比较SynchronizedMap、Hashtable和ConcurrentHashMap的效率
2019独角兽企业重金招聘Python工程师标准>>>
分别通过三种方式创建Map对象,使用ExecutorService
来并发运行5个线程,每个线程添加/获取500K个元素。
package cglib;
import java.util.Collections;
import java.util.HashMap;
import java.util.Hashtable;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;
public class StringNumber {
public final static int THREAD_POOL_SIZE = 5;
public static Map<String, Integer> crunchifyHashTableObject = null;
public static Map<String, Integer> crunchifySynchronizedMapObject = null;
public static Map<String, Integer> crunchifyConcurrentHashMapObject = null;
public static void main(String[] args) throws InterruptedException {
// Test with Hashtable Object
crunchifyHashTableObject = new Hashtable<>();
crunchifyPerformTest(crunchifyHashTableObject);
// Test with synchronizedMap Object
crunchifySynchronizedMapObject = Collections.synchronizedMap(new HashMap<String, Integer>());
crunchifyPerformTest(crunchifySynchronizedMapObject);
// Test with ConcurrentHashMap Object
crunchifyConcurrentHashMapObject = new ConcurrentHashMap<>();
crunchifyPerformTest(crunchifyConcurrentHashMapObject);
}
public static void crunchifyPerformTest(final Map<String, Integer> crunchifyThreads) throws InterruptedException {
System.out.println("Test started for: " + crunchifyThreads.getClass());
long averageTime = 0;
for (int i = 0; i < 5; i++) {
long startTime = System.nanoTime();
ExecutorService crunchifyExServer = Executors.newFixedThreadPool(THREAD_POOL_SIZE);
for (int j = 0; j < THREAD_POOL_SIZE; j++) {
crunchifyExServer.execute(new Runnable() {
@SuppressWarnings("unused")
@Override
public void run() {
for (int i = 0; i < 500000; i++) {
Integer crunchifyRandomNumber = (int) Math.ceil(Math.random() * 550000);
// Retrieve value. We are not using it anywhere
Integer crunchifyValue = crunchifyThreads.get(String.valueOf(crunchifyRandomNumber));
// Put value
crunchifyThreads.put(String.valueOf(crunchifyRandomNumber), crunchifyRandomNumber);
}
}
});
}
// Make sure executor stops
crunchifyExServer.shutdown();
// Blocks until all tasks have completed execution after a shutdown request
crunchifyExServer.awaitTermination(Long.MAX_VALUE, TimeUnit.DAYS);
long entTime = System.nanoTime();
long totalTime = (entTime - startTime) / 1000000L;
averageTime += totalTime;
System.out.println("2500K entried added/retrieved in " + totalTime + " ms");
}
System.out.println("For " + crunchifyThreads.getClass() + " the average time is " + averageTime / 5 + " ms\n");
}
}
输出:
Test started for: class java.util.Hashtable
2500K entried added/retrieved in 3816 ms
2500K entried added/retrieved in 2916 ms
2500K entried added/retrieved in 3040 ms
2500K entried added/retrieved in 2819 ms
2500K entried added/retrieved in 2855 ms
For class java.util.Hashtable the average time is 3089 ms
Test started for: class java.util.Collections$SynchronizedMap
2500K entried added/retrieved in 3430 ms
2500K entried added/retrieved in 2775 ms
2500K entried added/retrieved in 2891 ms
2500K entried added/retrieved in 2710 ms
2500K entried added/retrieved in 2811 ms
For class java.util.Collections$SynchronizedMap the average time is 2923 ms
Test started for: class java.util.concurrent.ConcurrentHashMap
2500K entried added/retrieved in 1814 ms
2500K entried added/retrieved in 1135 ms
2500K entried added/retrieved in 1051 ms
2500K entried added/retrieved in 1008 ms
2500K entried added/retrieved in 1560 ms
For class java.util.concurrent.ConcurrentHashMap the average time is 1313 ms
转载于:https://my.oschina.net/u/2822116/blog/785674
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