统一流控服务开源：基于.Net Core的流控服务

先前有一篇博文，梳理了流控服务的场景、业界做法和常用算法

统一流控服务开源-1：场景&业界做法&算法篇

最近完成了流控服务的开发，并在生产系统进行了大半年的验证，稳定可靠。今天整理一下核心设计和实现思路，开源到Github上，分享给大家

https://github.com/zhouguoqing/FlowControl

 一、令牌桶算法实现

先回顾一下令牌桶算法示意图

随着时间流逝,系统会按恒定1/QPS时间间隔(如果QPS=100,则间隔是10ms) 往桶里加入Token(想象和漏洞漏水相反,有个水龙头在不断的加水),

如果桶已经满了就不再加了. 新请求来临时, 会各自拿走一个Token,如果没有Token可拿了就阻塞或者拒绝服务.

令牌添加速度支持动态变化，实时控制处理的速率.

令牌桶有两个关键的属性：令牌桶容量（大小）和时间间隔，

有两个关键操作，从令牌桶中取Token；令牌桶定时的Reset重置。

  我们看TokenBucket类：

using System;

namespace CZ.FlowControl.Service
{
    using CZ.FlowControl.Spi;
    /// <summary>
    /// 令牌桶
    /// </summary>
    public abstract class TokenBucket : IThrottleStrategy
    {
        protected long bucketTokenCapacity;
        private static readonly object syncRoot = new object();
        protected readonly long ticksRefillInterval;
        protected long nextRefillTime;

        //number of tokens in the bucket
        protected long tokens;

        protected TokenBucket(long bucketTokenCapacity, long refillInterval, long refillIntervalInMilliSeconds)
        {
            if (bucketTokenCapacity <= )
                throw new ArgumentOutOfRangeException("bucketTokenCapacity", "bucket token capacity can not be negative");
            if (refillInterval < )
                throw new ArgumentOutOfRangeException("refillInterval", "Refill interval cannot be negative");
            if (refillIntervalInMilliSeconds <= )
                throw new ArgumentOutOfRangeException("refillIntervalInMilliSeconds", "Refill interval in milliseconds cannot be negative");

            this.bucketTokenCapacity = bucketTokenCapacity;
            ticksRefillInterval = TimeSpan.FromMilliseconds(refillInterval * refillIntervalInMilliSeconds).Ticks;
        }

        /// <summary>
        /// 是否流控
        /// </summary>
        /// <param name="n"></param>
        /// <returns></returns>
        public bool ShouldThrottle(long n = )
        {
            TimeSpan waitTime;
            return ShouldThrottle(n, out waitTime);
        }
        public bool ShouldThrottle(long n, out TimeSpan waitTime)
        {
            if (n <= ) throw new ArgumentOutOfRangeException("n", "Should be positive integer");

            lock (syncRoot)
            {
                UpdateTokens();
                if (tokens < n)
                {
                    var timeToIntervalEnd = nextRefillTime - SystemTime.UtcNow.Ticks;
                    if (timeToIntervalEnd < ) return ShouldThrottle(n, out waitTime);

                    waitTime = TimeSpan.FromTicks(timeToIntervalEnd);
                    return true;
                }
 tokens -= n;

                waitTime = TimeSpan.Zero;
                return false;
            }
        }

        /// <summary>
        /// 更新令牌
        /// </summary>
        protected abstract void UpdateTokens();

        public bool ShouldThrottle(out TimeSpan waitTime)
        {
            return ShouldThrottle(, out waitTime);
        }

        public long CurrentTokenCount
        {
            get
            {
                lock (syncRoot)
                {
                    UpdateTokens();
                    return tokens;
                }
            }
        }
    }
}

这个抽象类中，将UpdateToken作为抽象方法暴露出来，给实现类更多的灵活去控制令牌桶重置操作。基于此实现了“固定令牌桶”FixedTokenBucket

    /// <summary>
    /// 固定令牌桶
    /// </summary>
    class FixedTokenBucket : TokenBucket
    {
        public FixedTokenBucket(long maxTokens, long refillInterval, long refillIntervalInMilliSeconds)
            : base(maxTokens, refillInterval, refillIntervalInMilliSeconds)
        {
        }

        protected override void UpdateTokens()
        {
            var currentTime = SystemTime.UtcNow.Ticks;

            if (currentTime < nextRefillTime)
                return;

            tokens = bucketTokenCapacity;
            nextRefillTime = currentTime + ticksRefillInterval;
        }
    }

固定令牌桶在每次取Token时，都要执行方法ShouldThrottle。这个方法中：

并发取Token是线程安全的，这个地方用了Lock控制，损失了一部分性能。同时每次获取可用Token的时候，都会实时Check一下是否需要到达Reset令牌桶的时间。

获取到可用令牌后，令牌桶中令牌的数量-1。如果没有足够的可用令牌，则返回等待到下次Reset令牌桶的时间。如下代码：

        public bool ShouldThrottle(long n, out TimeSpan waitTime)
        {
            if (n <= ) throw new ArgumentOutOfRangeException("n", "Should be positive integer");

            lock (syncRoot)
            {
UpdateTokens();
                if (tokens < n)
                {
                    var timeToIntervalEnd = nextRefillTime - SystemTime.UtcNow.Ticks;
                    if (timeToIntervalEnd < ) return ShouldThrottle(n, out waitTime);

                    waitTime = TimeSpan.FromTicks(timeToIntervalEnd);
                    return true;
                }
tokens -= n;

                waitTime = TimeSpan.Zero;
                return false;
            }
        }

以上就是令牌桶算法的实现。我们继续看漏桶算法。

 二、漏桶算法实现

首先回顾一下漏桶算法的原理：

‘

水(请求)先进入到漏桶里，漏桶以一定的速度出水(接口有响应速率),

当水流入速度过大会直接溢出(访问频率超过接口响应速率), 然后就拒绝请求,

可以看出漏桶算法能强行限制数据的传输速率.

有两个变量：

• 一个是桶的大小,支持流量突发增多时可以存多少的水(burst),
• 另一个是水桶漏洞的大小(rate)。

漏桶抽象类：LeakTokenBucket，继承与令牌桶抽象父类 TokenBucket，说明了获取令牌(漏出令牌)在底层的方式是一致的，不一样的是重置令牌的方式（务必理解这一点）

using System;

namespace CZ.FlowControl.Service
{
    /// <summary>
    /// 漏桶
    /// </summary>
    abstract class LeakyTokenBucket : TokenBucket
    {
        protected readonly long stepTokens;
        protected long ticksStepInterval;

        protected LeakyTokenBucket(long maxTokens, long refillInterval, int refillIntervalInMilliSeconds,
            long stepTokens, long stepInterval, int stepIntervalInMilliseconds)
            : base(maxTokens, refillInterval, refillIntervalInMilliSeconds)
        {
            this.stepTokens = stepTokens;
            if (stepInterval < ) throw new ArgumentOutOfRangeException("stepInterval", "Step interval cannot be negative");
            if (stepTokens < ) throw new ArgumentOutOfRangeException("stepTokens", "Step tokens cannot be negative");
            if (stepIntervalInMilliseconds <= ) throw new ArgumentOutOfRangeException("stepIntervalInMilliseconds", "Step interval in milliseconds cannot be negative");

            ticksStepInterval = TimeSpan.FromMilliseconds(stepInterval * stepIntervalInMilliseconds).Ticks;
        }
    }
}

可以看出，漏桶是在令牌桶的基础上增加了二个重要的属性：这两个属性决定了重置令牌桶的方式

stepTokens：每间隔时间内漏的数量

ticksStepInterval：漏的间隔时间

举个例子：TPS 100，即每秒漏出100个Token，stepTokens =100， ticksStepInterval=1000ms

漏桶的具体实现有两种：空桶和满桶

StepDownTokenBucket 满桶：即一把将令牌桶填充满

using System;

namespace CZ.FlowControl.Service
{
    /// <summary>
    /// 漏桶(满桶)
    /// </summary>
    /// <remarks>
    /// StepDownLeakyTokenBucketStrategy resembles a bucket which has been filled with tokens at the beginning but subsequently leaks tokens at a fixed interval
    /// </remarks>
    class StepDownTokenBucket : LeakyTokenBucket
    {
        public StepDownTokenBucket(long maxTokens, long refillInterval, int refillIntervalInMilliSeconds, long stepTokens, long stepInterval, int stepIntervalInMilliseconds) : base(maxTokens, refillInterval, refillIntervalInMilliSeconds, stepTokens, stepInterval, stepIntervalInMilliseconds)
        {
        }

        protected override void UpdateTokens()
        {
            var currentTime = SystemTime.UtcNow.Ticks;

            if (currentTime >= nextRefillTime)
            {
                //set tokens to max
                tokens = bucketTokenCapacity;

                //compute next refill time
                nextRefillTime = currentTime + ticksRefillInterval;
                return;
            }

            //calculate max tokens possible till the end
            var timeToNextRefill = nextRefillTime - currentTime;
            var stepsToNextRefill = timeToNextRefill/ticksStepInterval;

            var maxPossibleTokens = stepsToNextRefill*stepTokens;

            if ((timeToNextRefill%ticksStepInterval) > ) maxPossibleTokens += stepTokens;
            if (maxPossibleTokens < tokens) tokens = maxPossibleTokens;
        }
    }
}

StepUpLeakyTokenBucket 空桶：即每次只将stepTokens个数的令牌放到桶中

 using System;

 namespace CZ.FlowControl.Service
 {
     /// <summary>
     /// 漏桶(空桶)
     /// </summary>
     /// <remarks>
     ///  StepUpLeakyTokenBucketStrategy resemembles an empty bucket at the beginning but get filled will tokens over a fixed interval.
     /// </remarks>
     class StepUpLeakyTokenBucket : LeakyTokenBucket
     {
         private long lastActivityTime;

         public StepUpLeakyTokenBucket(long maxTokens, long refillInterval, int refillIntervalInMilliSeconds, long stepTokens, long stepInterval, int stepIntervalInMilliseconds)
             : base(maxTokens, refillInterval, refillIntervalInMilliSeconds, stepTokens, stepInterval, stepIntervalInMilliseconds)
         {
         }

         protected override void UpdateTokens()
         {
             var currentTime = SystemTime.UtcNow.Ticks;

             if (currentTime >= nextRefillTime)
             {
                 tokens = stepTokens;

                 lastActivityTime = currentTime;
                 nextRefillTime = currentTime + ticksRefillInterval;

                 return;
             }

             //calculate tokens at current step

             long elapsedTimeSinceLastActivity = currentTime - lastActivityTime;
             long elapsedStepsSinceLastActivity = elapsedTimeSinceLastActivity / ticksStepInterval;

             tokens += (elapsedStepsSinceLastActivity*stepTokens);

             if (tokens > bucketTokenCapacity) tokens = bucketTokenCapacity;
             lastActivityTime = currentTime;
         }
     }
 }

三、流控服务封装

第二章节，详细介绍了令牌桶和漏桶的具体实现。基于以上，要重点介绍接口：IThrottleStrategy：流控的具体方式

using System;

namespace CZ.FlowControl.Spi
{
    /// <summary>
    /// 流量控制算法策略
    /// </summary>
    public interface IThrottleStrategy
    {
        /// <summary>
        /// 是否流控
        /// </summary>
        /// <param name="n"></param>
        /// <returns></returns>
        bool ShouldThrottle(long n = );

        /// <summary>
        /// 是否流控
        /// </summary>
        /// <param name="n"></param>
        /// <param name="waitTime"></param>
        /// <returns></returns>
        bool ShouldThrottle(long n, out TimeSpan waitTime);

        /// <summary>
        /// 是否流控
        /// </summary>
        /// <param name="waitTime"></param>
        /// <returns></returns>
        bool ShouldThrottle(out TimeSpan waitTime);

        /// <summary>
        /// 当前令牌个数
        /// </summary>
        long CurrentTokenCount { get; }
    }
}

有了这个流控方式接口后，我们还需要一个流控策略定义类：FlowControlStrategy

即定义具体的流控策略：以下是这个类的详细属性和成员：  不仅定义了流控策略类型，还定义了流控的维度信息和流控阈值，这样流控就做成依赖注入的方式了！

using System;
using System.Collections.Generic;
using System.Text;

namespace CZ.FlowControl.Spi
{
    /// <summary>
    /// 流控策略
    /// </summary>
    public class FlowControlStrategy
    {
        /// <summary>
        /// 标识
        /// </summary>
        public string ID { get; set; }

        /// <summary>
        /// 名称
        /// </summary>
        public string Name { get; set; }

        /// <summary>
        /// 流控策略类型
        /// </summary>
        public FlowControlStrategyType StrategyType { get; set; }

        /// <summary>
        /// 流控阈值-Int
        /// </summary>
        public long IntThreshold { get; set; }

        /// <summary>
        /// 流控阈值-Double
        /// </summary>
        public decimal DoubleThreshold { get; set; }

        /// <summary>
        /// 时间区间跨度
        /// </summary>
        public FlowControlTimespan TimeSpan { get; set; }

        private Dictionary<string, string> flowControlConfigs;

        /// <summary>
        /// 流控维度信息
        /// </summary>
        public Dictionary<string, string> FlowControlConfigs
        {
            get
            {
                if (flowControlConfigs == null)
                    flowControlConfigs = new Dictionary<string, string>();

                return flowControlConfigs;
            }
            set
            {
                flowControlConfigs = value;
            }
        }

        /// <summary>
        /// 描述
        /// </summary>
        public string Descriptions { get; set; }

        /// <summary>
        /// 触发流控后是否直接拒绝请求
        /// </summary>
        public bool IsRefusedRequest { get; set; }

        /// <summary>
        /// 创建时间
        /// </summary>
        public DateTime CreateTime { get; set; }

        /// <summary>
        /// 创建人
        /// </summary>
        public string Creator { get; set; }

        /// <summary>
        /// 最后修改时间
        /// </summary>
        public DateTime LastModifyTime { get; set; }

        /// <summary>
        /// 最后修改人
        /// </summary>
        public string LastModifier { get; set; }
    }
}

同时，流控策略类型，我们抽象了一个枚举：FlowControlStrategyType

支持3种流控策略：TPS、Sum(指定时间段内请求的次数)，Delay延迟

using System;
using System.Collections.Generic;
using System.Text;

namespace CZ.FlowControl.Spi
{
    /// <summary>
    /// 流控策略类型枚举
    /// </summary>
    public enum FlowControlStrategyType
    {
        /// <summary>
        /// TPS控制策略
        /// </summary>
        TPS,
     /// <summary>
        /// 总数控制策略
        /// </summary>
        Sum,

        /// <summary>
        /// 延迟控制策略
        /// </summary>
        Delay
    }
}

面向每种流控策略类型，提供了一个对应的流控器，比如说TPS的流控器

TPSFlowController，内部使用了固定令牌桶算法

using System;

namespace CZ.FlowControl.Service
{
    using CZ.FlowControl.Spi;

    /// <summary>
    /// TPS流量控制器
    /// </summary>
    class TPSFlowController : IFlowController
    {
        public IThrottleStrategy InnerThrottleStrategy
        {
            get; private set;
        }

        public FlowControlStrategy FlowControlStrategy { get; private set; }

        public bool ShouldThrottle(long n, out TimeSpan waitTime)
        {
            return InnerThrottleStrategy.ShouldThrottle(n, out waitTime);
        }

        public TPSFlowController(FlowControlStrategy strategy)
        {
            FlowControlStrategy = strategy;

            InnerThrottleStrategy = new FixedTokenBucket(strategy.IntThreshold, , );
        }
    }
}

Sum(指定时间段内请求的次数)流控器：

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;

namespace CZ.FlowControl.Service
{
    using CZ.FlowControl.Spi;

    /// <summary>
    /// 一段时间内合计值流量控制器
    /// </summary>
    class SumFlowController : IFlowController
    {
        public IThrottleStrategy InnerThrottleStrategy
        {
            get; private set;
        }

        public FlowControlStrategy FlowControlStrategy { get; private set; }

        public bool ShouldThrottle(long n, out TimeSpan waitTime)
        {
            return InnerThrottleStrategy.ShouldThrottle(n, out waitTime);
        }

        public SumFlowController(FlowControlStrategy strategy)
        {
            FlowControlStrategy = strategy;

            var refillInterval = GetTokenBucketRefillInterval(strategy);

            InnerThrottleStrategy = new FixedTokenBucket(strategy.IntThreshold, refillInterval, );
        }

        private long GetTokenBucketRefillInterval(FlowControlStrategy strategy)
        {
            long refillInterval = ;

            switch (strategy.TimeSpan)
            {
                case FlowControlTimespan.Second:
                    refillInterval = ;
                    break;
                case FlowControlTimespan.Minute:
                    refillInterval = ;
                    break;
                case FlowControlTimespan.Hour:
                    refillInterval =  * ;
                    break;
                case FlowControlTimespan.Day:
                    refillInterval =  *  * ;
                    break;
            }

            return refillInterval;
        }
    }
}

同时，通过一个创建者工厂，根据不同的流控策略，创建对应的流控器（做了一层缓存，性能更好）：

using System;
using System.Collections.Generic;
using System.Text;

namespace CZ.FlowControl.Service
{
    using CZ.FlowControl.Spi;

    /// <summary>
    /// 流控策略工厂
    /// </summary>
    class FlowControllerFactory
    {
        private static Dictionary<string, IFlowController> fcControllers;
        private static object syncObj = new object();

        private static FlowControllerFactory instance;

        private FlowControllerFactory()
        {
            fcControllers = new Dictionary<string, IFlowController>();
        }

        public static FlowControllerFactory GetInstance()
        {
            if (instance == null)
            {
                lock (syncObj)
                {
                    if (instance == null)
                    {
                        instance = new FlowControllerFactory();
                    }
                }
            }

            return instance;
        }

        public IFlowController GetOrCreateFlowController(FlowControlStrategy strategy)
        {
            if (strategy == null)
                throw new ArgumentNullException("FlowControllerFactory.GetOrCreateFlowController.strategy");

            if (!fcControllers.ContainsKey(strategy.ID))
            {
                lock (syncObj)
                {
                    if (!fcControllers.ContainsKey(strategy.ID))
                    {
                        var fcController = CreateFlowController(strategy);
                        if (fcController != null)
                            fcControllers.Add(strategy.ID, fcController);
                    }
                }
            }

            if (fcControllers.ContainsKey(strategy.ID))
            {
                var controller = fcControllers[strategy.ID];
                return controller;
            }

            return null;
        }

        private IFlowController CreateFlowController(FlowControlStrategy strategy)
        {
            if (strategy == null)
                throw new ArgumentNullException("FlowControllerFactory.CreateFlowController.strategy");

            IFlowController controller = null;

            switch (strategy.StrategyType)
            {
                case FlowControlStrategyType.TPS:
                    controller = new TPSFlowController(strategy);
                    break;
                case FlowControlStrategyType.Delay:
                    controller = new DelayFlowController(strategy);
                    break;
                case FlowControlStrategyType.Sum:
                    controller = new SumFlowController(strategy);
                    break;
                default:
                    break;
            }

            return controller;
        }
    }
}

有了流控策略定义、我们更进一步，继续封装了流控Facade服务，这样把流控的变化封装到内部。对外只提供流控服务接口，流控时动态传入流控策略和流控个数：FlowControlService

using System;
using System.Collections.Generic;
using System.Text;

namespace CZ.FlowControl.Service
{
    using CZ.FlowControl.Spi;
    using System.Threading;

    /// <summary>
    /// 统一流控服务
    /// </summary>
    public class FlowControlService
    {
        /// <summary>
        /// 流控
        /// </summary>
        /// <param name="strategy">流控策略</param>
        /// <param name="count">请求次数</param>
        public static void FlowControl(FlowControlStrategy strategy, int count = )
        {
            var controller = FlowControllerFactory.GetInstance().GetOrCreateFlowController(strategy);

            TimeSpan waitTimespan = TimeSpan.Zero;

            var result = controller.ShouldThrottle(count, out waitTimespan);
            if (result)
            {
                if (strategy.IsRefusedRequest == false && waitTimespan != TimeSpan.Zero)
                {
                    WaitForAvailable(strategy, controller, waitTimespan, count);
                }
                else if (strategy.IsRefusedRequest)
                {
                    throw new Exception("触发流控！");
                }
            }
        }

        /// <summary>
        /// 等待可用
        /// </summary>
        /// <param name="strategy">流控策略</param>
        /// <param name="controller">流控器</param>
        /// <param name="waitTimespan">等待时间</param>
        /// <param name="count">请求次数</param>
        private static void WaitForAvailable(FlowControlStrategy strategy, IFlowController controller, TimeSpan waitTimespan, int count)
        {
            var timespan = waitTimespan;
            if (strategy.StrategyType == FlowControlStrategyType.Delay)
            {
                Thread.Sleep(timespan);
                return;
            }

            while (controller.ShouldThrottle(count, out timespan))
            {
                Thread.Sleep(timespan);
            }
        }
    }
}

以上，统一流控服务完成了第一个版本的封装。接下来我们看示例代码

 四、示例代码

    先安装Nuget：

Install-Package CZ.FlowControl.Service -Version 1.0.

是不是很简单。

大家如果希望了解详细的代码，请参考这个项目的GitHub地址：

https://github.com/zhouguoqing/FlowControl

同时也欢迎大家一起改进完善。

周国庆

2019/8/9