Apache Dubbo负载均衡策略源码
1、Random LoadBalance
1.1 随机，按权重设置随机概率。
1.2 在一个截面上碰撞的概率高，但调用量越大分布越均匀，而且按概率使用权重后也比较均匀，有利于动态调整提供者权重。
1.3 源码分析
public class RandomLoadBalance extends AbstractLoadBalance { public static final String NAME = "random"; /** * Select one invoker between a list using a random criteria * @param invokers List of possible invokers * @param url URL * @param invocation Invocation * @param <T> * @return The selected invoker */ @Override protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) { // Number of invokers int length = invokers.size(); // Every invoker has the same weight? boolean sameWeight = true; // the weight of every invokers int[] weights = new int[length]; // the first invoker's weight int firstWeight = getWeight(invokers.get(0), invocation); weights[0] = firstWeight; // The sum of weights int totalWeight = firstWeight; for (int i = 1; i < length; i++) { int weight = getWeight(invokers.get(i), invocation); // save for later use weights[i] = weight; // Sum totalWeight += weight; if (sameWeight && weight != firstWeight) { sameWeight = false; } } if (totalWeight > 0 && !sameWeight) { // If (not every invoker has the same weight & at least one invoker's weight>0), select randomly based on totalWeight. int offset = ThreadLocalRandom.current().nextInt(totalWeight); // Return a invoker based on the random value. for (int i = 0; i < length; i++) { offset -= weights[i]; if (offset < 0) { return invokers.get(i); } } } // If all invokers have the same weight value or totalWeight=0, return evenly. return invokers.get(ThreadLocalRandom.current().nextInt(length)); } }
说明：从源码可以看出随机负载均衡的策略分为两种情况
a. 如果总权重大于0并且权重不相同，就生成一个1~totalWeight(总权重数)的随机数，然后再把随机数和所有的权重值一一相减得到一个新的随机数，直到随机 数小于0，那么此时访问的服务器就是使得随机数小于0的权重所在的机器
b. 如果权重相同或者总权重数为0，就生成一个1~length(权重的总个数)的随机数，此时所访问的机器就是这个随机数对应的权重所在的机器
2、RoundRobin LoadBalance
2.1 轮循，按公约后的权重设置轮循比率。
2.2 存在慢的提供者累积请求的问题，比如：第二台机器很慢，但没挂，当请求调到第二台时就卡在那，久而久之，所有请求都卡在调到第二台上。
2.3 源码分析
/** * Round robin load balance. */ public class RoundRobinLoadBalance extends AbstractLoadBalance { public static final String NAME = "roundrobin"; private static final int RECYCLE_PERIOD = 60000; protected static class WeightedRoundRobin { private int weight; private AtomicLong current = new AtomicLong(0); 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>>(); /** * get invoker addr list cached for specified invocation * <p> * <b>for unit test only</b> * * @param invokers * @param invocation * @return */ 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.computeIfAbsent(key, k -> new ConcurrentHashMap<>()); int totalWeight = 0; long maxCurrent = Long.MIN_VALUE; long now = System.currentTimeMillis(); Invoker<T> selectedInvoker = null; WeightedRoundRobin selectedWRR = null; for (Invoker<T> invoker : invokers) { String identifyString = invoker.getUrl().toIdentityString(); int weight = getWeight(invoker, invocation); WeightedRoundRobin weightedRoundRobin = map.computeIfAbsent(identifyString, k -> { WeightedRoundRobin wrr = new WeightedRoundRobin(); wrr.setWeight(weight); return wrr; }); if (weight != weightedRoundRobin.getWeight()) { //weight changed weightedRoundRobin.setWeight(weight); } long cur = weightedRoundRobin.increaseCurrent(); weightedRoundRobin.setLastUpdate(now); if (cur > maxCurrent) { maxCurrent = cur; selectedInvoker = invoker; selectedWRR = weightedRoundRobin; } totalWeight += weight; } if (invokers.size() != map.size()) { map.entrySet().removeIf(item -> now - item.getValue().getLastUpdate() > RECYCLE_PERIOD); } if (selectedInvoker != null) { selectedWRR.sel(totalWeight); return selectedInvoker; } // should not happen here return invokers.get(0); } }
说明：从源码可以看出轮循负载均衡的算法是：
a. 如果权重不一样时，获取一个当前的权重基数，然后从权重集合中筛选权重大于当前权重基数的集合，如果筛选出的集合的长度为1，此时所访问的机器就是集合里面的权重对应的机器
b. 如果权重一样时就取模轮循
3、LeastActive LoadBalance
3.1 最少活跃调用数，相同活跃数的随机，活跃数指调用前后计数差(调用前的时刻减去响应后的时刻的值)。
3.2 使慢的提供者收到更少请求，因为越慢的提供者的调用前后计数差会越大
3.3 对应的源码
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) { // Number of invokers int length = invokers.size(); // The least active value of all invokers int 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 invokers int[] weights = new int[length]; // The sum of the warmup weights of all the least active invokers int totalWeight = 0; // The weight of the first least active invoker int firstWeight = 0; // Every least active invoker has the same weight value? boolean sameWeight = true; // Filter out all the least active invokers for (int i = 0; i < length; i++) { Invoker<T> invoker = invokers.get(i); // Get the active number of the invoker int 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 use weights[i] = afterWarmup; // If it is the first invoker or the active number of the invoker is less than the current least active number if (leastActive == -1 || active < leastActive) { // Reset the active number of the current invoker to the least active number leastActive = active; // Reset the number of least active invokers leastCount = 1; // Put the first least active invoker first in leastIndexes leastIndexes[0] = i; // Reset totalWeight totalWeight = afterWarmup; // Record the weight the first least active invoker firstWeight = 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 order leastIndexes[leastCount++] = i; // Accumulate the total weight of the least active invoker totalWeight += afterWarmup; // If every invoker has the same weight? if (sameWeight && afterWarmup != firstWeight) { sameWeight = false; } } } // Choose an invoker from all the least active invokers if (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)]); } }
4、ConsistentHash LoadBalance
4.1 一致性 Hash，相同参数的请求总是发到同一提供者。
4.2 当某一台提供者挂时，原本发往该提供者的请求，基于虚拟节点，平摊到其它提供者，不会引起剧烈变动。
4.3 缺省只对第一个参数 Hash，如果要修改，请配置 <dubbo:parameter key="hash.arguments" value="0,1" />
4.4 缺省用 160 份虚拟节点，如果要修改，请配置 <dubbo:parameter key="hash.nodes" value="320" />
4.5 源码分析
/** * ConsistentHashLoadBalance */ public class ConsistentHashLoadBalance extends AbstractLoadBalance { public static final String NAME = "consistenthash"; /** * Hash nodes name */ public static final String HASH_NODES = "hash.nodes"; /** * Hash arguments name */ public static final String HASH_ARGUMENTS = "hash.arguments"; private final ConcurrentMap<String, ConsistentHashSelector<?>> selectors = new ConcurrentHashMap<String, ConsistentHashSelector<?>>(); @SuppressWarnings("unchecked") @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; // using the hashcode of list to compute the hash only pay attention to the elements in the list int invokersHashCode = invokers.hashCode(); ConsistentHashSelector<T> selector = (ConsistentHashSelector<T>) selectors.get(key); if (selector == null || selector.identityHashCode != invokersHashCode) { selectors.put(key, new ConsistentHashSelector<T>(invokers, methodName, invokersHashCode)); 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; 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 = 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(); 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); } } } } 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(); } 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(); } } }
说明：根据传递的参数进行hash然后调用服务，如果两次传递的参数一样就调用的是同一个机器上的服务
现在人工智能非常火爆，很多朋友都想学，但是一般的教程都是为博硕生准备的，太难看懂了。最近发现了一个非常适合小白入门的教程，不仅通俗易懂而且还很风趣幽默。所以忍不住分享一下给大家
