snowflake(雪花算法)的Java实现
背景了解
分布式系统中，有一些需要使用全局唯一ID的场景，这种时候为了防止ID冲突可以使用36位的UUID，但是UUID有一些缺点，首先他相对比较长，另外UUID一般是无序的。
有些时候我们希望能使用一种简单一些的ID，并且希望ID能够按照时间有序生成。而twitter的snowflake解决了这种需求，最初Twitter把存储系统从MySQL迁移到Cassandra，因为Cassandra没有顺序ID生成机制，所以开发了这样一套全局唯一ID生成服务。
原版snowflake
原版的snowflake是twitter用Scala写的，不懂Scala语言。大概瞄一眼就好。
附上原版地址及代码:IdWorker
/** Copyright 2010-2012 Twitter, Inc.*/
package com.twitter.service.snowflake
import com.twitter.ostrich.stats.Stats
import com.twitter.service.snowflake.gen._
import java.util.Random
import com.twitter.logging.Logger
/**
* An object that generates IDs.
* This is broken into a separate class in case
* we ever want to support multiple worker threads
* per process
*/
class IdWorker(val workerId: Long, val datacenterId: Long, private val reporter: Reporter, var sequence: Long = 0L)
extends Snowflake.Iface {
private[this] def genCounter(agent: String) = {
Stats.incr("ids_generated")
Stats.incr("ids_generated_%s".format(agent))
}
private[this] val exceptionCounter = Stats.getCounter("exceptions")
private[this] val log = Logger.get
private[this] val rand = new Random
val twepoch = 1288834974657L
private[this] val workerIdBits = 5L
private[this] val datacenterIdBits = 5L
private[this] val maxWorkerId = -1L ^ (-1L << workerIdBits)
private[this] val maxDatacenterId = -1L ^ (-1L << datacenterIdBits)
private[this] val sequenceBits = 12L
private[this] val workerIdShift = sequenceBits
private[this] val datacenterIdShift = sequenceBits + workerIdBits
private[this] val timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits
private[this] val sequenceMask = -1L ^ (-1L << sequenceBits)
private[this] var lastTimestamp = -1L
// sanity check for workerId
if (workerId > maxWorkerId || workerId < 0) {
exceptionCounter.incr(1)
throw new IllegalArgumentException("worker Id can't be greater than %d or less than 0".format(maxWorkerId))
}
if (datacenterId > maxDatacenterId || datacenterId < 0) {
exceptionCounter.incr(1)
throw new IllegalArgumentException("datacenter Id can't be greater than %d or less than 0".format(maxDatacenterId))
}
log.info("worker starting. timestamp left shift %d, datacenter id bits %d, worker id bits %d, sequence bits %d, workerid %d",
timestampLeftShift, datacenterIdBits, workerIdBits, sequenceBits, workerId)
def get_id(useragent: String): Long = {
if (!validUseragent(useragent)) {
exceptionCounter.incr(1)
throw new InvalidUserAgentError
}
val id = nextId()
genCounter(useragent)
reporter.report(new AuditLogEntry(id, useragent, rand.nextLong))
id
}
def get_worker_id(): Long = workerId
def get_datacenter_id(): Long = datacenterId
def get_timestamp() = System.currentTimeMillis
protected[snowflake] def nextId(): Long = synchronized {
var timestamp = timeGen()
if (timestamp < lastTimestamp) {
exceptionCounter.incr(1)
log.error("clock is moving backwards. Rejecting requests until %d.", lastTimestamp);
throw new InvalidSystemClock("Clock moved backwards. Refusing to generate id for %d milliseconds".format(
lastTimestamp - timestamp))
}
if (lastTimestamp == timestamp) {
sequence = (sequence + 1) & sequenceMask
if (sequence == 0) {
timestamp = tilNextMillis(lastTimestamp)
}
} else {
sequence = 0
}
lastTimestamp = timestamp
((timestamp - twepoch) << timestampLeftShift) |
(datacenterId << datacenterIdShift) |
(workerId << workerIdShift) |
sequence
}
protected def tilNextMillis(lastTimestamp: Long): Long = {
var timestamp = timeGen()
while (timestamp <= lastTimestamp) {
timestamp = timeGen()
}
timestamp
}
protected def timeGen(): Long = System.currentTimeMillis()
val AgentParser = """([a-zA-Z][a-zA-Z\-0-9]*)""".r
def validUseragent(useragent: String): Boolean = useragent match {
case AgentParser(_) => true
case _ => false
}
}
snowflake的Java实现
public class IdWorker {
/** 计算时间差的起始时间戳（2020/4/16 21:02:09） */
private final long twepoch = 1587028431430L;
/** 机器id所占的位数 */
private final long workerIdBits = 5L;
/** 数据标识id所占的位数 */
private final long dataCenterIdBits = 5L;
/** 支持的最大机器id，结果是31 (这个移位算法可以很快的计算出几位二进制数所能表示的最大十进制数) */
private final long maxWorkerId = -1L ^ (-1L << workerIdBits);
/** 支持的最大数据标识id，结果是31 */
private final long maxDataCenterId = -1L ^ (-1L << dataCenterIdBits);
/** 序列在id中占的位数 */
private final long sequenceBits = 12L;
/** 机器ID向左移12位 */
private final long workerIdShift = sequenceBits;
/** 数据标识id向左移17位(12+5) */
private final long dataCenterIdShift = sequenceBits + workerIdBits;
/** 时间截向左移22位(5+5+12) */
private final long timestampLeftShift = sequenceBits + workerIdBits + dataCenterIdBits;
/** 生成序列的掩码，这里为4095 (0b111111111111=0xfff=4095) */
private final long sequenceMask = -1L ^ (-1L << sequenceBits);
/** 工作机器ID(0~31) */
private long workerId;
/** 数据中心ID(0~31) */
private long dataCenterId;
/** 毫秒内序列(0~4095) */
private long sequence = 0L;
/** 上次生成ID的时间截 */
private long lastTimestamp = -1L;
/**
* 构造函数
* @param workerId 工作ID (0~31)
* @param dataCenterId 数据中心ID (0~31)
*/
public IdWorker(long workerId, long dataCenterId) {
if (workerId > maxWorkerId || workerId < 0) {
throw new IllegalArgumentException(String.format("workerId can't be greater than %d or less than 0", maxWorkerId));
}
if (dataCenterId > maxDataCenterId || dataCenterId < 0) {
throw new IllegalArgumentException(String.format("dataCenterId can't be greater than %d or less than 0", maxDataCenterId));
}
this.workerId = workerId;
this.dataCenterId = dataCenterId;
}
/**
* 获得下一个ID (该方法是线程安全的)
* @return SnowflakeId
*/
public synchronized long nextId() {
long timestamp = timeGen();
//如果当前时间小于上一次ID生成的时间戳，说明系统时钟回退过这个时候应当抛出异常
if (timestamp < lastTimestamp) {
throw new RuntimeException(
String.format("Clock moved backwards. Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));
}
//如果是同一时间生成的，则进行毫秒内序列
if (lastTimestamp == timestamp) {
sequence = (sequence + 1) & sequenceMask;
//毫秒内序列溢出
if (sequence == 0) {
//阻塞到下一个毫秒,获得新的时间戳
timestamp = tilNextMillis(lastTimestamp);
}
}
//时间戳改变，毫秒内序列重置
else {
sequence = 0L;
}
//上次生成ID的时间截
lastTimestamp = timestamp;
//移位并通过或运算拼到一起组成64位的ID
return ((timestamp - twepoch) << timestampLeftShift) //
| (dataCenterId << dataCenterIdShift) //
| (workerId << workerIdShift) //
| sequence;
}
/**
* 阻塞到下一个毫秒，直到获得新的时间戳
* @param lastTimestamp 上次生成ID的时间截
* @return 当前时间戳
*/
protected long tilNextMillis(long lastTimestamp) {
long timestamp = timeGen();
while (timestamp <= lastTimestamp) {
timestamp = timeGen();
}
return timestamp;
}
/**
* 返回以毫秒为单位的当前时间
* @return 当前时间(毫秒)
*/
protected long timeGen() {
return System.currentTimeMillis();
}
public static void main(String[] args) {
System.out.println(System.currentTimeMillis());
IdWorker idWorker = new IdWorker(1, 1);
long startTime = System.nanoTime();
for (int i = 0; i < 50000; i++) {
long id = idWorker.nextId();
System.out.println(id);
}
System.out.println("耗时: " + (System.nanoTime() - startTime) / 1000000 + "ms");
}
}
snowflake的优缺点
优点：
- 快（哈哈，天下武功唯快不破）。
- 没有啥依赖，实现也特别简单。
- 知道原理之后可以根据实际情况调整各各位段，方便灵活。
缺点：
- 依赖机器时间，如果发生回拨会导致可能生成id重复。
- 只能趋势递增。
关于这点说明一下，比如说某个电商网站的下单场景，如果绝对递增的话，竞争对手中午下单，第二天在下单即可大概判断该公司的订单量，可能会暴露该公司订单增量！