今天我来学习一下Dubbo负载均衡之一的最小活跃策略-LeastActiveLoadBalance
首先，让我们对负载均衡做一个简单的介绍。所谓集负载均衡，其含义就是指将负载（工作任务）进行平衡、分摊到多个操作单元上进行运行。负载均衡、集群容错、服务降级这三个概念在微服务中非常重要。从调用顺序来看，一次完整的RPC调用首先是负载均衡、其次是集群容错、最后是服务降级：负载均衡解决了选哪一个的问题、集群容错解决了换哪一个的问题、而服务降级则是解决了全错了怎么办的问题
今天我们要学习的策略是最小活跃策略-LeastActiveLoadBalance。顾名思义，这种策略就是寻找调用活跃度最低的服务提供者。
话不多说，直接分析源码
protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {// Number of invokersint length = invokers.size();// The least active value of all invokersint leastActive = -1;// The number of invokers having the same least active value (leastActive)int leastCount = 0;// The index of invokers having the same least active value (leastActive)int[] leastIndexes = new int[length];// the weight of every invokersint[] weights = new int[length];// The sum of the warmup weights of all the least active invokersint totalWeight = 0;// The weight of the first least active invokerint firstWeight = 0;// Every least active invoker has the same weight value?boolean sameWeight = true;// Filter out all the least active invokersfor (int i = 0; i < length; i++) {Invoker<T> invoker = invokers.get(i);// Get the active number of the invokerint active = RpcStatus.getStatus(invoker.getUrl(), invocation.getMethodName()).getActive();// Get the weight of the invoker's configuration. The default value is 100.int afterWarmup = getWeight(invoker, invocation);// save for later useweights[i] = afterWarmup;// If it is the first invoker or the active number of the invoker is less than the current least active numberif (leastActive == -1 || active < leastActive) {// Reset the active number of the current invoker to the least active numberleastActive = active;// Reset the number of least active invokersleastCount = 1;// Put the first least active invoker first in leastIndexesleastIndexes[0] = i;// Reset totalWeighttotalWeight = afterWarmup;// Record the weight the first least active invokerfirstWeight = afterWarmup;// Each invoke has the same weight (only one invoker here)sameWeight = true;// If current invoker's active value equals with leaseActive, then accumulating.} else if (active == leastActive) {// Record the index of the least active invoker in leastIndexes orderleastIndexes[leastCount++] = i;// Accumulate the total weight of the least active invokertotalWeight += afterWarmup;// If every invoker has the same weight?if (sameWeight && afterWarmup != firstWeight) {sameWeight = false;}}}// Choose an invoker from all the least active invokersif (leastCount == 1) {// If we got exactly one invoker having the least active value, return this invoker directly.return invokers.get(leastIndexes[0]);}if (!sameWeight && totalWeight > 0) {// If (not every invoker has the same weight & at least one invoker's weight>0), select randomly based on // totalWeight.int offsetWeight = ThreadLocalRandom.current().nextInt(totalWeight);// Return a invoker based on the random value.for (int i = 0; i < leastCount; i++) {int leastIndex = leastIndexes[i];offsetWeight -= weights[leastIndex];if (offsetWeight < 0) {return invokers.get(leastIndex);}}}// If all invokers have the same weight value or totalWeight=0, return evenly.return invokers.get(leastIndexes[ThreadLocalRandom.current().nextInt(leastCount)]);}
通过代码我们可以看出， 作者通过
RpcStatus.getStatus(invoker.getUrl(),invocation.getMethodName()).getActive()获取了每个提供者的活跃度，通过 getWeight(invoker, invocation) 获取了每个提供者的权重；最后再返回最低活跃提供者
如果只有一个最低活跃提供者，直接返回
if (leastCount == 1) {// 如果只存在一个最低活跃的提供者 直接返回return invokers.get(leastIndexes[0]);}
如果存在多个、但是不是所有提供者的权重都是一样的：先生成一个随机数(0-最小总权重)，循环用这个随机数-最小活跃度提供者们的权重 当减到小于0时 返回此时的最小活跃提供者
if (!sameWeight && totalWeight > 0) {// 生成一个0-最小活跃度提供者总权重之间的整数int offsetWeight = ThreadLocalRandom.current().nextInt(totalWeight);// 循环用这个随机数-最小活跃度提供者们的权重 当减到小于0时 返回此时的最小活跃提供者for (int i = 0; i < leastCount; i++) {int leastIndex = leastIndexes[i];offsetWeight -= weights[leastIndex];if (offsetWeight < 0) {return invokers.get(leastIndex);}}}
当所有最小活跃提供者权重都一样：随机返回其中一个
invokers.get(leastIndexes[ThreadLocalRandom.current().nextInt(leastCount)]);
通过上面的代码我们可以看出这个最小活跃的大致逻辑，那么每个提供者的活跃度又是怎么算的呢？
public static RpcStatus getStatus(URL url, String methodName) {String uri = url.toIdentityString();ConcurrentMap<String, RpcStatus> map = puteIfAbsent(uri, k -> new ConcurrentHashMap<>());return puteIfAbsent(methodName, k -> new RpcStatus());}
是通过url和methodName来查出来的活跃度，url我们可以理解为一个提供者的ip，methodName的dubbo接口的方法，可见该活跃度是方法级别的活跃度而非接口级别！此处是取活跃度的地方，那么什么地方又是增加活跃度的呢？请接着往下看
public static boolean beginCount(URL url, String methodName, int max) {max = (max <= 0) ? Integer.MAX_VALUE : max;RpcStatus appStatus = getStatus(url);RpcStatus methodStatus = getStatus(url, methodName);if (methodStatus.active.get() == Integer.MAX_VALUE) {return false;}for (int i; ; ) {i = methodStatus.active.get();if (i == Integer.MAX_VALUE || i + 1 > max) {return false;}if (pareAndSet(i, i + 1)) {break;}}appStatus.active.incrementAndGet();return true;}
在RpcStatus中有一个beginCount的方法就是用来计数的，而调用这个方法的ActiveLimitFilter的dubbo的filter,这说明每一次发生rpc调用的时都会执行beginCount方法。
由此，我们可以清晰地看出最小活跃策略就是取目标rpc接口方法调用次数最少的一个提供者来作为本次rpc调用的提供者。但是这种最小活跃只是针对当前消费者，而不能针对整个集群，所以该最小活跃策略仍然不能将资源充分运用起来。
