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令牌桶限流算法简单实现

令牌桶限流算法简单实现

作者: justonemoretry | 来源:发表于2023-03-05 11:24 被阅读0次

简介

令牌桶作为限流算法之一,具体原理是一段时间内向桶里加入一定的token数,使用时进行扣减,判断扣减数和剩余令牌数之间的关系,这种限流的好处是更灵活,能够处理突发流量,有流量波峰时,令牌桶内提前已经放入了足够多的token数,流量一样可以通过。不过需要注意的是,限流保护的系统需要事先验证能扛住这么高的突发流量。

简单实现

使用变量availableTokens标识桶内令牌数,消费前先按时间加入对应的令牌数,然后对令牌数进行扣减。

public class TokenBucket {

    private final long capacity;
    private final double refillTokensPerOneMillis;
    private double availableTokens;
    private long lastRefillTimestamp;

    public TokenBucket(long capacity, long refillTokens, long refillPeriodMillis) {
        this.capacity = capacity;
        this.refillTokensPerOneMillis = (double) refillTokens / (double) refillPeriodMillis;
        this.availableTokens = capacity;
        this.lastRefillTimestamp = System.currentTimeMillis();
    }

    synchronized public boolean tryConsume(int numberTokens) {
        refill();
        if (availableTokens < numberTokens) {
            return false;
        } else {
            availableTokens -= numberTokens;
            return true;
        }
    }

    private void refill() {
        long currentTimeMillis = System.currentTimeMillis();
        if (currentTimeMillis > lastRefillTimestamp) {
            long millisSinceLastRefill = currentTimeMillis - lastRefillTimestamp;
            double refill = millisSinceLastRefill * refillTokensPerOneMillis;
            this.availableTokens = Math.min(capacity, availableTokens + refill);
            this.lastRefillTimestamp = currentTimeMillis;
        }
    }

    public static void main(String[] args) throws InterruptedException {
        TokenBucket tokenBucket = new TokenBucket(10, 10, 1000);
        while (true) {
            if (tokenBucket.tryConsume(2)) {
                System.out.println("pass");
            } else {
                System.out.println("block");
            }
            Thread.sleep(100);
            System.out.println("当前可用token数目为:" + tokenBucket.availableTokens);
        }
    }
}

sentinel中应用

利用token数进行预热限流,利用了令牌桶的思维,根据桶内的令牌数取判断预热是否完成,令牌数多的情况下说明当前流量较小,需要进行预热,预热时按warningQps进行计算。

public class WarmUpController implements TrafficShapingController {

    protected double count;
    private int coldFactor;
    protected int warningToken = 0;
    private int maxToken;
    protected double slope;

    protected AtomicLong storedTokens = new AtomicLong(0);
    protected AtomicLong lastFilledTime = new AtomicLong(0);

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

    public WarmUpController(double count, int warmUpPeriodInSec) {
        construct(count, warmUpPeriodInSec, 3);
    }

    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;

        this.coldFactor = coldFactor;

        // thresholdPermits = 0.5 * warmupPeriod / stableInterval.
        // 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);

    }

    @Override
    public boolean canPass(Node node, int acquireCount) {
        return canPass(node, acquireCount, false);
    }

    @Override
    public boolean canPass(Node node, int acquireCount, boolean prioritized) {
        long passQps = (long) node.passQps();

        long previousQps = (long) node.previousPassQps();
        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) {
            long aboveToken = restToken - warningToken;
            // 消耗的速度要比warning快,但是要比慢
            // current interval = restToken*slope+1/count
            double warningQps = Math.nextUp(1.0 / (aboveToken * slope + 1.0 / count));
            if (passQps + acquireCount <= warningQps) {
                return true;
            }
        } else {
            if (passQps + acquireCount <= count) {
                return true;
            }
        }

        return false;
    }

    protected void syncToken(long passQps) {
        long currentTime = TimeUtil.currentTimeMillis();
        currentTime = currentTime - currentTime % 1000;
        long oldLastFillTime = lastFilledTime.get();
        if (currentTime <= oldLastFillTime) {
            return;
        }

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

        if (storedTokens.compareAndSet(oldValue, newValue)) {
            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) {
            newValue = (long)(oldValue + (currentTime - lastFilledTime.get()) * count / 1000);
        } else if (oldValue > warningToken) {
            if (passQps < (int)count / coldFactor) {
                newValue = (long)(oldValue + (currentTime - lastFilledTime.get()) * count / 1000);
            }
        }
        return Math.min(newValue, maxToken);
    }

}

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