https://www.cnblogs.com/shiningrise/p/5727895.html
http://blog.csdn.net/w200221626/article/details/52064976
/// <summary> /// 动态生产有规律的ID Snowflake算法是Twitter的工程师为实现递增而不重复的ID实现的 /// http://blog.csdn.net/w200221626/article/details/52064976 /// C# 实现 Snowflake算法 /// </summary> public class Snowflake { private static long machineId;//机器ID private static long datacenterId = 0L;//数据ID private static long sequence = 0L;//计数从零开始 private static long twepoch = 687888001020L; //唯一时间随机量 private static long machineIdBits = 5L; //机器码字节数 private static long datacenterIdBits = 5L;//数据字节数 public static long maxMachineId = -1L ^ -1L << (int)machineIdBits; //最大机器ID private static long maxDatacenterId = -1L ^ (-1L << (int)datacenterIdBits);//最大数据ID private static long sequenceBits = 12L; //计数器字节数,12个字节用来保存计数码 private static long machineIdShift = sequenceBits; //机器码数据左移位数,就是后面计数器占用的位数 private static long datacenterIdShift = sequenceBits + machineIdBits; private static long timestampLeftShift = sequenceBits + machineIdBits + datacenterIdBits; //时间戳左移动位数就是机器码+计数器总字节数+数据字节数 public static long sequenceMask = -1L ^ -1L << (int)sequenceBits; //一微秒内可以产生计数,如果达到该值则等到下一微妙在进行生成 private static long lastTimestamp = -1L;//最后时间戳 private static object syncRoot = new object();//加锁对象 static Snowflake snowflake; public static Snowflake Instance() { if (snowflake == null) snowflake = new Snowflake(); return snowflake; } public Snowflake() { Snowflakes(0L, -1); } public Snowflake(long machineId) { Snowflakes(machineId, -1); } public Snowflake(long machineId, long datacenterId) { Snowflakes(machineId, datacenterId); } private void Snowflakes(long machineId, long datacenterId) { if (machineId >= 0) { if (machineId > maxMachineId) { throw new Exception("机器码ID非法"); } Snowflake.machineId = machineId; } if (datacenterId >= 0) { if (datacenterId > maxDatacenterId) { throw new Exception("数据中心ID非法"); } Snowflake.datacenterId = datacenterId; } } /// <summary> /// 生成当前时间戳 /// </summary> /// <returns>毫秒</returns> private static long GetTimestamp() { //让他2000年开始 return (long)(DateTime.UtcNow - new DateTime(2000, 1, 1, 0, 0, 0, DateTimeKind.Utc)).TotalMilliseconds; } /// <summary> /// 获取下一微秒时间戳 /// </summary> /// <param name="lastTimestamp"></param> /// <returns></returns> private static long GetNextTimestamp(long lastTimestamp) { long timestamp = GetTimestamp(); int count = 0; while (timestamp <= lastTimestamp)//这里获取新的时间,可能会有错,这算法与comb一样对机器时间的要求很严格 { count++; if (count > 10) throw new Exception("机器的时间可能不对"); Thread.Sleep(1); timestamp = GetTimestamp(); } return timestamp; } /// <summary> /// 获取长整形的ID /// </summary> /// <returns></returns> public long GetId() { lock (syncRoot) { long timestamp = GetTimestamp(); if (Snowflake.lastTimestamp == timestamp) { //同一微妙中生成ID sequence = (sequence + 1) & sequenceMask; //用&运算计算该微秒内产生的计数是否已经到达上限 if (sequence == 0) { //一微妙内产生的ID计数已达上限,等待下一微妙 timestamp = GetNextTimestamp(Snowflake.lastTimestamp); } } else { //不同微秒生成ID sequence = 0L; } if (timestamp < lastTimestamp) { throw new Exception("时间戳比上一次生成ID时时间戳还小,故异常"); } Snowflake.lastTimestamp = timestamp; //把当前时间戳保存为最后生成ID的时间戳 long Id = ((timestamp - twepoch) << (int)timestampLeftShift) | (datacenterId << (int)datacenterIdShift) | (machineId << (int)machineIdShift) | sequence; return Id; } } }
[TestClass] public class SnowflakeUnitTest1 { /// <summary> /// 动态生产有规律的ID Snowflake算法是Twitter的工程师为实现递增而不重复的ID实现的 /// </summary> [TestMethod] public void SnowflakeTestMethod1() { var ids = new List<long>(); for (int i = 0; i < 1000000; i++)//测试同时100W有序ID { ids.Add(Snowflake.Instance().GetId()); } for (int i = 0; i < ids.Count - 1; i++) { Assert.IsTrue(ids[i] < ids[i+1]); } } }
namespace ConsoleApplicationTester { class Program { static void Main(string[] args) { for (int i = 0; i < 1000; i++) { Console.WriteLine("开始执行 " + DateTime.Now.ToString("yyyy-MM-dd HH:mm:ss:ffffff") + " " + Snowflake.Instance().GetId()); Console.WriteLine("Snowflake.maxMachineId:" + Snowflake.maxMachineId); } } } }