Java NIO Pipe vs BlockingQueue

Question

我剛剛發現只有一個NIO工具，即Java NIO Pipe，用於在線程之間傳遞數據。使用這種機制比通過隊列傳遞更傳統的消息（如ArrayBlockingQueue）有什麼優勢嗎？

Answer 1

通常，將數據傳遞給另一個線程進行處理的最簡單方法是使用ExecutorService。這種包裝了兩個隊列和線程池（可以有一個線程）

您可以使用一個管道時，你有支持NIO通道庫。如果你想在線程之間傳遞數據的ByteBuffers，這也很有用。

否則其通常使用ArrayBlockingQueue更簡單/更快。

如果你想要一個更快的方式在線程之間交換數據，我建議你看看Exchanger，但它不像ArrayBlockingQueue那樣具有通用性。

The Exchanger and GC-less Java

Answer 2

我想管將有更好的延遲，因爲它可以很可能與幕後協同程序來實現。因此，當數據可用時，生產者立即向消費者屈服，而不是在線程調度器決定時。

管道通常佔一個消費者 - 生產者的問題，很可能使兩個線程合作來實現這種方式，而不是外部搶佔。

Answer 3

我相信NIO管的設計，讓您可以將數據發送到在一個線程安全的方式選擇內循環的通道，換句話說，任何線程可以寫信給管和數據將在其他處理管道的極端，在選擇器迴路內部。當你寫入管道時，你可以使另一側的通道可讀。

Answer 4

所以有很多的麻煩管（check here）之後，我決定贊成非阻塞併發隊列超過NIO管道。所以我在Java的ConcurrentLinkedQueue上做了一些基準測試。請看下圖：

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); 
    } 
} 

結果：

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) 

結論：

的MAXTIME很可怕，但我認爲它是安全的結論，我們是在50毫微秒範圍輪詢併發隊列。