Java NIO Pipe vs BlockingQueue

Question

我刚刚发现只有一个NIO工具，即Java NIO Pipe，用于在线程之间传递数据。使用这种机制比通过队列传递更传统的消息（如ArrayBlockingQueue）有什么优势吗？

Answer 1

通常，将数据传递给另一个线程进行处理的最简单方法是使用ExecutorService。这种包装了两个队列和线程池（可以有一个线程）

您可以使用一个管道时，你有支持NIO通道库。如果你想在线程之间传递数据的ByteBuffers，这也很有用。

否则其通常使用ArrayBlockingQueue更简单/更快。

如果你想要一个更快的方式在线程之间交换数据，我建议你看看Exchanger，但它不像ArrayBlockingQueue那样具有通用性。

The Exchanger and GC-less Java

Answer 2

我想管将有更好的延迟，因为它可以很可能与幕后协同程序来实现。因此，当数据可用时，生产者立即向消费者屈服，而不是在线程调度器决定时。

管道通常占一个消费者 - 生产者的问题，很可能使两个线程合作来实现这种方式，而不是外部抢占。

Answer 3

我相信NIO管的设计，让您可以将数据发送到在一个线程安全的方式选择内循环的通道，换句话说，任何线程可以写信给管和数据将在其他处理管道的极端，在选择器回路内部。当你写入管道时，你可以使另一侧的通道可读。

Answer 4

所以有很多的麻烦管（check here）之后，我决定赞成非阻塞并发队列超过NIO管道。所以我在Java的ConcurrentLinkedQueue上做了一些基准测试。请看下图：

public static void main(String[] args) throws Exception { 

    ConcurrentLinkedQueue<String> queue = new ConcurrentLinkedQueue<String>(); 

    // first test nothing: 

    for (int j = 0; j < 20; j++) { 

     Benchmarker bench = new Benchmarker(); 

     String s = "asd"; 

     for (int i = 0; i < 1000000; i++) { 
      bench.mark(); 
      // s = queue.poll(); 
      bench.measure(); 
     } 

     System.out.println(bench.results()); 

     Thread.sleep(100); 
    } 

    System.out.println(); 

    // first test empty queue: 

    for (int j = 0; j < 20; j++) { 

     Benchmarker bench = new Benchmarker(); 

     String s = "asd"; 

     for (int i = 0; i < 1000000; i++) { 
      bench.mark(); 
      s = queue.poll(); 
      bench.measure(); 
     } 

     System.out.println(bench.results()); 

     Thread.sleep(100); 
    } 

    System.out.println(); 

    // now test polling one element on a queue with size one 

    for (int j = 0; j < 20; j++) { 

     Benchmarker bench = new Benchmarker(); 

     String s = "asd"; 
     String x = "pela"; 

     for (int i = 0; i < 1000000; i++) { 
      queue.offer(x); 
      bench.mark(); 
      s = queue.poll(); 
      bench.measure(); 
      if (s != x) throw new Exception("bad!"); 
     } 

     System.out.println(bench.results()); 

     Thread.sleep(100); 
    } 

    System.out.println(); 

    // now test polling one element on a queue with size two 

    for (int j = 0; j < 20; j++) { 

     Benchmarker bench = new Benchmarker(); 

     String s = "asd"; 
     String x = "pela"; 

     for (int i = 0; i < 1000000; i++) { 
      queue.offer(x); 
      queue.offer(x); 
      bench.mark(); 
      s = queue.poll(); 
      bench.measure(); 
      if (s != x) throw new Exception("bad!"); 
      queue.poll(); 
     } 

     System.out.println(bench.results()); 

     Thread.sleep(100); 
    } 
} 

结果：

totalLogs=1000000, minTime=0, maxTime=85000, avgTime=58.61 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=5281000, avgTime=63.35 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=725000, avgTime=59.71 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=25000, avgTime=58.13 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=378000, avgTime=58.45 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=15000, avgTime=57.71 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=170000, avgTime=58.11 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=1495000, avgTime=59.87 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=232000, avgTime=63.0 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=184000, avgTime=57.89 (times in nanos) 

totalLogs=1000000, minTime=0, maxTime=2600000, avgTime=65.22 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=850000, avgTime=60.5 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=150000, avgTime=63.83 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=43000, avgTime=59.75 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=276000, avgTime=60.02 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=457000, avgTime=61.69 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=204000, avgTime=60.44 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=154000, avgTime=63.67 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=355000, avgTime=60.75 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=338000, avgTime=60.44 (times in nanos) 

totalLogs=1000000, minTime=0, maxTime=345000, avgTime=110.93 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=396000, avgTime=100.32 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=298000, avgTime=98.93 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=1891000, avgTime=101.9 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=254000, avgTime=103.06 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=1894000, avgTime=100.97 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=230000, avgTime=99.21 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=348000, avgTime=99.63 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=922000, avgTime=99.53 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=168000, avgTime=99.12 (times in nanos) 

totalLogs=1000000, minTime=0, maxTime=686000, avgTime=107.41 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=320000, avgTime=95.58 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=248000, avgTime=94.94 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=217000, avgTime=95.01 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=159000, avgTime=93.62 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=155000, avgTime=95.28 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=106000, avgTime=98.57 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=370000, avgTime=95.01 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=1836000, avgTime=96.21 (times in nanos) 
totalLogs=1000000, minTime=0, maxTime=212000, avgTime=98.62 (times in nanos) 

结论：

的MAXTIME很可怕，但我认为它是安全的结论，我们是在50毫微秒范围轮询并发队列。