Functional Programming in Java
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Explores and discusses benefits of functional programming in Java and how to program in a functional style. Watch Venkat Subramaniam's talk at https://youtu.be/Ee5t_EGjv0A if you would like to learn more.
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Functional Programming in Java
- 1. 09 Sept 2015 Functional Programming in Java Jim Bethancourt @jimbethancourt
- 2. 1 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional Agenda
- 3. 2 What is Functional Programming? Functions have no state They don’t change • the world around them • their input • access global state Given the same input, they produce the same output Just like a function in math: f(x) = y What is Functional Programming
- 4. 3 What is Functional Programming? Let’s face it – Computer application codebases have gotten HUGE! How big are the codebases you work on? Mutable state is frequently difficult to manage Have you forgotten to set a flag too? Imperative style code is often difficult to read Don’t tell me what you’re doing – just do it! Why Functional Programming?
- 5. 4 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 6. 5 What are the Benefits? Functional Programs are easier to: • Scale • Read • Test Faster!
- 7. 6 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 8. 7 Functions Functions are first-class citizens: • They can be assigned, passed, and returned just like variables! • They don’t mutate state, they transform values • Treating functions as values is known as First- class Functions or Higher-order Programming Functions as First-Class Citizens
- 9. 8 Functions Functions are isolated But what if I want secondary output / side effects? Use a Consumer<T>! Functions Are Isolated
- 10. 9 Functions public void convertTemps() { DoubleUnaryOperator convertFtoC = expandedCurriedConverter(-32, 5.0/9, 0); S.o.p(convertFtoC.applyAsDouble(98.6)); } static DoubleUnaryOperator expandedCurriedConverter(double w, double y, double z) { return (double x) -> (x + w) * y + z; } Example of Functional Programming
- 11. 10 Functions Imperative vs Declarative Imperative How Mutate Side-effects Pass objects Hard to compose Declarative What Transform Pure Pass functions too Functional composition
- 12. 11 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 13. 12 Lambdas A lambda is an inline functional definition They can see and use nearby values, but can’t change them Closures can change nearby variables. Java 8 lambdas are NOT closures since they require all variables to be final What are Lambdas
- 14. 13 Lambdas Before: Comparator<Apple> byWeight = new Comparator<Apple>() { public int compare(Apple a1, Apple a2){ return a1.getWeight().compareTo(a2.getWeight()); } }; After (with lambda expressions): Comparator<Apple> byWeight = (Apple a1, Apple a2) -> a1.getWeight().compareTo(a2.getWeight()); Before and After Lambdas
- 15. 14 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 16. 15 Streams A sequence of elements supporting sequential and parallel aggregate operations. (from Java 8 Javadoc) Streams allow for you to manipulate collections of data in a declarative way. Operations can be chained together to form an expressive processing pipeline Streams perform internal iteration behind the scenes, allowing you to focus on what rather than how Streams can easily be parallelized by calling parallelStream() -- YMMV What is a Stream?
- 17. 16 Streams Each intermediate operation performed on a stream returns another stream This allows for any number of intermediate operations to be performed on a stream of elements These operations include but are not limited to filter() map() flatMap() distinct() Streams don’t perform an evaluation until they reach their terminal operation is reached, such as reduce() collect() sum(), max(), min(), etc. Streams Return Streams, and they’re Lazy!!!
- 18. 17 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 19. 18 Terminal Operators Terminal operators trigger processing and return a value. reduce(), count(), anyMatch(), and summaryStatistics() are examples of terminal operations Once a terminal operation takes place, that instance of the stream is dead and no longer usable, and no further processing can take place on it. The End is Coming!
- 20. 19 Terminal Operators Streams can only be traversed once, so how can we reuse the code we wrote? Assign the nonterminated stream operation to a stream supplier object! Supplier<Stream<String>> streamSupplier = () -> Stream.of("d2", "a2", "b1", "b3", "c") .filter(s -> s.startsWith("a")); streamSupplier.get().anyMatch(s -> true); // ok streamSupplier.get().noneMatch(s -> true); // ok One Time Traversal and Stream Reuse
- 21. 20 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 22. 21 Intermediate Operators Intermediate Operators perform mapping / transforming and filtering operations on a stream of elements. Any number of intermediate operations can be performed on a stream of elements. people.stream() .filter(person -> person.getGender() == Gender.MALE) .map(person -> new Person(person.getName().toUpperCase(), person.getGender(), person.getAge())) .forEach(System.out::println); Transforming! More Than Meets the Eye!
- 23. 22 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 24. 23 Collectors Collectors allow for objects to be accumulated to be passed back to a result container such as a Collection or a Map Transformations may take place once all elements are accumulated, but this is not required. Collector<Employee, ?, Map<Department, Integer>> summingSalariesByDept = Collectors.groupingBy(Employee::getDepartment, summingSalaries); Finishing the Stream
- 25. 24 Collectors Collectors require 3 things: Empty instance of the output type Operator that can put an element into that output type Operation that can combine two of the output type Collectors within collectors are possible too! Requirements of Collectors
- 26. 25 1 What is Functional Programming? 2 What are the Benefits? 3 Functions 4 Lambdas 5 Streams 6 Terminal Operators 7 Intermediate Operators 8 Collectors 9 Optional
- 28. 27 Optional I’m tired of NPEs!
- 29. 28 Optional Instead of returning a null object, return an Optional! Use Optional as a Safe Return Type
- 30. 29 Optional public class Person { private Optional<Car> car; public Optional<Car> getCar() { return car; } } public class Car { private Optional<Insurance> insurance; public Optional<Insurance> getInsurance() { return insurance; } } public class Insurance { private String name; public String getName() { return name; } } Putting Optional to Work
- 31. 30 Optional Empty optional creation Optional<Car> optCar = Optional.empty(); Optional from a non-null value Optional<Car> optCar = Optional.of(car); Optional from a possibly null value Optional<Car> optCar = Optional.ofNullable(car); Creating Optionals
- 32. 31 Optional Optional<Insurance> optInsurance = Optional.ofNullable(insurance); Optional<String> name = optInsurance.map(Insurance::getName); Getting values in Optionals
- 33. 32 Optional This won’t compile! Optional<Person> optPerson = Optional.of(person); Optional<String> name = optPerson.map(Person::getCar) .map(Car::getInsurance) .map(Insurance::getName); Chaining Operations with Optionals
- 34. 33 Optional Instead, use flatMap() public String getCarInsuranceName(Optional<Person> person) { return person.flatMap(Person::getCar) .flatMap(Car::getInsurance) .map(Insurance::getName) .orElse("Unknown"); } Chaining Operations with Optionals
Editor's Notes
- Functions are isolated – they don’t know anything about and can‘t change the outside world To allow secondary output to be passed out of a function, pass in a Consumer that will accept() this output, handing over control
- Thank you to Venkat Subramaniam for this helpful comparison!
- filter() map() flatMap() -- Returns a stream consisting of the results of replacing each element of this stream with the contents of a mapped stream produced by applying the provided mapping function to each element. It lets you replace each value of a stream with another stream and then concatenates all the generated streams into a single stream.
- This will print out the name, gender, and age for each person object
- If we wanted to create a collector to tabulate the sum of salaries by department, we could reuse the "sum of salaries" logic usingCollectors.groupingBy(Function, Collector):
- Collectors within collectors are possible too! groupingBy() allows for a second downstream collector to be passed that will further reduce the elements of the stream and populate them as values in the resulting map instead of populating a list of values
- A person might or might not have a car A car might or might not be insured
- There are several ways of creating optionals Third example: If car were null, the resulting Optional object would be empty.
- The map operation applies the provided function to each element of a stream. You could also think of an Optional object as a particular collection of data, containing at most a single element. If the Optional contains a value, then the function passed as argument to map transforms that value. If the Optional is empty, then nothing happens.
- With streams, the flatMap method takes a function as an argument, which returns another stream. This function is applied to each element of a stream, which would result in a stream of streams. But flatMap has the effect of replacing each generated stream by the contents of that stream. In other words, all the separate streams that are generated by the function get amalgamated or flattened into a single stream. What we want here is something similar, but you want to flatten a two-level optional into one.