Beyond Paradigms — Berlin edition

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T h e o r y f o r p r a c t i c e BEYOND PARADIGMS Understand language features,  use the right patterns. Luciano Ramalho @ramalhoorg

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EMPORIO CELESTIAL By Jorge Luis Borges 2

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PARADIGMS Programming language categories 6

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CALCULATOR PROGRAMMING IS IMPERATIVE 7 HP-25 TI 58C

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HP-25 CALCULATOR PROGRAMMING LANGUAGE 8

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A SURVEY-STYLE PROGRAMMING LANGUAGES BOOK Programming Language Pragmatics,  4th edition (2015) Michael L. Scott 9

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GCD ASM X86 Greatest  common divisor in x86 Assembly (Scott, 2015) 10

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GCD IN C, OCAML AND PROLOG 11

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GCD IN PYTHON Imperative style Functional style 12

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GCD IN PYTHON Imperative style Functional style 13 Bad ﬁt for Python: no tail-call optimisation

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ONE CLASSIFICATION 14 Programming Language Pragmatics,  4th edition (2015) Michael L. Scott

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ONE CLASSIFICATION (ZOOM) 15

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ONE CLASSIFICATION (ZOOM) 16 ???

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ANOTHER BOOK Princípios de Linguagens de Programação  (2003) Ana Cristina Vieira de Melo Flávio Soares Corrêa da Silva 17

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19 Lógicas

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THE LANGUAGE LIST 20

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LANGUAGES ARE MISSING… 21

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LANGUAGE CATEGORIES 22

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LANGUAGE CATEGORIES (2) 23

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LANGUAGE CATEGORIES (3) 24

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LANGUAGE CATEGORIES (4) 25

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CATEGORIES? Ontologies are so 1900’s 26

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A CLASSIFICATION BASED ON HARD FACTS 27

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A CLASSIFICATION BASED ON HARD FACTS? 28 “Noble” gases!?

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“Ontology is overrated.” Clay Shirky 29

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A BETTER APPROACH Fundamental Features of Programming Languages 30

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TEACHING PROGRAMMING LANGUAGE THEORY 31

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A PAPER PRESENTING THE APPROACH 32

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A PAPER PRESENTING THE APPROACH 33

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A PAPER PRESENTING THE APPROACH 34

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A PAPER PRESENTING THE APPROACH 35

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THEORY IN PRACTICE WITH RACKET (A SCHEME DIALECT) 36

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THEORY IN PRACTICE WITH PASCAL (1990) 37

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FEATURES Core features, not mere syntax 38

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SAMPLE FEATURES ✖ LANGUAGES 39 Common Lisp C Java Python Go First-class functions ✔ ∗ ✔ ✔ ✔ First-class types ✔ ✔ Iterators ∗ ✔ ✔ ∗ Variable model reference value* value and reference reference value* and reference Type checking dynamic static static dynamic static Type expression structural nominal nominal structural structural

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SAMPLE FEATURES ✖ LANGUAGES 40 Common Lisp C Java Python Go First-class functions ✔ ∗ ✔ ✔ ✔ First class types ✔ ✔ Iterators ∗ ✔ ✔ ∗ Variable model reference value* value and reference reference value* and reference Type checking dynamic static static dynamic static Type expression structural nominal nominal structural structural Functions as objects Classes as objects

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SAMPLE FEATURES ✖ LANGUAGES 41 Common Lisp C Java Python Go First-class functions ✔ ∗ ✔ ✔ ✔ First-class types ✔ ✔ Iterators ∗ ✔ ✔ ∗ Variable model reference value* value and reference reference value* and reference Type checking dynamic static static dynamic static Type expression structural nominal nominal structural structural

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SAMPLE FEATURES ✖ LANGUAGES 42 Common Lisp C Java Python Go First-class functions ✔ ∗ ✔ ✔ ✔ First-class types ✔ ✔ Iterators ∗ ✔ ✔ ∗ Variable model reference value* value and reference reference value* and reference Type checking dynamic static static dynamic static Type expression structural nominal nominal structural structural

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SAMPLE FEATURES ✖ LANGUAGES 43 Common Lisp C Java Python Go First-class functions ✔ ∗ ✔ ✔ ✔ First-class types ✔ ✔ Iterators ∗ ✔ ✔ ∗ Variable model reference value* value and reference reference value* and reference Type checking dynamic static static dynamic static Type expression structural nominal nominal structural structural

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SAMPLE FEATURES ✖ LANGUAGES 44 Common Lisp C Java Python Go First-class functions ✔ ∗ ✔ ✔ ✔ First-class types ✔ ✔ Iterators ∗ ✔ ✔ ∗ Variable model reference value* value and reference reference value* and reference Type checking dynamic static static dynamic static Type expression structural nominal nominal structural structural

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DESIGN PATTERNS When languages fall short 45

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GOF: CLASSIC BOOK BY THE “GANG OF FOUR” Design Patterns: Elements of Reusable Object-Oriented Software (1995) Erich Gamma  Richard Helm  Ralph Johnson  John Vlissides 46

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NOT EVERY PATTERN IS UNIVERSAL Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley, 1995), p. 4. 47

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NOT EVERY PATTERN IS UNIVERSAL Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley, 1995), p. 4. 48

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NOT EVERY PATTERN IS UNIVERSAL Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides, Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley, 1995), p. 4. 49

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THE ITERATOR PATTERN The classic recipe 54

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56 Head First Design Patterns Poster  O'Reilly  ISBN 0-596-10214-3

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THE FOR LOOP MACHINERY • In Python, the for loop, automatically: •Obtains an iterator from the iterable •Repeatedly invokes next() on the iterator,   retrieving one item at a time •Assigns the item to the loop variable(s) 57 for item in an_iterable: process(item) for item in an_iterable: process(item) •Terminates when a call to next() raises StopIteration.

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ITERABLE VERSUS ITERATOR 58 • iterable: implements Iterable interface (__iter__ method) •__iter__ method returns an Iterator  • iterator: implements Iterator interface (__next__ method) •__next__ method returns next item in series and •raises StopIteration to signal end of the series Python iterators are also iterable!

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AN ITERABLE TRAIN An instance of Train can be iterated, car by car 59 >>> t = Train(3) >>> for car in t: ... print(car) car #1 car #2 car #3 >>>

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CLASSIC ITERATOR IMPLEMENTATION The pattern as described by Gamma et. al. 60 class Train: def __init__(self, cars): self.cars = cars def __iter__(self): return TrainIterator(self.cars) class TrainIterator: def __init__(self, cars): self.next = 0 self.last = cars - 1 def __next__(self): if self.next <= self.last: self.next += 1 return 'car #%s' % (self.next) else: raise StopIteration() >>> t = Train(4) >>> for car in t: ... print(car) car #1 car #2 car #3 car #4

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BARBARA LISKOV'S CLU LANGUAGE 61 CLU Reference Manual — B. Liskov et. al. — © 1981 Springer-Verlag — also available online from MIT: http://publications.csail.mit.edu/lcs/pubs/pdf/MIT-LCS-TR-225.pdf © 2010 Kenneth C. Zirkel — CC-BY-SA

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ITERABLE OBJECTS: THE KEY TO FOREACH • Python, Java & CLU let programmers define iterable objects              • Some languages don't offer this flexibility • C has no concept of iterables • In Go, only some built-in types are iterable and can be used with foreach (written as the for … range special form) 67 for item in an_iterable: process(item) for item in an_iterable: process(item)

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ITERABLE TRAIN WITH A GENERATOR METHOD The Iterator pattern as a language feature: 68 class Train: def __init__(self, cars): self.cars = cars def __iter__(self): for i in range(self.cars): yield 'car #%s' % (i+1) Train is now iterable because __iter__ returns a generator! >>> t = Train(3) >>> it = iter(t) >>> it    >>> next(it), next(it), next(it)  ('car #1', 'car #2', 'car #3')

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COMPARE: CLASSIC ITERATOR × GENERATOR METHOD The classic Iterator recipe is obsolete in Python since v.2.2 (2001) 69 class Train: def __init__(self, cars): self.cars = cars def __iter__(self): for i in range(self.cars): yield 'car #%s' % (i+1) class Train: def __init__(self, cars): self.cars = cars def __iter__(self): return IteratorTrem(self.cars) class TrainIterator: def __init__(self, cars): self.next = 0 self.last = cars - 1 def __next__(self): if self.next <= self.last: self.next += 1 return 'car #%s' % (self.next) else: raise StopIteration() Generator function handles the state of the iteration

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STRATEGY IN PYTHON Leveraging Python’s fundamental features 70

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CHOOSE AN ALGORITHM AT RUN TIME 71

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72 Context Strategy Concrete strategies CHOOSE AN ALGORITHM AT RUN TIME

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DOCTESTS FOR CONTEXT AND ONE CONCRETE STRATEGY 73 Instance of Strategy  is given to Order constructor

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DOCTESTS FOR TWO ADDITIONAL CONCRETE STRATEGIES 74

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VARIATIONS OF STRATEGY IN PYTHON Classic implementation using ABC First-class function implementation Parameterised closure implementation Parameterised callable implementation 75

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CLASSIC STRATEGY: THE CONTEXT CLASS 76 Strategy is given to constructor Strategy is used here

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STRATEGY ABC AND A CONCRETE STRATEGY 77

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TWO CONCRETE STRATEGIES 78

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FIRST-CLASS FUNCTION STRATEGY 79

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CONTEXT: STRATEGY FUNCTION AS ARGUMENT 80 Strategy function is passed to Order constructor

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CONTEXT: INVOKING THE STRATEGY FUNCTION 81

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CONCRETE STRATEGIES AS FUNCTIONS 82

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CONCRETE STRATEGIES AS FUNCTIONS (2) 83

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PARAMETERISED STRATEGY  WITH CLOSURE 84

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PARAMETERISED STRATEGY IMPLEMENTED AS CLOSURE 85 Promo is called with discount percent value

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PARAMETERISED STRATEGY IMPLEMENTED AS CLOSURE 86 Outer function gets percent argument Inner function carries percent binding  in its closure

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LAMBDA: SHORTCUT TO DECLARE INNER FUNCTION 87

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PARAMETERISED STRATEGY  WITH CALLABLE 88

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PARAMETERISED STRATEGY IMPLEMENTED AS CLOSURE 89 Promo is instantiated with discount percent value

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PROMOTION AS A CALLABLE CLASS 90

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CONCRETE STRATEGIES AS CALLABLE CONCRETE CLASSES 91

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WHICH IS MORE IDIOMATIC? Classes x functions 92

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WHICH IS MORE IDIOMATIC? Classic strategy feels too verbose for Python* 93 * Yes, this is subjective. I am talking about style!

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WHICH IS MORE IDIOMATIC? Classic strategy feels too verbose for Python* First-class functions are very common in the standard library •The sorted built-in key argument is one example. 94 * Yes, this is subjective. I am talking about style!

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WHICH IS MORE IDIOMATIC — WITH PARAMETER? Use of closure is common in Python •nonlocal was added in Python 3 to support it better 95

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WHICH IS MORE IDIOMATIC — WITH PARAMETER? Use of closure is common in Python •nonlocal was added in Python 3 to support it better Callable objects are uniquely Pythonic •Graham Dumpleton recommends callable classes as the best way to code decorators 96

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WRAPPING UP Learn the fundamentals 97

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INSTEAD OF PARADIGMS… 98

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FOCUS ON FUNDAMENTAL FEATURES 99

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FEATURES ARE THE BEST GUIDE… 100 …to decide whether a particular pattern or implementation makes the most of the language.

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WHY LEARN THE FUNDAMENTALS* Learn new languages faster Leverage language features Choose among alternative implementations Make sensible use of design patterns Debug hard problems Emulate missing features when they are helpful 101 Inspired by Programming Language Pragmatics Michael L. Scott *

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Luciano Ramalho luciano.ramalho@thoughtworks.com  Twitter: @ramalhoorg / @standupdev Repo: github.com/standupdev/beyond-paradigms Slides: speakerdeck.com/ramalho VIELEN DANK !