我们有时候会遇到这种需求,那就是根据权重,按照比例去获取相应的信息,比如配置信息获取,负载均衡RS获取等。
在此就举一个例子,然后简单的实现。
需求:后端有三台机器,信息分别为,
S1<ip:"10.0.0.1",port:8081,weight:20>,
S2<ip:"10.0.0.2",port:8082,weight:40>,
S3<ip:"10.0.0.3",port:8083,weight:60>,
根据weight按照比例返回响应的机器信息。
算法思路:将三个权重映射到一个一维空间中,那么S1对应区间[0, 20), S2对应区间[20, 60), [60, 120],然后在[0,120]之间生成随机数,看此数落在哪个区间,那么就返回对应机器的信息。
talk is cheap, show me the code:
private int getServerByWeight(int[] weightArr) {
int[][] randArr = new int[weightArr.length][2];
int totalRank = 0;
int index = 0;
for(int i=0;i<weightArr.length;i++) {
if (weightArr[i] <= 0) {
continue;
}
totalRank += weightArr[i];
randArr[i][0] = i;
randArr[i][1] = totalRank;
}
int hitRank = new Random().nextInt(totalRank) + 1;//[1, totalRand]
for (int i = 0; i < randArr.length; i++) {
if (hitRank <= randArr[i][1]) {
return randArr[i][0];
}
}
return randArr[0][0];
}
public Server choose(List<Server> serverList) {
if (null == serverList) {
return null;
}
int[] weightArr = new int[serverList.size()];
for(int i = 0; i < serverList.size(); i++) {
if (serverList.get(i).getWeight() > 0) {
weightArr[i] = serverList.get(i).getWeight();
}
}
if (weightArr.length == 0) {
return null;
}
int chosenIndex = getServerByWeight(weightArr);
return serverList.get(chosenIndex);
}
主要有两个函数:choose和getServerByWeight。测试代码如下:
WeightAllocationAlg.java:
import java.util.Arrays;
import java.util.List;
import java.util.Random;
import java.util.concurrent.atomic.AtomicInteger;
/**
* Created by iqiyi on 2017/10/17.
*/
class Server {
private String ip;
private int port;
private int weight;
public Server(String ip, int port, int weight) {
this.ip = ip;
this.port = port;
this.weight = weight;
}
public String getIp() {
return ip;
}
public void setIp(String ip) {
this.ip = ip;
}
public int getPort() {
return port;
}
public void setPort(int port) {
this.port = port;
}
public int getWeight() {
return weight;
}
public void setWeight(int weight) {
this.weight = weight;
}
public String toString() {
return "ip : " + ip + ", port : " + port + ", weight : " + weight;
}
}
public class WeightAllocationAlg {
private AtomicInteger[] completedCount = new AtomicInteger[3];
public WeightAllocationAlg() {
completedCount[0] = new AtomicInteger(0);
completedCount[1] = new AtomicInteger(0);
completedCount[2] = new AtomicInteger(0);
}
private int getServerByWeight(int[] weightArr) {
int[][] randArr = new int[weightArr.length][2];
int totalRank = 0;
int index = 0;
for(int i=0;i<weightArr.length;i++) {
if (weightArr[i] <= 0) {
continue;
}
totalRank += weightArr[i];
randArr[i][0] = i;
randArr[i][1] = totalRank;
}
int hitRank = new Random().nextInt(totalRank) + 1;//[1, totalRand]
for (int i = 0; i < randArr.length; i++) {
if (hitRank <= randArr[i][1]) {
return randArr[i][0];
}
}
return randArr[0][0];
}
public Server choose(List<Server> serverList) {
if (null == serverList) {
return null;
}
int[] weightArr = new int[serverList.size()];
for(int i = 0; i < serverList.size(); i++) {
if (serverList.get(i).getWeight() > 0) {
weightArr[i] = serverList.get(i).getWeight();
}
}
if (weightArr.length == 0) {
return null;
}
int chosenIndex = getServerByWeight(weightArr);
return serverList.get(chosenIndex);
}
public void doTestConcurrently(final List<Server> servers, int threadCount) {
class MyRunnable implements Runnable {
public void run() {
Server svr = choose(servers);
if (svr.getIp().equals("10.0.0.1")) {
completedCount[0].incrementAndGet();
} else if (svr.getIp().equals("10.0.0.2")) {
completedCount[1].incrementAndGet();
} else if(svr.getIp().equals("10.0.0.3")) {
completedCount[2].incrementAndGet();
}
}
}
try {
Thread [] ts = new Thread[threadCount];
for (int i=0; i<threadCount; i++) {
ts[i] = new Thread(new MyRunnable());
}
for (int i = 0; i < threadCount; i++) {
ts[i].start();
}
for (int i = 0; i < threadCount; i++) {
ts[i].join();
}
} catch (Exception ex) {
ex.printStackTrace();
} finally {
int totalCompleted = completedCount[0].get() + completedCount[1].get() + completedCount[2].get();
if (totalCompleted == threadCount) {
System.out.println((double)completedCount[0].get()/totalCompleted);
System.out.println((double)completedCount[1].get()/totalCompleted);
System.out.println((double)completedCount[2].get()/totalCompleted);
}
}
}
public static void main(String[] args) {
Server[] servers = {
new Server("10.0.0.1", 8081, 20),
new Server("10.0.0.2", 8082, 40),
new Server("10.0.0.3", 8083, 60)
};
WeightAllocationAlg weightAllocationAlg = new WeightAllocationAlg();
Server server = weightAllocationAlg.choose(Arrays.asList(servers));
System.out.println(server.toString());
int threadCount = 100;
weightAllocationAlg.doTestConcurrently(Arrays.asList(servers), threadCount);
}
}
结果如下:
ip : 10.0.0.2, port : 8082, weight : 40
0.18
0.34
0.48
可以看到基本上是按照1:2:3的比例返回的,跟预期的一致,当然略有偏差,如果在数据量大且随机函数分布较均匀的情况下结果应该就是按照1:2:3来的。
Author:忆之独秀
Email:[email protected]
注明出处:http://blog.csdn.net/lavorange/article/details/78320349