Ribbon客户端负载均衡策略

Ribbon客户端负载均衡策略

ILoadBalancer接口,有如下几个实现类:


其中BaseLoadBalancer实现了基本的负载均衡策略,NoOpLoadBalancer没有进行任何负载均衡策略,
而DynamicServerListLoadBalancer和ZoneAwareLoadBalancer都在BaseLoadBalancer的基础上进行了相应的扩展。
而它们在chooser()方法中,是通过委托IRule规则实现类,来实现服务实例的选择,如下:

  public Server chooseServer(Object key) {
        if (counter == null) {
            counter = createCounter();
        }
        counter.increment();
        if (rule == null) {
            return null;
        } else {
            try {
                return rule.choose(key);
            } catch (Exception e) {
                logger.warn("LoadBalancer [{}]:  Error choosing server for key {}", name, key, e);
                return null;
            }
        }
    }

其中IRule的实现类有如下图:


如上图所示,每一个实现类都代表了一个负载均衡策略。
RoundRobinRule:采取了最常见了负载均衡策略,即轮询的方式:

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

        Server server = null;
        int count = 0;
        while (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)) {
                log.warn("No up servers available from load balancer: " + lb);
                return null;
            }

            int nextServerIndex = incrementAndGetModulo(serverCount);
            server = allServers.get(nextServerIndex);

            if (server == null) {
                /* Transient. */
                Thread.yield();
                continue;
            }

            if (server.isAlive() && (server.isReadyToServe())) {
                return (server);
            }

            // Next.
            server = null;
        }

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

RandomRule:随机选择服务实例,可能因为高并发Bug导致该线程一直获取不到服务实例而陷入死循环,其具体代码如下:

 public Server choose(ILoadBalancer lb, Object key) {
        if (lb == null) {
            return null;
        }
        Server server = null;

        while (server == null) {
            if (Thread.interrupted()) {
                return null;
            }
            List<Server> upList = lb.getReachableServers();
            List<Server> allList = lb.getAllServers();

            int serverCount = allList.size();
            if (serverCount == 0) {
                /*
                 * No servers. End regardless of pass, because subsequent passes
                 * only get more restrictive.
                 */
                return null;
            }

            int index = chooseRandomInt(serverCount);
            server = upList.get(index);

            if (server == null) {
                /*
                 * The only time this should happen is if the server list were
                 * somehow trimmed. This is a transient condition. Retry after
                 * yielding.
                 */
                Thread.yield();
                continue;
            }

            if (server.isAlive()) {
                return (server);
            }

            // Shouldn't actually happen.. but must be transient or a bug.
            server = null;
            Thread.yield();
        }

        return server;

    }

RetryRule:重复尝试负载均衡策略,在截止时间前,尽最大努力获取服务实例,源码如下:

public Server choose(ILoadBalancer lb, Object key) {
        long requestTime = System.currentTimeMillis();
        long deadline = requestTime + maxRetryMillis;

        Server answer = null;

        answer = subRule.choose(key);

        if (((answer == null) || (!answer.isAlive()))
                && (System.currentTimeMillis() < deadline)) {

            InterruptTask task = new InterruptTask(deadline
                    - System.currentTimeMillis());

            while (!Thread.interrupted()) {
                answer = subRule.choose(key);

                if (((answer == null) || (!answer.isAlive()))
                        && (System.currentTimeMillis() < deadline)) {
                    /* pause and retry hoping it's transient */
                    Thread.yield();
                } else {
                    break;
                }
            }

            task.cancel();
        }

        if ((answer == null) || (!answer.isAlive())) {
            return null;
        } else {
            return answer;
        }
    }

WeightedResponseTimeRule:该Rule是RoundRobinRule的继承类,在其基础上进行了相应的功能扩展:

  • 定时任务,启动一个定时任务来统计每个服务实例响应情况,其源码如下:
 void initialize(ILoadBalancer lb) {        
        if (serverWeightTimer != null) {
            serverWeightTimer.cancel();
        }
        serverWeightTimer = new Timer("NFLoadBalancer-serverWeightTimer-"
                + name, true);
        serverWeightTimer.schedule(new DynamicServerWeightTask(), 0,
                serverWeightTaskTimerInterval);
        // do a initial run
        ServerWeight sw = new ServerWeight();
        sw.maintainWeights();

        Runtime.getRuntime().addShutdownHook(new Thread(new Runnable() {
            public void run() {
                logger
                        .info("Stopping NFLoadBalancer-serverWeightTimer-"
                                + name);
                serverWeightTimer.cancel();
            }
        }));
    }
  • 权重计算,权重计算算法是这样的,(1)获取所有实例总的响应时间total,计算 第i个服务实例的weight=total-server(i).avgResponseTime,(2)计算i个实例的加权值wSofar(i),加权值在每次计算完(i-1)实例的权重后都会加上该权重值即wSofar(i)+=weight(i),(3)加权值集合形成的权重区间[wSofar(i-1),wSofar(i)],如果该区间越宽的话,说明该区间越容易被选中
    权重计算源码如下:
class ServerWeight {

        public void maintainWeights() {
            ILoadBalancer lb = getLoadBalancer();
            if (lb == null) {
                return;
            }
            
            if (!serverWeightAssignmentInProgress.compareAndSet(false,  true))  {
                return; 
            }
            
            try {
                logger.info("Weight adjusting job started");
                AbstractLoadBalancer nlb = (AbstractLoadBalancer) lb;
                LoadBalancerStats stats = nlb.getLoadBalancerStats();
                if (stats == null) {
                    // no statistics, nothing to do
                    return;
                }
                double totalResponseTime = 0;
                // find maximal 95% response time
                for (Server server : nlb.getAllServers()) {
                    // this will automatically load the stats if not in cache
                    ServerStats ss = stats.getSingleServerStat(server);
                    totalResponseTime += ss.getResponseTimeAvg();
                }
                // weight for each server is (sum of responseTime of all servers - responseTime)
              // 计算每个实例的权重=加权值+totalReponseTime-实例平均响应时间
                // so that the longer the response time, the less the weight and the less likely to be chosen
                Double weightSoFar = 0.0;//加权值初始为0
                
                // create new list and hot swap the reference
                List<Double> finalWeights = new ArrayList<Double>();
                for (Server server : nlb.getAllServers()) {
                    ServerStats ss = stats.getSingleServerStat(server);
                    double weight = totalResponseTime - ss.getResponseTimeAvg();
                    weightSoFar += weight;//加权值每次都会加上一个计算过实例的权重值
                    finalWeights.add(weightSoFar);   
                }
                setWeights(finalWeights);
            } catch (Exception e) {
                logger.error("Error calculating server weights", e);
            } finally {
                serverWeightAssignmentInProgress.set(false);
            }

        }
    }

选择服务实例源码如下:

 public Server choose(ILoadBalancer lb, Object key) {
        if (lb == null) {
            return null;
        }
        Server server = null;

        while (server == null) {
            // get hold of the current reference in case it is changed from the other thread
            List<Double> currentWeights = accumulatedWeights;
            if (Thread.interrupted()) {
                return null;
            }
            List<Server> allList = lb.getAllServers();

            int serverCount = allList.size();

            if (serverCount == 0) {
                return null;
            }

            int serverIndex = 0;

            // last one in the list is the sum of all weights
            double maxTotalWeight = currentWeights.size() == 0 ? 0 : currentWeights.get(currentWeights.size() - 1); 
            // No server has been hit yet and total weight is not initialized
            // fallback to use round robin
            if (maxTotalWeight < 0.001d || serverCount != currentWeights.size()) {
                server =  super.choose(getLoadBalancer(), key);
                if(server == null) {
                    return server;
                }
            } else {
                // 在0到maxWeight中随机生一个权重值
                double randomWeight = random.nextDouble() * maxTotalWeight;
                // pick the server index based on the randomIndex
                int n = 0;
               //获取第一个权重值大于随机权重值
                for (Double d : currentWeights) {
                    if (d >= randomWeight) {
                        serverIndex = n;
                        break;
                    } else {
                        n++;
                    }
                }

                server = allList.get(serverIndex);
            }

            if (server == null) {
                /* Transient. */
                Thread.yield();
                continue;
            }

            if (server.isAlive()) {
                return (server);
            }

            // Next.
            server = null;
        }
        return server;
    }

ClientConfigEnableRoundRobinRule该负载均衡策略,本身没有特殊之处,其内部实现了一个RoundRobinRule来进行轮询选择服务实例,但是该策略继承子类在进行一些高级策略时,如果遇到无法处理情况时,就可以个通过该策略进行服务实例选取;

BestAvailableRule:该策略时ClientConfigEnableRoundRobinRule子类,该策略会遍历实例列表所有服务实例,过滤掉故障实例,并从中选择并发请求数最小的实例,故该实例的策略时选择最空闲的实例:

 @Override
    public Server choose(Object key) {
        if (loadBalancerStats == null) {
            return super.choose(key);
        }
        List<Server> serverList = getLoadBalancer().getAllServers();
        int minimalConcurrentConnections = Integer.MAX_VALUE;
        long currentTime = System.currentTimeMillis();
        Server chosen = null;
        for (Server server: serverList) {
            ServerStats serverStats = loadBalancerStats.getSingleServerStat(server);
            if (!serverStats.isCircuitBreakerTripped(currentTime)) {
                int concurrentConnections = serverStats.getActiveRequestsCount(currentTime);
                if (concurrentConnections < minimalConcurrentConnections) {
                    minimalConcurrentConnections = concurrentConnections;
                    chosen = server;
                }
            }
        }
        if (chosen == null) {
            return super.choose(key);
        } else {
            return chosen;
        }
    }

PredicateBaseRule:该策略主要是通过该子类实现getPredicate()方法,来过滤服务实例列表,然后从剩余的实例列表中以线性轮询的方式获取一个;
其源码如下:

 @Override
    public Server choose(Object key) {
        ILoadBalancer lb = getLoadBalancer();
        Optional<Server> server = getPredicate().chooseRoundRobinAfterFiltering(lb.getAllServers(), key);
        if (server.isPresent()) {
            return server.get();
        } else {
            return null;
        }       
    }

AvailabilityFilteringRule:该策略时PredicateBaseRule的子策略,其实现源码如下:


    @Override
    public AbstractServerPredicate getPredicate() {
        return predicate;
    }

public Optional<Server> chooseRoundRobinAfterFiltering(List<Server> servers) {
        List<Server> eligible = getEligibleServers(servers);
        if (eligible.size() == 0) {
            return Optional.absent();
        }
        return Optional.of(eligible.get(incrementAndGetModulo(eligible.size())));
    }

 public List<Server> getEligibleServers(List<Server> servers) {
        return getEligibleServers(servers, null);
    }

  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();
            for (Server server: servers) {
                if (this.apply(new PredicateKey(loadBalancerKey, server))) {
                    results.add(server);
                }
            }
            return results;            
        }
    }

AvailabilityFilteringPredicate中实现了apply方法,该方法主要判断该服务实例是否故障,或者该服务实例并发请求数是否超过阈值,如果超过了阈值,那么该服务实例将会被过滤掉,其源码如下:

 @Override
    public boolean apply(@Nullable PredicateKey input) {
        LoadBalancerStats stats = getLBStats();
        if (stats == null) {
            return true;
        }
        return !shouldSkipServer(stats.getSingleServerStat(input.getServer()));
    }
    
    
    private boolean shouldSkipServer(ServerStats stats) {        
        if ((CIRCUIT_BREAKER_FILTERING.get() && stats.isCircuitBreakerTripped()) 
                || stats.getActiveRequestsCount() >= activeConnectionsLimit.get()) {
            return true;
        }
        return false;
    }
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