3. Sentinel源码分析— QPS流量控制是如何实现的？

public static void main(String[] args) {
    List<FlowRule> rules = new ArrayList<FlowRule>();
    FlowRule rule1 = new FlowRule();
    rule1.setResource("abc"); 
    rule1.setCount(20);
    rule1.setGrade(RuleConstant.FLOW_GRADE_QPS);
    rule1.setLimitApp("default");
    rules.add(rule1);
    FlowRuleManager.loadRules(rules);

    Entry entry = null;

    try {
        entry = SphU.entry("abc");
        //dosomething 
    } catch (BlockException e1) {

    } catch (Exception e2) {
        // biz exception
    } finally {
        if (entry != null) {
            entry.exit();
        }
    }
}

//设置拒绝策略：直接拒绝、Warm Up、匀速排队，默认是DefaultController
TrafficShapingController rater = generateRater(rule);
rule.setRater(rater);

private static TrafficShapingController generateRater(/*@Valid*/ FlowRule rule) {
    if (rule.getGrade() == RuleConstant.FLOW_GRADE_QPS) {
        switch (rule.getControlBehavior()) {
            case RuleConstant.CONTROL_BEHAVIOR_WARM_UP:
                //warmUpPeriodSec默认是10 
                return new WarmUpController(rule.getCount(), rule.getWarmUpPeriodSec(),
                    ColdFactorProperty.coldFactor);
            case RuleConstant.CONTROL_BEHAVIOR_RATE_LIMITER:
                //rule.getMaxQueueingTimeMs()默认是500
                return new RateLimiterController(rule.getMaxQueueingTimeMs(), rule.getCount());
            case RuleConstant.CONTROL_BEHAVIOR_WARM_UP_RATE_LIMITER:
                return new WarmUpRateLimiterController(rule.getCount(), rule.getWarmUpPeriodSec(),
                    rule.getMaxQueueingTimeMs(), ColdFactorProperty.coldFactor);
            case RuleConstant.CONTROL_BEHAVIOR_DEFAULT:
            default:
                // Default mode or unknown mode: default traffic shaping controller (fast-reject).
        }
    }
    return new DefaultController(rule.getCount(), rule.getGrade());
}

public void entry(Context context, ResourceWrapper resourceWrapper, DefaultNode node, int count,
                  boolean prioritized, Object... args) throws Throwable {
    checkFlow(resourceWrapper, context, node, count, prioritized);

    fireEntry(context, resourceWrapper, node, count, prioritized, args);
}

void checkFlow(ResourceWrapper resource, Context context, DefaultNode node, int count, boolean prioritized)
    throws BlockException {
    checker.checkFlow(ruleProvider, resource, context, node, count, prioritized);
}

public void checkFlow(Function<String, Collection<FlowRule>> ruleProvider, ResourceWrapper resource,
                      Context context, DefaultNode node, int count, boolean prioritized) throws BlockException {
    if (ruleProvider == null || resource == null) {
        return;
    }
    //返回FlowRuleManager里面注册的所有规则
    Collection<FlowRule> rules = ruleProvider.apply(resource.getName());
    if (rules != null) {
        for (FlowRule rule : rules) {
            //如果当前的请求不能通过，那么就抛出FlowException异常
            if (!canPassCheck(rule, context, node, count, prioritized)) {
                throw new FlowException(rule.getLimitApp(), rule);
            }
        }
    }
}

private static boolean passLocalCheck(FlowRule rule, Context context, DefaultNode node, int acquireCount,
                                      boolean prioritized) {
    //节点选择
    Node selectedNode = selectNodeByRequesterAndStrategy(rule, context, node);
    if (selectedNode == null) {
        return true;
    }
    //根据设置的规则来拦截
    return rule.getRater().canPass(selectedNode, acquireCount, prioritized);
}

new RateLimiterController(rule.getMaxQueueingTimeMs(), rule.getCount());

public boolean canPass(Node node, int acquireCount, boolean prioritized) {
    // Pass when acquire count is less or equal than 0.
    if (acquireCount <= 0) {
        return true;
    }
    // Reject when count is less or equal than 0.
    // Otherwise,the costTime will be max of long and waitTime will overflow in some cases.
    if (count <= 0) {
        return false;
    }

    long currentTime = TimeUtil.currentTimeMillis();
    //两个请求预期通过的时间,也就是说把请求平均分配到1秒上
    // Calculate the interval between every two requests.
    long costTime = Math.round(1.0 * (acquireCount) / count * 1000);

    //latestPassedTime代表的是上一次调用请求的时间
    // Expected pass time of this request.
    long expectedTime = costTime + latestPassedTime.get();
    //如果预期通过的时间加上上次的请求时间小于当前时间，则通过
    if (expectedTime <= currentTime) {
        // Contention may exist here, but it's okay.
        latestPassedTime.set(currentTime);
        return true;
    } else {
        //默认是maxQueueingTimeMs
        // Calculate the time to wait.
        long waitTime = costTime + latestPassedTime.get() - TimeUtil.currentTimeMillis();

        //如果预提时间比当前时间大maxQueueingTimeMs那么多，那么就阻塞
        if (waitTime > maxQueueingTimeMs) {
            return false;
        } else {
            //将上次时间加上这次请求要耗费的时间
            long oldTime = latestPassedTime.addAndGet(costTime);
            try {
                waitTime = oldTime - TimeUtil.currentTimeMillis();
                //再次判断一下是否超过maxQueueingTimeMs设置的时间
                if (waitTime > maxQueueingTimeMs) {
                    //如果是的话就阻塞，并重置上次通过时间
                    latestPassedTime.addAndGet(-costTime);
                    return false;
                }
                //如果需要等待的时间大于零，那么就sleep
                // in race condition waitTime may <= 0
                if (waitTime > 0) {
                    Thread.sleep(waitTime);
                }
                return true;
            } catch (InterruptedException e) {
            }
        }
    }
    return false;
}

//默认为3
private int coldFactor;
//转折点的令牌数
protected int warningToken = 0;
//最大的令牌数
private int maxToken;
//斜线斜率
protected double slope;
//累积的令牌数
protected AtomicLong storedTokens = new AtomicLong(0);
//最后更新令牌的时间
protected AtomicLong lastFilledTime = new AtomicLong(0);

public WarmUpController(double count, int warmUpPeriodInSec, int coldFactor) {
    construct(count, warmUpPeriodInSec, coldFactor);
}

private void construct(double count, int warmUpPeriodInSec, int coldFactor) {

    if (coldFactor <= 1) {
        throw new IllegalArgumentException("Cold factor should be larger than 1");
    }

    this.count = count;
    //默认是3
    this.coldFactor = coldFactor;

    // thresholdPermits = 0.5 * warmupPeriod / stableInterval.
    // 10*20/2 = 100
    // warningToken = 100;
    warningToken = (int) (warmUpPeriodInSec * count) / (coldFactor - 1);
    // / maxPermits = thresholdPermits + 2 * warmupPeriod /
    // (stableInterval + coldInterval)
    // maxToken = 200
    maxToken = warningToken + (int) (2 * warmUpPeriodInSec * count / (1.0 + coldFactor));

    // slope
    // slope = (coldIntervalMicros - stableIntervalMicros) / (maxPermits
    // - thresholdPermits);
    slope = (coldFactor - 1.0) / count / (maxToken - warningToken);
}

public boolean canPass(Node node, int acquireCount, boolean prioritized) {
    //获取当前时间窗口的流量大小
    long passQps = (long) node.passQps();
    //获取上一个窗口的流量大小
    long previousQps = (long) node.previousPassQps();
    //设置 storedTokens 和 lastFilledTime 到正确的值
    syncToken(previousQps);

    // 开始计算它的斜率
    // 如果进入了警戒线，开始调整他的qps
    long restToken = storedTokens.get();
    if (restToken >= warningToken) {
        //通过计算当前的restToken和警戒线的距离来计算当前的QPS
        //离警戒线越接近，代表这个程序越“热”，从而逐步释放QPS
        long aboveToken = restToken - warningToken;
        //当前状态下能达到的最高 QPS
        // current interval = restToken*slope+1/count
        double warningQps = Math.nextUp(1.0 / (aboveToken * slope + 1.0 / count));

        // 如果不会超过，那么通过，否则不通过
        if (passQps + acquireCount <= warningQps) {
            return true;
        }
    } else {
        // count 是最高能达到的 QPS
        if (passQps + acquireCount <= count) {
            return true;
        }
    }
    return false;
}

protected void syncToken(long passQps) {
    long currentTime = TimeUtil.currentTimeMillis();
    //去掉毫秒的时间
    currentTime = currentTime - currentTime % 1000;
    long oldLastFillTime = lastFilledTime.get();
    if (currentTime <= oldLastFillTime) {
        return;
    }

    // 令牌数量的旧值
    long oldValue = storedTokens.get();
    // 计算新的令牌数量，往下看
    long newValue = coolDownTokens(currentTime, passQps);

    if (storedTokens.compareAndSet(oldValue, newValue)) {
        // 令牌数量上，减去上一分钟的 QPS，然后设置新值
        long currentValue = storedTokens.addAndGet(0 - passQps);
        if (currentValue < 0) {
            storedTokens.set(0L);
        }
        lastFilledTime.set(currentTime);
    } 
}

private long coolDownTokens(long currentTime, long passQps) {
    long oldValue = storedTokens.get();
    long newValue = oldValue;

    // 添加令牌的判断前提条件:
    // 当令牌的消耗程度远远低于警戒线的时候
    if (oldValue < warningToken) {
        // 根据count数每秒加上令牌
        newValue = (long) (oldValue + (currentTime - lastFilledTime.get()) * count / 1000);
    } else if (oldValue > warningToken) {
        //如果还在冷启动阶段
        // 如果当前通过的 QPS 大于 count/coldFactor，说明系统消耗令牌的速度，大于冷却速度
        //    那么不需要添加令牌，否则需要添加令牌
        if (passQps < (int) count / coldFactor) {
            newValue = (long) (oldValue + (currentTime - lastFilledTime.get()) * count / 1000);
        }
    }
    return Math.min(newValue, maxToken);
}