On day 2, you learned that Stream API can help you work with collections in a declarative manner. We looked at the collect, which is a terminal operation that collects the result set of a stream pipeline in a List. collect is a reduction operation that reduces a stream to a value. The value could be a Collection, Map, or a value object. You can use collect to achieve following:
-
Reducing stream to a single value: Result of the stream execution can be reduced to a single value. Single value could be a
Collectionor numeric value like int, double, etc or a custom value object. -
Group elements in a stream: Group all the tasks in a stream by TaskType. This will result in a
Map<TaskType, List<Task>>with each entry containing a TaskType and its associated Tasks. You can use any other Collection instead of a List as well. If you don't need all the tasks associated with a TaskType you can also produceMap<TaskType, Task>as well. One example could be grouping tasks by type and obtaining the first created task. -
Partition elements in a stream: You can partition a stream into two groups -- due and completed tasks.
To feel the power of Collector let us look at the example where we have to group tasks by their type. In Java 8, we can achieve grouping by TaskType by writing code shown below. Please refer to day 2 blog where we talked about the example domain we will use in this series
private static Map<TaskType, List<Task>> groupTasksByType(List<Task> tasks) {
return tasks.stream().collect(Collectors.groupingBy(task -> task.getType()));
}The code shown above uses groupingBy Collector defined in the Collectors utility class. It creates a Map with key as the TaskType and value as the list containing all the tasks which have same TaskType. To achieve the same in Java 7 you have to write following code.
public static void main(String[] args) {
List<Task> tasks = getTasks();
Map<TaskType, List<Task>> allTasksByType = new HashMap<>();
for (Task task : tasks) {
List<Task> existingTasksByType = allTasksByType.get(task.getType());
if (existingTasksByType == null) {
List<Task> tasksByType = new ArrayList<>();
tasksByType.add(task);
allTasksByType.put(task.getType(), tasksByType);
} else {
existingTasksByType.add(task);
}
}
for (Map.Entry<TaskType, List<Task>> entry : allTasksByType.entrySet()) {
System.out.println(String.format("%s =>> %s", entry.getKey(), entry.getValue()));
}
}Collectors utility class provides a lot of static utility methods for creating collectors for most common use cases like accumulating elements into a Collection, grouping and partitioning elements, summarizing elements according to various criteria. We will cover most common Collectors in this blog.
As discussed above, collectors can be used to collect stream output to a Collection or produce a single value.
Let's write our first test case -- given a list of Tasks we want to collect all the titles into a List.
import static java.util.stream.Collectors.toList;
public class Example2_ReduceValue {
public List<String> allTitles(List<Task> tasks) {
return tasks.stream().map(Task::getTitle).collect(toList());
}
}The toList collector uses the List's add method to add elements into the resulting List. toList collector uses ArrayList as the List implementation.
If we want to make sure only unique titles are returned and we don't care about order then we can use toSet collector.
import static java.util.stream.Collectors.toSet;
public Set<String> uniqueTitles(List<Task> tasks) {
return tasks.stream().map(Task::getTitle).collect(toSet());
}toSet method uses HashSet as the Set implementation to store the result set.
You can convert a stream to a Map by using the toMap collector. The toMap collector takes two mapper functions to extract the key and values for the Map. In the code shown below, Task::getTitle is Function that takes a task and produces a key with only title. The task -> task is a lambda expression that just returns itself i.e. task in this case.
private static Map<String, Task> taskMap(List<Task> tasks) {
return tasks.stream().collect(toMap(Task::getTitle, task -> task));
}We can improve the code shown above by using the identity default method in the Function interface to make code cleaner and better convey developer intent to use identity function as shown below.
import static java.util.function.Function.identity;
private static Map<String, Task> taskMap(List<Task> tasks) {
return tasks.stream().collect(toMap(Task::getTitle, identity()));
}The code to create a Map from the stream will throw an exception when duplicate keys are present. You will get an error like the one shown below.
Exception in thread "main" java.lang.IllegalStateException: Duplicate key Task{title='Read Version Control with Git book', type=READING}
at java.util.stream.Collectors.lambda$throwingMerger$105(Collectors.java:133)
You can handle duplicates by using another variant of the toMap function which allows us to specify a merge function. The merge function allows a client to specify how they want to resolve collisions between values associated with the same key. In the code shown below, we just used the last value but you can write intelligent algorithm to resolve the collision.
private static Map<String, Task> taskMap_duplicates(List<Task> tasks) {
return tasks.stream().collect(toMap(Task::getTitle, identity(), (t1, t2) -> t2));
}You can use any other Map implementation by using the third variant of toMap method. This requires you to specify Map Supplier that will be used to store the result.
public Map<String, Task> collectToMap(List<Task> tasks) {
return tasks.stream().collect(toMap(Task::getTitle, identity(), (t1, t2) -> t2, LinkedHashMap::new));
}
Similar to the toMap collector there is also toConcurrentMap collector that produces ConcurrentMap instead of a HashMap.
The specific collectors like toList and toSet does not allow you to specify the underlying List or Set implementation. You can use toCollection collector when you want to collect the result to other types of collections as shown below.
private static LinkedHashSet<Task> collectToLinkedHaskSet(List<Task> tasks) {
return tasks.stream().collect(toCollection(LinkedHashSet::new));
}
public Task taskWithLongestTitle(List<Task> tasks) {
return tasks.stream().collect(collectingAndThen(maxBy((t1, t2) -> t1.getTitle().length() - t2.getTitle().length()), Optional::get));
}public int totalTagCount(List<Task> tasks) {
return tasks.stream().collect(summingInt(task -> task.getTags().size()));
}public String titleSummary(List<Task> tasks) {
return tasks.stream().map(Task::getTitle).collect(joining(";"));
}One of the most common use case of Collector is to group elements. Let's look at various examples to understand how we can perform grouping.
Let's look the example shown below where we want to group all the tasks based on their TaskType. You can very easily perform this task by using the groupingBy Collector of the Collectors utility class as shown below. You can make it more succinct by using method references and static imports.
import static java.util.stream.Collectors.groupingBy;
private static Map<TaskType, List<Task>> groupTasksByType(List<Task> tasks) {
return tasks.stream().collect(groupingBy(Task::getType));
}It will produce the output shown below.
{CODING=[Task{title='Write a mobile application to store my tasks', type=CODING, createdOn=2015-07-03}], WRITING=[Task{title='Write a blog on Java 8 Streams', type=WRITING, createdOn=2015-07-04}], READING=[Task{title='Read Version Control with Git book', type=READING, createdOn=2015-07-01}, Task{title='Read Java 8 Lambdas book', type=READING, createdOn=2015-07-02}, Task{title='Read Domain Driven Design book', type=READING, createdOn=2015-07-05}]}
private static Map<String, List<Task>> groupingByTag(List<Task> tasks) {
return tasks.stream().
flatMap(task -> task.getTags().stream().map(tag -> new TaskTag(tag, task))).
collect(groupingBy(TaskTag::getTag, mapping(TaskTag::getTask,toList())));
}
private static class TaskTag {
final String tag;
final Task task;
public TaskTag(String tag, Task task) {
this.tag = tag;
this.task = task;
}
public String getTag() {
return tag;
}
public Task getTask() {
return task;
}
}Combining classifiers and Collectors
private static Map<String, Long> tagsAndCount(List<Task> tasks) {
return tasks.stream().
flatMap(task -> task.getTags().stream().map(tag -> new TaskTag(tag, task))).
collect(groupingBy(TaskTag::getTag, counting()));
}private static Map<TaskType, Map<LocalDate, List<Task>>> groupTasksByTypeAndCreationDate(List<Task> tasks) {
return tasks.stream().collect(groupingBy(Task::getType, groupingBy(Task::getCreatedOn)));
}There are times when you want to partition a dataset into two dataset based on a predicate. For example, we can partition tasks into two groups by defining a partitioning function that partition tasks into two groups -- one with due date before today and one with due date after today.
private static Map<Boolean, List<Task>> partitionOldAndFutureTasks(List<Task> tasks) {
return tasks.stream().collect(partitioningBy(task -> task.getDueOn().isAfter(LocalDate.now())));
}Another group of collectors that are very helpful are collectors that produce statistics. These work on the primitive datatypes like int,double, long and can be used to produce statistics like the one shown below.
IntSummaryStatistics summaryStatistics = tasks.stream().map(Task::getTitle).collect(summarizingInt(String::length));
System.out.println(summaryStatistics.getAverage()); //32.4
System.out.println(summaryStatistics.getCount()); //5
System.out.println(summaryStatistics.getMax()); //44
System.out.println(summaryStatistics.getMin()); //24
System.out.println(summaryStatistics.getSum()); //162There are other variants as well for other primitive types like LongSummaryStatistics and DoubleSummaryStatistics
You can also combine one IntSummaryStatistics with another using the combine operation.
firstSummaryStatistics.combine(secondSummaryStatistics);
System.out.println(firstSummaryStatistics)private static String allTitles(List<Task> tasks) {
return tasks.stream().map(Task::getTitle).collect(joining(", "));
}import com.google.common.collect.HashMultiset;
import com.google.common.collect.Multiset;
import java.util.Collections;
import java.util.EnumSet;
import java.util.Set;
import java.util.function.BiConsumer;
import java.util.function.BinaryOperator;
import java.util.function.Function;
import java.util.function.Supplier;
import java.util.stream.Collector;
public class MultisetCollector<T> implements Collector<T, Multiset<T>, Multiset<T>> {
@Override
public Supplier<Multiset<T>> supplier() {
return HashMultiset::create;
}
@Override
public BiConsumer<Multiset<T>, T> accumulator() {
return (set, e) -> set.add(e, 1);
}
@Override
public BinaryOperator<Multiset<T>> combiner() {
return (set1, set2) -> {
set1.addAll(set2);
return set1;
};
}
@Override
public Function<Multiset<T>, Multiset<T>> finisher() {
return Function.identity();
}
@Override
public Set<Characteristics> characteristics() {
return Collections.unmodifiableSet(EnumSet.of(Characteristics.IDENTITY_FINISH));
}
}import com.google.common.collect.Multiset;
import java.util.Arrays;
import java.util.List;
public class MultisetCollectorExample {
public static void main(String[] args) {
List<String> names = Arrays.asList("shekhar", "rahul", "shekhar");
Multiset<String> set = names.stream().collect(new MultisetCollector<>());
set.forEach(str -> System.out.println(str + ":" + set.count(str)));
}
}We will end this section by writing famous word count example in Java 8 using Streams and Collectors.
public static void wordCount(Path path) throws IOException {
Map<String, Long> wordCount = Files.lines(path)
.parallel()
.flatMap(line -> Arrays.stream(line.trim().split("\\s")))
.map(word -> word.replaceAll("[^a-zA-Z]", "").toLowerCase().trim())
.filter(word -> word.length() > 0)
.map(word -> new SimpleEntry<>(word, 1))
.collect(groupingBy(SimpleEntry::getKey, counting()));
wordCount.forEach((k, v) -> System.out.println(String.format("%s ==>> %d", k, v)));
}