撸一撸Spring Cloud Ribbon的原理-负载均衡策略

在前两篇《撸一撸Spring Cloud Ribbon的原理》《撸一撸Spring Cloud Ribbon的原理-负载均衡器》中,整理了Ribbon如何通过负载均衡拦截器植入RestTemplate,以及调用负载均衡器获取服务列表,如何过滤,如何更新等的处理过程。

因为,负载均衡器最终是调用负载均衡策略的choose方法来选择一个服务,所以这一篇,整理Ribbon的负载均衡策略。

 

策略类

  • RandomRule
  • RoundRobinRule
  • RetryRule
  • WeightedResponseTimeRule
  • ClientConfigEnabledRoundRobinRule
  • BestAvailableRule
  • PredicateBasedRule
  • AvailabilityFilteringRule
  • ZoneAvoidanceRule

 

他们的位置在:

ribbon-loadbalancer-2.2.2.jar

com.netflix.loadbalancer

 

类继承关系

RandomRule

随机选取负载均衡策略。

choose方法中,通过随机Random对象,在所有服务实例数量中随机找一个服务的索引号,然后从上线的服务中获取对应的服务。

这时候,很可能会有不在线的服务,就有可能从上线的服务中获取不到,那么休息会儿再获取知道随机获取到一个上线的服务为止。

public class RandomRule extends AbstractLoadBalancerRule {
    Random rand;

    public RandomRule() {
        rand = new Random();
    }

    @edu.umd.cs.findbugs.annotations.SuppressWarnings(value = "RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE")
    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 = rand.nextInt(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;

    }

    //
}

 

RoundRobinRule

线性轮询负载均衡策略。

choose方法中,通过incrementAndGetModulo方法以线性轮询方式获取服务。

在incrementAndGetModulo中,实际上在类中维护了一个原子性的nextServerCyclicCounter成员变量作为当前服务的索引号,每次在所有服务数量的限制下,就是将服务的索引号加1,到达服务数量限制时再从头开始。

public class RoundRobinRule extends AbstractLoadBalancerRule {

    private AtomicInteger nextServerCyclicCounter;

    //

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

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

    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;
    }

    private int incrementAndGetModulo(int modulo) {
        for (;;) {
            int current = nextServerCyclicCounter.get();
            int next = (current + 1) % modulo;
            if (nextServerCyclicCounter.compareAndSet(current, next))
                return next;
        }
    }
    
    //
}

 

WeightedResponseTimeRule

响应时间作为选取权重的负载均衡策略。

其含义就是,响应时间越短的服务被选中的可能性大。

继承自RoundRobinRule类。

public class WeightedResponseTimeRule extends RoundRobinRule {

    //// holds the accumulated weight from index 0 to current index
    // for example, element at index 2 holds the sum of weight of servers from 0 to 2
    private volatile List<Double> accumulatedWeights = new ArrayList<Double>();

    //

    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();
            }
        }));
    }

    //

    @edu.umd.cs.findbugs.annotations.SuppressWarnings(value = "RCN_REDUNDANT_NULLCHECK_OF_NULL_VALUE")
    @Override
    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) {
                server =  super.choose(getLoadBalancer(), key);
                if(server == null) {
                    return server;
                }
            } else {
                // generate a random weight between 0 (inclusive) to maxTotalWeight (exclusive)
                double randomWeight = random.nextDouble() * maxTotalWeight;
                // pick the server index based on the randomIndex
                int n = 0;
                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;
    }

    class DynamicServerWeightTask extends TimerTask {
        public void run() {
            ServerWeight serverWeight = new ServerWeight();
            try {
                serverWeight.maintainWeights();
            } catch (Exception e) {
                logger.error("Error running DynamicServerWeightTask for {}", name, e);
            }
        }
    }

    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)
                // so that the longer the response time, the less the weight and the less likely to be chosen
                Double weightSoFar = 0.0;
                
                // 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;
                    finalWeights.add(weightSoFar);   
                }
                setWeights(finalWeights);
            } catch (Exception e) {
                logger.error("Error calculating server weights", e);
            } finally {
                serverWeightAssignmentInProgress.set(false);
            }

        }
    }

    void setWeights(List<Double> weights) {
        this.accumulatedWeights = weights;
    }
    
    //

}

既然是按照响应时间权重来选择服务,那么先整理一下权重算法是怎么做的。

观察initialize方法,启动了定时器定时执行DynamicServerWeightTask的run来调用计算服务权重,计算权重是通过内部类ServerWeight的maintainWeights方法来进行。

整理一下maintainWeights方法的逻辑,里面有两个for循环,第一个for循环拿到所有服务的总响应时间,第二个for循环计算每个服务的权重以及总权重。

第一个for循环。

假设有4个服务,每个服务的响应时间(ms):

A: 200

B: 500

C: 30

D: 1200

总响应时间:

200+500+30+1200=1930ms

接下来第二个for循环,计算每个服务的权重。

服务的权重=总响应时间-服务自身的响应时间:

A: 1930-200=1730

B: 1930-500=1430

C: 1930-30=1900

D: 1930-1200=730

总权重:

1730+1430+1900+730=5790

响应时间及权重计算结果示意图:

 

结果就是响应时间越短的服务,它的权重就越大。

 

再看一下choose方法。

重点在while循环的第3个if这里。

首先如果判定没有服务或者权重还没计算出来时,会采用父类RoundRobinRule以线性轮询的方式选择服务器。

有服务,有权重计算结果后,就是以总权重值为限制,拿到一个随机数,然后看随机数落到哪个区间,就选择对应的服务。

所以选取服务的结论就是:

响应时间越短的服务,它的权重就越大,被选中的可能性就越大。

 

RetryRule

这个策略默认就是用RoundRobinRule策略选取服务,当然可以通过配置,在构造RetryRule的时候传进想要的策略。

为了应对在有可能出现无法选取出服务的情况,比如瞬时断线的情况,那么就要提供一种重试机制,在最大重试时间的限定下重复尝试选取服务,直到选取出一个服务或者超时。

最大重试时间maxRetryMillis是可配置的。

public class RetryRule extends AbstractLoadBalancerRule {
    IRule subRule = new RoundRobinRule();
    long maxRetryMillis = 500;

    public RetryRule() {
    }

    public RetryRule(IRule subRule) {
        this.subRule = (subRule != null) ? subRule : new RoundRobinRule();
    }

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

    //

    public void setMaxRetryMillis(long maxRetryMillis) {
        if (maxRetryMillis > 0) {
            this.maxRetryMillis = maxRetryMillis;
        } else {
            this.maxRetryMillis = 500;
        }
    }

    //

    /*
     * Loop if necessary. Note that the time CAN be exceeded depending on the
     * subRule, because we're not spawning additional threads and returning
     * early.
     */
    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;
        }
    }
        
    //
    
}

 

ClientConfigEnabledRoundRobinRule

该策略本身很简单,就是默认通过RoundRobinRule策略选取服务,策略还不能自定义。

这个策略的的目的就是通过继承该类,并且通过对choose的重写,来实现更多的功能,继承后保底是有RoundRobinRule策略。

public class ClientConfigEnabledRoundRobinRule extends AbstractLoadBalancerRule {

    RoundRobinRule roundRobinRule = new RoundRobinRule();

    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        roundRobinRule = new RoundRobinRule();
    }

    @Override
    public void setLoadBalancer(ILoadBalancer lb) {
        super.setLoadBalancer(lb);
        roundRobinRule.setLoadBalancer(lb);
    }
    
    @Override
    public Server choose(Object key) {
        if (roundRobinRule != null) {
            return roundRobinRule.choose(key);
        } else {
            throw new IllegalArgumentException(
                    "This class has not been initialized with the RoundRobinRule class");
        }
    }

}

 

BestAvailableRule

继承自上面的ClientConfigEnabledRoundRobinRule,重写choose方法。

从所有的服务中,从那些没有断开的服务中,找到,到目前为止,请求数量最小的服务。

public class BestAvailableRule extends ClientConfigEnabledRoundRobinRule {

    private LoadBalancerStats loadBalancerStats;
    
    @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;
        }
    }

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

 

PredicateBasedRule

这是一个抽象类,提供一个choose的模板,通过调用AbstractServerPredicate实现类的过滤方法来过滤出目标的服务,再通过轮询方法选出一个服务。

PredicateBasedRule的子类ZoneAvoidanceRule使用了该模板选择服务。

public abstract class PredicateBasedRule extends ClientConfigEnabledRoundRobinRule {
   
    public abstract AbstractServerPredicate getPredicate();
        
    @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

按可用性进行过滤服务的负载均衡策略。

继承自PredicateBasedRule,但是重写了choose方法。

public class AvailabilityFilteringRule extends PredicateBasedRule {    

    private AbstractServerPredicate predicate;
    
    //
@Override public Server choose(Object key) { int count = 0; Server server = roundRobinRule.choose(key); while (count++ <= 10) { if (predicate.apply(new PredicateKey(server))) { return server; } server = roundRobinRule.choose(key); } return super.choose(key); } // }

先用线性轮询策略选出一个服务,通过predicate对象判断是否符合可用性要求,符合的就作为目标服务。这里的做法就是先选择后过滤。

如果尝试了十次都没有可用的服务,调用父类PredicateBasedRule的choose。

在构造函数中会看到AvailabilityFilteringRule用的是AvailabilityPredicate对象对服务进行判定。

public class AvailabilityFilteringRule extends PredicateBasedRule {    

    private AbstractServerPredicate predicate;
    
    public AvailabilityFilteringRule() {
        super();
        predicate = CompositePredicate.withPredicate(new AvailabilityPredicate(this, null))
                .addFallbackPredicate(AbstractServerPredicate.alwaysTrue())
                .build();
    }
    
    
    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        predicate = CompositePredicate.withPredicate(new AvailabilityPredicate(this, clientConfig))
                    .addFallbackPredicate(AbstractServerPredicate.alwaysTrue())
                    .build();
    }

    //
}

 在AvailabilityPredicate中,通过shouldSkipServer函数判定服务状态。

public class AvailabilityPredicate extends  AbstractServerPredicate {
        
    //
    
    @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;
    }

}

判定规则:

以下两项有一项成立,就表示该服务不可用,不能使用该服务。

1.

配置项niws.loadbalancer.availabilityFilteringRule.filterCircuitTripped为true(未配置则默认为true),并且已经触发断路。

2.

服务的活动请求数 > 配置项niws.loadbalancer.availabilityFilteringRule.activeConnectionsLimit(未配则默认为Interge.MAX_VALUE)。

 

在AvailabilityFilteringRule的choose中无法选出服务的情况下,会调用父类PredicateBasedRule的choose,PredicateBasedRule采用先过滤后线性轮行方法选择服务,不过,用来判定的predicate还是AvailabilityPredicate,所以过滤用的判定规则和上面是一样的。

 

ZoneAvoidanceRule

可以发现在ZoneAvoidanceRule本身并没有重写choose方法,用的还是抽象父类PredicateBasedRule的choose。

上面记录过,PredicateBasedRule的choose本身使用的是predicate进行过滤。

看一下ZoneAvoidanceRule定义的predicate。

public class ZoneAvoidanceRule extends PredicateBasedRule {

    //
    
    private CompositePredicate compositePredicate;
    
    public ZoneAvoidanceRule() {
        super();
        ZoneAvoidancePredicate zonePredicate = new ZoneAvoidancePredicate(this);
        AvailabilityPredicate availabilityPredicate = new AvailabilityPredicate(this);
        compositePredicate = createCompositePredicate(zonePredicate, availabilityPredicate);
    }
    
    private CompositePredicate createCompositePredicate(ZoneAvoidancePredicate p1, AvailabilityPredicate p2) {
        return CompositePredicate.withPredicates(p1, p2)
                             .addFallbackPredicate(p2)
                             .addFallbackPredicate(AbstractServerPredicate.alwaysTrue())
                             .build();
        
    }
    
    
    @Override
    public void initWithNiwsConfig(IClientConfig clientConfig) {
        ZoneAvoidancePredicate zonePredicate = new ZoneAvoidancePredicate(this, clientConfig);
        AvailabilityPredicate availabilityPredicate = new AvailabilityPredicate(this, clientConfig);
        compositePredicate = createCompositePredicate(zonePredicate, availabilityPredicate);
    }

    //

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

在这里的predicate主要用了两个,ZoneAvoidancePredicate,AvailabilityPredicate,都被复合进了CompositePredicate。

PredicateBasedRule的choose会去调用CompositePredicate的getEligibleServers来获取符合要求的服务。

而在getEligibleServers内部会对所有服务逐一调用复合进来的predicate(ZoneAvoidancePredicate,AvailabilityPredicate)的getEligibleServers。

public class CompositePredicate extends AbstractServerPredicate {

    private AbstractServerPredicate delegate;
    
    private List<AbstractServerPredicate> fallbacks = Lists.newArrayList();
        
    private int minimalFilteredServers = 1;
    
    private float minimalFilteredPercentage = 0;    
    
    @Override
    public boolean apply(@Nullable PredicateKey input) {
        return delegate.apply(input);
    }

    //

    /**
     * Get the filtered servers from primary predicate, and if the number of the filtered servers
     * are not enough, trying the fallback predicates  
     */
    @Override
    public List<Server> getEligibleServers(List<Server> servers, Object loadBalancerKey) {
        List<Server> result = super.getEligibleServers(servers, loadBalancerKey);
        Iterator<AbstractServerPredicate> i = fallbacks.iterator();
        while (!(result.size() >= minimalFilteredServers && result.size() > (int) (servers.size() * minimalFilteredPercentage))
                && i.hasNext()) {
            AbstractServerPredicate predicate = i.next();
            result = predicate.getEligibleServers(servers, loadBalancerKey);
        }
        return result;
    }
}

 调用的复合进来的predicate的getEligibleServers方法是定义在抽象类AbstractServerPredicate中。

可以看到,最终还是调用各predicate的apply方法进行过滤。

public abstract class AbstractServerPredicate implements Predicate<PredicateKey> {
    
    protected IRule rule;
    private volatile LoadBalancerStats lbStats;
    
    private final Random random = new Random();
    
    private final AtomicInteger nextIndex = new AtomicInteger();

    //

    /**
     * Get servers filtered by this predicate from list of servers. Load balancer key
     * is presumed to be null. 
     * 
     * @see #getEligibleServers(List, Object)
     * 
     */
    public List<Server> getEligibleServers(List<Server> servers) {
        return getEligibleServers(servers, null);
    }
 
    /**
     * Get servers filtered by this predicate from list of servers. 
     */
    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;            
        }
    }

    //
}

AvailabilityPredicate过滤时的判定规则在上面的AvailabilityFilteringRule中已经写过。

现在主要看一下ZoneAvoidancePredicate的Apply方法。

public class ZoneAvoidancePredicate extends  AbstractServerPredicate {

    //

    @Override
    public boolean apply(@Nullable PredicateKey input) {
        if (!ENABLED.get()) {
            return true;
        }
        String serverZone = input.getServer().getZone();
        if (serverZone == null) {
            // there is no zone information from the server, we do not want to filter
            // out this server
            return true;
        }
        LoadBalancerStats lbStats = getLBStats();
        if (lbStats == null) {
            // no stats available, do not filter
            return true;
        }
        if (lbStats.getAvailableZones().size() <= 1) {
            // only one zone is available, do not filter
            return true;
        }
        Map<String, ZoneSnapshot> zoneSnapshot = ZoneAvoidanceRule.createSnapshot(lbStats);
        if (!zoneSnapshot.keySet().contains(serverZone)) {
            // The server zone is unknown to the load balancer, do not filter it out 
            return true;
        }
        logger.debug("Zone snapshots: {}", zoneSnapshot);
        Set<String> availableZones = ZoneAvoidanceRule.getAvailableZones(zoneSnapshot, triggeringLoad.get(), triggeringBlackoutPercentage.get());
        logger.debug("Available zones: {}", availableZones);
        if (availableZones != null) {
            return availableZones.contains(input.getServer().getZone());
        } else {
            return false;
        }
    }    
}

比较清楚的是,这里获取了可用区域,看一下每个服务是否都在可用区中,在的就作为符合ZoneAvoidancePredicate要求的服务。

获得可用区域可以看一下ZoneAvoidanceRule的getAvailableZones方法。

 

End