Functional programming for Java developers, Part 1

Java 8 introduced Java developers to functional programming with lambda expressions. This Java release effectively notified developers that it's no longer sufficient to think about Java programming only from the imperative, object-oriented perspective. A Java developer must also be able to think and code using the declarative functional paradigm.

This tutorial presents the basics of functional programming. I'll start with terminology, then we'll dig into functional programming concepts. I'll conclude by introducing you to five functional programming techniques. Code examples in these sections will get you started with pure functions, higher-order functions, lazy evaluation, closures, and currying.

What is functional programming?

Computers typically implement the Von Neumann architecture, which is a widely-used computer architecture based on a 1945 description by the mathematician and physicist John von Neumann (and others). This architecture is biased toward imperative programming, which is a programming paradigm that uses statements to change a program's state. C, C++, and Java are all imperative programming languages.

In 1977, distinguished computer scientist John Backus (notable for his work on FORTRAN), gave a lecture titled "Can programming be liberated from the von Neumann style?." Backus asserted that the Von Neumann architecture and its associated imperative languages are fundamentally flawed, and presented a functional-level programming language (FP) as a solution.

Functional programming concepts and terminology

Functional programming is a programming style in which computations are codified as functional programming functions. These are mathematical function-like constructs (e.g., lambda functions) that are evaluated in expression contexts.

Functional programming languages are declarative, meaning that a computation's logic is expressed without describing its control flow. In declarative programming, there are no statements. Instead, programmers use expressions to tell the computer what needs to be done, but not how to accomplish the task. If you are familiar with SQL or regular expressions, then you have some experience with the declarative style; both use expressions to describe what needs to be done, rather than using statements to describe how to do it.

A computation in functional programming is described by functions that are evaluated in expression contexts. These functions are not the same as the functions used in imperative programming, such as a Java method that returns a value. Instead, a functional programming function is like a mathematical function, which produces an output that typically depends only on its arguments. Each time a functional programming function is called with the same arguments, the same result is achieved. Functions in functional programming are said to exhibit referential transparency. This means you could replace a function call with its resulting value without changing the computation's meaning.

Functional programming favors immutability, which means the state cannot change. This is typically not the case in imperative programming, where an imperative function might be associated with state (such as a Java instance variable). Calling this function at different times with the same arguments might result in different return values because in this case state is mutable, meaning it changes.

Side effects in imperative and functional programming

State changes are a side effect of imperative programming, preventing referential transparency. There are many other side effects worth knowing about, especially as you evaluate whether to use the imperative or functional style in your programs.

One common side effect in imperative programming is when an assignment statement mutates a variable by changing its stored value. Functions in functional programming don't support variable assignments. Because a variable's initial value never changes, functional programming eliminates this side effect.

Another common side effect happens when modifying an imperative function's behavior based on a thrown exception, which is an observable interaction with the caller. For more information, see the Stack Overflow discussion, "Why is the raising of an exception a side effect?"

A third common side effect occurs when an I/O operation inputs text that cannot be unread, or outputs text that cannot be unwritten. See the Stack Exchange discussion "How can IO cause side effects in functional programming?" to learn more about this side effect.

Eliminating side effects makes it much easier to understand and predict computational behavior. It also helps make code more suitable for parallel processing, which often improves application performance. While there are side effects in functional programming, they are generally fewer than in imperative programming. Using functional programming can help you write code that's easier to understand, maintain, and test, and is also more reusable.

Object-oriented versus functional programming

I've created a Java application that contrasts the imperative, object-oriented and declarative, functional programming approaches to writing code. Study the code below and then I'll point out differences between the two examples.

Listing 1. Employees.java

import java.util.ArrayList;
import java.util.List;

public class Employees
{
   static class Employee
   {
      private String name;
      private int age;

      Employee(String name, int age)
      {
         this.name = name;
         this.age = age;
      }

      int getAge()
      {
         return age;
      }

      @Override
      public String toString()
      {
         return name + ": " + age;
      }
   }

   public static void main(String[] args)
   {
      List<Employee> employees = new ArrayList<>();
      employees.add(new Employee("John Doe", 63));
      employees.add(new Employee("Sally Smith", 29));
      employees.add(new Employee("Bob Jone", 36));
      employees.add(new Employee("Margaret Foster", 53));
      printEmployee1(employees, 50);
      System.out.println();
      printEmployee2(employees, 50);
   }

   public static void printEmployee1(List<Employee> employees, int age)
   {
      for (Employee emp: employees)
         if (emp.getAge() < age)
            System.out.println(emp);
   }

   public static void printEmployee2(List<Employee> employees, int age)
   {
      employees.stream()
               .filter(emp -> emp.age < age)
               .forEach(emp -> System.out.println(emp));
   }
}

Listing 1 reveals an Employees application that creates a few Employee objects, then prints a list of all employees who are younger than 50. This code demonstrates both object-oriented and functional programming styles.

The printEmployee1() method reveals the imperative, statement-oriented approach. As specified, this method iterates over a list of employees, compares each employee's age against an argument value, and (if the age is less than the argument), prints the employee's details.

The printEmployee2() method reveals the declarative, expression-oriented approach, in this case implemented with the Streams API. Instead of imperatively specifying how to print the employees (step-by-step), the expression specifies the desired outcome and leaves the details of how to do it to Java. Think of filter() as the functional equivalent of an if statement, and forEach() as functionally equivalent to the for statement.

You can compile Listing 1 as follows:

javac Employees.java

Use the following command to run the resulting application:

java Employees

The output should look something like this:

Sally Smith: 29
Bob Jone: 36

Sally Smith: 29
Bob Jone: 36

Functional programming examples

In the next sections, we'll explore five core techniques used in functional programming: pure functions, higher-order functions, lazy evaluation, closures, and currying. Examples in this section are coded in JavaScript because its simplicity, relative to Java, will allow us to focus on the techniques. In Part 2 we'll revisit these same techniques using Java code.

Listing 2 presents the source code to RunScript, a Java application that uses Java's Scripting API to facilitate running JavaScript code. RunScript will be the base program for all of the forthcoming examples.

Listing 2. RunScript.java

import java.io.FileReader;
import java.io.IOException;

import javax.script.ScriptEngine;
import javax.script.ScriptEngineManager;
import javax.script.ScriptException;

import static java.lang.System.*;

public class RunScript
{
   public static void main(String[] args)
   {
      if (args.length != 1)
      {
         err.println("usage: java RunScript script");
         return;
      }
      ScriptEngineManager manager = 
         new ScriptEngineManager();
      ScriptEngine engine = 
         manager.getEngineByName("nashorn");
      try
      {
         engine.eval(new FileReader(args[0]));
      }
      catch (ScriptException se)
      {
         err.println(se.getMessage());
      }
      catch (IOException ioe)
      {
         err.println(ioe.getMessage());
      }      
   }
}

The main() method in this example first verifies that a single command-line argument (the name of a script file) has been specified. Otherwise, it displays usage information and terminates the application.

Assuming the presence of this argument, main() instantiates the javax.script.ScriptEngineManager class. ScriptEngineManager is the entry-point into Java's Scripting API.

Next, the ScriptEngineManager object's ScriptEngine getEngineByName(String shortName) method is called to obtain a script engine corresponding to the desired shortName value. Java 10 supports the Nashorn script engine, which is obtained by passing "nashorn" to getEngineByName(). The returned object's class implements the javax.script.ScriptEngine interface.

ScriptEngine declares several eval() methods for evaluating a script. main() invokes the Object eval(Reader reader) method to read the script from its java.io.FileReader object argument and (assuming that java.io.IOException isn't thrown) then evaluate the script. This method returns any script return value, which I ignore. Also, this method throws javax.script.ScriptException when an error occurs in the script.

Compile Listing 2 as follows:

javac RunScript.java

I'll show you how to run this application after I have presented the first script.

Functional programming with pure functions

A pure function is a functional programming function that depends only on its input arguments and no external state. An impure function is a functional programming function that violates either of these requirements. Because pure functions have no interaction with the outside world (apart from calling other pure functions), a pure function always returns the same result for the same arguments. Pure functions also have no observable side effects.

Pure functions versus impure functions

The JavaScript in Listing 3 contrasts an impure calculatebonus() function with a pure calculatebonus2() function.

Listing 3. Comparing pure vs impure functions (script1.js)

// impure bonus calculation

var limit = 100;

function calculatebonus(numSales) 
{
   return(numSales > limit) ? 0.10 * numSales : 0
}

print(calculatebonus(174))

// pure bonus calculation

function calculatebonus2(numSales)
{
   return (numSales > 100) ? 0.10 * numSales : 0
}

print(calculatebonus2(174))

calculatebonus() is impure because it accesses the external limit variable. In contrast, calculatebonus2() is pure because it obeys both requirements for purity. Run script1.js as follows:

java RunScript script1.js

Here's the output you should observe:

17.400000000000002
17.400000000000002

Suppose calculatebonus2() was refactored to return calculatebonus(numSales). Would calculatebonus2() still be pure? The answer is no: when a pure function invokes an impure function, the "pure function" becomes impure.

When no data dependency exists between pure functions, they can be evaluated in any order without affecting the outcome, making them suitable for parallel execution. This is one of functional programming's benefits.

Functional programming with higher-order functions

A higher-order function is a mathematical function that receives functions as arguments, returns a function to its caller, or both. One example is calculus's differential operator, d/dx, which returns the derivative of function f.

The JavaScript in Listing 4 demonstrates passing anonymous comparison functions to a first-class sorting function.

Listing 4. Passing anonymous comparison functions (script2.js)

function sort(a, cmp)
{
   for (var pass = 0; pass < a.length - 1; pass++)
      for (var i = a.length - 1; i > pass; i--)
         if (cmp(a[i], a[pass]) < 0)
         {
            var temp = a[i]
            a[i] = a[pass]
            a[pass] = temp
         }
}

var a = [22, 91, 3, 45, 64, 67, -1]
sort(a, function(i, j)
        {
           return i - j;
        })
a.forEach(function(entry) { print(entry) })
print('\n')
sort(a, function(i, j)
        {
           return j - i;
        })
a.forEach(function(entry) { print(entry) })
print('\n')
a = ["X", "E", "Q", "A", "P"]
sort(a, function(i, j)
        {
           return i < j ? -1 : i > j;
        })
a.forEach(function(entry) { print(entry) })
print('\n')
sort(a, function(i, j)
        {
           return i > j ? -1 : i < j;
        })
a.forEach(function(entry) { print(entry) })

In this example, the initial sort() call receives an array as its first argument, followed by an anonymous comparison function. When called, the anonymous comparison function executes return i - j; to achieve an ascending sort. By reversing i and j, the second comparison function achieves a descending sort. The third and fourth sort() calls receive anonymous comparison functions that are slightly different in order to properly compare string values.

Run the script2.js example as follows:

java RunScript script2.js

Here's the expected output:

-1
3
22
45
64
67
91

91
67
64
45
22
3
-1

A
E
P
Q
X

X
Q
P
E
A

Filter and map

Functional programming languages typically provide several useful higher-order functions. Two common examples are filter and map.

• A filter processes a list in some order to produce a new list containing exactly those elements of the original list for which a given predicate (think Boolean expression) returns true.
• A map applies a given function to each element of a list, returning a list of results in the same order.

JavaScript supports filtering and mapping functionality via the filter() and map() higher-order functions. Listing 5 demonstrates these functions for filtering out odd numbers and mapping numbers to their cubes.

Listing 5. Filtering and mapping (script3.js)

print([1, 2, 3, 4, 5, 6].filter(function(num) { return num % 2 == 0 }))
print('\n')
print([3, 13, 22].map(function(num) { return num * 3 }))

Run the script3.js example as follows:

java RunScript script3.js

You should observe the following output:

2,4,6

9,39,66

Reduce

Another common higher-order function is reduce, which is more commonly known as a fold. This function reduces a list to a single value.

Listing 6 uses JavaScript's reduce() higher-order function to reduce an array of numbers to a single number, which is then divided by the array's length to obtain an average.

Listing 6. Reducing an array of numbers to a single number (script4.js)

var numbers = [22, 30, 43]
print(numbers.reduce(function(acc, curval) { return acc + curval })
      / numbers.length)

Run Listing 6's script (in script4.js) as follows:

java RunScript script4.js

You should observe the following output:

31.666666666666668

You might think that the filter, map, and reduce higher-order functions obviate the need for if-else and various looping statements, and you would be right. Their internal implementations take care of decisions and iteration.

A higher-order function uses recursion to achieve iteration. A recursive function invokes itself, allowing an operation to repeat until it reaches a base case. You can also leverage recursion to achieve iteration in your functional code.

Functional programming with lazy evaluation

Another important functional programming feature is lazy evaluation (also known as nonstrict evaluation), which is the deferral of expression evaluation for as long as possible. Lazy evaluation offers several benefits, including these two:

• Expensive (timewise) calculations can be deferred until they're absolutely necessary.
• Unbounded collections are possible. They'll keep delivering elements for as long as they're requested to do so.

Lazy evaluation is integral to Haskell. It won't calculate anything (including a function's arguments before the function is called) unless it's strictly necessary to do so.

Java's Streams API capitalizes on lazy evaluation. A stream's intermediate operations (e.g., filter()) are always lazy; they don't do anything until a terminal operation (e.g., forEach()) is executed.

Although lazy evaluation is an important part of functional languages, even many imperative languages provide builtin support for some forms of laziness. For example, most programming languages support short-circuit evaluation in the context of the Boolean AND and OR operators. These operators are lazy, refusing to evaluate their right-hand operands when the left-hand operand is false (AND) or true (OR).

Listing 7 is an example of lazy evaluation in a JavaScript script.

Listing 7. Lazy evaluation in JavaScript (script5.js)

var a = false && expensiveFunction("1")
var b = true && expensiveFunction("2")
var c = false || expensiveFunction("3")
var d = true || expensiveFunction("4")

function expensiveFunction(id)
{
   print("expensiveFunction() called with " + id)
}

Run the code in script5.js as follows:

java RunScript script5.js

You should observe the following output:

expensiveFunction() called with 2
expensiveFunction() called with 3

Lazy evaluation is often combined with memoization, an optimization technique used primarily to speed up computer programs by storing the results of expensive function calls and returning the cached result when the same inputs reoccur.

Because lazy evaluation doesn't work with side effects (such as code that produces exceptions and I/O), imperative languages mainly use eager evaluation (also known as strict evaluation), where an expression is evaluated as soon as it's bound to a variable.

Functional programming with closures

First-class functions are associated with the concept of a closure, which is a persistent scope that holds onto local variables even after the code execution has left the block in which the local variables were defined.

To help clarify this concept, Listing 8 presents a JavaScript script that introduces a simple closure. The script is based on the example presented here.

Listing 8. A simple closure (script6.js)

function add(x)
{
   function partialAdd(y)
   {
      return y + x
   }
   return partialAdd
}

var add10 = add(10)
var add20 = add(20)

print(add10(5))
print(add20(5))

Listing 8 defines a first-class function named add() with a parameter x and a nested function partialAdd(). The nested function partialAdd() has access to x because x is in add()'s lexical scope. Function add() returns a closure that contains a reference to partialAdd() and a copy of the environment around add(), in which x has the value assigned to it in a specific invocation of add().

Because add() returns a value of function type, variables add10 and add20 also have function type. The add10(5) invocation returns 15 because the invocation assigns 5 to parameter y in the call to partialAdd(), using the saved environment for partialAdd() where x is 10. The add20(5) invocation returns 25 because, although it also assigns 5 to y in the call to partialAdd(), it's now using another saved environment for partialAdd() where x is 20. Thus, while add10() and add20() use the same function partialAdd(), the associated environments differ and invoking the closures will bind x to two different values in the two invocations, evaluating the function to two different results.

Run Listing 8's script (in script6.js) as follows:

java RunScript script6.js

You should observe the following output:

15
25

Functional programming with currying

Currying is a way to translate the evaluation of a multi-argument function into the evaluation of an equivalent sequence of single-argument functions. For example, a function takes two arguments: x and y. Currying transforms the function into taking only x and returning a function that takes only y. Currying is related to but is not the same as partial application, which is the process of fixing a number of arguments to a function, producing another function of smaller arity.

Listing 9 presents a JavaScript script that demonstrates currying.

Listing 9. Currying in JavaScript (script7.js)

function multiply(x, y)
{
   return x * y
}

function curried_multiply(x)
{
   return function(y)
   {
      return x * y
   }
}

print(multiply(6, 7))
print(curried_multiply(6)(7))
var mul_by_4 = curried_multiply(4)
print(mul_by_4(2))

The script presents a noncurried two-argument multiply() function, followed by a first-class curried_multiply() function that receives multiplicand argument x and returns a closure containing a reference to an anonymous function (that receives multiplier argument y) and a copy of the environment around curried_multiply(), in which x has the value assigned to it in an invocation of curried_multiply().

The rest of the script first invokes multiply() with two arguments and prints the result. It then invokes curried_multiply() in two ways:

• curried_multiply(6)(7) results in curried_multiply(6) executing first. The returned closure executes the anonymous function with the closure's saved x value 6 being multiplied by 7.
• var mul_by_4 = curried_multiply(4) executes curried_multiply(4) and assigns the closure to mul_by_4. mul_by_4(2) executes the anonymous function with the closure's 4 value and the passed argument 2.

Run Listing 9's script (in script7.js) as follows:

java RunScript script7.js

You should observe the following output:

42
42
8

In conclusion

In this tutorial you've learned some basics of functional programming. We've used examples in JavaScript to study five core functional programming techniques, which we'll further explore using Java code in Part 2. In addition to touring Java 8's functional programming capabilities, the second half of this tutorial will help you begin to think functionally, by converting an example of object-oriented Java code to its functional equivalent.

Learn more about functional programming

I found the book Introduction to Functional Programming (Richard Bird and Philip Wadler, Prentice Hall International Series in Computing Science, 1992) helpful in learning the basics of functional programming.

This story, "Functional programming for Java developers, Part 1" was originally published by JavaWorld.