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详解SpringCloud的负载均衡

运维开发网 https://www.qedev.com 2021-03-24 11:35 出处:网络 作者: 拾万个为什么
一.什么是负载均衡   负载均衡(Load-balance LB),指的是将用户的请求平摊分配到各www.cppcns.com个服务器上,从而达到系统的高可用。常见的负载均衡软件有Nginx、lvs等。

一.什么是负载均衡

  负载均衡(Load-balance LB),指的是将用户的请求平摊分配到各www.cppcns.com个服务器上,从而达到系统的高可用。常见的负载均衡软件有Nginx、lvs等。

二.负载均衡的简单分类

  1)集中式LB:集中式负载均衡指的是,在服务消费者(client)和服务提供者(provider)之间提供负载均衡设施,通过该设施把消费者(client)的请求通过某种策略转发给服务提供者(provider),常见的集中式负载均衡是Nginx;

  2)进程式LB:将负载均衡的逻辑集成到消费者(client)身上,即消费者从服务注册中心获取服务列表,获知有哪些地址可用,再从这些地址里选出合适的服务器,springCloud的Ribbon就是一个进程式的负载均衡工具。

三.为什么需要做负载均衡

  1) 不做负载均衡,可能导致某台机子负荷太重而挂掉;

  2)导致资源浪费,比如某些机子收到太多的请求,肯定会导致某些机子收到很少请求甚至收不到请求,这样会浪费系统资源。 

四.springCloud如何开启负载均衡

  1)在消费者子工程的pom.xml文件的加入相关依赖(https://mvnrepository.com/artifact/org.springframework.cloud/spring-cloud-starter-ribbon/1.4.7.RELEASE);

<!-- https://mvnrepository.com/artifact/org.springframework.cloud/spring-cloud-starter-ribbon -->
<dependency>
 <groupId>org.springframework.cloud</groupId>
 <artifactId>spring-cloud-starter-ribbon</artifactId>
 <version>1.4.7.RELEASE</version>
</dependency>

   消费者需要获取服务注册中心的注册列表信息,把Eureka的依赖包也放进pom.xml

 <dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-starter-eureka-server</artifactId>
   <version>1.4.7.RELEASE</version>
 </dependency>

  2)在application.yml里配置服务注册中心的信息

  在该消费者(client)的application.yml里配置Eureka的信息

#配置Eureka
eureka:
 client:
 #是否注册自己到服务注册中心,消费者不用提供服务
 register-with-eureka: false
 service-url:
  #访问的url
  defaultZone: http://localhost:8002/eureka/

  3)在消费者启动类上面加上注解@EnableEurekaClient

@EnableEurekaClient

  4)在配置文件的Bean上加上

 @Bean
 @LoadBalanced
 public RestTemplate getRestTemplate(){
  return new RestTemplate();
 }

五.IRule

 什么是IRule

  IRule接口代表负载均衡的策略,它的不同的实现类代表不同的策略,它的四种实现类和它的关系如下()

详解SpringCloud的负载均衡

说明一下(idea找Irule的方法:ctrl+n   填入IRule进行查找)

1.RandomRule:表示随机策略,它将从服务清单中随机选择一个服务;

public class RandomRule extends AbstractLoadBalancerRule {
 public RandomRule() {
 }

 @SuppressWarnings({"RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE"})
 //传入一个负载均衡器
 public Server choose(ILoadBalancer lb, Object key) {
  if (lb == null) {
   return null;
  } else {
   Server server = null;
   while(server == null) {
    if (Thread.interrupted()) {
     return null;
    }
    //通过负载均衡器获取对应的服务列表
    List<Server> upList = lb.getReachableServers();
    //通过负载均衡器获取全部服务列表
    List<Server> allList = lb.getAllServers();
    int serverCount = allList.size();
    if (serverCount == 0) {
     return null;
    }
    //获取一个随机数
    int index = this.chooseRandomInt(serverCount);
    //通过这个随机数从列表里获取服务
    server = (Server)upList.get(index);
    if (server == null) {
     //当前线程转为就绪状态,让出cpu
     Thread.yield();
    } else {
     if (server.isAlive()) {
      return server;
     }

     server = null;
     Thread.yield();
    }
   }

   return server;
  }
 }

  小结:通过获取到的所有服务的数量,以这个数量为标准获取一个(0,服务数量)的数作为获取服务实例的下标,从而获取到服务实例

2.ClientConfigEnabledRoundRobinRule:ClientConfigEnabledRoundRobinRule并没有实现什么特殊的处理逻辑,但是他的子类可以实现一些高级策略, 当一些本身的策略无法实现某些需求的时候,它也可以做为父类帮助实现某些策略,一般情况下我们都不会使用它;

public class ClientConfigEnabledRoundRobinRule extends AbstractLoadBalancerRule {
 //使用“4”中的RoundRobinRule策略
 RoundRobinRule roundRobinRule = new RoundRobinRule();

 public ClientConfigEnabledRoundRobinRule() {
 }

 public void initWithNiwsConfig(IClientConfig clientConfig) {
  this.roundRobinRule = new RoundRobinRule();
 }

 public void setLoadBalancer(ILoadBalancer lb) {
  super.setLoadBalancer(lb);
  this.roundRobinRule.setLoadBalancer(lb);
 }

 public Server choose(Object key) {
  if (this.roundRobinRule != null) {
   return this.roundRobinRule.choose(key);
  } else {
   throw new IllegalArgumentException("This class has not been initialized with the RoundRobinRule class");
  }
 }
}

  小结:用来作为父类,子类通过实现它来实现一些高级负载均衡策略

1)ClientConfigEnabledRoundRobinRule的子类BestAvailableRule:从该策略的名字就可以知道,bestAvailable的意思是最好获取的,该策略的作用是获取到最空闲的服务实例;

public class BestAvailableRule extends ClientConfigEnabledRoundRobinRule {
 //注入负载均衡器,它可以选择服务实例
 private LoadBalancerStats loadBalancerStats;

 public BestAvailableRule() {
 }

 public Server choose(Object key) {
  //假如负载均衡器实例为空,采用它父类的负载均衡机制,也就是轮询机制,因为它的父类采用的就是轮询机制
  if (this.loadBalancerStats == null) {
   return super.choose(key);
  } else {
   //获取所有服务实例并放入列表里
   List<Server> serverList = this.getLoadBalancer().getAllServers();
   //并发量
 http://www.cppcns.com  int minimalConcurrentConnections = 2147483647;
   long currentTime = System.currentTimeMillis();
   Server chosen = null;
   Iterator var7 = serverList.iterator();
   //遍历服务列表
   while(var7.hasNext()) {
    Server server = (Server)var7.next();
    ServerStats serverStats = this.loadBalancerStats.getSingleServerStat(server);
    //淘汰掉已经负载的服务实例
    if (!serverStats.isCircuitBreakerTripped(currentTime)) {
     //获得当前服务的请求量(并发量)
     int concurrentConnections = serverStats.getActiveRequestsCount(currentTime);
     //找出并发了最小的服务
     if (concurrentConnections < minimalConcurrentConnections) {
      minimalConcurrentConnections = concurrentConnections;
      chosen = server;
     }
    }
   }

   if (chosen == null) {
    return super.choose(key);
   } else {
    return chosen;
   }
  }
 }

 public void setLoadBalancer(ILoadBalancer lb) {
  super.setLoadBalancer(lb);
  if (lb instanceof AbstractLoadBalancer) {
   this.loadBalancerStats = ((AbstractLoadBalancer)lb).getLoadBalancerStats();
  }

 }
}

   小结:ClientConfigEnabledRoundRobinRule子类之一,获取到并发了最少的服务

2)ClientConfigEnabledRoundRobinRule的另一个子类是PredicateBasedRule:通过源码可以看出它是一个抽象类,它的抽象方法getPredicate()返回一个AbstractServerPredicate的实例,然后它的choose方法调用AbstractServerPredicate类的chooseRoundRobinAfterFiltering方法获取具体的Server实例并返回

public abstract class PredicateBasedRule extends ClientConfigEnabledRoundRobinRule {
 public PredicateBasedRule() {
 }
 //获取AbstractServerPredicate对象
 public abstract AbstractServerPredicate getPredicate();

 public Server choose(Object key) {
  //获取当前策略的负载均衡器
  ILoadBalancer lb = this.getLoadBalancer();
  //通过AbstractServerPredicate的子类过滤掉一部分实例(它实现了Predicate)
  //以轮询的方式从过滤后的服务里选择一个服务
  Optional<Server> server = this.getPredicate().chooseRoundRobinAfterFiltering(lb.getAllServers(), key);
  return server.isPresent() ? (Server)server.get() : null;
 }
}

  再看看它的chooseRoundRobinAfterFiltering()方法是如何实现的

public Optional<Server> chooseRoundRobinAfterFiltering(List<Server> servers, Object loadBalancerKey) {
  List<Server> eligible = this.getEligibleServers(servers, loadBalancerKey);
  return eligible.size() == 0 ? Optional.absent() : Optional.of(eligible.get(this.incrementAndGetModulo(eligible.size())));
 }

  是这样的,先通过this.getEligibleServers(servers, loadBalancerKey)方法获取一部分实例,然后判断这部分实例是否为空,如果不为空则调用eligible.get(this.incrementAndGetModulo(eligible.size())方法从这部分实例里获取一个服务,点进this.getEligibleServers看

public List<Server> getEligibleServers(List<Server> servers, Object loadBalancerKey) {
  if (loadBalancerKey == null) {
   return ImmutableList.copyOf(Iterables.filter(servers, this.getServerOnlyPredicate()));
  } else {
   List<Server> results = Lists.newArrayList();
   Iterator var4 = servers.iterator();

   while(var4.hasNext()) {
    Server server = (Server)var4.next();
    //条件满足
    if (this.apply(new PredicateKey(loadBalancerKey, server))) {
     //添加到集合里
     results.add(server);
    }
   }

   return results;
  }
 }

  getEligibleServers方法是根据this.apply(new PredicateKey(loadBalancerKey, server))进行过滤的,如果满足,就添加到返回的集合中。符合什么条件才可以进行过滤呢?可以发现,apply是用this调用的,this指的是AbstractServerPredicate(它的类对象),但是,该类是个抽象类,该实例是不存在的,需要子类去实现,它的子类在这里暂时不是看了,以后有空再深入学习下,它的子类如下,实现哪个子类,就用什么 方式过滤。

详解SpringCloud的负载均衡

   再回到chooseRoundRobinAfterFiltering()方法,刚刚说完它通过 getEligibleServers方法过滤并获取到一部分实例,然后再通过this.incrementAndGetModulo(eligible.size())方法从这部分实例里选择一个实例返回,该方法的意思是直接返回下一个整数(索引值),通过该索引值从返回的实例列表中取得Server实例。

private int incrementAndGetModulo(int modulo) {
  //当前下标
  int current;
  //下一个下标
  int next;
  do {
   //获得当前下标值
   current = this.nextIndex.get();
   next = (current + 1) % modulo;
  } while(!this.nextIndex.compareAndSet(current, next) || current >= modulo);

  return current;
 }

  源码撸明白了,再来理一下chooseRoundRobinAfterFiltering()的思路:先通过getEligibleServers()方法获得一部分服务实例,再从这部分服务实例里拿到当前服务实例的下一个服务对象使用。

  小结:通过AbstractServerPredicate的chooseRoundRobinAfterFiltering方法进行过滤,获取备选的服务实例清单,然后用线性轮询选择一个实例,是一个抽象类,过滤策略在AbstractServerPredicate的子类中具体实现

3.RetryRule:是对选定的负载均衡策略加上重试机制,即在一个配置好的时间段内(默认500ms),当选择实例不成功,则一直尝试使用subRule的方式选择一个可用的实例,在调用时间到达阀值的时候还没找到可用服务,则返回空,如果没有配置负载策略,默认轮询(即“4”中的轮询);

  先贴上它的源码

public class RetryRule extends AbstractLoadBalancerRule {
 //从这可以看出,默认使用轮询机制
 IRule subRule = new RoundRobinRule();
 //500秒的阀值
 long maxRetryMillis = 500L;
 //无参构造函数
 public RetryRule() {
 }
 //使用轮询机制
 public RetryRule(IRule subRule) {
  this.subRule = (IRule)(subRule != null ? subRule : new RoundRobinRule());
 }

 public RetryRule(IRule subRule, long maxRetryMillis) {
  this.subRule = (IRule)(subRule != null ? subRule : new RoundRobinRule());
  this.maxRetryMillis = maxRetryMillis > 0L ? maxRetryMillis : 500L;
 }
 
 public void setRule(IRule subRule) {
  this.subRule = (IRule)(subRule != null ? subRule : new RoundRobinRule());
 }

 public IRule getRule() {
  return this.subRule;
 }
 //设置最大耗时时间(阀值),最多重试多久
 public void setMaxRetryMillis(long maxRetryMillis) {
  if (maxRetryMillis > 0L) {
   this.maxRetryMillis = maxRetryMillis;
  } else {
   this.maxRetryMillis = 500L;
  }

 }
 //获取重试的时间
 public long getMaxRetryMillis() {
  return this.maxRetryMillis;
 }
 //设置负载均衡器,用以获取服务
 public void setLoadBalancer(ILoadBalancer lb) {
  super.setLoadBalancer(lb);
  this.subRule.setLoadBalancer(lb);
 }
 //通过负载均衡器选择服务
 public Server choose(ILoadBalancer lb, Object key) {
  long requestTime = System.currentTimeMillis();
  //当前时间+阀值 = 截止时间
  long deadline = requestTime + this.maxRetryMillis;
  Server answer = null;
  answer = this.subRule.choose(key);
  //获取到服务直接返回
  if ((answer == null || !answer.isAlive()) && System.currentTimeMillis() < deadline) {
   InterruptTask task = new InterruptTask(deadline - System.currentTimeMillis());
   //获取不到服务的情况下反复获取
   while(!Thread.interrupted()) {
    answer = this.subRule.choose(key);
    if (answer != null && answer.isAlive() || System.currentTimeMillis() >= deadline) {
     break;
    }

    Thread.yield();
   }

   task.cancel();
  }

  return answer != null && answer.isAlive() ? answer : null;
 }

 public Server choose(Object key) {
  return this.choose(this.getLoadBalancer(), key);
 }

 public void initWithNiwsConfig(IClientConfig clientConfig) {
 }
}

  小结:采用RoundRobinRule的选择机制,进行反复尝试,当花费时间超过设置的阈值maxRetryMills时,就返回null

4.RoundRobinRule:轮询策略,它会从服务清单中按照轮询的方式依次选择每个服务实例,它的工作原理是:直接获取下一个可用实例,如果超过十次没有获取到可用的服务实例,则返回空且报出异常信息;

public class RoundRobinRule extends AbstractLoadBalancerRule {
 private AtomicInteger nextServerCyclicCounter;
 private static final boolean AVAILABLE_ONLY_SERVERS = true;
 private static final boolean ALL_SERVERS = false;
 private static Logger log = LoggerFactory.getLogger(RoundRobinRule.class);

 public RoundRobinRule() {
  this.nextServerCyclicCounter = new AtomicInteger(0);
 }

 public RoundRobinRule(ILoadBalancer lb) {
  this();
  this.setLoadBalancer(lb);
 }

 public Server choose(ILoadBalancer lb, Object key) {
  if (lb == null) {
   log.warn("no load balancer");
   return null;
  } else {
   Server server = null;
   int count = 0;

   while(true) {
    //选择十次,十次都没选到可用服务就返回空
    if (server == null && count++ < 10) {
     List<Server> reachableServers = lb.getReachableServers();
     List<Server> allServers = lb.getAllServers();
     int upCount = reachableServers.size();
     int serverCount = allServers.size();
     if (upCount != 0 && serverCount != 0) {
      int nextServerIndex = this.incrementAndGetModulo(serverCount);
      server = (Server)allServers.get(nextServerIndex);
      if (server == null) {
       Thread.yield();
      } else {
       if (server.isAlive() && server.isReadyToServe()) {
        return server;
       }

       server = null;
      }
      continue;
     }

     log.warn("No up servers available from load balancer: " + lb);
     return null;
    }

    if (count >= 10) {
     
     log.warn("No available alive servers after 10 tries from load balancer: " + lb);
    }

    return server;
   }
  }
 }
 
 //递增的形式实现轮询
 private int incrementAndGetModulo(int modulo) {
  int current;
  int next;
  do {
   current = this.nextServerCyclicCounter.get();
   next = (current + 1) % modulo;
  } while(!this.nextServerCyclicCounter.compareAndSet(current, next));

  return next;
 }

 public Server choose(Object key) {
  return this.choose(this.getLoadBalancer(), key);
 }

 public void initWithNiwsConfig(IClientConfig clientConfig) {
 }
}

  小结:采用线性轮询机制循环依次选择每个服务实例,直到选择到一个不为空的服务实例或循环次数达到10次   

它有个子类WeightedResponseTimeRule,WeightedResponseTimeRule是对RoundRobinRule的优化。WeightedResponseTimeRule在其父类的基础上,增加了定时任务这个功能,通过启动一个定时任务来计算每个服务的权重,然后遍历服务列表选择服务实例,从而达到更加优秀的分配效果。我们这里把这个类分为三部分:定时任务,计算权值,选择服务

1)定时任务

//定时任务
void initialize(ILoadBalancer lb) {
  if (this.serverWeightTimer != null) {
   this.serverWeightTimer.cancel();
  }

  this.serverWeightTimer = new Timer("NFLoadBalancer-serverWeightTimer-" + this.name, true);
  //开启一个任务,每30秒执行一次
  this.serverWeightTimer.schedule(new WeightedResponseTimeRule.DynamicServerWeightTask(), 0L, (long)this.serverWeightTaskTimerInterval);
  WeightedResponseTimeRule.ServerWeight sw = new WeightedResponseTimeRule.ServerWeight();
  sw.maintainWeights();
  Runtime.getRuntime().addShutdownHook(new Thread(new Runnable() {
   public void run() {
    WeightedResponseTimeRule.logger.info("Stopping NFLoadBalancer-serverWeightTimer-" + WeightedResponseTimeRule.this.name);
    WeightedResponseTimeRule.this.serverWeightTimer.cancel();
   }
  }));
 }

DynamicServerWeightTask()任务如下:

class DynamicServerWeightTask extends TimerTask {
  DynamicServerWeightTask() {
  }

  public void run() {
   WeightedResponseTimeRule.ServerWeight serverWeight = Weig编程客栈htedResponseTimeRule.this.new ServerWeight();

   try {
    //计算权重
    serverWeight.maintainWeights();
   } catch (Exception var3) {
    WeightedResponseTimeRule.logger.error("Error running DynamicServerWeightTask for {}", WeightedResponseTimeRule.this.name, var3);
   }

  }
 }

   小结:调用initialize方法开启定时任务,再在任务里计算服务的权重

2)计算权重:第一步,先算出所有实例的响应时间;第二步,再根据所有实例响应时间,算出每个实例的权重

//用来存储权重
private volatile List<Double> accumulatedWeights = new ArrayList();

//内部类
class ServerWeight {
  ServerWeight() {
  }
  //该方法用于计算权重
  public void maintainWeights() {
   //获取负载均衡器
   ILoadBalancer lb = WeightedResponseTimeRule.this.getLoadBalancer();
   if (lb != null) {
    if (WeightedResponseTimeRule.this.serverWeightAssignmentInProgress.compareAndSet(false, true)) {
     try {
      WeightedResponseTimeRule.logger.info("Weight adjusting job started");
      AbstractLoadBalancer nlb = (AbstractLoadBalancer)lb;
      //获得每个服务实例的信息
      LoadBalancerStats stats = nlb.getLoadBalancerStats();
      if (stats != null) {
       //实例的响应时间
       double totalResponseTime = 0.0D;

       ServerStats ss;
       //累加所有实例的响应时间
       for(Iterator var6 = nlb.getAllServers().iterator(); var6.hasNext(); totalResponseTime += ss.getResponseTimeAvg()) {
        Server server = (Server)var6.next();
        ss = stats.getSingleServerStat(server);
       }

       Double weightSoFar = 0.0D;
       List<Double> finalWeights = new ArrayList();
       Iterator var20 = nlb.getAllServers().iterator();
       //计算负载均衡器所有服务的权重,公式是weightSoFar = weightSoFar + weight-实例平均响应时间
       while(var20.hasNext()) {
        Server serverx = (Server)var20.next();
        ServerStats ssx = stats.getSingleServerStat(serverx);
        double weight = totalResponseTime - ssx.getResponseTimeAvg();
        weightSoFar = weightSoFar + weight;
        finalWeights.add(weightSoFar);
       }

       WeightedResponseTimeRule.this.setWeights(finalWeights);
       return;
      }
     } catch (Exception var16) {
      WeightedResponseTimeRule.logger.error("Error calculating server weights", var16);
      return;
     } finally {
      WeightedResponseTimeRule.this.serverWeightAssignmentInProgress.set(false);
     }

    }
   }
  }
 }

3)选择服务

@SuppressWarnings({"RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE"})
 public Server choose(ILoadBalancer lb, Object key) {
  if (lb == null) {
   return null;
  } else {
   Server server = null;

   while(server == null) {
    List<Double> currentWeights = this.accumulatedWeights;
    if (Thread.interrupted()) {
     return null;
    }

    List<Server> allList = lb.getAllServers();
    int serverCount = allList.size();
    if (serverCount == 0) {
     return null;
    }

    int serverIndex = 0;
    
    double maxTotalWeight = currentWeights.size() == 0 ? 0.0D : (Double)currentWeights.get(currentWeights.size() - 1);
    if (maxTotalWeight >= 0.001D && serverCount == currentWeights.size()) {
     //生产0到最大权重值的随机数
     double randomWeight = this.random.nextDouble() * maxTotalWeight;
     int n = 0;
     //循环权重区间
     for(Iterator var13 = currentWeights.iterator(); var13.hasNext(); ++n) {
      //获取到循环的数
      Double d = (Double)var13.next();
      //假如随机数在这个区间内,就拿该索引d服务列表获取对应的实例
      if (d >= randomWeight) {
       serverIndex = n;
       break;
      }
     }

     server = (Server)allList.get(serverIndex);
    } else {
     servNZseuLjZcNer = super.choose(this.getLoadBalancer(), key);
     if (server == null) {
      return server;
     }
    }

    if (server == null) {
     Thread.yield();
    } else {
     if (server.isAlive()) {
      return server;
     }

     server = null;
    }
   }

   return server;
  }
 }

  小结:首先生成了一个[0,最大权重值) 区间内的随机数,然后遍历权重列表,假如当前随机数在编程客栈这个区间内,就通过该下标获得对应的服务。

以上就是详解SpringCloud的负载均衡的详细内容,更多关于SpringCloud 负载均衡的资料请关注我们其它相关文章!

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