Functional programming principles and Java 8
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FUNCTIONAL PRINCIPLES Introduction to Functional Programming and Java 8
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Transcript of Functional programming principles and Java 8
FUNCTIONAL
PRINCIPLES
Introduction to Functional Programming and Java 8
WHY ?
WHY FUNCTIONAL PROGRAMMING ? / MOORE’S LAW
WHY FUNCTIONAL PROGRAMMING ? / MULTI CORE TREND
WHY FUNCTIONAL PROGRAMMING ? / SOME HISTORY
WHAT ?
•A programming paradigm where functions are first-class entities
• The main concepts are:
1. programming with functions
2. avoid mutation
•A new way of thinking
WHAT IS FP ?
•Object Immutability
• Functions:
– as first class citizens
– no side effects (Pure functions)
– Higher Order Functions
•No loops
• Lazy evaluation
WHAT IS FP ? / FUNCTIONAL PRINCIPLES
• Easier parallelization
• Less code
• Easy testing
• Results instead of steps
• Easy to understand code
WHAT IS FP ? / WHAT YOU GET ?
HOW ?
IMMUTABILITY
An immutable object is an object whose state cannot be modified after it is created
IMMUTABLE OBJECTS
“Classes should be immutable unless there’s very good reason to make them mutable… If a class cannot be made immutable, limit its mutability as much as possible”
Joshua Bloch
IMMUTABLE OBJECTS
•are thread-safe
•are simple to construct
•easy to test
•easy to use
• favors caching
IMMUTABLE OBJECTS / PROS
• Large object graphs
• Memory consumption
• Extra garbage collection cycles needed
IMMUTABLE OBJECTS / CONS
FUNCTIONS
F : X → Y
HIGHER ORDER
FUNCTIONS
(HOF)
HOF = functions that can take other functions as arguments and / or return other functions as result
HIGHER ORDER FUNCTIONS / DEFINITION
HIGHER ORDER FUNCTIONS / HOFS EXAMPLE IN F#
•Allows easy parallelism
•Encourages abstraction
•Reusing of common code
• Isolates the essential parts
•Allows easier unit testing
HIGHER ORDER FUNCTIONS
HIGHER ORDER FUNCTIONS/ HOFS IN JAVA
HIGHER ORDER FUNCTIONS / JAVA CLASSES
HIGHER ORDER FUNCTIONS / USAGE OF JAVA 8 FUNC. CLS.
HIGHER ORDER FUNCTIONS / LAMBDA EXPRESSIONS
• A lambda expression is an anonymous method
• Lambdas favor HOFs
• more powerful libraries
• more expressive, more readable, less
error-prone use code
• Boosts developer productivity
• key to an accessible parallelism strategy
HIGHER ORDER FUNCTIONS
CHECKPOINT
FUNCTIONS
(PURE
FUNCTIONS)
A function is said to be pure if
1. it returns same set of values for same set of inputs
2. It does not have any observable side effects
PURE FUNCTIONS / DEFINITION
PURE FUNCTIONS / EXAMPLE 2
Impure functions / Side effects :
1. Alter parameters passed by ref
2. Alter members of passed objects
3. Alter external objects
PURE FUNCTIONS / SIDE EFFECTS
PURE FUNCTIONS / EXAMPLE 2
• sin(x)
• length(a)
• random()
• println(String s)
• Insert values(x, y, z) into DB_TABLE
PURE FUNCTIONS / SAMPLE OF PURE AND IMPURE FNC
• easier to understand
• easy maintenance
• easy testing / unit-testing
• favor concurrency
PURE FUNCTIONS / PROS
“No side effects” is utopic
PURE FUNCTIONS / CONS
FUNCTION
COMPOSITION
FUNCTION COMPOSITION / MATH
FUNCTION COMPOSITION / SUPPORT IN JAVA 8
FUNCTION COMPOSITION / THE POWER OF COMPOSITION
CHECKPOINT
NO LOOPS
NO LOOPS
(RECURSION)
RECURSION / EXAMPLE OF AN ITERATIVE FUNCTION
A recursive function is a function that calls itself during its execution
RECURSION / RECURSIVE VS. ITERATION
RECURSION / TAIL RECURSION
Tail recursion = a recursive function calls itself as its last action
NO LOOPS / RECURSION - TAIL RECURSION
NO LOOPS / RECURSION - TAIL RECURSION OPTIMIZATION
NO LOOPS
(FUNCTION
CHAINING)
NO LOOPS / FUNCTION CHAINING
• Similar to unix pipes :
ps -ax | tee processes.txt | more
• Already used in java in fluent interfaces
• Eliminate the need for intermediate variables
NO LOOPS / FUNCTION CHAINING
persons.stream()
.filter(e -> e.getGender() == Person.Sex.MALE)
.forEach(e -> System.out.println(e.getName()));
for (Person p : persons) {
if (p.getGender() == Person.Sex.MALE) {
System.out.println(p.getName());
}
}
NO LOOPS / AGGREGATE OPERATIONS
• They use internal iteration
• They process elements from a stream
• They support behavior as parameters
NO LOOPS / FUNCTION CHAINING
double average = persons.stream()
.filter(p -> p.getGender() == Person.Sex.MALE)
.mapToInt(Person::getAge)
.average()
.getAsDouble();
FUNCTION CHAINING / DEFAULT METHODS
FUNCTION CHAINING / DEFAULT METHODS
FUNCTIONS CHAINING / STREAMS IN JAVA 8
• Streams do not provide a means to directly access or manipulate their elements
• are concerned with declaratively describing the computational operations which will be performed in aggregate on that source
• No storage: they carry values from a source through a pipeline
• Functional in nature ( operations do not modify its underlying data)
• Operations can be implemented lazily ( for single pass execution & efficient implementation of short-circuit operations)
• No bounds : streams can be infinite
FUNCTION CHAINING / SEQUENTIAL REDUCE
FUNCTION CHAINING / PARALLEL STREAMS
PERFORMANCE OF PARALLEL PROCESSING
FUNCTION CHAINING / OTHER EXAMPLES
OTHER
FUNCTIONAL
FEATURES
(OPTIONAL)
Shameless copy-paste from www.oracle.com/technetwork/articles/java/java8-optional-2175753.html
OPTIONAL / WHY OPTIONAL ?
OPTIONAL / NULL, THE BILLION DOLLAR MISTAKE
"I call it my billion-dollar mistake. It was the invention of the null reference in 1965. […]
I couldn't resist the temptation to put in a null reference, simply because it was so easy to implement.
This has led to innumerable errors, vulnerabilities, and system crashes, which have probably caused a billion dollars of pain and damage in the last forty years“
Tony Hoare
OPTIONAL / THE SOLUTION TO NULL
java.util.Optional<T> :
• A class that encapsulates an optional value
• A single-value container that either contains a value or doesn't (empty)
OPTIONAL / HOW TO CREATE IT
OPTIONAL / HOW TO USE IT
OPTIONAL / THE MOST IMPORTANT METHODS
OPTIONAL / RETURN TO ORIGINAL EXAMPLE
OPTIONAL / BENEFITS
• Idiot proof / Clear intent
• Cleaner code (no more null checks)
• Encourages method chaining
• End of NullPointerException
OPTIONAL / CONS
• Performance
• Serialization - Optional is not
• Certain operations involving parametric polymorphism become cumbersome
CHECKPOINT
FP CHANGES
EVERYTHING
EVERYTHING CHANGES / THREADS WITH LAMBDAS
EVERYTHING CHANGES / ACTION LISTENERS W/ LAMBDAS
EVERYTHING CHANGES / COMPARATORS
EVERYTHING CHANGES / LIST ITERATION, FILTERING, ETC.
EVERYTHING CHANGES / READING FILES
EVERYTHING CHANGES / SPRING
CHECKPOINT
• Immutability
• Higher Order Functions
• Pure functions
• No loops ( recursion, function chaining)
• Lazy evaluation
• Type inference
• Parallelism
• Easy coding / understanding
RECAP / FUNCTIONAL PROGRAMMING
• Introduced FP features
• Functional interfaces
• Function/Predicate/Producer/Consumer
• Default methods
• Lambda expressions
• Streams
• Map/Reduce/Filter/Collect
• Type inference
RECAP / JAVA 8
• Easier parallelization
• Less code
• Easy testing
• Results instead of steps
• Easy to understand code
WHAT IS FP ? / WHAT YOU GET ?
QUESTIONS
THE END
• Scheme, Lisp
• ML, OCaml
• Haskell
• Erlang
• Scala
• Clojure
• F#
WHAT’S NEXT ? / OTHER FUNCTIONAL LANGUAGES
•Retrolambda – backport of
java 8 lambdas in Java 7,6,5
• functionaljava.org
•Google Guava
WHAT’S NEXT ? / FP IN JAVA BEFORE JAVA8
•Reactive programming
WHAT’S NEXT ? / OTHER TECHNOLOGIES
WHAT’S/WHO’S
NEXT ?
![CMSC 330: Organization of Programming Languages · Why study functional programming? 9 Functional languages predict the future: •Garbage collection •Java [1995], LISP [1958] •Parametric](https://static.fdocuments.us/doc/165x107/5f02028d7e708231d40221a8/cmsc-330-organization-of-programming-languages-why-study-functional-programming.jpg)
