Java8 parallelStream并发安全原理讲解

private static List<Integer> list1 = new ArrayList<>();
private static List<Integer> list2 = new ArrayList<>();
private static List<Integer> list3 = new ArrayList<>();
private static Lock lock = new ReentrantLock();

public static void main(String[] args) {
 IntStream.range(0, 10000).forEach(list1::add);

 IntStream.range(0, 10000).parallel().forEach(list2::add);

 IntStream.range(0, 10000).forEach(i -> {
 lock.lock();
 try {
  list3.add(i);
 }finally {
  lock.unlock();
 }
 });

 System.out.println("串行执行的大小：" + list1.size());
 System.out.println("并行执行的大小：" + list2.size());
 System.out.println("加锁并行执行的大小：" + list3.size());
}

<R, A> R collect(Collector<? super T, A, R> collector);

Supplier<A> supplier();
BiConsumer<A, T> accumulator();
BinaryOperator<A> combiner();
Function<A, R> finisher();
Set<Characteristics> characteristics();

public class Compute {
public Double compute(int num) {
 return (double) (2 * num);
}
}

private List<Integer> nums = new ArrayList<>();
private Set<Double> result = new HashSet<>();

public void run() {
 // 填充原始数据，nums中填充0-9 10个数
 IntStream.range(0, 10).forEach(nums::add);
 //实现Collector接口
 result = nums.stream().parallel().collect(new Collector<Integer, Container, Set<Double>>() {

 @Override
 public Supplier<Container> supplier() {
  return Container::new;
 }

 @Override
 public BiConsumer<Container, Integer> accumulator() {
  return Container::accumulate;
 }

 @Override
 public BinaryOperator<Container> combiner() {
  return Container::combine;
 }

 @Override
 public Function<Container, Set<Double>> finisher() {
  return Container::getResult;
 }

 @Override
 public Set<Characteristics> characteristics() {
  // 固定写法
  return Collections.emptySet();
 }
 });
}

class Container {
 // 定义本地的result
 public Set<Double> set;

 public Container() {
 this.set = new HashSet<>();
 }

 public Container accumulate(int num) {
 this.set.add(compute.compute(num));
 return this;
 }

 public Container combine(Container container) {
 this.set.addAll(container.set);
 return this;
 }

 public Set<Double> getResult() {
 return this.set;
 }
}

public static void main(String[] args) {
 Main main = new Main();
 main.run();
 System.out.println("原始数据：");
 main.nums.forEach(i -> System.out.print(i + " "));
 System.out.println("\n\ncollect方法加工后的数据：");
 main.result.forEach(i -> System.out.print(i + " "));
}

@BenchmarkMode(Mode.AverageTime)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
@Warmup(iterations = 5, time = 3, timeUnit = TimeUnit.SECONDS)
@Measurement(iterations = 20, time = 3, timeUnit = TimeUnit.SECONDS)
@Fork(1)
@State(Scope.Benchmark)
public class StreamBenchTest {
  List<String> data = new ArrayList<>();
  @Setup
  public void init() {
    // prepare
    for(int i=0;i<100;i++){
      data.add(UUID.randomUUID().toString());
    }
  }
  @TearDown
  public void destory() {
    // destory
  }
  @Benchmark
  public void benchStream(){
    data.stream().forEach(e -> {
      e.getBytes();
      try {
        Thread.sleep(10);
      } catch (InterruptedException e1) {
        e1.printStackTrace();
      }
    });
  }
  @Benchmark
  public void benchParallelStream(){
    data.parallelStream().forEach(e -> {
      e.getBytes();
      try {
        Thread.sleep(10);
      } catch (InterruptedException e1) {
        e1.printStackTrace();
      }
    });
  }
  public static void main(String[] args) throws RunnerException {
    Options opt = new OptionsBuilder()
        .include(".*" +StreamBenchTest.class.getSimpleName()+ ".*")
        .forks(1)
        .build();
    new Runner(opt).run();
  }
}

# Run complete. Total time: 00:02:44
Benchmark              Mode Cnt      Score     Error Units
StreamBenchTest.benchParallelStream avgt  20  155868805.437 ± 1509175.840 ns/op
StreamBenchTest.benchStream     avgt  20 1147570372.950 ± 6138494.414 ns/op

Benchmark              Mode Cnt      Score     Error Units
StreamBenchTest.benchParallelStream avgt  20  414230854.631 ± 725294.455 ns/op
StreamBenchTest.benchStream     avgt  20 3107250608.500 ± 4805037.628 ns/op