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ConcurrentSkipListMap是Doug Lea在java6中新加入的一个并发集合,下面这个例子主要是测试ConcurrentSkipListMap的插入、读取和并发修改集合元素时的性能特征,代码如下:
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class="java">package test.caipiao.log; import java.io.File; import java.io.IOException; import java.nio.charset.Charset; import java.util.concurrent.ConcurrentNavigableMap; import java.util.concurrent.ConcurrentSkipListMap; import java.util.concurrent.atomic.AtomicInteger; import java.util.ArrayList; import java.util.Iterator; import java.util.Map; import java.util.Set; import com.caipiao.util.io.FileUtils; public class ConcurrentSkipListMapTest2 { public static void main(String[] args) throws InterruptedException, Exception { final ConcurrentNavigableMap<Integer, String> cslm = new ConcurrentSkipListMap<Integer, String>(); final Counter5 counter = new Counter5(); // create 100 threads ArrayList<MyThread5> threads = new ArrayList<MyThread5>(); for (int x = 0; x < 100; x++) { threads.add(new MyThread5(counter, cslm)); } long start1 = System.currentTimeMillis(); // start all of the threads Iterator i1 = threads.iterator(); while (i1.hasNext()) { MyThread5 mt = (MyThread5) i1.next(); mt.start(); } // wait for all the threads to finish Iterator i2 = threads.iterator(); while (i2.hasNext()) { MyThread5 mt = (MyThread5) i2.next(); mt.join(); } long end1 = System.currentTimeMillis(); System.out.println("100个线程 每个线程插入100000共1000万条记录,耗时: " + (end1 - start1) + " 毫秒"); System.out.println("Count: " + counter.getCount()); System.out.println("original size = " + cslm.size()); System.out.println("---------------------以上是测试插入---------------------------------------"); // create 1000 threads ArrayList<MyThread7> threads3 = new ArrayList<MyThread7>(); for (int x = 0; x < 1000; x++) { threads3.add(new MyThread7(counter, cslm)); } long start3 = System.currentTimeMillis(); // start all of the threads Iterator i3 = threads3.iterator(); while (i3.hasNext()) { MyThread7 mt = (MyThread7) i3.next(); mt.start(); } // wait for all the threads to finish Iterator i33 = threads3.iterator(); while (i33.hasNext()) { MyThread7 mt = (MyThread7) i33.next(); mt.join(); } long end3 = System.currentTimeMillis(); System.out.println("1000个线程 每个线程插入10000共1000万条记录,耗时: " + (end3 - start3) + " 毫秒"); System.out.println("---------------------以上是测试读取---------------------------------------"); long start2 = System.currentTimeMillis(); ArrayList<MyThread6> threads2 = new ArrayList<MyThread6>(); for (int x = 0; x < 5; x++) { threads2.add(new MyThread6(counter, cslm)); } // start all of the threads Iterator i12 = threads2.iterator(); //并发修改 访问 ConcurrentSkipListMap while (i12.hasNext()) { MyThread6 mt = (MyThread6) i12.next(); mt.start(); } Thread t = new Thread() { public void run() { for (int x = 0; x < 100000; x++) { String s = cslm.get(x); if (x % 20 == 0) { // System.out.println("key ---" + x + " s--- " + s); } } } }; t.start(); // wait for all the threads to finish Iterator i22 = threads2.iterator(); while (i22.hasNext()) { MyThread6 mt = (MyThread6) i22.next(); mt.join(); } t.join(); long end2 = System.currentTimeMillis(); System.out.println(end2 - start2); System.out.println("over"); System.out.println("new size = " + cslm.size()); System.out.println("---------------------以上是测试并发的修改ConcurrentSkipListMap里的元素---------------------------------------"); } } // thread that increments the counter 100000 times. class MyThread5 extends Thread { Counter5 counter; ConcurrentNavigableMap<Integer, String> cslm; MyThread5(Counter5 counter, ConcurrentNavigableMap<Integer, String> cslm) { this.counter = counter; this.cslm = cslm; } public void run() { for (int x = 0; x < 100000; x++) { int i = counter.incrementCount(); cslm.put(i, ""); } } } // class that uses AtomicInteger for counter class Counter5 { private AtomicInteger count = new AtomicInteger(0); public int incrementCount() { return count.incrementAndGet(); } public int decrementAndGet() { return count.decrementAndGet(); } public int getCount() { return count.get(); } } //class that uses to delete part data of the cslm class MyThread6 extends Thread { Counter5 counter; ConcurrentNavigableMap<Integer, String> cslm; MyThread6(Counter5 counter, ConcurrentNavigableMap<Integer, String> cslm) { this.counter = counter; this.cslm = cslm; } public void run() { for (int x = 0; x < 100000; x++) { cslm.remove(x); try { if (x % 100 == 0) { this.sleep(1); } } catch (InterruptedException e) { e.printStackTrace(); } } } } //thread that decrements the counter 100000 times. class MyThread7 extends Thread { Counter5 counter; ConcurrentNavigableMap<Integer, String> cslm; MyThread7(Counter5 counter, ConcurrentNavigableMap<Integer, String> cslm) { this.counter = counter; this.cslm = cslm; } public void run() { for (int x = 0; x < 10000; x++) { int i = counter.decrementAndGet(); cslm.get(i); } } }
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CPU配置为4核?intel i3-2100 3.1GHz,内存分配1G,多次运行输出大致如下:
100个线程 每个线程插入100000共1000万条记录,耗时: 4719 毫秒
Count: 10000000
original size ?= 10000000
---------------------以上是测试插入---------------------------------------
1000个线程 每个线程插入10000共1000万条记录,耗时: 2684 毫秒
---------------------以上是测试读取---------------------------------------
1025
over
new size ?= 9900001
-------------------以上是测试并发的修改、访问ConcurrentSkipListMap里的元素------------------------------------
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可以看到put操作的并发能达到200万/秒以上,get操作的并发能达到350万/秒以上,
这里的性能损耗有一部分是在AtomicInteger原子的递增和递减上,如果在真实的业务场景中,可以知道key的操作不需要使用AtomicInteger或加锁方式来获取,性能会更高。
通过第三个输出可以知道对ConcurrentSkipListMap访问的同时进行删除不会出现ConcurrentModificationException异常。
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此例子可以把ConcurrentSkipListMap换成ConcurrentHashMap等其它集合进行测试。
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