Java 8 Features
Java Interface Changes
Allow to add default and static methods in Interface.
One of the major reason for introducing default methods in interfaces is to enhance the Collections API in Java 8 to support lambda expressions.
Liked forEach method:
default void forEach(Consumer<? super T> action) {
Objects.requireNonNull(action);
for (T t : this) {
action.accept(t);
}
}
Java interface static method is similar to default method except that we can’t override them in the implementation classes.
Java Lambda Expression
An interface with exactly one abstract method is known as Functional Interface. It enables us to use lambda expressions to instantiate them.
public static int count(List<Integer> numList, Predicate<Integer> predicate) {
int sum = 0;
for (int number : numList) {
if (predicate.test(number))
sum++;
}
return sum;
}
public static void main(String[] args) {
List<Integer> numList = new ArrayList<>();
numList.add(new Integer(10));
numList.add(new Integer(20));
numList.add(new Integer(30));
numList.add(new Integer(40));
numList.add(new Integer(50));
// try 4 different predicates (functional interfaces)
assert count(numList, n -> true) == 5;
assert count(numList, n -> false) == 0;
assert count(numList, n -> n < 25) == 2;
assert count(numList, n -> n % 3 == 0) == 1;
}
- A lambda expression can be understood as a kind of anonymous function: it doesn’t have a name, but it has a list of parameters, a body, a return type, and also possibly a list of exceptions that can be thrown.
- Lambda expressions let you pass code concisely.
- A functional interface is an interface that declares exactly one abstract method.
- Lambda expressions can be used only where a functional interface is expected.
- Lambda expressions let you provide the implementation of the abstract method of a functional interface directly inline and treat the whole expression as an instance of a functional interface.
- Java 8 comes with a list of common functional interfaces in the java.util .function package, which includes Predicate
, Function<T, R>, Supplier , Consumer , and BinaryOperator , described in table 3.2. - There are primitive specializations of common generic functional interfaces such as Predicate
and Function<T, R> that can be used to avoid boxing operations: IntPredicate, IntToLongFunction, and so on. - The execute around pattern (that is, you need to execute a bit of behavior in the middle of code that’s always required in a method, for example, resource allocation and cleanup) can be used with lambdas to gain additional flexibility and reusability.
- The type expected for a lambda expression is called the target type.
- Method references let you reuse an existing method implementation and pass it around directly.
- Functional interfaces such as Comparator, Predicate, and Function have several default methods that can be used to combine lambda expressions.
/**
* Behavior parameterization is the ability for a method to take multiple different behaviors as
* parameters and use them internally to accomplish different behaviors.
*/
// Executing a block of code with Runnable
public void threadABlockOfCode() {
Thread thread = new Thread(() -> {
System.out.println("Hello World!");
});
thread.run();
}
// Methods and lambdas as first-class citizens
public void filterAllHiddenFiles() {
File[] hiddenFiles = new File(".").listFiles(File::isHidden);
// Arrays.stream(hiddenFiles).forEach((File file) -> System.out.println(file.getPath()));
Arrays.stream(hiddenFiles).forEach(System.out::println);
}
/**
* You can use a lambda expression in the context of a functional interface. A functional
* interface is an interface that specifies exactly one abstract method. But they can have
* multiple default methods. Also these interfaces will be annotated as
* {@code @FunctionalInterface}
*
* <br>
*
* {@code java.util.Comparator} <br>
* {@code java.lang.Runnable} <br>
* {@code java.util.function.Consumer} <br>
* {@code java.util.function.Function} <br>
* {@code java.util.function.Predicate} <br>
* {@code java.awt.event.ActionListener} <br>
* {@code java.util.concurrent.Callable} <br>
* {@code java.security.PriviledgedAction}
*/
public void useLambdaFunctions() {
Stream<String> stream = Stream.of("Java 8", "Lambdas", "In", "Action");
stream.map(String::toUpperCase).forEach(System.out::println);
}
/**
* Boxed values are essentially a wrapper around primitive types and are stored on the heap.
* Therefore, boxed values use more memory and require additional memory lookups to fetch the
* wrapped primitive value.
*
* Java8 defines a list of named functional interfaces those use appropriate primitive types
* directly!
*
*/
public void methodComparation() {
List<String> str = Arrays.asList("a", "b", "A", "B");
str.sort((a, b) -> a.compareToIgnoreCase(b));
System.out.println(str);
str = Arrays.asList("a", "b", "A", "B");
str.sort(String::compareToIgnoreCase);
System.out.println(str);
}
public void appleSamples() {
// filtering with lambdas
List<Apple> inventory = Arrays.asList(new Apple(80, "green"), new Apple(155, "green"), new Apple(120, "red"));
inventory.stream().filter(a -> "green".equals(a.getColor())).forEach(System.out::println);
inventory.sort(new Comparator<Apple>() {
@Override
public int compare(Apple o1, Apple o2) {
return o1.getWeight().compareTo(o2.getWeight());
}
});
// Functional Interface
inventory.sort((a, b) -> (a.getWeight().compareTo(b.getWeight())));
System.out.println(inventory);
// Static Assistant Method
inventory.sort(Comparator.comparing(a -> a.getWeight()));
System.out.println(inventory);
// Method Reference
inventory.sort(Comparator.comparing(Apple::getWeight).reversed().thenComparing(Apple::getColor));
System.out.println(inventory);
}
public void andThenCompose() {
Function<Integer, Integer> f = x -> x + 1;
Function<Integer, Integer> g = x -> x * 2;
Function<Integer, Integer> h = f.andThen(g);
System.out.println(f.andThen(g).apply(1));
System.out.println(f.compose(g).apply(1));
System.out.println(f.andThen(g).apply(3)); // (3 + 1) * 2 = 8
System.out.println(f.compose(g).apply(3)); // (3 * 2) + 1 = 7
}
public void transformLetters() {
Function<String, String> addHeader = Letter::addHeader;
addHeader.andThen(Letter::checkSpelling).andThen(Letter::addFooter);
}
public static class Letter {
public static String addHeader(String text) {
return "From Raoul, Mario and Alan: " + text;
}
public static String addFooter(String text) {
return text + " Kind regards";
}
public static String checkSpelling(String text) {
return text.replaceAll("labda", "lambda");
}
}
Java Stream API
Especially helpful for Bulk Data Operations on Collections.
A new java.util.stream has been added in Java 8 to perform filter/map/reduce like operations with the collection. Stream API will allow sequential as well as parallel execution.
- The Streams API lets you express complex data processing queries.
- You can filter and slice a stream using the filter, distinct, skip, and limit methods.
- You can extract or transform elements of a stream using the map and flatMap methods.
- You can find elements in a stream using the findFirst and findAny methods. You can match a given predicate in a stream using the allMatch, noneMatch, and anyMatch methods.
- These methods make use of short-circuiting: a computation stops as soon as a result is found; there’s no need to process the whole stream.
- You can combine all elements of a stream iteratively to produce a result using the reduce method, for example, to calculate the sum or find the maximum of a stream.
- Some operations such as filter and map are stateless; they don’t store any state. Some operations such as reduce store state to calculate a value. Some operations such as sorted and distinct also store state because they need to buffer all the elements of a stream before returning a new stream. Such operations are called stateful operations.
- There are three primitive specializations of streams: IntStream, DoubleStream, and LongStream. Their operations are also specialized accordingly.
- Streams can be created not only from a collection but also from values, arrays, files, and specific methods such as iterate and generate.
- An infinite stream is a stream that has no fixed size.
public class Java8Stream {
public static void main(String... args) {
Trader raoul = new Trader("Raoul", "Cambridge");
Trader mario = new Trader("Mario", "Milan");
Trader alan = new Trader("Alan", "Cambridge");
Trader brian = new Trader("Brian", "Cambridge");
List<Transaction> transactions = Arrays.asList(new Transaction(brian, 2011, 300), new Transaction(raoul, 2012, 1000),
new Transaction(raoul, 2011, 400), new Transaction(mario, 2012, 710), new Transaction(mario, 2012, 700),
new Transaction(alan, 2012, 950));
// Query 1: Find all transactions from year 2011 and sort them by value (small to high).
List<Transaction> tr2011 = transactions.stream()
.filter(transaction -> transaction.getYear() == 2011)
.sorted(comparingInt(Transaction::getValue))
.collect(toList());
System.out.println(tr2011);
// Query 2: What are all the unique cities where the traders work?
List<String> cities = transactions.stream()
.map(transaction -> transaction.getTrader()
.getCity())
.distinct()
.collect(toList());
System.out.println(cities);
// Query 3: Find all traders from Cambridge and sort them by name.
List<Trader> traders = transactions.stream()
.map(Transaction::getTrader)
.filter(trader -> trader.getCity()
.equals("Cambridge"))
.distinct()
.sorted(comparing(Trader::getName))
.collect(toList());
System.out.println(traders);
// Query 4: Return a string of all traders’ names sorted alphabetically.
String traderStr = transactions.stream()
.map(transaction -> transaction.getTrader()
.getName())
.distinct()
.sorted()
.collect(joining(", "));
System.out.println(traderStr);
// Query 5: Are there any trader based in Milan?
boolean milanBased = transactions.stream()
.anyMatch(transaction -> transaction.getTrader()
.getCity()
.equals("Milan"));
System.out.println(milanBased);
// Query 6: Update all transactions so that the traders from Milan are set to Cambridge.
transactions.stream()
.map(Transaction::getTrader)
.filter(trader -> trader.getCity()
.equals("Milan"))
.forEach(trader -> trader.setCity("Cambridge"));
System.out.println(transactions);
// Query 7: What's the highest value in all the transactions?
int highestValue = transactions.stream()
.map(Transaction::getValue)
.reduce(0, Integer::max);
System.out.println(highestValue);
String[] arrayOfWords = { "Hello", "World" };
List<String> result = Arrays.stream(arrayOfWords)
.map(w -> w.split(""))
.flatMap(Arrays::stream) // flat all arrays to a single stream!
.distinct()
.collect(toList());
System.out.println(result);
}
static class Trader {
private String name;
private String city;
public Trader(String n, String c) {
this.name = n;
this.city = c;
}
public String getName() {
return this.name;
}
public String getCity() {
return this.city;
}
public void setCity(String newCity) {
this.city = newCity;
}
public String toString() {
return "Trader:" + this.name + " in " + this.city;
}
}
static class Transaction {
private Trader trader;
private int year;
private int value;
public Transaction(Trader trader, int year, int value) {
this.trader = trader;
this.year = year;
this.value = value;
}
public Trader getTrader() {
return this.trader;
}
public int getYear() {
return this.year;
}
public int getValue() {
return this.value;
}
public String toString() {
return "{" + this.trader + ", " + "year: " + this.year + ", " + "value:" + this.value + "}";
}
}
}
Java Collector API
- collect is a terminal operation that takes as argument various recipes (called collectors) for accumulating the elements of a stream into a summary result.
- Predefined collectors include reducing and summarizing stream elements into a single value, such as calculating the minimum, maximum, or average.
- Predefined collectors let you group elements of a stream with groupingBy and partition elements of a stream with partitioningBy.
- Collectors compose effectively to create multilevel groupings, partitions, and reductions.
- You can develop your own collectors by implementing the methods defined in the Collector interface.
private static Map<Dish.Type, List<Dish>> groupDishesByType() {
return menu.stream()
.collect(groupingBy(Dish::getType));
}
private static Map<Dish.Type, List<String>> groupDishNamesByType() {
return menu.stream()
.collect(groupingBy(Dish::getType, mapping(Dish::getName, toList())));
}
private static Map<Dish.Type, Set<String>> groupDishTagsByType() {
return menu.stream()
.collect(groupingBy(Dish::getType, mapping(Dish::getName, toSet())));
}
private static Map<Dish.Type, List<Dish>> groupCaloricDishesByType() {
return menu.stream()
.filter(dish -> dish.getCalories() > 500)
.collect(groupingBy(Dish::getType));
}
private static Map<CaloricLevel, List<Dish>> groupDishesByCaloricLevel() {
return menu.stream()
.collect(groupingBy(dish -> {
if (dish.getCalories() <= 400)
return CaloricLevel.DIET;
else if (dish.getCalories() <= 700)
return CaloricLevel.NORMAL;
else
return CaloricLevel.FAT;
}));
}
private static Map<Dish.Type, Map<CaloricLevel, List<Dish>>> groupDishedByTypeAndCaloricLevel() {
return menu.stream()
.collect(groupingBy(Dish::getType, groupingBy((Dish dish) -> {
if (dish.getCalories() <= 400)
return CaloricLevel.DIET;
else if (dish.getCalories() <= 700)
return CaloricLevel.NORMAL;
else
return CaloricLevel.FAT;
})));
}
private static Map<Dish.Type, Long> countDishesInGroups() {
return menu.stream()
.collect(groupingBy(Dish::getType, counting()));
}
private static Map<Dish.Type, Optional<Dish>> mostCaloricDishesByType() {
return menu.stream()
.collect(groupingBy(Dish::getType, reducing((Dish d1, Dish d2) -> d1.getCalories() > d2.getCalories() ? d1 : d2)));
}
private static Map<Dish.Type, Dish> mostCaloricDishesByTypeWithoutOprionals() {
return menu.stream()
.collect(groupingBy(Dish::getType,
collectingAndThen(reducing((d1, d2) -> d1.getCalories() > d2.getCalories() ? d1 : d2), Optional::get)));
}
private static Map<Dish.Type, Integer> sumCaloriesByType() {
return menu.stream()
.collect(groupingBy(Dish::getType, summingInt(Dish::getCalories)));
}
private static Map<Dish.Type, Set<CaloricLevel>> caloricLevelsByType() {
return menu.stream()
.collect(groupingBy(Dish::getType, mapping(dish -> {
if (dish.getCalories() <= 400)
return CaloricLevel.DIET;
else if (dish.getCalories() <= 700)
return CaloricLevel.NORMAL;
else
return CaloricLevel.FAT;
}, toSet())));
}
private static Map<Boolean, List<Dish>> partitionByVegeterian() {
return menu.stream()
.collect(partitioningBy(Dish::isVegetarian));
}
private static Map<Boolean, Map<Dish.Type, List<Dish>>> vegetarianDishesByType() {
return menu.stream()
.collect(partitioningBy(Dish::isVegetarian, groupingBy(Dish::getType)));
}
private static Object mostCaloricPartitionedByVegetarian() {
return menu.stream()
.collect(partitioningBy(Dish::isVegetarian, collectingAndThen(maxBy(comparingInt(Dish::getCalories)), Optional::get)));
}
private static int calculateTotalCaloriesUsingSum() {
return menu.stream()
.mapToInt(Dish::getCalories)
.sum();
}
private static long howManyDishes() {
return menu.stream()
.collect(counting());
}
private static Dish findMostCaloricDish() {
return menu.stream()
.collect(reducing((d1, d2) -> d1.getCalories() > d2.getCalories() ? d1 : d2))
.get();
}
private static Dish findMostCaloricDishUsingComparator() {
Comparator<Dish> dishCaloriesComparator = Comparator.comparingInt(Dish::getCalories);
BinaryOperator<Dish> moreCaloricOf = BinaryOperator.maxBy(dishCaloriesComparator);
return menu.stream()
.collect(reducing(moreCaloricOf))
.get();
}
private static int calculateTotalCalories() {
return menu.stream()
.collect(summingInt(Dish::getCalories));
}
private static Double calculateAverageCalories() {
return menu.stream()
.collect(averagingInt(Dish::getCalories));
}
private static IntSummaryStatistics calculateMenuStatistics() {
return menu.stream()
.collect(summarizingInt(Dish::getCalories));
}
private static String getShortMenu() {
return menu.stream()
.map(Dish::getName)
.collect(joining());
}
private static String getShortMenuCommaSeparated() {
return menu.stream()
.map(Dish::getName)
.collect(joining(", "));
}
Java Parallel Stream
- Stream sources and decomposability: Excellent (ArrayList, IntStream.range); Good (HashSet, TreeSet); Poor (LinkedList, Stream.iterate).
- The fork/join framework was designed to recursively split a parallelizable task into smaller tasks and then combine the results of each subtask to produce the overall result.
- Internal iteration allows you to process a stream in parallel without the need to explicitly use and coordinate different threads in your code.
- Even if processing a stream in parallel is so easy, there’s no guarantee that doing so will make your programs run faster under all circumstances.
- Behavior and performance of parallel software can sometimes be counterintuitive, and for this reason it’s always necessary to measure them and be sure that you’re not actually slowing your programs down.
- Parallel execution of an operation on a set of data, as done by a parallel stream, can provide a performance boost, especially when the number of elements to be processed is huge or the processing of each single element is particularly time consuming.
- From a performance point of view, using the right data structure, for instance, employing primitive streams instead of nonspecialized ones whenever possible, is almost always more important than trying to parallelize some operations.
- The fork/join framework lets you recursively split a parallelizable task into smaller tasks, execute them on different threads, and then combine the results of each subtask in order to produce the overall result.
- Spliterators define how a parallel stream can split the data it traverses.
Work Stealing
The tasks are more or less evenly divided on all the threads in the ForkJoinPool. Each of these threads holds a doubly linked queue of the tasks assigned to it, and as soon as it completes a task it pulls another one from the head of the queue and starts executing it. For the reasons we listed previously, one thread might complete all the tasks assigned to it much faster than the others, which means its queue will become empty while the other threads are still pretty busy. In this case, instead of becoming idle, the thread randomly chooses a queue of a different thread and “steals” a task, taking it from the tail of the queue. This process continues until all the tasks are executed, and then all the queues become empty. That’s why having many smaller tasks, instead of only a few bigger ones, can help in better balancing the workload among the worker threads.
public class Java8ParallelStream {
private static final long N = 10_000_000L;
private static final ForkJoinPool FORK_JOIN_POOL = new ForkJoinPool();
public static <T, R> long measurePerf(Function<T, R> function, T input) {
long fastest = Long.MAX_VALUE;
for (int i = 0; i < 2; i++) {
long start = System.nanoTime();
R result = function.apply(input);
long duration = (System.nanoTime() - start) / 1_000_000;
System.out.println("Result: " + result);
if (duration < fastest)
fastest = duration;
}
return fastest;
}
static class ParallelStreams {
public static long iterativeSum(long n) {
long result = 0;
for (long i = 0; i <= n; i++) {
result += i;
}
return result;
}
public static long sequentailSum(long n) {
return Stream.iterate(1L, i -> i + 1).limit(n).reduce(Long::sum).get();
}
public static long parallelSum(long n) {
return Stream.iterate(1L, i -> i + 1).limit(n).parallel().reduce(Long::sum).get();
}
public static long rangedSum(long n) {
return LongStream.rangeClosed(1, n).reduce(Long::sum).getAsLong();
}
public static long parallelRangedSum(long n) {
return LongStream.rangeClosed(1, n).parallel().reduce(Long::sum).getAsLong();
}
public static long forkJoinSum(long n) {
long[] numbers = LongStream.rangeClosed(1, n).toArray();
ForkJoinTask<Long> task = new ForkJoinSumCalculator(numbers);
return FORK_JOIN_POOL.invoke(task);
}
public static long sideEffectSum(long n) {
Accumulator accumulator = new Accumulator();
LongStream.rangeClosed(1, n).forEach(accumulator::add);
return accumulator.getTotal();
}
public static long sideEffectParrallelSum(long n) {
Accumulator accumulator = new Accumulator();
LongStream.rangeClosed(1, n).parallel().forEach(accumulator::add);
return accumulator.getTotal();
}
}
static class ForkJoinSumCalculator extends RecursiveTask<Long> {
private static final long serialVersionUID = -2754919233589478904L;
private static final long THRESHOLD = 10_000;
private final long[] numbers;
private final int start, end;
public ForkJoinSumCalculator(long[] numbers) {
this(numbers, 0, numbers.length);
}
private ForkJoinSumCalculator(long[] numbers, int start, int end) {
this.numbers = numbers;
this.start = start;
this.end = end;
}
@Override
protected Long compute() {
int length = end - start;
if (length <= THRESHOLD) {
return computeSequentially();
}
ForkJoinSumCalculator leftTask = new ForkJoinSumCalculator(numbers, start, start + length / 2);
leftTask.fork();
ForkJoinSumCalculator rightTask = new ForkJoinSumCalculator(numbers, start + length / 2, end);
Long rightResult = rightTask.compute();
Long leftResult = leftTask.join();
return leftResult + rightResult;
}
private long computeSequentially() {
long sum = 0;
for (int i = start; i < end; i++) {
sum += numbers[i];
}
return sum;
}
}
static class Accumulator {
private long total = 0;
public void add(long value) {
total += value;
}
public long getTotal() {
return total;
}
}
public static void main(String[] args) {
System.out.println("Iterative Sum done in: " + measurePerf(ParallelStreams::iterativeSum, N) + " msecs");
System.out.println("Sequential Sum done in: " + measurePerf(ParallelStreams::sequentailSum, N) + " msecs");
System.out.println("Parallel forkJoinSum done in: " + measurePerf(ParallelStreams::parallelSum, N) + " msecs");
System.out.println("Range forkJoinSum done in: " + measurePerf(ParallelStreams::rangedSum, N) + " msecs");
System.out.println("Parallel range forkJoinSum done in: " + measurePerf(ParallelStreams::parallelRangedSum, N) + " msecs");
System.out.println("ForkJoin sum done in: " + measurePerf(ParallelStreams::forkJoinSum, N) + " msecs");
System.out.println("SideEffect sum done in: " + measurePerf(ParallelStreams::sideEffectSum, N) + " msecs");
System.out.println("SideEffect parallel sum done in: " + measurePerf(ParallelStreams::sideEffectParrallelSum, N) + " msecs");
}
}
Java Default Methods
- Interfaces in Java 8 can have implementation code through default methods and static methods.
- Default methods start with a default keyword and contain a body like class methods do.
- Adding an abstract method to a published interface is a source incompatibility.
- Default methods help library designers evolve APIs in a backward-compatible way.
- Default methods can be used for creating optional methods and multiple inheritance of behavior.
- There are resolution rules to resolve conflicts when a class inherits from several default methods with the same signature.
- A method declaration in the class or a superclass takes priority over any default method declaration. Otherwise, the method with the same signature in the most specific default-providing interface is selected.
- When two methods are equally specific, a class can explicitly override a method and select which one to call.
Java Optional vs. null
- Java 8 introduces the class java.util.Optional
to model the presence or absence of a value. - You can create Optional objects with the static factory methods Optional.empty, Optional.of, and Optional.ofNullable.
- The Optional class supports many methods such as map, flatMap, and filter, which are conceptually similar to the methods of a stream.
- Using Optional forces you to actively unwrap an optional to deal with the absence of a value; as a result, you protect your code against unintended null pointer exceptions.
- Using Optional can help you design better APIs in which, just by reading the signature of a method, users can tell whether to expect an optional value.
public int readDuration(Properties props, String name) {
return Optional.ofNullable(props.getProperty(name))
.flatMap(OptionalUtility::stringToInt)
.filter(i -> i > 0)
.orElse(0);
}
Java CompletableFuture
CompletableFuture is to a plain Future what Stream is to a Collection.
Right Pool Size
The right pool size to approximate a desired CPU utilization rate can be calculated with the following formula:
\[N_{threads} = N_{CPU} * U_{CPU} * (1 + W/C)\]- \(N_{CPU}\) is the number of cores, available through
Runtime.getRuntime().availableProcessors() - \(U_{CPU}\) is the target CPU utilization (between 0 and 1), and
- \(W/C\) is the ratio of wait time to compute time (between 0.01 and 100)
Say an application is spending about the 99% of the time waiting for the shops’ responses, so you could estimate a W/C ratio of 100. This means that if your target is 100% CPU utilization, you should have a pool with 400 threads. But In practice it will be wasteful to have more threads than shops, because in doing so you’ll have threads in your pool that are never used. For this reason, we can calculate a thread size like: Math.min(shops.size(), 100).
Two Ways Parallel
There are two different ways to do parallel computing on a collection: either convert it to a parallel stream and use operations like map on it, or iterate over the collection and spawn operations within a CompletableFuture. The latter provides more control using resizing of thread pools, which helps ensure that your overall computation doesn’t block just because all of your fixed number of threads are waiting for I/O.
Our advice for using these APIs is as follows:
- If you’re doing computation-heavy operations with no I/O, then the Stream interface gives the simplest implementation and one likely to be the most efficient (if all threads are compute-bound, then there’s no point in having more threads than processor cores).
- On the other hand, if your parallel units of work involve waiting for I/O (including network connections), then CompletableFutures give more flexibility and the ability to match the number of threads to the wait/computer, or W/C, ratio as discussed previously. Another reason to avoid using parallel streams when I/O waits are involved in the stream-processing pipeline is that the laziness of streams can make it harder to reason about when the waits actually happen.
Java 8 Refactor/Test/Debug
- Lambda expressions can make your code more readable and flexible.
- Consider converting anonymous classes to lambda expressions, but be wary of subtle semantic differences such as the meaning of the keyword this and shadowing of variables.
- Method references can make your code more readable compared to lambda expressions.
- Consider converting iterative collection processing to use the Streams API.
- Lambda expressions can help remove boilerplate code associated with several object-oriented design patterns such as strategy, template method, observer, chain of responsibility, and factory.
- Lambda expressions can be unit tested, but in general you should focus on testing the behavior of the methods where the lambda expressions appear.
- Consider extracting complex lambda expressions into regular methods.
- Lambda expressions can make stack traces less readable.
- The peek method of a stream is useful to log intermediate values as they flow past at certain points in a stream pipeline.
public class Java8Refactor {
private Logger logger = Logger.getAnonymousLogger();
interface Task {
public void execute();
}
public static void doSomething(Runnable r) {
r.run();
}
public static void doSomething(Task t) {
t.execute();
}
// Lambda can help to defer the construction of message
public void log(Level level, Supplier<String> msgSupplier) {
if (logger.isLoggable(level)) {
logger.log(level, msgSupplier.get());
}
}
// Execute around
public static String processFile(BufferedReaderProcessor p) throws IOException {
try (BufferedReader br = new BufferedReader(new FileReader("data.txt"))) {
return p.process(br);
}
}
@FunctionalInterface
interface BufferedReaderProcessor {
String process(BufferedReader b) throws IOException;
}
// Strategy Pattern
@FunctionalInterface
public interface ValidationStrategy {
boolean execute(String s);
}
public static class Validator {
private final ValidationStrategy strategy;
public Validator(ValidationStrategy v) {
this.strategy = v;
}
public boolean validate(String s) {
return strategy.execute(s);
}
}
public static void debugWithPeekLog() {
List<Integer> numbers = Arrays.asList(2, 3, 4, 5, 6, 7, 8, 9);
List<Integer> result =
numbers.stream()
.peek(x -> System.out.println("from stream: " + x))
.map(x -> x + 17)
.peek(x -> System.out.println("after map: " + x))
.filter(x -> x % 2 == 0)
.peek(x -> System.out.println("after filter: " + x))
.limit(3)
.peek(x -> System.out.println("after limit: " + x))
.collect(Collectors.toList());
System.out.println(result);
}
public static void main(String[] args) throws Exception {
// anonymous class
doSomething(new Task() {
@Override
public void execute() {
System.out.println("Danger danger!!");
}
});
// lambda expression
doSomething((Task) () -> System.out.println("Danger danger!!"));
// execute around
// processFile(b -> b.readLine());
// strategy pattern
Validator validator = new Validator(s -> s.matches("[a-z]+"));
System.out.println(validator.validate("aaaaa"));
// chain of responsiblity
// Chain of responsibility
UnaryOperator<String> headerProcessing = (String text) -> "From Raoul, Mario and Alan: " + text;
UnaryOperator<String> spellCheckerProcessing = (String text) -> text.replaceAll("labda", "lambda");
Function<String, String> pipeline = headerProcessing.andThen(spellCheckerProcessing);
System.out.println(pipeline.apply("Aren't labdas really sexy?!!"));
debugWithPeekLog();
}
}
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