在集群负载均衡时，Dubbo 提供了多种均衡策略，缺省为 random 随机调用。
我们还可以扩展自己的负责均衡策略，前提是你已经从一个小白变成了大牛，嘻嘻
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
package com.alibaba.dubbo.rpc.cluster.loadbalance;
import java.util.List;
import java.util.Random;
import com.alibaba.dubbo.common.URL;
import com.alibaba.dubbo.rpc.Invocation;
import com.alibaba.dubbo.rpc.Invoker;
/**
* random load balance.
*
* @author qianlei
* @author william.liangf
*/
public class RandomLoadBalance extends AbstractLoadBalance {
public static final String NAME = "random";
private final Random random = new Random();
protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
int length = invokers.size(); // 总个数
int totalWeight = 0; // 总权重
boolean sameWeight = true; // 权重是否都一样
for (int i = 0; i < length; i++) {
int weight = getWeight(invokers.get(i), invocation);
totalWeight += weight; // 累计总权重
if (sameWeight && i > 0
&& weight != getWeight(invokers.get(i - 1), invocation)) {
sameWeight = false; // 计算所有权重是否一样
}
}
if (totalWeight > 0 && ! sameWeight) {
// 如果权重不相同且权重大于0则按总权重数随机
int offset = random.nextInt(totalWeight);
// 并确定随机值落在哪个片断上
for (int i = 0; i < length; i++) {
offset -= getWeight(invokers.get(i), invocation);
if (offset < 0) {
return invokers.get(i);
}
}
}
// 如果权重相同或权重为0则均等随机
return invokers.get(random.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
package com.alibaba.dubbo.rpc.cluster.loadbalance;
import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import com.alibaba.dubbo.common.URL;
import com.alibaba.dubbo.common.utils.AtomicPositiveInteger;
import com.alibaba.dubbo.rpc.Invocation;
import com.alibaba.dubbo.rpc.Invoker;
/**
* Round robin load balance.
*
* @author qian.lei
* @author william.liangf
*/
public class RoundRobinLoadBalance extends AbstractLoadBalance {
public static final String NAME = "roundrobin";
private final ConcurrentMap<String, AtomicPositiveInteger> sequences = new ConcurrentHashMap<String, AtomicPositiveInteger>();
private final ConcurrentMap<String, AtomicPositiveInteger> weightSequences = new ConcurrentHashMap<String, AtomicPositiveInteger>();
protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
String key = invokers.get(0).getUrl().getServiceKey() + "." + invocation.getMethodName();
int length = invokers.size(); // 总个数
int maxWeight = 0; // 大权重
int minWeight = Integer.MAX_VALUE; // 小权重
for (int i = 0; i < length; i++) {
int weight = getWeight(invokers.get(i), invocation);
maxWeight = Math.max(maxWeight, weight); // 累计大权重
minWeight = Math.min(minWeight, weight); // 累计小权重
}
if (maxWeight > 0 && minWeight < maxWeight) { // 权重不一样
AtomicPositiveInteger weightSequence = weightSequences.get(key);
if (weightSequence == null) {
weightSequences.putIfAbsent(key, new AtomicPositiveInteger());
weightSequence = weightSequences.get(key);
}
int currentWeight = weightSequence.getAndIncrement() % maxWeight;
List<Invoker<T>> weightInvokers = new ArrayList<Invoker<T>>();
for (Invoker<T> invoker : invokers) { // 筛选权重大于当前权重基数的Invoker
if (getWeight(invoker, invocation) > currentWeight) {
weightInvokers.add(invoker);
}
}
int weightLength = weightInvokers.size();
if (weightLength == 1) {
return weightInvokers.get(0);
} else if (weightLength > 1) {
invokers = weightInvokers;
length = invokers.size();
}
}
AtomicPositiveInteger sequence = sequences.get(key);
if (sequence == null) {
sequences.putIfAbsent(key, new AtomicPositiveInteger());
sequence = sequences.get(key);
}
// 取模轮循
return invokers.get(sequence.getAndIncrement() % length);
}
}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 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
package com.alibaba.dubbo.rpc.cluster.loadbalance;
import java.util.List;
import java.util.Random;
import com.alibaba.dubbo.common.Constants;
import com.alibaba.dubbo.common.URL;
import com.alibaba.dubbo.rpc.Invocation;
import com.alibaba.dubbo.rpc.Invoker;
import com.alibaba.dubbo.rpc.RpcStatus;
/**
* LeastActiveLoadBalance
*
* @author william.liangf
*/
public class LeastActiveLoadBalance extends AbstractLoadBalance {
public static final String NAME = "leastactive";
private final Random random = new Random();
protected <T> Invoker<T> doSelect(List<Invoker<T>> invokers, URL url, Invocation invocation) {
int length = invokers.size(); // 总个数
int leastActive = -1; // 小的活跃数
int leastCount = 0; // 相同小活跃数的个数
int[] leastIndexs = new int[length]; // 相同小活跃数的下标
int totalWeight = 0; // 总权重
int firstWeight = 0; // 个权重，用于于计算是否相同
boolean sameWeight = true; // 是否所有权重相同
for (int i = 0; i < length; i++) {
Invoker<T> invoker = invokers.get(i);
int active = RpcStatus.getStatus(invoker.getUrl(), invocation.getMethodName()).getActive(); // 活跃数
int weight = invoker.getUrl().getMethodParameter(invocation.getMethodName(), Constants.WEIGHT_KEY, Constants.DEFAULT_WEIGHT); // 权重
if (leastActive == -1 || active < leastActive) { // 发现更小的活跃数，重新开始
leastActive = active; // 记录小活跃数
leastCount = 1; // 重新统计相同小活跃数的个数
leastIndexs[0] = i; // 重新记录小活跃数下标
totalWeight = weight; // 重新累计总权重
firstWeight = weight; // 记录个权重
sameWeight = true; // 还原权重相同标识
} else if (active == leastActive) { // 累计相同小的活跃数
leastIndexs[leastCount ++] = i; // 累计相同小活跃数下标
totalWeight += weight; // 累计总权重
// 判断所有权重是否一样
if (sameWeight && i > 0
&& weight != firstWeight) {
sameWeight = false;
}
}
}
// assert(leastCount > 0)
if (leastCount == 1) {
// 如果只有一个小则直接返回
return invokers.get(leastIndexs[0]);
}
if (! sameWeight && totalWeight > 0) {
// 如果权重不相同且权重大于0则按总权重数随机
int offsetWeight = random.nextInt(totalWeight);
// 并确定随机值落在哪个片断上
for (int i = 0; i < leastCount; i++) {
int leastIndex = leastIndexs[i];
offsetWeight -= getWeight(invokers.get(leastIndex), invocation);
if (offsetWeight <= 0)
return invokers.get(leastIndex);
}
}
// 如果权重相同或权重为0则均等随机
return invokers.get(leastIndexs[random.nextInt(leastCount)]);
}
}说明：源码里面的注释已经很清晰了，大致的意思就是活跃数越小的的机器分配到的请求越多
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
/*
* Copyright 1999-2012 Alibaba Group.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.alibaba.dubbo.rpc.cluster.loadbalance;
import java.io.UnsupportedEncodingException;
import java.security.MessageDigest;
import java.security.NoSuchAlgorithmException;
import java.util.List;
import java.util.SortedMap;
import java.util.TreeMap;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import com.alibaba.dubbo.common.Constants;
import com.alibaba.dubbo.common.URL;
import com.alibaba.dubbo.rpc.Invocation;
import com.alibaba.dubbo.rpc.Invoker;
/**
* ConsistentHashLoadBalance
*
* @author william.liangf
*/
public class ConsistentHashLoadBalance extends AbstractLoadBalance {
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 key = invokers.get(0).getUrl().getServiceKey() + "." + invocation.getMethodName();
int identityHashCode = System.identityHashCode(invokers);
ConsistentHashSelector<T> selector = (ConsistentHashSelector<T>) selectors.get(key);
if (selector == null || selector.getIdentityHashCode() != identityHashCode) {
selectors.put(key, new ConsistentHashSelector<T>(invokers, invocation.getMethodName(), 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;
public ConsistentHashSelector(List<Invoker<T>> invokers, String methodName, int identityHashCode) {
this.virtualInvokers = new TreeMap<Long, Invoker<T>>();
this.identityHashCode = System.identityHashCode(invokers);
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) {
for (int i = 0; i < replicaNumber / 4; i++) {
byte[] digest = md5(invoker.getUrl().toFullString() + i);
for (int h = 0; h < 4; h++) {
long m = hash(digest, h);
virtualInvokers.put(m, invoker);
}
}
}
}
public int getIdentityHashCode() {
return identityHashCode;
}
public Invoker<T> select(Invocation invocation) {
String key = toKey(invocation.getArguments());
byte[] digest = md5(key);
Invoker<T> invoker = sekectForKey(hash(digest, 0));
return invoker;
}
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> sekectForKey(long hash) {
Invoker<T> invoker;
Long key = hash;
if (!virtualInvokers.containsKey(key)) {
SortedMap<Long, Invoker<T>> tailMap = virtualInvokers.tailMap(key);
if (tailMap.isEmpty()) {
key = virtualInvokers.firstKey();
} else {
key = tailMap.firstKey();
}
}
invoker = virtualInvokers.get(key);
return invoker;
}
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[0 + 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 = null;
try {
bytes = value.getBytes("UTF-8");
} catch (UnsupportedEncodingException e) {
throw new IllegalStateException(e.getMessage(), e);
}
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.
5、dubbo官方的文档的负载均衡配置示例.
服务端服务级别.
<dubbo:service interface="..." loadbalance="roundrobin" />
客户端服务级别.
<dubbo:reference interface="..." loadbalance="roundrobin" />
服务端方法级别.
<dubbo:service interface="...">
<dubbo:method name="..." loadbalance="roundrobin"/>
</dubbo:service>
客户端方法级别.
<dubbo:reference interface="...">
<dubbo:method name="..." loadbalance="roundrobin"/>
</dubbo:reference>
各大公司面试题集合：
整理的 pdf 文档：
各专题视频集合：
源码分析专题部分课程：
来源：https://www.cnblogs.com/leeSmall/p/7620467.html
