Lazy evaluation — deferral of expression evaluation for as long as possible — is a feature of many functional programming languages. Lazy collections deliver their elements as needed rather than precalculating them, offering several benefits. First, you can defer expensive calculations until they're absolutely needed. Second, you can create infinite collections, which keep delivering elements as long as they keep receiving requests. Third, lazy use of functional concepts such as map
and filter
enable you to generate moreefficient code (see Resources for a link to a relevant discussion by Brian Goetz). Java doesn't natively support laziness, but several frameworks and successor languages do, which I explore in this installment and the next.
Consider this snippet of pseudo code for printing the length of a list:
print length([2+1, 3*2, 1/0, 54])
If you try to execute this code, the result will vary depending on the type of programming
language it's written in: strict or nonstrict (also known as lazy). In a strict programming language, executing (or perhaps even compiling) this code results in a DivByZero
exception because of the list's third element. In a nonstrict language, the result is 4
, which accurately reports the number of items in the list. After all, the method I'm calling is length()
, not lengthAndThrowExceptionWhenDivByZero()
! Haskell is one of the few nonstrict languages in use (see Resources). Alas, Java doesn't support nonstrict evaluation, but you can still take advantage of the concept of laziness in Java.
Lazy iterator in Java
Java's lack of native support for lazy collections doesn't mean you can't simulate one using an Iterator
. As in several previous installments of this series, I'll use a simple primenumber algorithm to illustrate functional concepts. I'll build on the optimized class presented in the last installment with the enhancements that appear in Listing 1:
Listing 1. Simple algorithm for determining prime numbers
import java.util.HashSet; import java.util.Set; import static java.lang.Math.sqrt; public class Prime { public static boolean isFactor(int potential, int number) { return number % potential == 0; } public static Set<Integer> getFactors(int number) { Set<Integer> factors = new HashSet<Integer>(); factors.add(1); factors.add(number); for (int i = 2; i < sqrt(number) + 1; i++) if (isFactor(i, number)) { factors.add(i); factors.add(number / i); } return factors; } public static int sumFactors(int number) { int sum = 0; for (int i : getFactors(number)) sum += i; return sum; } public static boolean isPrime(int number) { return number == 2  sumFactors(number) == number + 1; } public static Integer nextPrimeFrom(int lastPrime) { lastPrime++; while (! isPrime(lastPrime)) lastPrime++; return lastPrime; } }
A previous installment discusses at length the inner details of how this class determines if an integer is a prime number. In Listing 1, I add the nextPrimeFrom()
method to generate the next prime number based on the input parameter. That method plays a role in this article's upcoming examples.
Generally, developers think of iterators as using collections as backing stores, but anything that supports the Iterator
interface qualifies. Thus, I can create an infinite iterator of prime numbers, as shown in Listing 2:
Listing 2. Creating a lazy iterator
public class PrimeIterator implements Iterator<Integer> { private int lastPrime = 1; public boolean hasNext() { return true; } public Integer next() { return lastPrime = Prime.nextPrimeFrom(lastPrime); } public void remove() { throw new RuntimeException("Can't change the fundamental nature of the universe!"); } }
In Listing 2, the hasNext()
method always returns true
because, as far as we know, the number of prime numbers is infinite. The remove()
method doesn't apply here, so I throw an exception in case of accidental invocation. The workhorse method is the next()
method, which handles two chores with its single line. First, it generates the next prime number based on the last one by calling the nextPrimeFrom()
method that I added in Listing 1. Second, it exploits Java's ability to assign and return in a single statement, updating the internal lastPrime
field. I exercise the lazy iterator in Listing 3:
Listing 3. Testing the lazy iterator
public class PrimeTest { private ArrayList<Integer> PRIMES_BELOW_50 = new ArrayList<Integer>() {{ add(2); add(3); add(5); add(7); add(11); add(13); add(17); add(19); add(23); add(29); add(31); add(37); add(41); add(43); add(47); }}; @Test public void prime_iterator() { Iterator<Integer> it = new PrimeIterator(); for (int i : PRIMES_BELOW_50) { assertTrue(i == it.next()); } } }
In Listing 3, I create a PrimeIterator
and verify that it reports the first 50 prime numbers. Although not the typical use of an iterator, it does mimic some of the useful behavior of lazy collections.
Using LazyList
Jakarta Commons includes a LazyList
class (see Resources), which uses a combination of the Decorator design pattern and a factory. To use Commons LazyList
, you must wrap an existing list to make it lazy, and create a factory for new values. Consider the usage of LazyList
in Listing 4:
Listing 4. Testing a Commons LazyList
public class PrimeTest { private ArrayList<Integer> PRIMES_BELOW_50 = new ArrayList<Integer>() {{ add(2); add(3); add(5); add(7); add(11); add(13); add(17); add(19); add(23); add(29); add(31); add(37); add(41); add(43); add(47); }}; @Test public void prime_factory() { List<Integer> primes = new ArrayList<Integer>(); List<Integer> lazyPrimes = LazyList.decorate(primes, new PrimeFactory()); for (int i = 0; i < PRIMES_BELOW_50.size(); i++) assertEquals(PRIMES_BELOW_50.get(i), lazyPrimes.get(i)); } }
In Listing 4, I create a new empty ArrayList
and wrap it in the Commons LazyList.decorate()
method, along with a PrimeFactory
for generating new values. The Commons LazyList
will use whatever values already reside in the list, but when the get()
method is called for an index that doesn't yet have a value, LazyList
uses the factory (in this case, PrimeFactory()
) to generate and populate the values. PrimeFactory
appears in Listing 5:
Listing 5. PrimeFactory
used by LazyList
public class PrimeFactory implements Factory { private int index = 0; @Override public Object create() { return Prime.indexedPrime(index++); } }
All lazy lists need a way to generate subsequent values. In Listing 2, I use the combination of the next()
method and Prime
's nextPrimeFrom()
method. For Commons LazyList
s in Listing 4, I use the PrimeFactory
instance.
One quirk of the Commons LazyList
implementation is the dearth of information passed to the factory method when a new value is requested. As designed, it doesn't even pass the index of the requested element, forcing the maintenance of the current state upon the PrimeFactory
class. This creates an undesirable dependence on the backing list (because it must initialize as empty to sync up the index numbers with PrimeFactory
's internal state). Commons LazyList
is a rudimentary implementation at best; much better open source alternatives exist, such as Totally Lazy.
Totally Lazy
Totally Lazy is a framework that adds firstclass laziness to Java (see Resources). In a previous installment, I introduced Totally Lazy but didn't do it idiomatic justice. One of the framework's goals is to create highly readable Java code by using combinations of static imports. The simple primenumber finder in Listing 6 is written to exploit this Totally Lazy feature fully:
Listing 6. Totally Lazy, fully utilizing static imports
import com.googlecode.totallylazy.Predicate; import com.googlecode.totallylazy.Sequence; import static com.googlecode.totallylazy.Predicates.is; import static com.googlecode.totallylazy.numbers.Numbers.equalTo; import static com.googlecode.totallylazy.numbers.Numbers.increment; import static com.googlecode.totallylazy.numbers.Numbers.range; import static com.googlecode.totallylazy.numbers.Numbers.remainder; import static com.googlecode.totallylazy.numbers.Numbers.sum; import static com.googlecode.totallylazy.numbers.Numbers.zero; import static com.googlecode.totallylazy.predicates.WherePredicate.where; public class Prime { public static Predicate<Number> isFactor(Number n) { return where(remainder(n), is(zero)); } public static Sequence<Number> factors(Number n){ return range(1, n).filter(isFactor(n)); } public static Number sumFactors(Number n){ return factors(n).reduce(sum); } public static boolean isPrime(Number n){ return equalTo(increment(n), sumFactors(n)); } }
In Listing 6, after the static imports are completed, the code is atypical of Java yet quite readable. Totally Lazy was partly inspired by the Hamcrest testing extension fluent interface for JUnit (see Resources) and uses some of Hamcrest's classes. The isFactor()
method becomes a call to the where()
method, using Totally Lazy's remainder()
method in conjunction with the Hamcrest is()
method. Similarly, the factors()
method becomes a filter()
call on a range()
object, and I use the nowfamiliar reduce()
method to determine the sum. Finally, the isPrime()
method uses Hamcrest's equalTo()
method to determine if the sum of factors equals the incremented number.
Astute readers will note that the implementation in Listing 6 does implement the optimization I wrote about in the preceding installment, using a more efficient algorithm to determine factors. The optimized version appears in Listing 7:
Listing 7. Totally Lazy implementation of the optimized primenumber finder
public class PrimeFast { public static Predicate<Number> isFactor(Number n) { return where(remainder(n), is(zero)); } public static Sequence<Number> getFactors(final Number n){ Sequence<Number> lowerRange = range(1, squareRoot(n)).filter(isFactor(n)); return lowerRange.join(lowerRange.map(divide().apply(n))); } public static Sequence<Number> factors(final Number n) { return getFactors(n).memorise(); } public static Number sumFactors(Number n){ return factors(n).reduce(sum); } public static boolean isPrime(Number n){ return equalTo(increment(n), sumFactors(n)); } }
Two primary changes appear in Listing 7. First, I improve the getFactors()
algorithm to harvest the factors below the square root, then generate the symmetrical ones above the square root. In Totally Lazy, even operations like divide()
can be expressed in its fluentinterface style. The second change involves memoization, which automatically caches function invocations with the same parameters; I've changed the sumFactors()
method to use the factors()
method, which is the memoized getFactors()
method. Totally Lazy implements memoization as part of the framework, so no further code is necessary to implement this optimization; however, the framework author spells it memorise()
instead of the more traditional (as in Groovy) memoize()
.
True to its name, Totally Lazy tries to use laziness as much as possible throughout the framework. In fact, the Totally Lazy framework itself includes a primes()
generator that implements an infinite sequence of prime numbers using the framework's building blocks. Consider the excerpts from the Numbers
class that are shown in Listing 8:
Listing 8. Totally Lazy excerpts implementing infinite prime numbers
public static Function1<Number, Number> nextPrime = new Function1<Number, Number>() { @Override public Number call(Number number) throws Exception { return nextPrime(number); } }; public static Computation<Number> primes = computation(2, computation(3, nextPrime)); public static Sequence<Number> primes() { return primes; } public static LogicalPredicate<Number> prime = new LogicalPredicate<Number>() { public final boolean matches(final Number candidate) { return isPrime(candidate); } }; public static Number nextPrime(Number number) { return iterate(add(2), number).filter(prime).second(); }
The nextPrime()
method creates a new Function1
, which is Totally Lazy's implementation of a pseudo higherorder function, this one designed to accept a single Number
parameter and produce a Number
result. In this case, it returns the result from the nextPrime()
method. The primes
variable is created to hold the state of the prime numbers, performing a computation with 2
(the first prime number) as the seed value, and using a new computation for the next prime number. This is a typical pattern in lazy implementations: hold the next element plus a method for generating subsequent values. The prime()
method is merely a wrapper around the prime
computation performed earlier.
To determine the nextPrime()
in Listing 8, Totally Lazy creates a new LogicalPredicate
to encapsulate the determination of primeness, then creates the nextPrime()
method, which uses the fluent interfaces within Totally Lazy to determine the next prime number.
Totally Lazy does an excellent job of using the lowly static import in Java to facilitate quite readable code. Many developers believe Java is a poor host for internal domainspecific languages, but Totally Lazy debunks that attitude. And it uses laziness aggressively, deferring every possible operation.
Conclusion
In this installment, I explored laziness, first by creating a simulated lazy collection in Java using an iterator, then by using the rudimentary LazyList
class from Jakarta Commons Collections. Finally, I implemented the sample code with Totally Lazy, using lazy collections both internally for the determination of prime numbers and in the lazy infinite collection of prime numbers. Totally Lazy also illustrates the expressiveness of the fluentinterface style, using static imports to improve code readability.
In the next installment, I'll continue the exploration of laziness, moving to Groovy, Scala, and Clojure.
Resources
Learn
 Haskell: Haskell is an open source advanced functional programming language, the product of many years of research.
LazyList
: This is the API page for the Jakarta CommonsLazyList
implementation. "State of the Lambda: Libraries Edition": Brian Goetz discusses the benefits of laziness in code generation.
 Totally Lazy: The Totally Lazy framework adds tons of functional extensions to Java, using an intuitive DSLlike interface.
 Hamcrest: Hamcrest provides a library of matcher objects (also known as constraints or predicates) that enable "match" rules to be defined declaratively, for use in other frameworks. (Hamcrest is being ported to GitHub.)
 "Evolutionary architecture and emergent design: Fluent interfaces" (Neal Ford, developerWorks, July 2010): See how fluent interfaces remove unnecessary noise from code syntax, making it more readable.
 Design Patterns: Elements of Reusable ObjectOriented Software (Erich Gamma et al., AddisonWesley, 1994): You can read about the Decorator pattern in the Gang of Four's classic work on design patterns.
 Scala: Scala is a modern, functional language on the JVM.
 Clojure: Clojure is a modern, functional Lisp that runs on the JVM.
 "Execution in the Kingdom of Nouns" (Steve Yegge, March 2006): An entertaining rant about some aspects of Java language design.
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