a cast. So short answer: You can't call these things from Java because instead of declaration side variance you have only a broken wildcard system.” ! — Martin Odersky W
  • 21. Lightning Round • My time is running out and I can hear you saying… • “Just give us a laundry list of collections issues” • Okay, you asked for it (in my mind)
  • 22. • Implementation details infest everything • And every detail is implementation-defined • Capabilities should be designed around the laws of variance; instead variance checks are suppressed and key method contains is untyped • Specificity rules render contravariance useless • Implicit selection and type inference inextricably bound - so type inference is largely frozen because any change will break existing code
  • 23. • Extreme pollution of base objects - all collections have “size: Int”, all Seqs have “apply”, etc. • Bundling of concerns (e.g. invariant Set) • Inheritance of implementation is the hammer for every nail… • …yet “final” and “private”, critical for a hope of correctness under inheritance, are almost unknown • Semantics discovered instead of designed
  • 24. assume the worst In Set(x) ++ Set(x), which x wins? ! Can xs filter (_ => true) return xs? ! Are defaults preserved across operations? Which operations? Is sortedness? Will views and Streams retain laziness when zipped?
  • 25. xs map identity scala> val m = Map(1 -> 2) withDefaultValue 10 m: Map[Int,Int] = Map(1 -> 2) ! scala> m(1000) res0: Int = 10 ! scala> (m map identity)(1000) <console>:9: error: type mismatch; found : Int(1000) required: CanBuildFrom[Map[Int,Int],(Int, Int),?] (m map identity)(1000) ^ ! scala> m map identity apply 1000 java.util.NoSuchElementException: key not found: 1000 at MapLike$class.default(MapLike.scala:228)
  • 26. types are for suckers % find collection -name ‘*.scala’ | xargs egrep asInstanceOf | wc -l 556
  • 27. How could 556 casts ever go wrong scala> val xs: Set[Int] = (1 to 3).view.map(x => x)(breakOut) ! java.lang.ClassCastException: SeqViewLike$$anon$3 cannot be cast to immutable.Set
  • 28. get and apply trivially fall into disagreement ! scala> Map[Int,Int]() withDefaultValue 123 res0: Map[Int,Int] = Map() ! scala> res0 contains 55 res1: Boolean = false ! scala> res0 get 55 res2: Option[Int] = None ! scala> res0 apply 55 res3: Int = 123
  • 29. Why is covariance such an object of worship? Types exist so we don’t have to live like this! // WHY infer this utterly useless type? scala> List(1, 2) ::: List(3, 4.0) res0: List[AnyVal] = List(1, 2, 3.0, 4.0) ! scala> PspList(1, 2) ::: PspList(3, 4.0) <console>:23: error: type mismatch; found : PspList[Int] required: PspList[Double]
  • 31. Abstracting over mutability • • • An inherited implementation is ALWAYS wrong somewhere!! • Half the overrides in collections exist to stave off the incorrectness which looms above. This is nuts.! • Not to mention “Map”, “Set”, etc. in three namespaces Example: how do you write "drop" so it's reusable?! In a mutable class, drop MUST NOT share, but in an immutable class, drop MUST share!
  • 32. How many ways are there to write ‘slice’ ? % ack --no-filename 'def slice(' src/library/ ! 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 override def slice(from: Int, until: Int): Iterator[A] = def slice(from: Int, until: Int): Iterator[A] = { def slice(from: Int, until: Int): Repr = def slice(from: Int, until: Int): Repr = { def slice(from: Int, until: Int): Repr = { def slice(start: Int): PagedSeq[T] = slice(start, UndeterminedEnd) def slice(unc_from: Int, unc_until: Int): Repr override /*IterableLike*/ def slice(from: Int, until: Int): Vector[A] = override /*TraversableLike*/ def slice(from: Int, until: Int): Repr = { override def slice(_start: Int, _end: Int): PagedSeq[T] = { override def slice(from1: Int, until1: Int): IterableSplitter[T] = override def slice(from1: Int, until1: Int): SeqSplitter[T] = override def slice(from: Int, until: Int) = { override def slice(from: Int, until: Int) = { override def slice(from: Int, until: Int): List[A] = { override def slice(from: Int, until: Int): Repr = self.slice(from, until) override def slice(from: Int, until: Int): Repr = { override def slice(from: Int, until: Int): Stream[A] = { override def slice(from: Int, until: Int): String = { override def slice(from: Int, until: Int): This = override def slice(from: Int, until: Int): This = override def slice(from: Int, until: Int): Traversable[A] override def slice(from: Int, until: Int): WrappedString = { override def slice(unc_from: Int, unc_until: Int): Repr = {
  • 33. scala.conflation • Every collection must have size • Every sequence must have apply • Every call to map includes a "builder factory" • Every set must be invariant • Everything must suffer universal equality
  • 34. predictability One of these expressions returns 2 and one returns never. Feeling lucky? ! scala> (Stream from 1) zip (Stream from 1) map { case (x, y) => x + y } head ! scala> (Stream from 1, Stream from 1).zipped map (_ + _) head
  • 35. sets Two complementary ways to define Set[A]. Complementary - and NOT the same thing! Intensional Extensional Specification Membership test Members Variance Set[-A] Set[+A] Defining Signature A => Boolean Iterable[A] Size Unknowable Known Duplicates(*) Meaningless Disallowed
  • 36. What's going on here? scala> class xs[A] extends Set[A] error: class xs has 4 unimplemented members. ! // Intensional/extensional, conflated. // Any possibility of variance eliminated. def iterator: Iterator[A] def contains(elem: A): Boolean // What are these doing in the interface? // Why can I define a Seq without them? def -(elem: A): Set[A] def +(elem: A): Set[A]
  • 37. todo: also add all other methods % git grep 'todo: also add' 607cb4250d SynchronizedMap.scala: // !!! todo: also add all other methods ! % git grep 'todo: also add' origin/master SynchronizedMap.scala: // !!! todo: also add all other methods ! commit 607cb4250d Author: Martin Odersky <odersky@gmail.com> Date: Mon May 25 15:18:48 2009 (4 years, 8 months ago) ! added SynchronizedMap; changed Set.put to Set.add, implemented LinkedHashMap/Set more efficiently.
  • 38. tyranny of the interface • Mandating "def size: Int" for all collections is the fast track to Glacialville! • Countless times have I fixed xs.size != 0 • Collections are both worlds: all performance/ termination trap, no exploiting of size information! • A universal size method must be SAFE and CHEAP
  • 39. Psp Collections • So here is a little of what I would do differently • I realized since agreeing to this talk that I may have to go cold turkey to escape scala’s orbit. It’s just too frustrating to use. • Which means this may never go anywhere • But you can have whatever gets done
  • 40. Conceptual Integrity trait Collections { type CC[+X] type Min[+X] type Opt[+X] type CCPair[+X] type ~>[-V1, +V2] ! ! } type type type type type type type type // // // // // Iso[A] Map[-A, +B] FlatMap[-A, +B] Grouped[A, DD[X]] Fold[-A, +R] Flatten[A] Build[A] Pure[A] the overarching container type (in scala: any covariant collection, e.g. List, Vector) least type constructor which can be reconstituted to CC[X] (scala: GenTraversableOnce) the container type for optional results (in scala: Option) some representation of a divided CC[A] (at simplest, (CC[A], CC[A])) some means of composing operations (at simplest, Function1) = = = = = = = = CC[A] ~> CC[A] CC[A] ~> CC[B] CC[A] ~> Min[B] CC[A] ~> CC[DD[A]] CC[A] ~> R CC[Min[A]] ~> CC[A] Min[A] ~> CC[A] A ~> CC[A] trait Relations[A] { type MapTo[+B] = Map[A, B] type FoldTo[+R] = Fold[A, R] type This = CC[A] type Twosome = CCPair[A] type Self = Iso[A] type Select = FoldTo[A] type Find = FoldTo[Opt[A]] type Split = FoldTo[Twosome] } // // // // // // // // // // // // // // // // e.g. filter, take, drop, reverse, etc. e.g. map, collect e.g. flatMap e.g. sliding e.g. fold, but also subsumes all operations on CC[A] e.g. flatten for use in e.g. sliding, flatMap we may not need an alias incorporating the known A another one the CC[A] under consideration a (CC[A], CC[A]) representation a.k.a. CC[A] => CC[A], e.g. tail, filter, reverse a.k.a. CC[A] => A, e.g. head, reduce, max a.k.a. CC[A] => Opt[A], e.g. find a.k.a. CC[A] => (CC[A], CC[A]), e.g. partition, span
  • 41. “Do not multiply entities unnecessarily” • mutable / immutable • Seq / Set / Map • parallel / sequential • view / regular 24 Combinations!
  • 42. Surface Area Reduced 96% • A Set is a Seq without duplicates. • A Map is a Set paired with a function K => V. • A mutable collection has nothing useful in common with an immutable collection. Write your own mutable collections. • If we can’t get sequential collections right, we have no hope of parallel collections. Write your own parallel collections. • “Views” should be how it always works.
  • 43. predictability: size matters scala> def f(xs: Iterable[Int]) = xs.size f: (xs: Seq[Int])Int ! // O(1) scala> f(Set(1)) res0: Int = 1 ! // O(n) scala> f(List(1)) res1: Int = 1 ! // O(NOES) scala> f(Stream continually 1) <ctrl-C>
  • 44. Asking the right question SizeInfo / Atomic / Infinite Precise Bounded
  • 45. Don’t ask unanswerable questions (Unless you enjoy hearing lies) scala> val xs = Foreach from BigInt(1) xs: psp.core.Foreach[BigInt] = unfold(1)(<function1>) ! scala> xs.size <console>:22: error: value size is not a member of psp.core.Foreach[BigInt] xs.size ^ ! scala> xs.sizeInfo res0: psp.core.SizeInfo = <inf>
  • 46. the joy of the invariant leaf scala> List(1, 2, 3) contains "1" res0: Boolean = false ! scala> PspList(1, 2, 3) contains "1" <console>:23: error: type mismatch; found : String("1") required: Int PspList(1, 2, 3) contains "1" ^
  • 47. HEY! MAP NEED NOT BE RUINED! scala> "abc" map (_.toInt.toChar) res1: String = abc ! scala> "abc" map (_.toInt) map (_.toChar) res2: IndexedSeq[Char] = Vector(a, b, c) ! // psp to the rescue scala> "abc".m map (_.toInt) map (_.toChar) res3: psp.core.View[String,Char] = view of abc ! scala> res3.force res4: String = abc