代码详解七种负载均衡策略（长文多图）

╰+哭是因爲堅強的太久メ 2023-09-24 22:25 45阅读 0赞

# 1 三组概念 #

负载均衡、集群容错、服务降级这三个概念在DUBBO中非常重要，同理其它分布式框架也都有相同或者相近之概念。

从调用顺序角度分析，调用顺序依次是负载均衡、集群容错、服务降级。从解决问题角度分析，负载均衡解决了「选哪一个」问题，集群容错解决了「换哪一个」问题，服务降级解决了「全错怎么办」问题。

假设有1个服务消费者面对10个提供者，这时面临第一个问题就是「选哪一个」进行调用，所以负载均衡最先调用，假设选定了5号服务提供者进行服务调用。

假设消费者调用5号提供者发生了超时异常，这时面临第二个问题就是「换哪一个」进行调用：5号超时要不要换1号试一试，或者直接返回不进行重试，所以集群容错第二个调用。

假设已经重试了1号、3号、6号提供者全部超时，这时面临「全错怎么办」这第三个问题，这时可以直接返回一个固定值或者提示文案，所以服务降级第三个调用。

负载均衡作为整个调用链路第一个节点非常重要，本文结合DUBBO源码分析以下七种负载均衡策略：

*  简单随机
 *  加权随机
 *  简单轮询
 *  简单加权轮询
 *  平滑加权轮询
 *  一致性哈希
 *  最少活跃数

# 2 简单随机 #

简单随机含义是服务消费者每次会任意访问一个服务提供者，并且从概率角度看每个提供者被访问概率一致，可以通过指定范围随机数实现。第一步编写服务器代码

public class MyServer {
    
        private String ip;
    
        public MyServer(String ip) {
            this.ip = ip;
        }
    
        public String getIp() {
            return ip;
        }
    
        public void setIp(String ip) {
            this.ip = ip;
        }
    }
    复制代码

第二步编写基础负载均衡策略，其它策略可以复用

public abstract class AbstractLoadBalance {
    
        public MyServer select(List<MyServer> serverList) {
            return doSelect(serverList);
        }
    
        public abstract MyServer doSelect(List<MyServer> serverList);
    }
    复制代码

第三步编写简单随机策略

public class RandomBalance extends AbstractLoadBalance {
    
        @Override
        public MyServer doSelect(List<MyServer> serverList) {
            // 随机数范围[0,serverListSize)
            int index = ThreadLocalRandom.current().nextInt(serverList.size());
            return serverList.get(index);
        }
    }
    复制代码

第四步编写测试代码

public class LoadBalanceTest {
    
        public static void main(String[] args) {
            List<MyServer> serverList = buildData();
            testRandomBalance(serverList);
        }
    
        public static void testRandomBalance(List<MyServer> serverList) {
            AbstractLoadBalance randomBalance = new RandomBalance();
            for (int i = 0; i < 10; i++) {
                MyServer server = randomBalance.select(serverList);
                System.out.println("RandomBalance route server=" + server);
            }
        }
    
        public static List<MyServer> buildData() {
            List<MyServer> serverList = new ArrayList<MyServer>();
            MyServer server1 = new MyServer("192.1.1.1");
            MyServer server2 = new MyServer("192.1.1.2");
            MyServer server3 = new MyServer("192.1.1.3");
            serverList.add(server1);
            serverList.add(server2);
            serverList.add(server3);
            return serverList;
        }
    }
    复制代码

第五步输出结果，循环次数越多结果越准确

RandomBalance route server=MyServer(ip=192.1.1.2)
    RandomBalance route server=MyServer(ip=192.1.1.1)
    RandomBalance route server=MyServer(ip=192.1.1.3)
    RandomBalance route server=MyServer(ip=192.1.1.2)
    RandomBalance route server=MyServer(ip=192.1.1.1)
    RandomBalance route server=MyServer(ip=192.1.1.1)
    RandomBalance route server=MyServer(ip=192.1.1.2)
    RandomBalance route server=MyServer(ip=192.1.1.2)
    RandomBalance route server=MyServer(ip=192.1.1.3)
    RandomBalance route server=MyServer(ip=192.1.1.3)
    复制代码

# 3 加权随机 #

## 3.1 设计思路 ##

加权随机新增了权重这个概念，假设服务器A权重等于1，服务器B权重等于5，从概率角度看B服务器被访问概率5倍于A服务器。实现按照权重访问有很多种方式，我们选择使用概率区间这个思路。

假设现在有三台服务器，服务器权重分别是3、5、2，那么三者构成如下图概率区间：

![format_png][]

概率区间计算步骤如下图：

![format_png 1][]

## 3.2 代码实例 ##

第一步编写服务器代码

public class MyServer {
    
        private String ip;
    
        private int weight;
    
        public MyServer(String ip) {
            this.ip = ip;
        }
    
        public String getIp() {
            return ip;
        }
    
        public void setIp(String ip) {
            this.ip = ip;
        }
    
        public int getWeight() {
            return weight;
        }
    
        public void setWeight(int weight) {
            this.weight = weight;
        }
    }
    复制代码

第二步编写加权随机策略

public class RandomWeightBalance extends AbstractLoadBalance {
    
        @Override
        public MyServer doSelect(List<MyServer> serverList) {
            // 所有服务器总权重
            int totalWeight = 0;
            // 第一个服务器权重
            int firstWeight = serverList.get(0).getWeight();
            // 所有服务器权重相等
            boolean sameWeight = true;
            // 遍历所有服务器
            for (MyServer server : serverList) {
                // 计算总权重
                totalWeight += server.getWeight();
                // 任意一个invoker权重不等于第一个权重则设置sameWeight=false
                if (sameWeight && server.getWeight() != firstWeight) {
                    sameWeight = false;
                }
            }
            // 权重不相等则根据权重选择
            if (!sameWeight) {
                // 在总区间范围[0,totalWeight)生成随机数A
                Integer offset = ThreadLocalRandom.current().nextInt(totalWeight);
                // 遍历所有服务器区间
                for (MyServer server : serverList) {
                    // 如果A在server区间直接返回
                    if (offset < server.getWeight()) {
                        return server;
                    }
                    // 如果A不在server区间则减去此区间范围并继续匹配其它区间
                    offset -= server.getWeight();
                }
            }
            // 所有服务器权重相等则随机选择
            return serverList.get(ThreadLocalRandom.current().nextInt(serverList.size()));
        }
    }
    复制代码

第三步编写测试代码

public class LoadBalanceTest {
    
        public static void main(String[] args) {
            List<MyServer> serverList = buildData();
            testRandomWeightBalance(serverList);
        }
    
        public static void testRandomWeightBalance(List<MyServer> serverList) {
            AbstractLoadBalance randomBalance = new RandomWeightBalance();
            for (int i = 0; i < 10; i++) {
                MyServer server = randomBalance.select(serverList);
                System.out.println("RandomWeightBalance route server=" + server);
            }
        }
    
        public static List<MyServer> buildData() {
            List<MyServer> serverList = new ArrayList<MyServer>();
            MyServer server1 = new MyServer("192.1.1.1", 3);
            MyServer server2 = new MyServer("192.1.1.2", 5);
            MyServer server3 = new MyServer("192.1.1.3", 2);
            serverList.add(server1);
            serverList.add(server2);
            serverList.add(server3);
            return serverList;
        }
    }
    复制代码

第四步输出结果，循环次数越多结果越准确

RandomWeightBalance route server=MyServer(ip=192.1.1.2, weight=2)
    RandomWeightBalance route server=MyServer(ip=192.1.1.2, weight=3)
    RandomWeightBalance route server=MyServer(ip=192.1.1.1, weight=3)
    RandomWeightBalance route server=MyServer(ip=192.1.1.1, weight=3)
    RandomWeightBalance route server=MyServer(ip=192.1.1.3, weight=2)
    RandomWeightBalance route server=MyServer(ip=192.1.1.3, weight=2)
    RandomWeightBalance route server=MyServer(ip=192.1.1.2, weight=5)
    RandomWeightBalance route server=MyServer(ip=192.1.1.1, weight=3)
    RandomWeightBalance route server=MyServer(ip=192.1.1.2, weight=5)
    RandomWeightBalance route server=MyServer(ip=192.1.1.2, weight=5)
    复制代码

## 3.3 DUBBO源码 ##

public class RandomLoadBalance extends AbstractLoadBalance {
        public static final String NAME = "random";
    
        @Override
        protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
    
            // invoker数量
            int length = invokers.size();
    
            // 所有权重是否相等
            boolean sameWeight = true;
    
            // 权重数组
            int[] weights = new int[length];
    
            // 第一个权重
            int firstWeight = getWeight(invokers.get(0), invocation);
            weights[0] = firstWeight;
    
            // 权重之和
            int totalWeight = firstWeight;
    
            // 遍历所有invoker
            for (int i = 1; i < length; i++) {
    
                // 获取权重
                int weight = getWeight(invokers.get(i), invocation);
                weights[i] = weight;
    
                // 计算总权重
                totalWeight += weight;
    
                // 任意一个invoker权重不等于第一个权重则设置sameWeight=false
                if (sameWeight && weight != firstWeight) {
                    sameWeight = false;
                }
            }
            // 权重不相等则根据权重选择
            if (totalWeight > 0 && !sameWeight) {
                int offset = ThreadLocalRandom.current().nextInt(totalWeight);
                for (int i = 0; i < length; i++) {
                    offset -= weights[i];
                    if (offset < 0) {
                        return invokers.get(i);
                    }
                }
            }
            // 所有服务权重相等则随机选择
            return invokers.get(ThreadLocalRandom.current().nextInt(length));
        }
    }
    复制代码

# 4 简单轮询 #

简单轮询含义是服务消费者每次会依次访问一个服务提供者，并且从概率角度看每个提供者被访问概率一致，可以通过原子变量累加实现。第一步编写简单轮询策略

public class RoundRobinBalance extends AbstractLoadBalance {
        private AtomicInteger atomicIndex = new AtomicInteger(0);
    
        @Override
        public MyServer doSelect(List<MyServer> serverList) {
            // atomicIndex自增大于服务器数量时取余
            int index = atomicIndex.getAndIncrement() % serverList.size();
            return serverList.get(index);
        }
    }
    复制代码

第二步编写测试代码

public class LoadBalanceTest {
    
        public static void main(String[] args) {
            List<MyServer> serverList = buildData();
            testRoundRobinBalance(serverList);
        }
    
        public static void testRoundRobinBalance(List<MyServer> serverList) {
            AbstractLoadBalance roundRobinBalance = new RoundRobinBalance();
            for (int i = 0; i < 10; i++) {
                MyServer server = roundRobinBalance.select(serverList);
                System.out.println("RoundRobinBalance route server=" + server);
            }
        }
    
        public static List<MyServer> buildData() {
            List<MyServer> serverList = new ArrayList<MyServer>();
            MyServer server1 = new MyServer("192.1.1.1");
            MyServer server2 = new MyServer("192.1.1.2");
            MyServer server3 = new MyServer("192.1.1.3");
            serverList.add(server1);
            serverList.add(server2);
            serverList.add(server3);
            return serverList;
        }
    }
    复制代码

第三步输出结果

RoundRobinBalance route server=MyServer(ip=192.1.1.1)
    RoundRobinBalance route server=MyServer(ip=192.1.1.2)
    RoundRobinBalance route server=MyServer(ip=192.1.1.3)
    RoundRobinBalance route server=MyServer(ip=192.1.1.1)
    RoundRobinBalance route server=MyServer(ip=192.1.1.2)
    RoundRobinBalance route server=MyServer(ip=192.1.1.3)
    RoundRobinBalance route server=MyServer(ip=192.1.1.1)
    RoundRobinBalance route server=MyServer(ip=192.1.1.2)
    RoundRobinBalance route server=MyServer(ip=192.1.1.3)
    RoundRobinBalance route server=MyServer(ip=192.1.1.1)
    复制代码

# 5 简单加权轮询 #

简单加权轮询新增了权重这个概念，假设服务器A权重等于1，服务器B权重等于5，从概率角度看B服务器被访问概率5倍于A服务器，我们还是使用概率区间这个思路，相较于加权随机会有一些变化。

![format_png 2][]

第一步编写简单加权轮询策略

public class RoundRobinWeightBalance1 extends AbstractLoadBalance {
        private AtomicInteger atomicIndex = new AtomicInteger(0);
    
        @Override
        public MyServer doSelect(List<MyServer> serverList) {
            int totalWeight = 0;
            int firstWeight = serverList.get(0).getWeight();
            boolean sameWeight = true;
            for (MyServer server : serverList) {
                totalWeight += server.getWeight();
                if (sameWeight && server.getWeight() != firstWeight) {
                    sameWeight = false;
                }
            }
            if (!sameWeight) {
                // 自增方式计算offset
                int offset = atomicIndex.getAndIncrement() % totalWeight;
                for (MyServer server : serverList) {
                    if (offset < server.getWeight()) {
                        return server;
                    }
                    offset -= server.getWeight();
                }
            }
            int index = atomicIndex.getAndIncrement() % serverList.size();
            return serverList.get(index);
        }
    }
    复制代码

第二步编写测试代码

public class LoadBalanceTest {
    
        public static void main(String[] args) {
            List<MyServer> serverList = buildData();
            testRoundRobinWeightBalance1(serverList);
        }
    
        public static void testRoundRobinWeightBalance1(List<MyServer> serverList) {
            AbstractLoadBalance roundRobinBalance = new RoundRobinWeightBalance1();
            for (int i = 0; i < 10; i++) {
                MyServer server = roundRobinBalance.select(serverList);
                System.out.println("RoundRobinWeightBalance1 route server=" + server);
            }
        }
    
        public static List<MyServer> buildData() {
            List<MyServer> serverList = new ArrayList<MyServer>();
            MyServer server1 = new MyServer("192.1.1.1", 3);
            MyServer server2 = new MyServer("192.1.1.2", 5);
            MyServer server3 = new MyServer("192.1.1.3", 2);
            serverList.add(server1);
            serverList.add(server2);
            serverList.add(server3);
            return serverList;
        }
    }
    复制代码

第三步输出结果

RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.1, weight=3)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.1, weight=3)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.1, weight=3)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.2, weight=5)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.2, weight=5)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.2, weight=5)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.2, weight=5)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.2, weight=5)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.3, weight=2)
    RoundRobinWeightBalance1 route server=MyServer(ip=192.1.1.3, weight=2)
    复制代码

# 6 平滑加权轮询 #

## 6.1 设计思路 ##

简单加权轮询有什么问题？我们分析其输出结果发现，连续3次访问服务器1，连续5次访问服务器2，连续2次访问服务器3，所以简单加权轮询策略会导致请求集中问题。

所以需要使用平滑加权轮询策略，将请求比较均匀地分散至各个服务器，计算步骤如下：

![format_png 3][]

## 6.2 代码实例 ##

第一步编写服务器代码

public class MyServer {
    
        private String ip;
    
        private int weight;
    
        private int currentWeight = 0;
    
        public MyServer(String ip) {
            this.ip = ip;
        }
    
        public MyServer(String ip, int weight) {
            this.ip = ip;
            this.weight = weight;
        }
    
        public int getWeight() {
            return weight;
        }
    
        public void setWeight(int weight) {
            this.weight = weight;
        }
    
        public int getCurrentWeight() {
            return currentWeight;
        }
    
        public void setCurrentWeight(int currentWeight) {
            this.currentWeight = currentWeight;
        }
    
        public String getIp() {
            return ip;
        }
    
        public void setIp(String ip) {
            this.ip = ip;
        }
    }
    复制代码

第二步编写平滑加权轮询策略

public class RoundRobinWeightBalance2 extends AbstractLoadBalance {
        private AtomicInteger atomicIndex = new AtomicInteger(0);
    
        @Override
        public MyServer doSelect(List<MyServer> serverList) {
            int totalWeight = 0;
            int firstWeight = serverList.get(0).getWeight();
            boolean sameWeight = true;
            for (MyServer server : serverList) {
                totalWeight += server.getWeight();
                if (sameWeight && server.getWeight() != firstWeight) {
                    sameWeight = false;
                }
                // 设置动态权重 -> currentWeight += weight
                server.setCurrentWeight(server.getCurrentWeight() + server.getWeight());
            }
            if (!sameWeight) {
                // 最大动态权重服务器 -> max(currentWeight)
                MyServer maxCurrentWeightServer = serverList.stream().max((s1, s2) -> s1.getCurrentWeight() - s2.getCurrentWeight()).get();
                // 设置最大动态权重 -> max(currentWeight) - totalWeight
                maxCurrentWeightServer.setCurrentWeight(maxCurrentWeightServer.getCurrentWeight() - totalWeight);
                // 返回最大动态权重服务器
                return maxCurrentWeightServer;
            }
            // 权重相同依次轮询
            int index = atomicIndex.getAndIncrement() % serverList.size();
            return serverList.get(index);
        }
    }
    复制代码

第三步编写测试代码

public class LoadBalanceTest {
    
        public static void main(String[] args) {
            List<MyServer> serverList = buildData();
            testRoundRobinWeightBalance2(serverList);
        }
    
        public static void testRoundRobinWeightBalance2(List<MyServer> serverList) {
            AbstractLoadBalance roundRobinBalance = new RoundRobinWeightBalance2();
            for (int i = 0; i < 10; i++) {
                MyServer server = roundRobinBalance.select(serverList);
                System.out.println("RoundRobinWeightBalance2 route server=" + server);
            }
        }
    
        public static List<MyServer> buildData() {
            List<MyServer> serverList = new ArrayList<MyServer>();
            MyServer server1 = new MyServer("192.1.1.1", 3);
            MyServer server2 = new MyServer("192.1.1.2", 5);
            MyServer server3 = new MyServer("192.1.1.3", 2);
            serverList.add(server1);
            serverList.add(server2);
            serverList.add(server3);
            return serverList;
        }
    }
    复制代码

第四步输出结果

RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.2, weight=5, currentWeight=-5)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.1, weight=3, currentWeight=-4)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.3, weight=2, currentWeight=-4)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.2, weight=5, currentWeight=0)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.1, weight=3, currentWeight=-5)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.2, weight=5, currentWeight=0)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.2, weight=5, currentWeight=-5)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.3, weight=2, currentWeight=-4)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.1, weight=3, currentWeight=-3)
    RoundRobinWeightBalance2 route server=MyServer(ip=192.1.1.2, weight=5, currentWeight=0)
    复制代码

## 6.3 DUBBO源码 ##

public class RoundRobinLoadBalance extends AbstractLoadBalance {
        public static final String NAME = "roundrobin";
    
        private static int RECYCLE_PERIOD = 60000;
    
        protected static class WeightedRoundRobin {
    
            // 权重
            private int weight;
    
            // 动态权重
            private AtomicLong current = new AtomicLong(0);
    
            // 更新时间
            private long lastUpdate;
    
            public int getWeight() {
                return weight;
            }
    
            public void setWeight(int weight) {
                this.weight = weight;
                current.set(0);
            }
    
            public long increaseCurrent() {
                return current.addAndGet(weight);
            }
    
            public void sel(int total) {
                current.addAndGet(-1 * total);
            }
    
            public long getLastUpdate() {
                return lastUpdate;
            }
    
            public void setLastUpdate(long lastUpdate) {
                this.lastUpdate = lastUpdate;
            }
        }
    
        private ConcurrentMap<String, ConcurrentMap<String, WeightedRoundRobin>> methodWeightMap = new ConcurrentHashMap<String, ConcurrentMap<String, WeightedRoundRobin>>();
        private AtomicBoolean updateLock = new AtomicBoolean();
    
        protected <T> Collection<String> getInvokerAddrList(List<Invoker<T>> invokers, Invocation invocation) {
            String key = invokers.get(0).getUrl().getServiceKey() + "." + invocation.getMethodName();
            Map<String, WeightedRoundRobin> map = methodWeightMap.get(key);
            if (map != null) {
                return map.keySet();
            }
            return null;
        }
    
        @Override
        protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
            String key = invokers.get(0).getUrl().getServiceKey() + "." + invocation.getMethodName();
            ConcurrentMap<String, WeightedRoundRobin> map = methodWeightMap.get(key);
            if (map == null) {
                methodWeightMap.putIfAbsent(key, new ConcurrentHashMap<String, WeightedRoundRobin>());
                map = methodWeightMap.get(key);
            }
            // 总权重
            int totalWeight = 0;
    
            // 最大当前权重
            long maxCurrent = Long.MIN_VALUE;
    
            // 当前时间
            long now = System.currentTimeMillis();
    
            // 选中提供者
            Invoker<T> selectedInvoker = null;
    
            // 选中提供者权重对象
            WeightedRoundRobin selectedWRR = null;
    
            // 遍历所有提供者
            for (Invoker<T> invoker : invokers) {
                String identifyString = invoker.getUrl().toIdentityString();
                WeightedRoundRobin weightedRoundRobin = map.get(identifyString);
    
                // 获取权重
                int weight = getWeight(invoker, invocation);
                if (weightedRoundRobin == null) {
                    weightedRoundRobin = new WeightedRoundRobin();
                    weightedRoundRobin.setWeight(weight);
                    map.putIfAbsent(identifyString, weightedRoundRobin);
                }
                if (weight != weightedRoundRobin.getWeight()) {
                    weightedRoundRobin.setWeight(weight);
                }
                // 选择动态权重最大提供者
                long cur = weightedRoundRobin.increaseCurrent();
                weightedRoundRobin.setLastUpdate(now);
                if (cur > maxCurrent) {
                    maxCurrent = cur;
                    selectedInvoker = invoker;
                    selectedWRR = weightedRoundRobin;
                }
                // 计算总权重
                totalWeight += weight;
            }
            // 更新负载均衡容器
            if (!updateLock.get() && invokers.size() != map.size()) {
                if (updateLock.compareAndSet(false, true)) {
                    try {
                        ConcurrentMap<String, WeightedRoundRobin> newMap = new ConcurrentHashMap<String, WeightedRoundRobin>();
                        newMap.putAll(map);
                        Iterator<Entry<String, WeightedRoundRobin>> it = newMap.entrySet().iterator();
                        while (it.hasNext()) {
                            Entry<String, WeightedRoundRobin> item = it.next();
                            if (now - item.getValue().getLastUpdate() > RECYCLE_PERIOD) {
                                it.remove();
                            }
                        }
                        methodWeightMap.put(key, newMap);
                    } finally {
                        updateLock.set(false);
                    }
                }
            }
            if (selectedInvoker != null) {
                // 最大动态权重减去总权重
                selectedWRR.sel(totalWeight);
                return selectedInvoker;
            }
            return invokers.get(0);
        }
    }
    复制代码

# 7 一致性哈希 #

一致性哈希策略具有三个特性：第一是在不新增或者删除提供者情况下，同一个客户端总是可以访问同一个提供者。第二是一致性哈希可以有效分散新增或者删除提供者带来的波动性。第三是一致性哈希虚拟节点可以更加有效分散特性二之波动性。

## 7.1 特性分析 ##

(1) 特性一

在不新增或者删除提供者情况下，同一个客户端总是可以访问同一个提供者。第一步6个提供者分布在哈希环中，第二步client1发起访问请求，此时计算客户端哈希值位于哈希环位置，第三步沿着哈希环顺时针旋转，找到与客户端哈希值最近提供者server5。当哈希环结构不发生改变时，client1总是路由到server5。

![format_png 4][]

哈希环结构应该选择什么数据结构呢？我们可以选择TreeMap构建哈希环，其底层使用了红黑树。

public class TreeMapTest {
    
        public static void main(String[] args) {
        
            // 构建哈希环
            TreeMap<Integer, MyServer> treeMap = new TreeMap<Integer, MyServer>();
            treeMap.put(1, new MyServer("1"));
            treeMap.put(2, new MyServer("2"));
            treeMap.put(3, new MyServer("3"));
            treeMap.put(4, new MyServer("4"));
            treeMap.put(5, new MyServer("5"));
            treeMap.put(6, new MyServer("6"));
    
            // 找到第一个大于客户端哈希值的服务器
            Integer clientHashCode = 5;
            SortedMap<Integer, MyServer> tailMap = treeMap.tailMap(clientHashCode, false);
            MyServer server = tailMap.get(tailMap.firstKey());
    
            // MyServer(ip=6)
            System.out.println(server);
        }
    }
    复制代码

(2) 特性二

一致性哈希可以有效分散新增或者删除提供者带来的波动性，例如新增服务器server7，但是并不影响client1路由结果：

![format_png 5][]

服务器server5发生宕机只会影响client1路由结果，并不会影响其它客户端路由结果：

![format_png 6][]

(3) 特性三

一致性哈希虚拟节点可以更加有效分散特性二之波动性，例如我们可以为每个服务器节点新增一个虚拟节点，使得服务器分散得更加均匀。

![format_png 7][]

## 7.2 代码实例 ##

第一步编写基础负载均衡策略

public abstract class AbstractConsistentHashLoadBalance {
    
        public MyServer select(String clientIP, List<MyServer> serverList) {
            return doSelect(clientIP, serverList);
        }
    
        public abstract MyServer doSelect(String clientIP, List<MyServer> serverList);
    }
    复制代码

第二步编写一致性哈希策略

public class ConsistentHashBalance1 extends AbstractConsistentHashLoadBalance {
        private ConsistentHashSelector consistentHashSelector;
    
        @Override
        public MyServer doSelect(String clientIP, List<MyServer> serverList) {
            initialConsistentHashSelector(serverList);
            return consistentHashSelector.select(clientIP);
        }
    
        private class ConsistentHashSelector {
            private Integer identityHashCode;
            private TreeMap<Integer /* hashcode */, MyServer> serverNodes = new TreeMap<Integer, MyServer>();
    
            // 构建哈希环
            public ConsistentHashSelector(Integer identityHashCode, List<MyServer> serverList) {
                this.identityHashCode = identityHashCode;
                TreeMap<Integer, MyServer> newServerNodes = new TreeMap<Integer, MyServer>();
                for (MyServer server : serverList) {
                    newServerNodes.put(hashCode(server.getIp()), server);
                }
                serverNodes = newServerNodes;
            }
    
            // 根据客户端IP路由
            public MyServer select(String clientIP) {
    
                // 计算客户端哈希值
                int clientHashCode = hashCode(clientIP);
    
                // 找到第一个大于客户端哈希值的服务器
                SortedMap<Integer, MyServer> tailMap = serverNodes.tailMap(clientHashCode, false);
                if (CollectionUtils.isEmpty(tailMap)) {
                    Integer firstKey = serverNodes.firstKey();
                    return serverNodes.get(firstKey);
                }
    
                // 找不到表示在最后一个节点和第一个节点之间 ->选择第一个节点
                Integer firstKey = tailMap.firstKey();
                return tailMap.get(firstKey);
            }
    
            // 计算哈希值
            private int hashCode(String key) {
                return Objects.hashCode(key);
            }
    
            // 提供者列表哈希值 -> 如果新增或者删除提供者会发生变化
            public Integer getIdentityHashCode() {
                return identityHashCode;
            }
        }
    
        private void initialConsistentHashSelector(List<MyServer> serverList) {
    
            // 计算提供者列表哈希值
            Integer newIdentityHashCode = System.identityHashCode(serverList);
    
            // 提供者列表哈希值没有变化则无需重新构建哈希环
            if (null != consistentHashSelector && (null != consistentHashSelector.getIdentityHashCode() && newIdentityHashCode == consistentHashSelector.getIdentityHashCode())) {
                return;
            }
            // 提供者列表哈希值发生变化则重新构建哈希环
            consistentHashSelector = new ConsistentHashSelector(newIdentityHashCode, serverList);
        }
    }
    复制代码

第三步编写测试代码

public class LoadBalanceTest {
    
        public static void main(String[] args) {
            testConsistentHashBalance1();
        }
    
        public static void testConsistentHashBalance1() {
            List<MyServer> serverList = new ArrayList<MyServer>();
            MyServer server1 = new MyServer("1");
            MyServer server2 = new MyServer("2");
            MyServer server3 = new MyServer("3");
            MyServer server4 = new MyServer("4");
            MyServer server5 = new MyServer("5");
            MyServer server6 = new MyServer("6");
            serverList.add(server1);
            serverList.add(server2);
            serverList.add(server3);
            serverList.add(server4);
            serverList.add(server5);
            serverList.add(server6);
            AbstractConsistentHashLoadBalance consistentHashBalance = new ConsistentHashBalance1();
            for (int i = 0; i < 10; i++) {
                String clientIP = "5";
                MyServer server = consistentHashBalance.select(clientIP, serverList);
                System.out.println("clientIP=" + clientIP + ",consistentHashBalance1 route server=" + server);
            }
        }
    }
    复制代码

第四步输出结果

clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    clientIP=5,consistentHashBalance1 route server=MyServer(ip=6)
    复制代码

如果新增虚拟节点参看以下代码

public class ConsistentHashBalance2 extends AbstractConsistentHashLoadBalance {
        private ConsistentHashSelector consistentHashSelector;
    
        @Override
        public MyServer doSelect(String clientIP, List<MyServer> serverList) {
            initialSelector(serverList);
            return consistentHashSelector.select(clientIP);
        }
    
        private class ConsistentHashSelector {
            private Integer identityHashCode;
            private Integer VIRTUAL_NODES_NUM = 16;
            private TreeMap<Integer /* hashcode */, MyServer> serverNodes = new TreeMap<Integer, MyServer>();
    
            public ConsistentHashSelector(Integer identityHashCode, List<MyServer> serverList) {
                this.identityHashCode = identityHashCode;
                TreeMap<Integer, MyServer> newServerNodes = new TreeMap<Integer, MyServer>();
                for (MyServer server : serverList) {
                    // 虚拟节点
                    for (int i = 0; i < VIRTUAL_NODES_NUM; i++) {
                        int virtualKey = hashCode(server.getIp() + "_" + i);
                        newServerNodes.put(virtualKey, server);
                    }
                }
                serverNodes = newServerNodes;
            }
    
            public MyServer select(String clientIP) {
                int clientHashCode = hashCode(clientIP);
                SortedMap<Integer, MyServer> tailMap = serverNodes.tailMap(clientHashCode, false);
                if (CollectionUtils.isEmpty(tailMap)) {
                    Integer firstKey = serverNodes.firstKey();
                    return serverNodes.get(firstKey);
                }
                Integer firstKey = tailMap.firstKey();
                return tailMap.get(firstKey);
            }
    
            private int hashCode(String key) {
                return Objects.hashCode(key);
            }
    
            public Integer getIdentityHashCode() {
                return identityHashCode;
            }
        }
    
        private void initialSelector(List<MyServer> serverList) {
            Integer newIdentityHashCode = System.identityHashCode(serverList);
            if (null != consistentHashSelector && (null != consistentHashSelector.getIdentityHashCode() && newIdentityHashCode == consistentHashSelector.getIdentityHashCode())) {
                return;
            }
            consistentHashSelector = new ConsistentHashSelector(newIdentityHashCode, serverList);
        }
    }
    复制代码

## 7.3 DUBBO源码 ##

public class ConsistentHashLoadBalance extends AbstractLoadBalance {
        public static final String NAME = "consistenthash";
        private final ConcurrentMap<String, ConsistentHashSelector<?>> selectors = new ConcurrentHashMap<String, ConsistentHashSelector<?>>();
    
        @Override
        protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
            String methodName = RpcUtils.getMethodName(invocation);
            String key = invokers.get(0).getUrl().getServiceKey() + "." + methodName;
            int identityHashCode = System.identityHashCode(invokers);
            ConsistentHashSelector<T> selector = (ConsistentHashSelector<T>) selectors.get(key);
    
            // 提供者列表哈希值发生变化则重新构建哈希环
            if (selector == null || selector.identityHashCode != identityHashCode) {
                selectors.put(key, new ConsistentHashSelector<T>(invokers, methodName, identityHashCode));
                selector = (ConsistentHashSelector<T>) selectors.get(key);
            }
            return selector.select(invocation);
        }
    
        private static final class ConsistentHashSelector<T> {
    
            private final TreeMap<Long, Invoker<T>> virtualInvokers;
    
            private final int replicaNumber;
    
            private final int identityHashCode;
    
            private final int[] argumentIndex;
    
            // 构建哈希环
            ConsistentHashSelector(List<Invoker<T>> invokers, String methodName, int identityHashCode) {
                this.virtualInvokers = new TreeMap<Long, Invoker<T>>();
                this.identityHashCode = identityHashCode;
                URL url = invokers.get(0).getUrl();
                this.replicaNumber = url.getMethodParameter(methodName, "hash.nodes", 160);
                String[] index = Constants.COMMA_SPLIT_PATTERN.split(url.getMethodParameter(methodName, "hash.arguments", "0"));
                argumentIndex = new int[index.length];
                for (int i = 0; i < index.length; i++) {
                    argumentIndex[i] = Integer.parseInt(index[i]);
                }
                for (Invoker<T> invoker : invokers) {
                    String address = invoker.getUrl().getAddress();
                    // 新增虚拟节点（默认160个）
                    for (int i = 0; i < replicaNumber / 4; i++) {
                        byte[] digest = md5(address + i);
                        for (int h = 0; h < 4; h++) {
                            long m = hash(digest, h);
                            virtualInvokers.put(m, invoker);
                        }
                    }
                }
            }
    
            // 负载均衡
            public Invoker<T> select(Invocation invocation) {
                String key = toKey(invocation.getArguments());
                byte[] digest = md5(key);
                return selectForKey(hash(digest, 0));
            }
    
            private String toKey(Object[] args) {
                StringBuilder buf = new StringBuilder();
                for (int i : argumentIndex) {
                    if (i >= 0 && i < args.length) {
                        buf.append(args[i]);
                    }
                }
                return buf.toString();
            }
    
            private Invoker<T> selectForKey(long hash) {
                Map.Entry<Long, Invoker<T>> entry = virtualInvokers.ceilingEntry(hash);
                if (entry == null) {
                    entry = virtualInvokers.firstEntry();
                }
                return entry.getValue();
            }
    
            // 哈希运算
            private long hash(byte[] digest, int number) {
                return (((long) (digest[3 + number * 4] & 0xFF) << 24)
                        | ((long) (digest[2 + number * 4] & 0xFF) << 16)
                        | ((long) (digest[1 + number * 4] & 0xFF) << 8)
                        | (digest[number * 4] & 0xFF))
                       & 0xFFFFFFFFL;
            }
    
            private byte[] md5(String value) {
                MessageDigest md5;
                try {
                    md5 = MessageDigest.getInstance("MD5");
                } catch (NoSuchAlgorithmException e) {
                    throw new IllegalStateException(e.getMessage(), e);
                }
                md5.reset();
                byte[] bytes = value.getBytes(StandardCharsets.UTF_8);
                md5.update(bytes);
                return md5.digest();
            }
        }
    }
    复制代码

# 8 最少活跃数策略 #

每个提供者维护并发处理的任务个数，任务个数越大活跃度越高。在服务消费者进行负载均衡时，第一查询提供者负载情况，第二选择活跃度最低的提供者，我们直接分析DUBBO源码：

public class LeastActiveLoadBalance extends AbstractLoadBalance {
        public static final String NAME = "leastactive";
    
        @Override
        protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
    
            // invoker数量
            int length = invokers.size();
    
            // 最小调用次数
            int leastActive = -1;
    
            // 调用次数等于最小次数invoker数量
            int leastCount = 0;
    
            // 调用次数等于最小调用次数invoker下标集合
            int[] leastIndexes = new int[length];
    
            // 每个服务提供者权重
            int[] weights = new int[length];
    
            // 总权重
            int totalWeight = 0;
    
            // 第一个调用者权重
            int firstWeight = 0;
    
            // 权重值是否相同
            boolean sameWeight = true;
    
            // 遍历invokers
            for (int i = 0; i < length; i++) {
                Invoker<T> invoker = invokers.get(i);
    
                // 调用次数
                int active = RpcStatus.getStatus(invoker.getUrl(), invocation.getMethodName()).getActive();
    
                // 获取权重
                int afterWarmup = getWeight(invoker, invocation);
    
                // 设置权重
                weights[i] = afterWarmup;
    
                // 第一个invoker或者调用次数小于最小调用次数
                if (leastActive == -1 || active < leastActive) {
                    leastActive = active;
                    leastCount = 1;
                    leastIndexes[0] = i;
                    totalWeight = afterWarmup;
                    firstWeight = afterWarmup;
                    sameWeight = true;
                }
                // 当前服务提供者调用次数等于最小调用次数
                else if (active == leastActive) {
                    // 记录下标
                    leastIndexes[leastCount++] = i;
                    // 新增总权重值
                    totalWeight += afterWarmup;
                    // 权重值是否相同
                    if (sameWeight && i > 0 && afterWarmup != firstWeight) {
                        sameWeight = false;
                    }
                }
            }
    
            // 只有一个invoker调用次数等于最小调用次数直接返回
            if (leastCount == 1) {
                return invokers.get(leastIndexes[0]);
            }
    
            // 多个invoker调用次数等于最小调用次数并且权重值不相同->根据权重值选择
            if (!sameWeight && totalWeight > 0) {
                int offsetWeight = ThreadLocalRandom.current().nextInt(totalWeight);
                for (int i = 0; i < leastCount; i++) {
                    int leastIndex = leastIndexes[i];
                    offsetWeight -= weights[leastIndex];
                    if (offsetWeight < 0) {
                        return invokers.get(leastIndex);
                    }
                }
            }
            // 多个invoker调用次数等于最小调用次数并且权重值相同->随机选择
            return invokers.get(leastIndexes[ThreadLocalRandom.current().nextInt(leastCount)]);
        }
    }
    复制代码

# 9 文章总结 #

第一本文首先分析了负载均衡、集群容错、服务降级这三组概念，第二结合代码分析了简单随机，加权随机，简单轮询，简单加权轮询，平滑加权轮询，一致性哈希，最少活跃数七种负载均衡策略，其中权重计算、平滑加权轮询，一致性哈希算法值得注意，希望本文对大家有所帮助。

[format_png]: https://img-blog.csdnimg.cn/img_convert/23f7aaefc64717bc60ed17d2630cfa7e.webp?x-oss-process=image/format,png
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[format_png 2]: https://img-blog.csdnimg.cn/img_convert/7b728a9a4e182e0eda2ba3ba01d4ef52.webp?x-oss-process=image/format,png
[format_png 3]: https://img-blog.csdnimg.cn/img_convert/7a2f9395d2ccb050a8ab0cef2a3f907d.webp?x-oss-process=image/format,png
[format_png 4]: https://img-blog.csdnimg.cn/img_convert/5730dec6c44a9323aa5f245274ae56b9.webp?x-oss-process=image/format,png
[format_png 5]: https://img-blog.csdnimg.cn/img_convert/a9452488ac76012931bea5d75187104e.webp?x-oss-process=image/format,png
[format_png 6]: https://img-blog.csdnimg.cn/img_convert/e875ffa5486b7f608650a47f8c374907.webp?x-oss-process=image/format,png
[format_png 7]: https://img-blog.csdnimg.cn/img_convert/574700a44860efd32d98e4a4eced8ed8.webp?x-oss-process=image/format,png