Snowflake.cs
5.4 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading;
using System.Threading.Tasks;
namespace RemoteSheardObject
{
/// <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;
}
}
}
}