1. More Stream Processing
Computer Science 312
More Stream Processing 1

2. Taking and Dropping In Haskell: Prelude> take 3 [1..10] [1,2,3]
Prelude> drop 3 [1..10]
[4,5,6,7,8,9,10]
Prelude> take 3 [1..]

3. Taking and Dropping In Java: List<Dish> dishes = menu.stream()
.filter(d -> d.getCalories() > 300)
.limit(3) // Like take
.collect(toList());
List<Dish> dishes = menu.stream()
.filter(d -> d.getCalories() > 300)
.skip(2) // Like drop
.collect(toList());

4. Chained Mappings A single mapping: Consecutive mappings:
List<String> words = Arrays.asList("Java8", "Lambdas",
"In", "Action");
List<Integer> wordLengths = words.stream()
.map(String::length)
.collect(toList());
Consecutive mappings:
List<Integer> dishNameLengths = menu.stream()
.map(Dish::getName)
.map(String::length)
.collect(toList());
Are three complete streams generated in the second code segment?

5. Flattening during Map
Try to obtain a list of unique characters from a list of words:
words.stream()
.map(word -> word.split(""))
.distinct()
.collect(toList());
Actually sends a stream of arrays char out of the map

6. Flattening during Map
Create a stream of characters from an array of words:
String[] arrayOfWords = {"Goodbye", "World"};
Stream<String> streamOfwords = Arrays.stream(arrayOfWords);
Do this on the stream of words resulting from the first map:
But now we get a stream of streams of characters from the second map

7. Flattening during Map
Use flatMap for the second mapping, which flattens the stream of streams into a single stream:

8. Detecting Items
Several operations for detecting or finding items in a stream:
anyMatch
allMatch
noneMatch
findFirst
findAny
The first three are Boolean ops, and the last two return an item

9. Sample Boolean Detections
if(menu.stream().anyMatch(Dish::isVegetarian)){
System.out.println("The menu is kind of vegetarian friendly!!"); }
boolean isHealthy = menu.stream()
.allMatch(d -> d.getCalories() < 1000);
.noneMatch(d -> d.getCalories() >= 1000);
They all use short-circuit evaluation in the stream for optimization

10. Sample Item Detection
Optional<Dish> dish =
menu.stream()
.filter(Dish::isVegetarian)
.findAny();
Optional is Java’s version of Haskell’s Maybe type
Optional<T> methods:
isPresent()
ifPresent(Consumer<T> block)
T get()
T orElse(T other)

11. Sample Item Detection
Optional is Java’s version of Haskell’s Maybe type
Optional<T> methods:
isPresent()
ifPresent(Consumer<T> block)
T get()
T orElse(T other)

12. Reducing (Folding)
int sum = numbers.stream().reduce(0, (a, b) -> a + b);
int product = numbers.stream().reduce(1, (a, b) -> a * b);
int sum = numbers.stream().reduce(0, Integer::sum);
Optional<Integer> sum = numbers.stream().reduce((a, b) -> (a + b));

13. Reducing (Folding)
Optional<Integer> min = numbers.stream().reduce(Integer::min);
Optional<Integer> min =
numbers.stream().reduce((x,y)-> x < y ? x : y);
Reduction operation must be associative and not include side effects, for parallelization

14. Streams from Values
Stream<String> stream = Stream.of("Java 8 ", "Lambdas ",
"In ", "Action");
stream.map(String::toUpperCase).forEach(System.out::println);
Stream<String> emptyStream = Stream.empty();

15. Streams from Arrays and Files

16. Infinite Streams In Haskell: In Java:
take 10 (map (\n -> n + 2) [1..])
In Java:
Stream.iterate(0, n -> n + 2)
.limit(10)
.forEach(System.out::println);

17. Terminal Operations Chapter 6
For next time
Terminal Operations
Chapter 6