Bend them to your will!

Lambdas in Java 8, Part 2


In the second instalment of a two part series, Ted Neward explains how you can use lambda expressions to your advantage.

Looking for the first part of this series? Find it here

Learn how to use lambda expressions to your

The release of Java SE 8 swiftly approaches. With it come
not only the new linguistic lambda expressions (also
closures or anonymous
)—along with some supporting language
features—but also API and library enhancements that will make parts
of the traditional Java core libraries easier to use. Many of these
enhancements and additions are on the Collections API, and because
the Collections API is pretty ubiquitous across applications, it
makes the most sense to spend the majority of this article on

However, it’s likely that most Java developers will be
unfamiliar with the concepts behind lambdas and with how designs
incorporating lambdas look and behave. So, it’s best to examine why
these designs look the way they do before showing off the final
stage. Thus, we’ll look at some before and after approaches to see
how to approach a problem pre-lambda and post-lambda.

Note: This article was written against the
b92 (May 30, 2013) build of Java SE 8, and the APIs, syntax, or
semantics might have changed by the time you read this or by the
time Java SE 8 is released. However, the concepts behind these
APIs, and the approach taken by the Oracle engineers, should be
close to what we see here.

Collections and Algorithms


Algorithms, a more functional-centric way of interacting
with collections,
 have been a part of the Collections API
since its initial release, but they often get little attention,
despite their usefulness.

The Collections API has been with us since JDK 1.2, but not all
parts of it have received equal attention or love from the
developer community. Algorithms, a more functional-centric way of
interacting with collections, have been a part of the Collections
API since its initial release, but they often get little attention,
despite their usefulness. For example,
the Collections class sports a dozen or so
methods all designed to take a collection as a parameter and
perform some operation against the collection or its

Consider, for example, the Person class shown
in Listing 1, which in turn is used by
List that holds a dozen or
so Person objects, as shown in Listing 2.

Listing 1

public class Person {
  public Person(String fn, String ln, int a) {
    this.firstName = fn; this.lastName = ln; this.age = a;

  public String getFirstName() { return firstName; }
  public String getLastName() { return lastName; }
        public int getAge() { return age; }


Listing 2 

List<Person> people = Arrays.asList(
      new Person("Ted", "Neward", 42),
      new Person("Charlotte", "Neward", 39),
      new Person("Michael", "Neward", 19),
      new Person("Matthew", "Neward", 13),
      new Person("Neal", "Ford", 45),
      new Person("Candy", "Ford", 39),
      new Person("Jeff", "Brown", 43),
      new Person("Betsy", "Brown", 39)

Now, assuming we want to examine or sort this list by last name
and then by age, a naive approach is to write
for loop (in other words, implement the sort
by hand each time we need to sort). The problem with this, of
course, is that this violates DRY (the Don’t Repeat Yourself
principle) and, worse, we have to reimplement it each time,
because for loops are not reusable.

The Collections API has a better approach:
the Collections class sports
sort method that will sort the contents of
the List. However, using this requires
the Person class to implement
the Comparable method (which is called
natural ordering, and defines a default ordering
for all Person types) or you have to pass in
Comparator instance to define
how Person objects should be sorted.

So, if we want to sort first by last name and then by age (in
the event the last names are the same), the code will look
something like Listing 3. But that’s a lot of work to do something
as simple as sort by last name and then by age. This is exactly
where the new closures feature will be of help, making it easier to
write the Comparator (see Listing 4).

Listing 3 

 Collections.sort(people, new Comparator<Person>() {
      public int compare(Person lhs, Person rhs) {
        if (lhs.getLastName().equals(rhs.getLastName())) {
          return lhs.getAge() - rhs.getAge();
          return lhs.getLastName().compareTo(rhs.getLastName());

Listing 4 

Collections.sort(people, (lhs, rhs) -> {
      if (lhs.getLastName().equals(rhs.getLastName()))
        return lhs.getAge() - rhs.getAge();
        return lhs.getLastName().compareTo(rhs.getLastName());

The Comparator is a prime example of the need
for lambdas in the language: it’s one of the dozens of places where
a one-off anonymous method is useful. (Bear in mind, this is
probably the easiest—and weakest—benefit of lambdas. We’re
essentially trading one syntax for another, admittedly terser,
syntax, but even if you put this article down and walk away right
now, a significant amount of code will be saved just from that

If this particular comparison is something that we use over
time, we can always capture the lambda as
Comparator instance, because that is the
signature of the method—in this case, "int compare(Person,
—that the lambda fits, and store it on
the Person class directly, making the
implementation of the lambda easier (see Listing 5) and its use
even more readable (see Listing 6).

Listing 5 

public class Person {
  // . . .

  public static final Comparator<Person> BY_LAST_AND_AGE =
    (lhs, rhs) -> {
      if (lhs.lastName.equals(rhs.lastName))
        return lhs.age - rhs.age;
        return lhs.lastName.compareTo(rhs.lastName);

Listing 6 

 Collections.sort(people, Person.BY_LAST_AND_AGE); 


The Comparator is a prime example of the need for
lambdas in the language: it’s one of the dozens of places where a
one-off anonymous method is useful.

Storing a Comparator<Person> instance
on the Person class is a bit odd, though. It
would make more sense to define a method that does the comparison,
and use that instead of a Comparator instance.
Fortunately, Java will allow any method to be used that satisfies
the same signature as the method on Comparator, so
it’s equally possible to write the BY_LAST_AND_AGE
 as a standard instance or static method
on Person (see Listing 7) and use it instead
(see Listing 8).

Listing 7 

  public static int compareLastAndAge(Person lhs, Person rhs) {
    if (lhs.lastName.equals(rhs.lastName))
      return lhs.age - rhs.age;
      return lhs.lastName.compareTo(rhs.lastName);

Listing 8 

Collections.sort(people, Person::compareLastAndAge); 

Thus, even without any changes to the Collections API, lambdas
are already helpful and useful. Again, if you walk away from this
article right here, things are pretty good. But they’re about to
get a lot better.

Changes in the Collections API

With some additional APIs on
the Collection classes themselves, a variety of
new and more powerful approaches and techniques open up, most often
leveraging techniques drawn from the world of functional
programming. No knowledge of functional programming is necessary to
use them, fortunately, as long you can open your mind to the idea
that functions are just as valuable to manipulate and reuse as are
classes and objects.

Comparisons. One of the drawbacks to
the Comparator approach shown earlier is hidden
inside the Comparator implementation. The code
is actually doing two comparisons, one as a “dominant” comparison
over the other, meaning that last names are compared first, and age
is compared only if the last names are identical. If project
requirements later demand that sorting be done by age first and by
last names second, a new Comparator must be
written—no parts of compareLastAndAge can be

This is where taking a more functional approach can add some
powerful benefits. If we look at that comparison as entirely
separate Comparator instances, we can combine
them to create the precise kind of comparison needed (see Listing

Listing 9 

public static final Comparator<Person> BY_FIRST =
    (lhs, rhs) -> lhs.firstName.compareTo(rhs.firstName);
  public static final Comparator<Person> BY_LAST =
    (lhs, rhs) -> lhs.lastName.compareTo(rhs.lastName);
  public static final Comparator<Person> BY_AGE =
    (lhs, rhs) -> lhs.age – rhs.age; 

Historically, writing the combination by hand has been less
productive, because by the time you write the code to do the
combination, it would be just as fast (if not faster) to write the
multistage comparison by hand.

As a matter of fact, this “I want to compare these two X things
by comparing values returned to me by a method on
each X” approach is such a common thing, the platform
gave us that functionality out of the box. On
the Comparator class,
acomparing method takes a function (a lambda) that
extracts a comparison key out of the object and returns
Comparator that sorts based on that. This
means that Listing 9 could be rewritten even more easily as shown
in Listing 10.

Listing 10 

 public static final Comparator<Person> BY_FIRST =
  public static final Comparator<Person> BY_LAST =
  public static final Comparator<Person> BY_AGE =


Doing this bypasses an interesting opportunity to
explore one of the more powerful features of the new Java
API, that of doing a reduction
—coalescing a collection of
values down into a single one through some custom operations.

Think for a moment about what this is doing:
the Person is no longer about sorting, but just
about extracting the key by which the sort should be done. This is
a good thing—Person shouldn’t have to think about how
to sort; Person should just focus on being

It gets better, though, particularly when we want to compare
based on two or more of those values.

Composition. As of Java 8,
the Comparator interface comes with several
methods to combine Comparator instances in
various ways by stringing them together. For example,
the Comparator .thenComparing() method takes
Comparator to use for comparison after the
first one compares. So, re-creating the “last name then age”
comparison can now be written in terms of the
two Comparator instances LAST and AGE,
as shown in Listing 11. Or, if you prefer to use methods rather
thanComparator instances, use the code in Listing

Listing 11 

 Collections.sort(people, Person.BY_LAST.

Listing 12 


By the way, for those who didn’t grow up
using Collections.sort(), there’s now
sort() method directly on List.
This is one of the neat things about the introduction of interface
default methods: where we used to have to put that kind of
noninheritance-based reusable behavior in static methods, now it
can be hoisted up into interfaces. (See the previous
article in this series
 for more details.)

Similarly, if the code needs to sort the collection
of Person objects by last name and then by first
name, no new Comparator needs to be written,
because this comparison can, again, be made of the two particular
atomic comparisons shown in Listing 13.

Listing 13 


This combinatory “connection” of methods, known
as functional composition, is common in functional
programming and at the heart of why functional programming is as
powerful as it is.

It’s important to understand that the real benefit here isn’t
just in the APIs that enable us to do comparisons, but the ability
to pass bits of executable code (and then combine them in new and
interesting ways) to create opportunities for reuse and
design.Comparator is just the tip of the iceberg.
Lots of things can be made more flexible and powerful, particularly
when combining and composing them.

Iteration. As another example of how
lambdas and functional approaches change the approach to code,
consider one of the fundamental operations done with collections:
that of iterating over them. Java 8 will bring to collections a
change via theforEach() default method defined on
the Iterator and Iterable interfaces.
Using it to print each of the items in the collection, for example,
requires passing a lambda to the forEach method
on an Iterator, as shown in Listing 14.

Listing 14 

people.forEach((it) -> System.out.println("Person: " + it)); 

Officially, the type of lambda being passed in is
Consumer instance, defined in
the java.util.function package. Unlike
traditional Java interfaces,
however, Consumer is one of the new functional
interfaces, meaning that direct implementations will likely never
happen—instead, the new way to think about it is solely in terms of
its single, important method, accept, which is the
method the lambda provides. The rest (such
as compose and andThen) are
utility methods defined in terms of the important method, and they
are designed to support the important method.

For example, andThen() chains
two Consumer instances together, so the first
one is called first and the second is called immediately after into
a single Consumer. This provides useful composition
techniques that are a little outside the scope of this article.


It is ugly enough to fix. The code is actually a lot
easier to write if we use the built-in Collector
 and its partner Collectors, which specifically
do this kind of mutable-reduction operation.

Many of the use cases involved in walking through a collection
have the purpose of finding items that fit a particular
criterion—for example, determining which of
the Person objects in the collection are of
drinking age, because the automated code system needs to send
everyone in that collection a beer. This “act upon a thing coming
from a group of things” is actually far more widespread than just
operating upon a collection. Think about operating on each line in
a file, each row from a result set, each value generated by a
random-number generator, and so on. Java SE 8 generalized this
concept one step further, outside collections, by lifting it into
its own interface: Stream.

Stream. Like several other interfaces in
the JDK, the Stream interface is a fundamental
interface that is intended for use in a variety of scenarios,
including the Collections API. It represents a stream of objects,
and on the surface of things, it feels similar to
how Iterator gives us access one object at a
time through a collection.

However, unlike collections, Stream does not
guarantee that the collection of objects is finite. Thus, it is a
viable candidate for pulling strings from a file, for example, or
other kinds of on-demand operations, particularly because it is
designed not only to allow for composition of functions, but also
to permit parallelization “under the hood.”

Consider the earlier requirement: the code needs to filter out
any Person object that is not at least 21 years
of age. Once a Collection converts to
Stream (via
the stream() method defined on
the Collection interface),
the filter method can be used to produce a
new Stream through which only the filtered
objects come (see Listing 15).

Listing 15 

      .filter(it -> it.getAge() >= 21) 

The parameter to filter is
Predicate, an interface defined as taking one
genericized parameter and returning a Boolean. The intent of
the Predicate is to determine whether the
parameter object is included as part of the returned set.

The return from filter() is
another Stream, which means that the
filtered Stream is also available for further
manipulation, such as to forEach() through each
of the elements that come through the Stream, in this
case to display the results (see Listing 16).

Listing 16
      .filter((it) -> it.getAge() >= 21)
      .forEach((it) -> 
        System.out.println("Have a beer, " + it.getFirstName())); 

This neatly demonstrates the composability of streams—we can
take streams and run them through a variety of atomic operations,
each of which do one—and only one—thing to the stream.
Additionally, it’s important to note
that filter() is lazy—it will filter only as it
needs to, on demand, rather than going through the entire
collection of Person objects and filtering ahead
of time (which is what we’re used to with the Collections API).

Predicates. It might seem odd at first
that the filter() method takes only a
single Predicate. After all, if a goal was to find
all the Person objects whose age is greater than
21 and whose last name is Neward, it would seem
that filter() could or should take a pair
of Predicate instances. Of course, this opens a
Pandora’s box of possibilities. What if the goal is to find
all Person objects with an age greater than 21
and less than 65, and with a first name of at least four or more
characters? Infinite possibilities suddenly open up, and
the filter() API would need to somehow approach
all of these.

Unless, of course, a mechanism were available to somehow
coalesce all of these possibilities down into a
single Predicate. Fortunately, it’s fairly easy to
see that any combination of Predicate instances
can themselves be a single Predicate. In other words,
if a given filter needs to have condition A
be true and condition B
be true before an object can be included in the
filtered stream, that is itself a Predicate (A and
, and we can combine those two together into a
single Predicate by writing
Predicate that takes any
two Predicate instances and
returns true only if
both A and B each
yield true.

This “and”ing Predicate is—by virtue of the
fact that it knows only about the
two Predicate instances that it needs to call
(and nothing about the parameters being passed in to each of
those)— completely generic and can be written well ahead of

If the Predicate closures are stored
in Predicate references (similar to
how Comparator references were used earlier, as
members on Person), they can be strung together using
the and() method on them, as shown in Listing

Listing 17 

 Predicate<Person> drinkingAge = (it) -> it.getAge() >= 21;
    Predicate<Person> brown = (it) -> it.getLastName().equals("Brown");
      .forEach((it) ->
                System.out.println("Have a beer, " +

As might be expected, and()or(),
and xor() are all available. Make sure to check
the Javadoc for a full introduction to all the possibilities.

map() and reduce(). Other
common Stream operations
include map(), which applies a function across each
element present within a Stream to produce a
result out of each element. So, for example, we can obtain the age
of each Person in the collection by applying a
simple function to retrieve the age out of
each Person, as shown in Listing 18.

Listing 18

  IntStream ages =
            .mapToInt((it) -> it.getAge()); 

For all practical purposes, IntStream (and
cousins LongStream and DoubleStream)
is a specialization of
the Stream<T> interface (meaning that it
creates custom versions of that interface) for those primitive

This, then, produces a Stream of integers out
Collection of Person instances.
This is also sometimes known as a transformation
, because the code is transforming or projecting
Person into an int.

Similarly, reduce() is an operation that
takes a stream of values and, through some kind of operation,
reduces them into a single value. Reduction is an operation already
familiar to developers, though they might not recognize it at
first: the COUNT()operator from SQL is one such
operation (reducing from a collection of rows to a single integer),
as are the SUM()MAX(),
and MIN() operators. Each of these takes a
stream of values (rows) and produces a single value (the integer)
by applying some operation (for example, increment a counter, add
the value to a running total, select the highest, or select the
lowest) to each of the values in the stream.

So, for example, you could sum the values prior to dividing by
the number of elements in the stream to obtain an average age.
Given the new APIs, it’s easiest to just use the built-in methods,
as shown in Listing 19.

Listing 19

int sum =

But doing this bypasses an interesting opportunity to explore
one of the more powerful features of the new Java API, that of
doing a reduction—coalescing a collection of values down into a
single one through some custom operation. So, let’s rewrite the
summation part of this using the
new reduce() method: 

.reduce(0, (l, r) -> l + r); 

This reduction, also known in functional circles as
fold, starts with a seed value (0, in this case),
and applies the closure to the seed and the first element in the
stream, taking the result and storing it as the accumulated value
that will be used as the seed for the next element in the

In other words, in a list of integers such as 1, 2, 3, 4, and 5,
the seed 0 is added to 1 and the result (1) is stored as the
accumulated value, which then serves as the left-hand value in
addition to serving as the next number in the stream (1+2). The
result (3) is stored as the accumulated value and used in the next
addition (3+3). The result (6) is stored and used in the next
addition (6+4), and the result is used in the final addition
(10+5), yielding the final result 15. And, sure enough, if we run
the code in Listing 20, we get that result.

Listing 20 

List<Integer> values = Arrays.asList(1, 2, 3, 4, 5);
    int sum =, (l,r) -> l+r);

Note that the type of closure accepted as the second argument
to reduce is an IntBinaryOperator,
defined as taking two integers and returning
an int result. IntBinaryOperator and IntBiFunction are
examples of specialized functional interfaces—including other
specialized versions
for Double and Long—which take two
parameters (of one or two different types) and return
an int. These specialized versions were created
mostly to ease the work required for using the common primitive

IntStream also has a couple of helper methods,
including the average()min(),
and max() methods, that do some of the more
common integer operations. Additionally, binary operations (such as
summing two numbers) are also often defined on the primitive
wrapper classes for that type
(Integer::sumLong::max, and so on).

More maps and reduction. Maps and
reduction are useful in a variety of situations beyond just simple
math. After all, in any case where a collection of objects can be
transformed into a different object (or value) and then collected
into a single value, map and reduction operations work.

The map operation, for example, can be useful as an extraction
or projection operation to take an object and extract portions of
it, such as extracting the last name out of
Person object: 

Stream lastNames =      .map(Person::getLastName);  

Once the last names have been retrieved from
the Person stream, the reduction can concatenate
strings together, such as transforming the last name into a data
representation for XML. See Listing 21.

Listing 21 

String xml =
      "<people data='lastname'>" +
            .map(it -> "<person>" + it.getLastName() + "</person>")
            .reduce("", String::concat)
      + "</people>";

And, naturally, if different XML formats are required, different
operations can be used to control the contents of each format,
supplied either ad hoc, as in Listing 21, or from methods defined
on other classes, such as from the Person class
itself, as shown in Listing 22, which can then be used as part of
the map() operation to transform the stream
of Person objects into a JSON array of object
elements, as shown in Listing 23.

Listing 22

public class Person {
  // . . .
  public static String toJSON(Person p) {
      "{" +
        "firstName: "" + p.firstName + "", " +
        "lastName: "" + p.lastName + "", " +
        "age: " + p.age + " " +


Listing 23 

String json =
        .reduce("[", (l, r) -> l + (l.equals("[") ? "" : ",") + r)
        + "]";


The release of Java SE 8 swiftly approaches. With
it come not only the new linguistic lambda expressions (also called
closures or anonymous methods)—along with some supporting language
features—but also API and library enhancements that will make parts
of the traditional Java core libraries easier to use.

The ternary operation in the middle of
reduce operation is there to
avoid putting a comma in front of the
Person serialized to JSON.
Some JSON parsers might accept this format, but that is not
guaranteed, and it looks ugly to have it there.

It is ugly enough, in fact, to fix. The code is actually a lot
easier to write if we use the
built-in Collector interface and its
partner Collectors, which specifically do this kind
of mutable-reduction operation (see Listing 24). This has the added
benefit of being much faster than the versions using the
explicit reduce and String::concat from
the earlier examples, so it’s generally a better bet.

Listing 24

  String joined =
                          .collect(Collectors.joining(", "));System.out.println("[" + joined + "]"); 

Oh, and lest we forget our old friend Comparator,
note that Stream also has an operation to sort a
stream in-flight, so the sorted JSON representation of
the Person list looks like Listing 25.

Listing 25

String json =
                        .collect(Collectors.joining(", " "[", "]"));

This is powerful stuff.

Parallelization. What’s even more powerful
is that these operations are entirely independent of the logic
necessary to pull each object through
the Stream and act on each one, which means that
the traditional for loop will break down when
attempting to iterate, map, or reduce a large collection by
breaking the collection into segments that will each be processed
by a separate thread.

Learn More


The Stream API, however, already has that
covered, making the XML or
JSON map() and reduce() operations
shown earlier a slightly different operation—instead of
calling stream() to obtain
aStream from the collection,
use parallelStream() instead, as demonstrated in
Listing 26.

Listing 26

      .filter((it) -> it.getAge() >= 21)
      .forEach((it) ->
                System.out.println("Have a beer " + it.getFirstName() +

For a collection of at least a dozen items, at least on my
laptop, two threads are used to process the collection: the thread
named main, which is the traditional one used to
invoke the main() method of a Java class, and
another thread namedForkJoinPool.commonPool worker-1,
which is obviously not of our creation.

Obviously, for a collection of a dozen items, this would be
hideously unnecessary, but for several hundred or more, this would
be the difference between “good enough” and “needs to go faster.”
Without these new methods and approaches, you would be staring at
some significant code and algorithmic study. With them, you can
write parallelized code literally by adding eight keystrokes (nine
if you count the Shift key required to capitalize the s
in stream) to the previously sequential

And, where necessary, a parallel Stream can
be brought back to a sequential one by calling—you can probably
guess—sequential() on it.

The important thing to note is that regardless of whether the
processing is better done sequentially or in parallel, the
same Stream interface is used for both. The
sequential or parallel implementation becomes entirely an
implementation detail, which is exactly where we want it to be when
working on code that focuses on business needs (and value); we
don’t want to focus on the low-level details of firing up threads
in thread pools and synchronizing across them.


Lambdas will bring a lot of change to Java, both in terms of how
Java code will be written and how it will be designed. Some of
these changes are already taking place within the Java SE
libraries, and they will slowly make their way through many other
libraries—both those owned by the Java platform and those out in
“the wilds” of open source—as developers grow more comfortable with
the abilities (and drawbacks) of lambdas.

Numerous other changes are present within the Java SE 8 release.
But if you understand how lambdas on collections work, you will
have a strong advantage when thinking about how to leverage lambdas
within your own designs and code, and you can create
better-decoupled code for years to come.

Originally published in the July/Aug 2013 issue of Java
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About the author

 (@tedneward) is an architectural consultant for
Neudesic.He has served on several Expert Groups; authored many
books, including Effective Enterprise
Java (Addison-Wesley Professional, 2004) and Professional
F# 2.0 (Wrox, 2010); written hundreds of articles on Java,
Scala, and other technologies; and spoken at hundreds of

(1) Originally published in the Sep/Oct 2013 Edition of
Java Magazine 
(2) Copyright © [2013] Oracle.

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