/**
 * 普通轮询算法
 */public class RoundRobin {
    private static Integer index = 0;
    private static List<String> nodes = new ArrayList<>();
    // 准备模拟数据
    static {
        nodes.add("192.168.1.101");
        nodes.add("192.168.1.103");
        nodes.add("192.168.1.102");
        System.out.println("普通轮询算法的所有节点："+nodes);//打印所有节点
    }
    
    // 关键代码
    public String selectNode(){
        String ip = null;
        synchronized (index){
            // 下标复位
            if(index>=nodes.size()) index = 0;
            ip = nodes.get(index);
            index++;
        }
        return ip;
    }

    // 并发测试：两个线程循环获取节点
    public static void main(String[] args) {
        new Thread(() -> {
            RoundRobin roundRobin1 = new RoundRobin();
            for (int i=1;i<=5;i++){
                String serverIp = roundRobin1.selectNode();
                System.out.println(Thread.currentThread().getName()+"==第"+i+"次获取节点："+serverIp);
            }
        }).start();

        RoundRobin roundRobin2 = new RoundRobin();
        for (int i=1;i<=nodes.size();i++){
            String serverIp = roundRobin2.selectNode();
            System.out.println(Thread.currentThread().getName()+"==第"+i+"次获取节点："+serverIp);
        }
    }
}

/**
 *  简单版的加权轮询
 */public class WeightedRoundRobinSimple {
    private static Integer index = 0;
    private static Map<String,Integer> mapNodes = new HashMap<>();

    // 准备模拟数据
    static {
        mapNodes.put("192.168.1.101",1);
        mapNodes.put("192.168.1.102",3);
        mapNodes.put("192.168.1.103",2);
        /* -- 以下代码只为了方便查看所有节点，删除不影响 -- S */
        List<String> nodes = new ArrayList<>();
        Iterator<Map.Entry<String, Integer>> iterator = mapNodes.entrySet().iterator();
        while (iterator.hasNext()){
            Map.Entry<String, Integer> entry = iterator.next();
            String key = entry.getKey();
            for (int i=0;i<entry.getValue();i++){
                nodes.add(key);
            }
        }
        System.out.println("简单版的加权轮询："+nodes);//打印所有节点
        /* -- 以上代码只为了方便查看所有节点，删除不影响-- E */
    }

    // 关键代码：类似于二维数组 降维成 一维数组，然后使用普通轮询
    public String selectNode(){
        List<String> nodes = new ArrayList<>();
        Iterator<Map.Entry<String, Integer>> iterator = mapNodes.entrySet().iterator();
        while (iterator.hasNext()){
            Map.Entry<String, Integer> entry = iterator.next();
            String key = entry.getKey();
            for (int i=0;i<entry.getValue();i++){
                nodes.add(key);
            }
        }
        String ip = null;
        synchronized (index){
            // 下标复位
            if(index>=nodes.size()) index = 0;
            ip = nodes.get(index);
            index++;
        }
        return ip;
    }

    // 并发测试：两个线程循环获取节点
    public static void main(String[] args) {
        new Thread(() -> {
            WeightedRoundRobinSimple roundRobin1 = new WeightedRoundRobinSimple();
            for (int i=1;i<=6;i++){
                String serverIp = roundRobin1.selectNode();
                System.out.println(Thread.currentThread().getName()+"==第"+i+"次获取节点："+serverIp);
            }
        }).start();

        WeightedRoundRobinSimple roundRobin2 = new WeightedRoundRobinSimple();
        for (int i=1;i<=6;i++){
            String serverIp = roundRobin2.selectNode();
            System.out.println(Thread.currentThread().getName()+"==第"+i+"次获取节点："+serverIp);
        }
    }
}

/**
 * String ip：负载IP
 * final Integer weight：权重，保存配置的权重
 * Integer effectiveWeight：有效权重，轮询的过程权重可能变化
 * Integer currentWeight：当前权重，比对该值大小获取节点
 *   第一次加权轮询时：currentWeight = weight = effectiveWeight
 *   后面每次加权轮询时：currentWeight 的值都会不断变化，其他权重不变
 */public class Node implements Comparable<Node>{
    private String ip;
    private final Integer weight;
    private Integer effectiveWeight;
    private Integer currentWeight;

    public Node(String ip,Integer weight){
        this.ip = ip;
        this.weight = weight;
        this.effectiveWeight = weight;
        this.currentWeight = weight;
    }

    public Node(String ip, Integer weight, Integer effectiveWeight, Integer currentWeight) {
        this.ip = ip;
        this.weight = weight;
        this.effectiveWeight = effectiveWeight;
        this.currentWeight = currentWeight;
    }

    public String getIp() {
        return ip;
    }

    public void setIp(String ip) {
        this.ip = ip;
    }

    public Integer getWeight() {
        return weight;
    }

    public Integer getEffectiveWeight() {
        return effectiveWeight;
    }

    public void setEffectiveWeight(Integer effectiveWeight) {
        this.effectiveWeight = effectiveWeight;
    }

    public Integer getCurrentWeight() {
        return currentWeight;
    }

    public void setCurrentWeight(Integer currentWeight) {
        this.currentWeight = currentWeight;
    }

    @Override
    public int compareTo(Node node) {
        return currentWeight > node.currentWeight ? 1 : (currentWeight.equals(node.currentWeight) ? 0 : -1);
    }

    @Override
    public String toString() {
        return "{ip='" + ip + "', weight=" + weight + ", effectiveWeight=" + effectiveWeight + ", currentWeight=" + currentWeight + "}";
    }
}

/**
 * 加权轮询算法
 */public class WeightedRoundRobin {

    private static List<Node> nodes = new ArrayList<>();
    // 权重之和
    private static Integer totalWeight = 0;
    // 准备模拟数据
    static {
        nodes.add(new Node("192.168.1.101",1));
        nodes.add(new Node("192.168.1.102",3));
        nodes.add(new Node("192.168.1.103",2));
        nodes.forEach(node -> totalWeight += node.getEffectiveWeight());
    }

    /**
     * 按照当前权重（currentWeight）最大值获取IP
     * @return Node
     */
    public Node selectNode(){
        if (nodes ==null || nodes.size()<=0) return null;
        if (nodes.size() == 1)  return nodes.get(0);

        Node nodeOfMaxWeight = null; // 保存轮询选中的节点信息
        synchronized (nodes){
            // 打印信息对象：避免并发时打印出来的信息太乱，不利于观看结果
            StringBuffer sb = new StringBuffer();
            sb.append(Thread.currentThread().getName()+"==加权轮询--[当前权重]值的变化："+printCurrentWeight(nodes));

            // 选出当前权重最大的节点
            Node tempNodeOfMaxWeight = null;
            for (Node node : nodes) {
                if (tempNodeOfMaxWeight == null)
                    tempNodeOfMaxWeight = node;
                else
                    tempNodeOfMaxWeight = tempNodeOfMaxWeight.compareTo(node) > 0 ? tempNodeOfMaxWeight : node;
            }
            // 必须new个新的节点实例来保存信息，否则引用指向同一个堆实例，后面的set操作将会修改节点信息
            nodeOfMaxWeight = new Node(tempNodeOfMaxWeight.getIp(),tempNodeOfMaxWeight.getWeight(),tempNodeOfMaxWeight.getEffectiveWeight(),tempNodeOfMaxWeight.getCurrentWeight());

            // 调整当前权重比：按权重（effectiveWeight）的比例进行调整，确保请求分发合理。
            tempNodeOfMaxWeight.setCurrentWeight(tempNodeOfMaxWeight.getCurrentWeight() - totalWeight);
            sb.append(" -> "+printCurrentWeight(nodes));

            nodes.forEach(node -> node.setCurrentWeight(node.getCurrentWeight()+node.getEffectiveWeight()));

            sb.append(" -> "+printCurrentWeight(nodes));
            System.out.println(sb); //打印权重变化过程
        }
        return nodeOfMaxWeight;
    }

    // 格式化打印信息
    private String printCurrentWeight(List<Node> nodes){
        StringBuffer stringBuffer = new StringBuffer("[");
        nodes.forEach(node -> stringBuffer.append(node.getCurrentWeight()+",") );
        return stringBuffer.substring(0, stringBuffer.length() - 1) + "]";
    }

    // 并发测试：两个线程循环获取节点
    public static void main(String[] args){
        Thread thread = new Thread(() -> {
            WeightedRoundRobin weightedRoundRobin1 = new WeightedRoundRobin();
            for(int i=1;i<=totalWeight;i++){
                Node node = weightedRoundRobin1.selectNode();
                System.out.println(Thread.currentThread().getName()+"==第"+i+"次轮询选中[当前权重最大]的节点：" + node + "\n");
            }
        });
        thread.start();
        //
        WeightedRoundRobin weightedRoundRobin2 = new WeightedRoundRobin();
        for(int i=1;i<=totalWeight;i++){
            Node node = weightedRoundRobin2.selectNode();
            System.out.println(Thread.currentThread().getName()+"==第"+i+"次轮询选中[当前权重最大]的节点：" + node + "\n");
        }

    }
}