1、直接使用uuid

public static String getUUID() {String replaceUUID = UUID.randomUUID().toString().replace("-", "");return replaceUUID;}

但由于生成的數(shù)據(jù)沒(méi)有規(guī)律性，并且太長(zhǎng)；

測(cè)試：循環(huán)1000w次

?測(cè)試代碼：

public static void main(String[] args) {long startTime = System.currentTimeMillis();Set set=new HashSet<>();for(int i=0;i<10000000;i++){String uuid = getUUID();System.out.println("uuid---"+i+"======="+uuid);set.add(uuid);}long endTime = System.currentTimeMillis();System.out.println("set.size():"+set.size());System.out.println("endTime-startTime:"+(endTime-startTime));}

控制臺(tái)提示：

2、用時(shí)間（精確到毫秒）+隨機(jī)數(shù)

//時(shí)間（精確到毫秒）DateTimeFormatter ofPattern = DateTimeFormatter.ofPattern("yyyyMMddHHmmssSSS");String localDate = LocalDateTime.now().format(ofPattern);//隨機(jī)數(shù)String randomNumeric = RandomStringUtils.randomNumeric(8);

for循環(huán)1000w次，發(fā)現(xiàn)重復(fù)數(shù)據(jù)太多。因此光靠隨機(jī)數(shù)并不可靠。

3、使用 時(shí)間（精確到毫秒）+隨機(jī)數(shù)+用戶(hù)id（業(yè)務(wù)id）

ps:如果是類(lèi)似用戶(hù)id，項(xiàng)目當(dāng)中集成了權(quán)限框架，使用工具類(lèi)獲取即可，就不用傳參了

/*** 生成訂單號(hào)（25位）：時(shí)間（精確到毫秒）+3位隨機(jī)數(shù)+5位用戶(hù)id*/public static synchronized String getOrderNum(Long userId) {//時(shí)間（精確到毫秒）DateTimeFormatter ofPattern = DateTimeFormatter.ofPattern("yyyyMMddHHmmssSSS");String localDate = LocalDateTime.now().format(ofPattern);//3位隨機(jī)數(shù)String randomNumeric = RandomStringUtils.randomNumeric(3);//5位用戶(hù)idint subStrLength = 5;String sUserId = userId.toString();int length = sUserId.length();String str;if (length >= subStrLength) {str = sUserId.substring(length - subStrLength, length);} else {str = String.format("%0" + subStrLength + "d", userId);}String orderNum = localDate + randomNumeric + str;log.info("訂單號(hào):{}", orderNum);return orderNum;}

在2的基礎(chǔ)上改造，加入用戶(hù)的id等其他的業(yè)務(wù)id。

4.Java實(shí)現(xiàn)Snowflake算法的方案（高并發(fā)下，推薦使用這個(gè)）

package com.lucifer.order.util.idgenerate;/*** Twitter_Snowflake<br>* SnowFlake的結(jié)構(gòu)如下(每部分用-分開(kāi)):<br>* 0 - 0000000000 0000000000 0000000000 0000000000 0 - 00000 - 00000 - 000000000000 <br>* 1位標(biāo)識(shí)，由于long基本類(lèi)型在Java中是帶符號(hào)的，最高位是符號(hào)位，正數(shù)是0，負(fù)數(shù)是1，所以id一般是正數(shù)，最高位是0<br>* 41位時(shí)間截(毫秒級(jí))，注意，41位時(shí)間截不是存儲(chǔ)當(dāng)前時(shí)間的時(shí)間截，而是存儲(chǔ)時(shí)間截的差值（當(dāng)前時(shí)間截 - 開(kāi)始時(shí)間截)* 得到的值），這里的的開(kāi)始時(shí)間截，一般是我們的id生成器開(kāi)始使用的時(shí)間，由我們程序來(lái)指定的（如下下面程序IdWorker類(lèi)的startTime屬性）。41位的時(shí)間截，可以使用69年，年T = (1L << 41) / (1000L * 60 * 60 * 24 * 365) = 69<br>* 10位的數(shù)據(jù)機(jī)器位，可以部署在1024個(gè)節(jié)點(diǎn)，包括5位datacenterId和5位workerId<br>* 12位序列，毫秒內(nèi)的計(jì)數(shù)，12位的計(jì)數(shù)順序號(hào)支持每個(gè)節(jié)點(diǎn)每毫秒(同一機(jī)器，同一時(shí)間截)產(chǎn)生4096個(gè)ID序號(hào)<br>* 加起來(lái)剛好64位，為一個(gè)Long型。<br>* SnowFlake的優(yōu)點(diǎn)是，整體上按照時(shí)間自增排序，并且整個(gè)分布式系統(tǒng)內(nèi)不會(huì)產(chǎn)生ID碰撞(由數(shù)據(jù)中心ID和機(jī)器ID作區(qū)分)，并且效率較高，經(jīng)測(cè)試，SnowFlake每秒能夠產(chǎn)生26萬(wàn)ID左右。** @author Lucifer*/ public class SnowFlake {// ==============================Fields===========================================/*** 開(kāi)始時(shí)間截 (2018-07-03)*/private final long twepoch = 1530607760000L;/*** 機(jī)器id所占的位數(shù)*/private final long workerIdBits = 5L;/*** 數(shù)據(jù)標(biāo)識(shí)id所占的位數(shù)*/private final long datacenterIdBits = 5L;/*** 支持的最大機(jī)器id，結(jié)果是31 (這個(gè)移位算法可以很快的計(jì)算出幾位二進(jìn)制數(shù)所能表示的最大十進(jìn)制數(shù))*/private final long maxWorkerId = -1L ^ (-1L << workerIdBits);/*** 支持的最大數(shù)據(jù)標(biāo)識(shí)id，結(jié)果是31*/private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);/*** 序列在id中占的位數(shù)*/private final long sequenceBits = 12L;/*** 機(jī)器ID向左移12位*/private final long workerIdShift = sequenceBits;/*** 數(shù)據(jù)標(biāo)識(shí)id向左移17位(12+5)*/private final long datacenterIdShift = sequenceBits + workerIdBits;/*** 時(shí)間截向左移22位(5+5+12)*/private final long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;/*** 生成序列的掩碼，這里為4095 (0b111111111111=0xfff=4095)*/private final long sequenceMask = -1L ^ (-1L << sequenceBits);/*** 工作機(jī)器ID(0~31)*/private long workerId;/*** 數(shù)據(jù)中心ID(0~31)*/private long datacenterId;/*** 毫秒內(nèi)序列(0~4095)*/private long sequence = 0L;/*** 上次生成ID的時(shí)間截*/private long lastTimestamp = -1L;//==============================Constructors=====================================/*** 構(gòu)造函數(shù)** @param workerId 工作ID (0~31)* @param datacenterId 數(shù)據(jù)中心ID (0~31)*/public SnowFlake(long workerId, long datacenterId) {if (workerId > maxWorkerId || workerId < 0) {throw new IllegalArgumentException(String.format("worker Id can't be greater than %d or less than 0", maxWorkerId));}if (datacenterId > maxDatacenterId || datacenterId < 0) {throw new IllegalArgumentException(String.format("datacenter Id can't be greater than %d or less than 0", maxDatacenterId));}this.workerId = workerId;this.datacenterId = datacenterId;}// ==============================Methods==========================================/*** 獲得下一個(gè)ID (該方法是線(xiàn)程安全的)** @return SnowflakeId*/public synchronized long nextId() {long timestamp = timeGen();//如果當(dāng)前時(shí)間小于上一次ID生成的時(shí)間戳，說(shuō)明系統(tǒng)時(shí)鐘回退過(guò)這個(gè)時(shí)候應(yīng)當(dāng)拋出異常if (timestamp < lastTimestamp) {throw new RuntimeException(String.format("Clock moved backwards. Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));}//如果是同一時(shí)間生成的，則進(jìn)行毫秒內(nèi)序列if (lastTimestamp == timestamp) {sequence = (sequence + 1) & sequenceMask;//毫秒內(nèi)序列溢出if (sequence == 0) {//阻塞到下一個(gè)毫秒,獲得新的時(shí)間戳timestamp = tilNextMillis(lastTimestamp);}}//時(shí)間戳改變，毫秒內(nèi)序列重置else {sequence = 0L;}//上次生成ID的時(shí)間截lastTimestamp = timestamp;//移位并通過(guò)或運(yùn)算拼到一起組成64位的IDreturn (((timestamp - twepoch) << timestampLeftShift)| (datacenterId << datacenterIdShift)| (workerId << workerIdShift)| sequence);}/*** 阻塞到下一個(gè)毫秒，直到獲得新的時(shí)間戳** @param lastTimestamp 上次生成ID的時(shí)間截* @return 當(dāng)前時(shí)間戳*/protected long tilNextMillis(long lastTimestamp) {long timestamp = timeGen();while (timestamp <= lastTimestamp) {timestamp = timeGen();}return timestamp;}/*** 返回以毫秒為單位的當(dāng)前時(shí)間** @return 當(dāng)前時(shí)間(毫秒)*/protected long timeGen() {return System.currentTimeMillis();}//==============================Test=============================================/*** 測(cè)試*/public static void main(String[] args) {long startTime = System.currentTimeMillis();SnowFlake idWorker = new SnowFlake(0, 0);Set set = new HashSet();for (int i = 0; i < 10000000; i++) {long id = idWorker.nextId();set.add(id);System.out.println("id----"+i+":"+id);}long endTime = System.currentTimeMillis();System.out.println("set.size():" + set.size());System.out.println("endTime-startTime:" + (endTime - startTime));} }

也可以在雪花算法生成的id的基礎(chǔ)上拼接日期，不過(guò)性能有所損耗。?

public static String timestampConversionDate(String param) {Instant timestamp = Instant.ofEpochMilli(new Long(param));System.out.println("timestamp:"+param);LocalDateTime localDateTime = LocalDateTime.ofInstant(timestamp, ZoneId.systemDefault());String format = localDateTime.format(DateTimeFormatter.ofPattern("yyyyMMdd"));return format;}

測(cè)試1：

循環(huán)1000w次，發(fā)現(xiàn)并無(wú)重復(fù)

測(cè)試2：100個(gè)線(xiàn)程，每個(gè)線(xiàn)程負(fù)責(zé)生成10w個(gè)id

//多線(xiàn)程測(cè)試 public static void main(String[] args) throws InterruptedException {long startTime = System.currentTimeMillis();CountDownLatch countDownLatch=new CountDownLatch(10000000);final SnowFlake idWorker = new SnowFlake(0, 0);Set set = Collections.synchronizedSet(new HashSet());for (int i = 0; i < 100; i++) {Thread thread = new Thread(() -> {for (int i1 = 0; i1 < 100000; i1++) {long id = idWorker.nextId();System.out.println("id:"+id);set.add(id);countDownLatch.countDown();}});thread.start();}countDownLatch.await();long endTime = System.currentTimeMillis();System.out.println("set.size():" + set.size());System.out.println("endTime-startTime:" + (endTime - startTime));}

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