SynchronousQueue、LinkedBlockingQueue、ArrayBlockingQueue性能测试
JDK6对SynchronousQueue做了性能优化,避免对竞争资源加锁,所以想试试到底平时是选择SynchronousQueue还是其他BlockingQueue。
对于容器类在并发环境下的比较,一是是否线程安全,二是并发性能如何。BlockingQueue的实现都是线程安全的,所以只能比比它们的并发性能了。在 不同的应用场景中,对容器的使用情况不同,有的读取操作多修改写入操作少,有的修改写入操作多,这对容器的性能会造成不同的影响。但对于Queue的使 用,个人认为是比较一致的,简单点就是put和get,不会修改某个元素的内容再被读取,也很少只读取的操作,那是不是有最佳实践了?
LinkedBlockingQueue性能表现远超ArrayBlcokingQueue,不管线程 多少,不管Queue长短,LinkedBlockingQueue都胜过ArrayBlockingQueue。SynchronousQueue表现 很稳定,而且在20个线程之内不管Queue长短,SynchronousQueue性能表现是最好的,(其实SynchronousQueue跟 Queue长短没有关系),如果Queue的capability只能是1,那么毫无疑问选择SynchronousQueue,这也是设计 SynchronousQueue的目的吧。但大家也可以看到当超过1000个线程时,SynchronousQueue性能就直线下降了,只有最高峰的 一半左右,而且当Queue大于30时,LinkedBlockingQueue性能就超过SynchronousQueue。
结论:
线程多(>20),Queue长度长(>30),使用LinkedBlockingQueue
线程少 (<20) ,Queue长度短 (<30) , 使用SynchronousQueue
当然,使用SynchronousQueue的时候不要忘记应用的扩展,如果将来需要进行扩展还是选择LinkedBlockingQueue好,尽量把SynchronousQueue限制在特殊场景中使用。
少用ArrayBlcokingQueue,似乎没找到它的好处,高手给给建议吧!
最后看看测试代码和结果:(Win7 64bit + JDK7 + CPU4 + 4GB)
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import java.util.concurrent.ArrayBlockingQueue;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.Callable;
import java.util.concurrent.CompletionService;
import java.util.concurrent.ExecutorCompletionService;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.LinkedBlockingQueue;
import java.util.concurrent.SynchronousQueue;
public class TestSynchronousQueue {
private static int THREAD_NUM;
private static int N = 1000000 ;
private static ExecutorService executor;
public static void main(String[] args) throws Exception {
System.out.println( "Producer\tConsumer\tcapacity \t LinkedBlockingQueue \t ArrayBlockingQueue \t SynchronousQueue" );
for ( int j = 0 ; j< 10 ; j++){
THREAD_NUM = ( int ) Math.pow( 2 , j);
executor = Executors.newFixedThreadPool(THREAD_NUM * 2 );
for ( int i = 0 ; i < 10 ; i++) {
int length = (i == 0 ) ? 1 : i * 10 ;
System.out.print(THREAD_NUM + "\t\t" );
System.out.print(THREAD_NUM + "\t\t" );
System.out.print(length + "\t\t" );
System.out.print(doTest2( new LinkedBlockingQueue<Integer>(length), N) + "/s\t\t\t" );
System.out.print(doTest2( new ArrayBlockingQueue<Integer>(length), N) + "/s\t\t\t" );
System.out.print(doTest2( new SynchronousQueue<Integer>(), N) + "/s" );
System.out.println();
}
executor.shutdown();
}
}
private static class Producer implements Runnable{
int n;
BlockingQueue<Integer> q;
public Producer( int initN, BlockingQueue<Integer> initQ){
n = initN;
q = initQ;
}
public void run() {
for ( int i = 0 ; i < n; i++)
try {
q.put(i);
} catch (InterruptedException ex) {
}
}
}
private static class Consumer implements Callable<Long>{
int n;
BlockingQueue<Integer> q;
public Consumer( int initN, BlockingQueue<Integer> initQ){
n = initN;
q = initQ;
}
public Long call() {
long sum = 0 ;
for ( int i = 0 ; i < n; i++)
try {
sum += q.take();
} catch (InterruptedException ex) {
}
return sum;
}
}
private static long doTest2( final BlockingQueue<Integer> q, final int n)
throws Exception {
CompletionService<Long> completionServ = new ExecutorCompletionService<Long>(executor);
long t = System.nanoTime();
for ( int i= 0 ; i<THREAD_NUM; i++){
executor.submit( new Producer(n/THREAD_NUM, q));
}
for ( int i= 0 ; i<THREAD_NUM; i++){
completionServ.submit( new Consumer(n/THREAD_NUM, q));
}
for ( int i= 0 ; i<THREAD_NUM; i++){
completionServ.take().get();
}
t = System.nanoTime() - t;
return ( long ) ( 1000000000.0 * N / t); // Throughput, items/sec
}
} |
程序运行结果:
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Producer Consumer capacity LinkedBlockingQueue ArrayBlockingQueue SynchronousQueue 1 1 1 154567 /s 154100 /s 3655071 /s
1 1 10 1833165 /s 1967491 /s 3622405 /s
1 1 20 3011779 /s 2558451 /s 3744037 /s
1 1 30 3145926 /s 2632099 /s 3354525 /s
1 1 40 3289673 /s 2879696 /s 3581858 /s
1 1 50 3201828 /s 3008838 /s 3600100 /s
1 1 60 3171374 /s 2541672 /s 3922617 /s
1 1 70 3159786 /s 2844493 /s 3423066 /s
1 1 80 3042835 /s 2536290 /s 3443517 /s
1 1 90 3025808 /s 3026241 /s 3307096 /s
2 2 1 141555 /s 135653 /s 2897927 /s
2 2 10 1627066 /s 785082 /s 2908671 /s
2 2 20 2199668 /s 1604847 /s 2937085 /s
2 2 30 2309495 /s 2115986 /s 2922671 /s
2 2 40 2335737 /s 2424491 /s 2942621 /s
2 2 50 2394045 /s 2405210 /s 2918222 /s
2 2 60 2499445 /s 2471052 /s 2881591 /s
2 2 70 2368143 /s 2454153 /s 2914038 /s
2 2 80 2381024 /s 2457910 /s 2937337 /s
2 2 90 2509167 /s 2461035 /s 2789278 /s
4 4 1 138177 /s 138101 /s 2736238 /s
4 4 10 1654165 /s 478171 /s 2693045 /s
4 4 20 2443373 /s 779452 /s 2728493 /s
4 4 30 2646300 /s 1169313 /s 2787315 /s
4 4 40 2755774 /s 1487883 /s 2874789 /s
4 4 50 2774736 /s 1579152 /s 2804046 /s
4 4 60 2804725 /s 1998602 /s 2803680 /s
4 4 70 2797524 /s 2388276 /s 2936613 /s
4 4 80 2887786 /s 2557358 /s 2899823 /s
4 4 90 2878895 /s 2539458 /s 2839990 /s
8 8 1 140745 /s 135621 /s 2711703 /s
8 8 10 1650143 /s 526018 /s 2730710 /s
8 8 20 2477902 /s 798799 /s 2696374 /s
8 8 30 2658511 /s 983456 /s 2783054 /s
8 8 40 2694167 /s 1185732 /s 2677500 /s
8 8 50 2758267 /s 1110716 /s 2766695 /s
8 8 60 2831922 /s 1003692 /s 2762232 /s
8 8 70 2763751 /s 1409142 /s 2791901 /s
8 8 80 2771897 /s 1654843 /s 2838479 /s
8 8 90 2740467 /s 1718642 /s 2806164 /s
16 16 1 131843 /s 137943 /s 2694036 /s
16 16 10 1637213 /s 491171 /s 2725893 /s
16 16 20 2523193 /s 660559 /s 2709892 /s
16 16 30 2601176 /s 899163 /s 2689270 /s
16 16 40 2794088 /s 1054763 /s 2759321 /s
16 16 50 2777807 /s 1111479 /s 2663346 /s
16 16 60 2893566 /s 931713 /s 2778294 /s
16 16 70 2822779 /s 1286067 /s 2704785 /s
16 16 80 2828238 /s 1430581 /s 2724927 /s
16 16 90 2860943 /s 1249650 /s 2791520 /s
32 32 1 132098 /s 130805 /s 2676121 /s
32 32 10 1586372 /s 402270 /s 2674953 /s
32 32 20 2467754 /s 886059 /s 2580989 /s
32 32 30 2569709 /s 772173 /s 2599466 /s
32 32 40 2659883 /s 963633 /s 2677042 /s
32 32 50 2721213 /s 910607 /s 2677578 /s
32 32 60 2779272 /s 861786 /s 2676874 /s
32 32 70 2757921 /s 1111937 /s 2696416 /s
32 32 80 2915294 /s 1323776 /s 2655641 /s
32 32 90 2798313 /s 1193225 /s 2630231 /s
64 64 1 126035 /s 123764 /s 2526632 /s
64 64 10 1539034 /s 394597 /s 2582590 /s
64 64 20 2449850 /s 703790 /s 2598631 /s
64 64 30 2672792 /s 758256 /s 2529693 /s
64 64 40 2797081 /s 661028 /s 2573380 /s
64 64 50 2789848 /s 1162143 /s 2659469 /s
64 64 60 2726806 /s 1145495 /s 2567020 /s
64 64 70 2731554 /s 1359939 /s 2607615 /s
64 64 80 2871116 /s 1305428 /s 2494839 /s
64 64 90 2774416 /s 1339611 /s 2560153 /s
128 128 1 223305 /s 112828 /s 2390234 /s
128 128 10 1419592 /s 404611 /s 2401086 /s
128 128 20 2365301 /s 793815 /s 2516045 /s
128 128 30 2647136 /s 915702 /s 2463175 /s
128 128 40 2721664 /s 1081728 /s 2400299 /s
128 128 50 2688304 /s 1149251 /s 2489667 /s
128 128 60 2774212 /s 1145298 /s 2453444 /s
128 128 70 2782905 /s 1165408 /s 2403510 /s
128 128 80 2818388 /s 1392486 /s 2389275 /s
128 128 90 2738468 /s 1546247 /s 2425994 /s
256 256 1 160146 /s 80530 /s 2369297 /s
256 256 10 1214041 /s 364460 /s 2142039 /s
256 256 20 1915432 /s 901668 /s 2156774 /s
256 256 30 2371862 /s 1124997 /s 2237464 /s
256 256 40 2630812 /s 1123016 /s 2216475 /s
256 256 50 2666827 /s 1239640 /s 2267322 /s
256 256 60 2635269 /s 1276851 /s 2318122 /s
256 256 70 2663477 /s 1333002 /s 2188256 /s
256 256 80 2672080 /s 1659850 /s 2315438 /s
256 256 90 2804828 /s 1497635 /s 2194905 /s
512 512 1 123294 /s 68426 /s 1892168 /s
512 512 10 1028250 /s 296454 /s 1728199 /s
512 512 20 1545215 /s 604512 /s 1963526 /s
512 512 30 1968728 /s 762240 /s 2000386 /s
512 512 40 2273678 /s 854483 /s 1948188 /s
512 512 50 2295335 /s 939350 /s 1858429 /s
512 512 60 2419257 /s 1056918 /s 1884224 /s
512 512 70 2346088 /s 980795 /s 1852387 /s
512 512 80 2341964 /s 928496 /s 1867498 /s
512 512 90 2375789 /s 1290064 /s 1923461 /s
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