深入浅出负载均衡

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Nginx 为例，如下所示：","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/50/50aae959161035ed1ed6404a6bf573da.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"首先，在代理服务器上设定好负载均衡规则。然后，当收到客户端请求，反向代理服务器拦截指定的域名或 IP 请求，根据负载均衡算法，将请求分发到候选服务器上。其次，如果某台候选服务器宕机，反向代理服务器会有容错处理，比如分发请求失败 3 次以上，将请求分发到其他候选服务器上。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"反向代理的 优点：","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"1) 多种负载均衡算法：支持多种负载均衡算法，以应对不同的场景需求。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":"br"}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"2) 可以监控服务器：基于 HTTP 协议，可以监控转发服务器的状态，如：系统负载、响应时间、是否可用、连接数、流量等，从而根据这些数据调整负载均衡的策略。","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"反向代理的 缺点：","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"1) 额外的转发开销：反向代理的转发操作本身是有性能开销的，可能会包括创建连接，等待连接响应，分析响应结果等操作。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"2) 增加系统复杂度：反向代理常用于做分布式应用的水平扩展，但反向代理服务存在以下问题，为了解决以下问题会给系统整体增加额外的复杂度和运维成本：","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"反向代理服务如果自身宕机，就无法访问站点，所以需要有 高可用 方案，常见的方案有：主备模式（一主一备）、双主模式（互为主备）。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":"br"},"content":[{"type":"text","text":"反向代理服务自身也存在性能瓶颈，随着需要转发的请求量不断攀升，需要有 可扩展 方案。","attrs":{}}]}]}],"attrs":{}},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"2.2.4 IP负载均衡","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"IP 负载均衡是在网络层通过修改请求目的地址进行负载均衡。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/e6/e6b307e7492c6b7ee3149fbccb8d8c1e.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如上图所示，IP 均衡处理流程大致为：","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"客户端请求 192.168.137.10，由负载均衡服务器接收到报文。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":"br"}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"负载均衡服务器根据算法选出一个服务节点 192.168.0.1，然后将报文请求地址改为该节点的 IP。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":"br"}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"真实服务节点收到请求报文，处理后，返回响应数据到负载均衡服务器。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":"br"}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"负载均衡服务器将响应数据的源地址改负载均衡服务器地址，返回给客户端。","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"IP 负载均衡在内核进程完成数据分发，较反向代理负载均衡有更好的从处理性能。但是，由于所有请求响应都要经过负载均衡服务器，集群的吞吐量受制于负载均衡服务器的带宽。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"2.2.5 数据链路层负载均衡","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"数据链路层负载均衡是指在通信协议的数据链路层修改 mac 地址进行负载均衡。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/28/28e74446bf201c13805d2687e92de38c.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在 Linux 平台上最好的链路层负载均衡开源产品是 LVS (Linux Virtual Server)。LVS 是基于 Linux 内核中 netfilter 框架实现的负载均衡系统。netfilter 是内核态的 Linux 防火墙机制，可以在数据包流经过程中，根据规则设置若干个关卡（hook 函数）来执行相关的操作。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"LVS 的工作流程大致如下：","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"当用户访问 www.sina.com.cn 时，用户数据通过层层网络，最后通过交换机进入 LVS 服务器网卡，并进入内核网络层。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"进入 PREROUTING 后经过路由查找，确定访问的目的 VIP 是本机 IP 地址，所以数据包进入到 INPUT 链上；","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"IPVS 是工作在 INPUT 链上，会根据访问的 ","attrs":{}},{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"vip+port","attrs":{}},{"type":"text","text":" 判断请求是否 IPVS 服务，如果是则调用注册的 IPVS HOOK 函数，进行 IPVS 相关主流程，强行修改数据包的相关数据，并将数据包发往 POSTROUTING 链上；","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"POSTROUTING 上收到数据包后，根据目标 IP 地址（后端服务器），通过路由选路，将数据包最终发往后端的服务器上。","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"开源 LVS 版本有 3 种工作模式，每种模式工作原理截然不同，说各种模式都有自己的优缺点，分别适合不同的应用场景，不过最终本质的功能都是能实现均衡的流量调度和良好的扩展性。主要包括三种模式：DR 模式、NAT 模式、Tunnel 模式。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"三、负载均衡算法","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"负载均衡器的实现可以分为两个部分：","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"根据负载均衡算法在候选服务器列表选出一个服务器;","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":"br"}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"将请求数据发送到该服务器上。","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"负载均衡算法是负载均衡服务核心中的核心。负载均衡产品多种多样，但是各种负载均衡算法原理是共性的。负载均衡算法有很多种，分别适用于不同的应用场景，本文仅介绍最为常见的负载均衡算法的特性及原理：","attrs":{}},{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"轮询、随机、最小活跃数、源地址哈希、一致性哈希","attrs":{}},{"type":"text","text":"。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"注：","attrs":{}},{"type":"text","text":"负载均衡算法的实现，推荐阅读 ","attrs":{}},{"type":"link","attrs":{"href":"https://dubbo.apache.org/zh/docs/v2.7/dev/source/loadbalance/","title":null,"type":null},"content":[{"type":"text","text":"Dubbo 官方负载均衡算法说明","attrs":{}}]},{"type":"text","text":" ，源码讲解非常详细，非常值得借鉴。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"3.1 随机","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"3.1.1 随机算法","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"随机（Random）","attrs":{}},{"type":"text","text":" 算法将请求随机分发到候选服务器。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"随机算法 适合服务器硬件相同的场景。学习过概率论的都知道，调用量较小的时候，可能负载并不均匀，调用量越大，负载越均衡。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/db/db8a87707531558572c5dbf982774882.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"【示例】随机算法实现示例","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"负载均衡接口","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public interface LoadBalance {\n\n N select(List nodes, String ip);\n\n}","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"负载均衡抽象类","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public abstract class BaseLoadBalance implements LoadBalance {\n\n @Override\n public N select(List nodes, String ip) {\n if (CollectionUtil.isEmpty(nodes)) {\n return null;\n }\n\n // 如果 nodes 列表中仅有一个 node，直接返回即可，无需进行负载均衡\n if (nodes.size() == 1) {\n return nodes.get(0);\n }\n\n return doSelect(nodes, ip);\n }\n\n protected abstract N doSelect(List nodes, String ip);\n\n}","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"服务器节点类","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class Node implements Comparable {\n\n protected String url;\n\n protected Integer weight;\n\n protected Integer active;\n\n // ...\n}","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"随机算法实现","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class RandomLoadBalance extends BaseLoadBalance implements LoadBalance {\n\n private final Random random = new Random();\n\n @Override\n protected N doSelect(List nodes, String ip) {\n // 在列表中随机选取一个节点\n int index = random.nextInt(nodes.size());\n return nodes.get(index);\n }\n\n}","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"3.1.2 加权随机算法","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"加权随机（Weighted Random","attrs":{}},{"type":"text","text":"） 算法在随机算法的基础上，按照概率调整权重，进行负载分配。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"【示例】加权随机算法实现示例","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class WeightRandomLoadBalance extends BaseLoadBalance implements LoadBalance {\n\n private final Random random = ThreadLocalRandom.current();\n\n @Override\n protected N doSelect(List nodes, String ip) {\n\n int length = nodes.size();\n AtomicInteger totalWeight = new AtomicInteger(0);\n for (N node : nodes) {\n Integer weight = node.getWeight();\n totalWeight.getAndAdd(weight);\n }\n\n if (totalWeight.get() > 0) {\n int offset = random.nextInt(totalWeight.get());\n for (N node : nodes) {\n // 让随机值 offset 减去权重值\n offset -= node.getWeight();\n if (offset < 0) {\n // 返回相应的 Node\n return node;\n }\n }\n }\n\n // 直接随机返回一个\n return nodes.get(random.nextInt(length));\n }\n\n}","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"3.2 轮询","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"3.2.1 轮询算法","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"轮询（Round Robin）","attrs":{}},{"type":"text","text":"算法的策略是：将请求依次分发到候选服务器。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如下图所示，负载均衡器收到来自客户端的 6 个请求，(1, 3, 5) 的请求会被发送到服务器 1，(2, 4, 6) 的请求会被发送到服务器 2。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/c1/c19e290329384726b3f3379f6eec250d.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"该算法适合场景：各服务器处理能力相近，且每个事务工作量差异不大。如果存在较大差异，那么处理较慢的服务器就可能会积压请求，最终无法承担过大的负载。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/33/3332a6f9a33847df3e7bf08cc55ccbb2.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"【示例】轮询算法示例","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"轮询负载均衡算法实现","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class RoundRobinLoadBalance extends BaseLoadBalance implements LoadBalance {\n\n private final AtomicInteger position = new AtomicInteger(0);\n\n @Override\n protected N doSelect(List nodes, String ip) {\n int length = nodes.size();\n // 如果位置值已经等于节点数，重置为 0\n position.compareAndSet(length, 0);\n N node = nodes.get(position.get());\n position.getAndIncrement();\n return node;\n }\n\n}","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":4},"content":[{"type":"text","text":"3.2.2 加权轮询算法","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"加权轮询（Weighted Round Robbin）","attrs":{}},{"type":"text","text":"算法在轮询算法的基础上，增加了权重属性来调节转发服务器的请求数目。性能高、处理速度快的节点应该设置更高的权重，使得分发时优先将请求分发到权重较高的节点上。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"如下图所示，服务器 A 设置权重为 5，服务器 B 设置权重为 1，负载均衡器收到来自客户端的 6 个请求，那么 (1, 2, 3, 4, 5) 请求会被发送到服务器 A，(6) 请求会被发送到服务器 B。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/07/07072e320d6d5d2ca38b2942b71e465a.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"【示例】加权轮询算法实现示例","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"以下实现基于 Dubbo 加权轮询算法做了一些简化。","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class WeightRoundRobinLoadBalance extends BaseLoadBalance implements LoadBalance {\n\n /**\n * 60秒\n */\n private static final int RECYCLE_PERIOD = 60000;\n\n /**\n * Node hashcode 到 WeightedRoundRobin 的映射关系\n */\n private ConcurrentMap weightMap = new ConcurrentHashMap<>();\n\n /**\n * 原子更新锁\n */\n private AtomicBoolean updateLock = new AtomicBoolean();\n\n @Override\n protected N doSelect(List nodes, String ip) {\n\n int totalWeight = 0;\n long maxCurrent = Long.MIN_VALUE;\n\n // 获取当前时间\n long now = System.currentTimeMillis();\n N selectedNode = null;\n WeightedRoundRobin selectedWRR = null;\n\n // 下面这个循环主要做了这样几件事情：\n // 1. 遍历 Node 列表，检测当前 Node 是否有相应的 WeightedRoundRobin，没有则创建\n // 2. 检测 Node 权重是否发生了变化，若变化了，则更新 WeightedRoundRobin 的 weight 字段\n // 3. 让 current 字段加上自身权重，等价于 current += weight\n // 4. 设置 lastUpdate 字段，即 lastUpdate = now\n // 5. 寻找具有最大 current 的 Node，以及 Node 对应的 WeightedRoundRobin，\n // 暂存起来，留作后用\n // 6. 计算权重总和\n for (N node : nodes) {\n int hashCode = node.hashCode();\n WeightedRoundRobin weightedRoundRobin = weightMap.get(hashCode);\n int weight = node.getWeight();\n if (weight < 0) {\n weight = 0;\n }\n\n // 检测当前 Node 是否有对应的 WeightedRoundRobin，没有则创建\n if (weightedRoundRobin == null) {\n weightedRoundRobin = new WeightedRoundRobin();\n // 设置 Node 权重\n weightedRoundRobin.setWeight(weight);\n // 存储 url 唯一标识 identifyString 到 weightedRoundRobin 的映射关系\n weightMap.putIfAbsent(hashCode, weightedRoundRobin);\n weightedRoundRobin = weightMap.get(hashCode);\n }\n // Node 权重不等于 WeightedRoundRobin 中保存的权重，说明权重变化了，此时进行更新\n if (weight != weightedRoundRobin.getWeight()) {\n weightedRoundRobin.setWeight(weight);\n }\n\n // 让 current 加上自身权重，等价于 current += weight\n long current = weightedRoundRobin.increaseCurrent();\n // 设置 lastUpdate，表示近期更新过\n weightedRoundRobin.setLastUpdate(now);\n // 找出最大的 current\n if (current > maxCurrent) {\n maxCurrent = current;\n // 将具有最大 current 权重的 Node 赋值给 selectedNode\n selectedNode = node;\n // 将 Node 对应的 weightedRoundRobin 赋值给 selectedWRR，留作后用\n selectedWRR = weightedRoundRobin;\n }\n\n // 计算权重总和\n totalWeight += weight;\n }\n\n // 对 weightMap 进行检查，过滤掉长时间未被更新的节点。\n // 该节点可能挂了，nodes 中不包含该节点，所以该节点的 lastUpdate 长时间无法被更新。\n // 若未更新时长超过阈值后，就会被移除掉，默认阈值为60秒。\n if (!updateLock.get() && nodes.size() != weightMap.size()) {\n if (updateLock.compareAndSet(false, true)) {\n try {\n // 遍历修改，即移除过期记录\n weightMap.entrySet().removeIf(item -> now - item.getValue().getLastUpdate() > RECYCLE_PERIOD);\n } finally {\n updateLock.set(false);\n }\n }\n }\n\n if (selectedNode != null) {\n // 让 current 减去权重总和，等价于 current -= totalWeight\n selectedWRR.decreaseCurrent(totalWeight);\n // 返回具有最大 current 的 Node\n return selectedNode;\n }\n\n // should not happen here\n return nodes.get(0);\n }\n\n protected static class WeightedRoundRobin {\n\n // 服务提供者权重\n private int weight;\n // 当前权重\n private AtomicLong current = new AtomicLong(0);\n // 最后一次更新时间\n private long lastUpdate;\n\n public long increaseCurrent() {\n // current = current + weight；\n return current.addAndGet(weight);\n }\n\n public long decreaseCurrent(int total) {\n // current = current - total;\n return current.addAndGet(-1 * total);\n }\n\n public int getWeight() {\n return weight;\n }\n\n public void setWeight(int weight) {\n this.weight = weight;\n // 初始情况下，current = 0\n current.set(0);\n }\n\n public AtomicLong getCurrent() {\n return current;\n }\n\n public void setCurrent(AtomicLong current) {\n this.current = current;\n }\n\n public long getLastUpdate() {\n return lastUpdate;\n }\n\n public void setLastUpdate(long lastUpdate) {\n this.lastUpdate = lastUpdate;\n }\n\n }\n\n}","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"3.3 最小活跃数","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"最小活跃数（Least Active）","attrs":{}},{"type":"text","text":"算法 将请求分发到连接数/请求数最少的候选服务器（目前处理请求最少的服务器）。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"特点：根据候选服务器当前的请求连接数，动态分配。","attrs":{}}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"场景：适用于对系统负载较为敏感或请求连接时长相差较大的场景。","attrs":{}}]}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"由于每个请求的连接时长不一样，如果采用简单的轮循或随机算法，都可能出现某些服务器当前连接数过大，而另一些服务器的连接过小的情况，这就造成了负载并非真正均衡。虽然，轮询或算法都可以通过加权重属性的方式进行负载调整，但加权方式难以应对动态变化。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"例如下图中，(1, 3, 5) 请求会被发送到服务器 1，但是 (1, 3) 很快就断开连接，此时只有 (5) 请求连接服务器 1；(2, 4, 6) 请求被发送到服务器 2，只有 (2) 的连接断开。该系统继续运行时，服务器 2 会承担过大的负载。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/42/421e8bff0463932aba2b1ed14ee51087.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"最小活跃数算法会记录当前时刻，每个候选节点正在处理的连接数，然后选择连接数最小的节点。该策略能够动态、实时地反应服务器的当前状况，较为合理地将负责分配均匀，适用于对当前系统负载较为敏感的场景。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"例如下图中，服务器 1 当前连接数最小，那么新到来的请求 6 就会被发送到服务器 1 上。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/e0/e0303e8eaad543889d0ebd86a1821a6d.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"加权最小活跃数（Weighted Least Connection）","attrs":{}},{"type":"text","text":"在最小活跃数的基础上，根据服务器的性能为每台服务器分配权重，再根据权重计算出每台服务器能处理的连接数。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"最小活跃数算法实现要点：活跃调用数越小，表明该服务节点处理能力越高，单位时间内可处理更多的请求，应优先将请求分发给该服务。在具体实现中，每个服务节点对应一个活跃数 active。初始情况下，所有服务提供者活跃数均为 0。每收到一个请求，活跃数加 1，完成请求后则将活跃数减 1。在服务运行一段时间后，性能好的服务提供者处理请求的速度更快，因此活跃数下降的也越快，此时这样的服务提供者能够优先获取到新的服务请求、这就是最小活跃数负载均衡算法的基本思想。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"【示例】最小活跃数算法实现","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"以下实现基于 Dubbo 最小活跃数负载均衡算法做了些许改动。","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class LeastActiveLoadBalance extends BaseLoadBalance implements LoadBalance {\n\n private final Random random = new Random();\n\n @Override\n protected N doSelect(List nodes, String ip) {\n int length = nodes.size();\n // 最小的活跃数\n int leastActive = -1;\n // 具有相同“最小活跃数”的服务者提供者（以下用 Node 代称）数量\n int leastCount = 0;\n // leastIndexs 用于记录具有相同“最小活跃数”的 Node 在 nodes 列表中的下标信息\n int[] leastIndexs = new int[length];\n int totalWeight = 0;\n // 第一个最小活跃数的 Node 权重值，用于与其他具有相同最小活跃数的 Node 的权重进行对比，\n // 以检测是否“所有具有相同最小活跃数的 Node 的权重”均相等\n int firstWeight = 0;\n boolean sameWeight = true;\n\n // 遍历 nodes 列表\n for (int i = 0; i < length; i++) {\n N node = nodes.get(i);\n // 发现更小的活跃数，重新开始\n if (leastActive == -1 || node.getActive() < leastActive) {\n // 使用当前活跃数更新最小活跃数 leastActive\n leastActive = node.getActive();\n // 更新 leastCount 为 1\n leastCount = 1;\n // 记录当前下标值到 leastIndexs 中\n leastIndexs[0] = i;\n totalWeight = node.getWeight();\n firstWeight = node.getWeight();\n sameWeight = true;\n\n // 当前 Node 的活跃数 node.getActive() 与最小活跃数 leastActive 相同\n } else if (node.getActive() == leastActive) {\n // 在 leastIndexs 中记录下当前 Node 在 nodes 集合中的下标\n leastIndexs[leastCount++] = i;\n // 累加权重\n totalWeight += node.getWeight();\n // 检测当前 Node 的权重与 firstWeight 是否相等，\n // 不相等则将 sameWeight 置为 false\n if (sameWeight && i > 0\n && node.getWeight() != firstWeight) {\n sameWeight = false;\n }\n }\n }\n\n // 当只有一个 Node 具有最小活跃数，此时直接返回该 Node 即可\n if (leastCount == 1) {\n return nodes.get(leastIndexs[0]);\n }\n\n // 有多个 Node 具有相同的最小活跃数，但它们之间的权重不同\n if (!sameWeight && totalWeight > 0) {\n // 随机生成一个 [0, totalWeight) 之间的数字\n int offsetWeight = random.nextInt(totalWeight);\n // 循环让随机数减去具有最小活跃数的 Node 的权重值，\n // 当 offset 小于等于0时，返回相应的 Node\n for (int i = 0; i < leastCount; i++) {\n int leastIndex = leastIndexs[i];\n // 获取权重值，并让随机数减去权重值\n offsetWeight -= nodes.get(leastIndex).getWeight();\n if (offsetWeight <= 0) {\n return nodes.get(leastIndex);\n }\n }\n }\n // 如果权重相同或权重为0时，随机返回一个 Node\n return nodes.get(leastIndexs[random.nextInt(leastCount)]);\n }\n\n}","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"3.4 源地址哈希","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"源地址哈希（IP Hash）","attrs":{}},{"type":"text","text":"算法 根据请求源 IP，通过哈希计算得到一个数值，用该数值在候选服务器列表的进行取模运算，得到的结果便是选中的服务器。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"可以保证同一 IP 的客户端的请求会转发到同一台服务器上，用来实现会话粘滞（Sticky Session）。","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"特点：保证特定用户总是请求到相同的服务器，若服务器宕机，会话会丢失。","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"【示例】源地址哈希算法实现示例","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class IpHashLoadBalance extends BaseLoadBalance implements LoadBalance {\n\n @Override\n protected N doSelect(List nodes, String ip) {\n if (StrUtil.isBlank(ip)) {\n ip = \"127.0.0.1\";\n }\n\n int length = nodes.size();\n int index = hash(ip) % length;\n return nodes.get(index);\n }\n\n public int hash(String text) {\n return HashUtil.fnvHash(text);\n }\n\n}","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","text":"3.5 一致性哈希","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"一致性哈希（Consistent Hash）算法的目标是：相同的请求尽可能落到同一个服务器上。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"一致性哈希 可以很好的解决 稳定性问题，可以将所有的 存储节点 排列在 首尾相接 的 Hash 环上，每个 key 在计算 Hash 后会 顺时针 找到 临接 的 存储节点 存放。而当有节点 加入 或 退出 时，仅影响该节点在 Hash环上顺时针相邻的后续节点。","attrs":{}}]},{"type":"image","attrs":{"src":"https://static001.geekbang.org/infoq/03/0395dd03dd099cb3e06e5a644c6a9b7c.png","alt":null,"title":null,"style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":null,"fromPaste":true,"pastePass":true}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"1）相同的请求是指：一般在使用一致性哈希时，需要指定一个 key 用于 hash 计算，可能是：","attrs":{}}]},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"用户 ID","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"请求方 IP","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"请求服务名称，参数列表构成的串","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"2）尽可能是指：服务器可能发生上下线，少数服务器的变化不应该影响大多数的请求。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"当某台候选服务器宕机时，原本发往该服务器的请求，会基于虚拟节点，平摊到其它候选服务器，不会引起剧烈变动。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"优点：","attrs":{}},{"type":"text","text":"加入 和 删除 节点只影响 哈希环 中 顺时针方向 的 相邻的节点，对其他节点无影响。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"缺点：","attrs":{}},{"type":"text","text":"加减节点 会造成 哈希环 中部分数据 无法命中。当使用 少量节点 时，节点变化 将大范围影响 哈希","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"环 中 数据映射，不适合 少量数据节点 的分布式方案。普通 的 一致性哈希分区 在增减节点时需要 增加一倍 或 减去一半 节点才能保证 数据 和 负载的均衡。","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"blockquote","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong","attrs":{}}],"text":"注意：","attrs":{}},{"type":"text","text":"因为 一致性哈希分区 的这些缺点，一些分布式系统采用 虚拟槽 对 一致性哈希 进行改进，比如 Dynamo 系统。","attrs":{}}]}],"attrs":{}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"【示例】一致性哈希算法示例","attrs":{}}]},{"type":"codeblock","attrs":{"lang":"java"},"content":[{"type":"text","text":"public class ConsistentHashLoadBalance extends BaseLoadBalance implements LoadBalance {\n\n private final ConcurrentMap> selectors = new ConcurrentHashMap<>();\n\n @SuppressWarnings(\"unchecked\")\n @Override\n protected N doSelect(List nodes, String ip) {\n // 分片数，这里设为节点数的 4 倍\n Integer replicaNum = nodes.size() * 4;\n // 获取 nodes 原始的 hashcode\n int identityHashCode = System.identityHashCode(nodes);\n\n // 如果 nodes 是一个新的 List 对象，意味着节点数量发生了变化\n // 此时 selector.identityHashCode != identityHashCode 条件成立\n ConsistentHashSelector selector = (ConsistentHashSelector) selectors.get(ip);\n if (selector == null || selector.identityHashCode != identityHashCode) {\n // 创建新的 ConsistentHashSelector\n selectors.put(ip, new ConsistentHashSelector<>(nodes, identityHashCode, replicaNum));\n selector = (ConsistentHashSelector) selectors.get(ip);\n }\n // 调用 ConsistentHashSelector 的 select 方法选择 Node\n return selector.select(ip);\n }\n\n /**\n * 一致性哈希选择器\n */\n private static final class ConsistentHashSelector {\n\n /**\n * 存储虚拟节点\n */\n private final TreeMap virtualNodes;\n\n private final int identityHashCode;\n\n /**\n * 构造器\n *\n * @param nodes 节点列表\n * @param identityHashCode hashcode\n * @param replicaNum 分片数\n */\n ConsistentHashSelector(List nodes, int identityHashCode, Integer replicaNum) {\n this.virtualNodes = new TreeMap<>();\n this.identityHashCode = identityHashCode;\n // 获取虚拟节点数，默认为 100\n if (replicaNum == null) {\n replicaNum = 100;\n }\n for (N node : nodes) {\n for (int i = 0; i < replicaNum / 4; i++) {\n // 对 url 进行 md5 运算，得到一个长度为16的字节数组\n byte[] digest = md5(node.getUrl());\n // 对 digest 部分字节进行 4 次 hash 运算，得到四个不同的 long 型正整数\n for (int j = 0; j < 4; j++) {\n // h = 0 时，取 digest 中下标为 0 ~ 3 的4个字节进行位运算\n // h = 1 时，取 digest 中下标为 4 ~ 7 的4个字节进行位运算\n // h = 2, h = 3 时过程同上\n long m = hash(digest, j);\n // 将 hash 到 node 的映射关系存储到 virtualNodes 中，\n // virtualNodes 需要提供高效的查询操作，因此选用 TreeMap 作为存储结构\n virtualNodes.put(m, node);\n }\n }\n }\n }\n\n public N select(String key) {\n // 对参数 key 进行 md5 运算\n byte[] digest = md5(key);\n // 取 digest 数组的前四个字节进行 hash 运算，再将 hash 值传给 selectForKey 方法，\n // 寻找合适的 Node\n return selectForKey(hash(digest, 0));\n }\n\n private N selectForKey(long hash) {\n // 查找第一个大于或等于当前 hash 的节点\n Map.Entry entry = virtualNodes.ceilingEntry(hash);\n // 如果 hash 大于 Node 在哈希环上最大的位置，此时 entry = null，\n // 需要将 TreeMap 的头节点赋值给 entry\n if (entry == null) {\n entry = virtualNodes.firstEntry();\n }\n // 返回 Node\n return entry.getValue();\n }\n\n }\n\n /**\n * 计算 hash 值\n */\n public static long hash(byte[] digest, int number) {\n return (((long) (digest[3 + number * 4] & 0xFF) << 24)\n | ((long) (digest[2 + number * 4] & 0xFF) << 16)\n | ((long) (digest[1 + number * 4] & 0xFF) << 8)\n | (digest[number * 4] & 0xFF))\n & 0xFFFFFFFFL;\n }\n\n /**\n * 计算 MD5 值\n */\n public static byte[] md5(String value) {\n MessageDigest md5;\n try {\n md5 = MessageDigest.getInstance(\"MD5\");\n } catch (NoSuchAlgorithmException e) {\n throw new IllegalStateException(e.getMessage(), e);\n }\n md5.reset();\n byte[] bytes = value.getBytes(StandardCharsets.UTF_8);\n md5.update(bytes);\n return md5.digest();\n }\n\n}","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"以上示例基于 Dubbo 的一致性哈希负载均衡算法做了一些简化。","attrs":{}}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"四、参考资料","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"1. ","attrs":{}},{"type":"link","attrs":{"href":"https://www.youtube.com/watch?reload=9&app=desktop&v=iqOTT7_7qXY","title":null,"type":null},"content":[{"type":"text","text":"Comparing Load Balancing Algorithms","attrs":{}}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"2. 《大型网站技术架构：核心原理与案例分析》","attrs":{}}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"3. ","attrs":{}},{"type":"link","attrs":{"href":"https://www.cnblogs.com/itfly8/p/5043435.html","title":null,"type":null},"content":[{"type":"text","text":"大型网站架构系列：负载均衡详解（1）","attrs":{}}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"4. ","attrs":{}},{"type":"link","attrs":{"href":"https://zhuanlan.zhihu.com/p/32841479","title":null,"type":null},"content":[{"type":"text","text":"什么是负载均衡","attrs":{}}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"5. ","attrs":{}},{"type":"link","attrs":{"href":"https://avinetworks.com/what-is-load-balancing/","title":null,"type":null},"content":[{"type":"text","text":"What Is Load Balancing","attrs":{}}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"6. ","attrs":{}},{"type":"link","attrs":{"href":"https://dubbo.apache.org/zh/docs/v2.7/dev/source/loadbalance/","title":null,"type":null},"content":[{"type":"text","text":"Dubbo 官方负载均衡算法说明","attrs":{}}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"7. ","attrs":{}},{"type":"link","attrs":{"href":"https://segmentfault.com/a/1190000004492447","title":null,"type":null},"content":[{"type":"text","text":"负载均衡算法及手段","attrs":{}}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"8. ","attrs":{}},{"type":"link","attrs":{"href":"https://segmentfault.com/a/1190000002578457","title":null,"type":null},"content":[{"type":"text","text":"利用 dns 解析来实现网站的负载均衡","attrs":{}}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"作者：vivo互联网团队-Zhang Peng","attrs":{}}]}]}