数据结构和算法学习五 之 提取出某日访问网站次数最多的那K个IP之并发版_JAVA_编程开发_程序员俱乐部

前边提到了单线程的实现，这里贴出多线程版，此处主要用多线程去处理hash后的小文件：

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package com.kingdee.gmis.mass.data.ips;

import static com.kingdee.gmis.mass.data.ips.MassIP.K10;
import static com.kingdee.gmis.mass.data.ips.MassIP.getPartitionFile;
import static com.kingdee.gmis.mass.data.ips.MassIP.partationCount;
import static com.kingdee.gmis.mass.data.ips.MassIP.printResult;
import static com.kingdee.gmis.mass.data.ips.MassIP.*;

import java.io.BufferedInputStream;
import java.io.DataInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.atomic.AtomicInteger;

import com.kingdee.gmis.common.TopNHeap;
import com.kingdee.gmis.mass.data.ips.MassIP.IPInfo;

public class ConcurrentMassIP {

	private static int workerCnt = 3;

	/**
	 * @param args
	 * @throws Exception
	 */
	public static void main(String[] args) throws Exception {
//		 generateMassIp("ip", "ips.txt", 100000000);
//		 generatePartitionFile("ip", "ips.txt", 100);
		searchTopN(20);
	}

	private static AtomicInteger curIdx = new AtomicInteger(-1);
	static File[] smallFiles;
	static TopNHeap<IPInfo> destHeap;

	/**
	 * 查找出现次数最多的K个ip地址
	 * 
	 * @param count
	 * @throws Exception
	 */
	public static void searchTopN(int count) throws Exception {
		Thread.sleep(10000);
		long start = System.currentTimeMillis();
		smallFiles = getPartitionFile("ip", partationCount);
		destHeap = new TopNHeap<MassIP.IPInfo>(count);
		int cnt = workerCnt;
		synchronized (lock) {
			for (int i = 0; i < cnt; i++) {
				new Thread(new Worker(i, count)).start();
			}
			lock.wait();
			printResult(destHeap);
		}
		System.out.println("Total spend " + (System.currentTimeMillis() - start) + " ms");
	}

	static Object lock = new Object();

	public static synchronized void mergeToResult(TopNHeap<IPInfo> srcHeap) {
		try {
			destHeap.mergeHeap(srcHeap);
		} finally {
			if (--workerCnt == 0) {
				synchronized (lock) {
					lock.notify();
				}
			}
		}
	}

	static class Worker implements Runnable {
		private int topCount;
		private int id;

		public Worker(int id, int count) {
			this.id = id;
			this.topCount = count;
		}

		@Override
		public void run() {
			int curFileIdx;
			DataInputStream dis = null;
			Map<Integer, Integer> ipCountMap = new HashMap<Integer, Integer>();
			TopNHeap<IPInfo> heap = new TopNHeap<IPInfo>(topCount);
			int processCnt = 0;
			while ((curFileIdx = curIdx.addAndGet(1)) < partationCount) {
				processCnt++;
				ipCountMap.clear();
				try {
					dis = new DataInputStream(new BufferedInputStream(
							new FileInputStream(smallFiles[curFileIdx]), K10));
					while (dis.available() > 0) {
						int ip = dis.readInt();
						Integer cnt = ipCountMap.get(ip);
						ipCountMap.put(ip, cnt == null ? 1 : cnt + 1);
					}
					searchMostCountIps(ipCountMap, heap);
				} catch (Exception e) {
					throw new RuntimeException(e);
				} finally {
					if (dis != null) {
						try {
							dis.close();
						} catch (IOException e) {
							e.printStackTrace();
						}
					}
				}
			}
			System.out.println("Thread " + this.id + " process " + processCnt
					+ " files.");
			mergeToResult(heap);
		}
	}
}

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? ? ? 测试了下，3线程是120s左右，串行是320s左右，的确提高了很多。测试机子是4核cpu，如果启4个线程，机器都变得很卡，cpu居高不下。另jvm启动参数为：

-server
-Xmx1024m
-Xms1024m
-Xmn600m
-XX:+UseConcMarkSweepGC
-XX:ParallelGCThreads=4

? ? ? 因为此处理过程中，大多数对象存活周期并不长，所以可以把新生代设置大一些。堆初始化设置大些，避免minor gc的时候才去扩展内存大小，因为可以预料到程序一旦启动，加载的内存的东西就会很多。

另外，此处垃圾收集器是用了cms，老生代内存回收就用了cms，新生代用了PurNew，并行收集的，设置gc线程数等于cpu内核数。当然这里也可以设置成-XX:+UseParallelGC、-XX:+UseParallelOldGC都可以，此处主要是新生代内存回收频繁，所以一定要把新生代设置成并发或并行版本的。

通过-server把虚拟机启动为server模式，这样运行时候会启用c2级别的JIT优化，能获得更高质量的编译代码。当然server模式下启动的jvm，默认使用的gc收集器跟-XX:UseParallelGC使用的一样

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