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  • springCloud的负载均衡

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,至于如何启动一个springCloud项目,请看这篇博客https://www.cnblogs.com/fengrongriup/p/14464208.html

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#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/
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  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()

 

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;

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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;
        }
    }
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  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;

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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");
        }
    }
}
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  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;

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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
            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();
        }

    }
}
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   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

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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;
    }
}
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  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

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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;
        }
    }
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  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。

   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。

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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;
    }
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  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

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public class RetryRule extends AbstractLoadBalancerRule {
    //从这可以看出,默认使用轮询机制
    IRule subRule = new RoundRobinRule();
    //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) {
    }
}
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  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;

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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) {
    }
}
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  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

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//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();
            }
        }));
    }
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DynamicServerWeightTask()任务如下:

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class DynamicServerWeightTask extends TimerTask {
        DynamicServerWeightTask() {
        }

        public void run() {
            WeightedResponseTimeRule.ServerWeight serverWeight = WeightedResponseTimeRule.this.new ServerWeight();

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

        }
    }
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   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

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//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);
                    }

                }
            }
        }
    }
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3)选择服务

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@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 {
                    server = 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;
        }
    }
复制代码

  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,假如当前随机数在这个区间内,就通过该下标获得对应的服务。

 原文:https://www.cnblogs.com/fengrongriup/p/14505755.html


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