병렬 스트림: 성능과 주의점

long count = list.parallelStream()
    .filter(n -> n % 2 == 0)
    .count();
System.out.println(count);

// Good candidate: CPU-heavy computation on large dataset
long sum = LongStream.rangeClosed(1, 10_000_000)
    .parallel()
    .filter(n -> isPrime(n))
    .sum();
System.out.println(sum);

// Bad: I/O bound — blocking ForkJoinPool threads starves other tasks
List<String> result = urls.parallelStream()
    .map(url -> httpGet(url)) // BLOCKS the common pool
    .collect(Collectors.toList());

// RACE CONDITION — never do this:
List<Integer> results = new ArrayList<>(); // not thread-safe
numbers.parallelStream().forEach(n -> results.add(n)); // corrupts list!
// Fix:
List<Integer> safe = numbers.parallelStream().collect(Collectors.toList());

// sorted() forces collect-all, then sort — parallel overhead usually not worth it:
list.parallelStream().sorted().collect(Collectors.toList());

// Faster in parallel (order-insensitive):
Optional<Integer> any = list.parallelStream().filter(n -> n > 10).findAny();
// Slower in parallel (must preserve order):
Optional<Integer> first = list.parallelStream().filter(n -> n > 10).findFirst();

ForkJoinPool custom = new ForkJoinPool(8);
long result = custom.submit(
    () -> LongStream.rangeClosed(1, 1_000_000).parallel().sum()
).get();

@Benchmark
public long sequential() { return LongStream.rangeClosed(1,1_000_000).sum(); }
@Benchmark
public long parallel()   { return LongStream.rangeClosed(1,1_000_000).parallel().sum(); }

// Associative reduce — safe in parallel:
int sum = list.parallelStream().reduce(0, Integer::sum);
// Non-associative: subtraction — NOT safe in parallel:
int bad = list.parallelStream().reduce(0, (a, b) -> a - b); // wrong result!