- 最近项目用的BaseModel，主键生成方案用的雪花算法，感觉通用性比较强，做个存档，有需要直接复制过来用就行了：
import com.fasterxml.jackson.annotation.JsonIgnore;
import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
import com.fasterxml.jackson.databind.annotation.JsonSerialize;
import com.fasterxml.jackson.databind.ser.std.ToStringSerializer;
import com.test.utils.IdWorker;
import org.springframework.data.annotation.CreatedDate;
import org.springframework.data.annotation.LastModifiedDate;
import javax.persistence.*;
import java.io.Serializable;
import java.sql.Timestamp;
import java.util.Objects;
@MappedSuperclass
public class BaseModel implements Serializable {
@Transient
@JsonIgnore
private final IdWorker idWorker = new IdWorker();
@Id
@Column(nullable = false, updatable = false)
private Long id;
@CreatedDate
@Column(nullable = false, updatable = false)
private Timestamp createdTime;
@LastModifiedDate
@Column(nullable = false)
private Timestamp lastUpdatedTime;
/**
* 软删除
*/
@Column(nullable = false,columnDefinition = "bit(1) default 0")
private Integer isDel ;
public BaseModel(){
Timestamp now =new Timestamp(System.currentTimeMillis());
this.id = id == null? idWorker.nextId() : id;
this.createdTime = now;
this.lastUpdatedTime =now;
this.isDel = 0;
}
public BaseModel(Long id, Integer isDel) {
this.id = id == null? idWorker.nextId() : id;
this.createdTime = new Timestamp(System.currentTimeMillis());
this.lastUpdatedTime = new Timestamp(System.currentTimeMillis());
this.isDel = isDel;
}
@JsonSerialize(using = ToStringSerializer.class)
public Long getId() {
return id;
}
public void setId(Long id) {
this.id = id == null? idWorker.nextId() : id;
}
public Timestamp getCreatedTime() {
return createdTime;
}
public void setCreatedTime(Timestamp createdTime) {
this.createdTime = createdTime;
}
public Timestamp getLastUpdatedTime() {
return lastUpdatedTime;
}
public void setLastUpdatedTime(Timestamp lastUpdatedTime) {
this.lastUpdatedTime = lastUpdatedTime;
}
public Integer getIsDel(){
return isDel;
}
public Integer GetIsDel() {
return isDel;
}
public void setIsDel(Integer del) {
isDel = del;
}
public void eraseSystemFields(){
this.setCreatedTime(null);
this.setId(null);
this.setIsDel(0);
this.setLastUpdatedTime(null);
}
@Override
public boolean equals(Object o) {
if (this == o) {
return true;
}
if (!(o instanceof BaseModel)) {
return false;
}
BaseModel baseModel = (BaseModel) o;
return Objects.equals(getId(), baseModel.getId()) &&
Objects.equals(getCreatedTime(), baseModel.getCreatedTime()) &&
Objects.equals(getIsDel(), baseModel.getIsDel()) &&
Objects.equals(getLastUpdatedTime(), baseModel.getLastUpdatedTime());
}
@Override
public int hashCode() {
return Objects.hash(getId(), getCreatedTime(), getIsDel(), getLastUpdatedTime());
}
}
- IdWorker:
import java.lang.management.ManagementFactory;
import java.net.InetAddress;
import java.net.NetworkInterface;
/**
* <p>名称：IdWorker.java</p>
* <p>描述：分布式自增长ID</p>
* <pre>
* Twitter的 Snowflake JAVA实现方案
* </pre>
* 核心代码为其IdWorker这个类实现，其原理结构如下，我分别用一个0表示一位，用—分割开部分的作用：
* 1||0---0000000000 0000000000 0000000000 0000000000 0 --- 00000 ---00000 ---000000000000
* 在上面的字符串中，第一位为未使用（实际上也可作为long的符号位），接下来的41位为毫秒级时间，
* 然后5位datacenter标识位，5位机器ID（并不算标识符，实际是为线程标识），
* 然后12位该毫秒内的当前毫秒内的计数，加起来刚好64位，为一个Long型。
* 这样的好处是，整体上按照时间自增排序，并且整个分布式系统内不会产生ID碰撞（由datacenter和机器ID作区分），
* 并且效率较高，经测试，snowflake每秒能够产生26万ID左右，完全满足需要。
* <p>
* 64位ID (42(毫秒)+5(机器ID)+5(业务编码)+12(重复累加))
*
* @author Polim
*/
public class IdWorker {
// 时间起始标记点，作为基准，一般取系统的最近时间（一旦确定不能变动）
private final static long twepoch = 1288834974657L;
// 机器标识位数
private final static long workerIdBits = 5L;
// 数据中心标识位数
private final static long datacenterIdBits = 5L;
// 机器ID最大值
private final static long maxWorkerId = -1L ^ (-1L << workerIdBits);
// 数据中心ID最大值
private final static long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);
// 毫秒内自增位
private final static long sequenceBits = 12L;
// 机器ID偏左移12位
private final static long workerIdShift = sequenceBits;
// 数据中心ID左移17位
private final static long datacenterIdShift = sequenceBits + workerIdBits;
// 时间毫秒左移22位
private final static long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;
private final static long sequenceMask = -1L ^ (-1L << sequenceBits);
/* 上次生产id时间戳 */
private static long lastTimestamp = -1L;
// 0，并发控制
private long sequence = 0L;
private final long workerId;
// 数据标识id部分
private final long datacenterId;
public IdWorker(){
this.datacenterId = getDatacenterId(maxDatacenterId);
this.workerId = getMaxWorkerId(datacenterId, maxWorkerId);
}
/**
* @param workerId
* 工作机器ID
* @param datacenterId
* 序列号
*/
public IdWorker(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;
}
/**
* 获取下一个ID
*
* @return
*/
public synchronized long nextId() {
long timestamp = timeGen();
if (timestamp < lastTimestamp) {
throw new RuntimeException(String.format("Clock moved backwards. Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));
}
if (lastTimestamp == timestamp) {
// 当前毫秒内，则+1
sequence = (sequence + 1) & sequenceMask;
if (sequence == 0) {
// 当前毫秒内计数满了，则等待下一秒
timestamp = tilNextMillis(lastTimestamp);
}
} else {
sequence = 0L;
}
lastTimestamp = timestamp;
// ID偏移组合生成最终的ID，并返回ID
long nextId = ((timestamp - twepoch) << timestampLeftShift)
| (datacenterId << datacenterIdShift)
| (workerId << workerIdShift) | sequence;
return nextId;
}
private long tilNextMillis(final long lastTimestamp) {
long timestamp = this.timeGen();
while (timestamp <= lastTimestamp) {
timestamp = this.timeGen();
}
return timestamp;
}
private long timeGen() {
return System.currentTimeMillis();
}
/**
* <p>
* 获取 maxWorkerId
* </p>
*/
protected static long getMaxWorkerId(long datacenterId, long maxWorkerId) {
StringBuffer mpid = new StringBuffer();
mpid.append(datacenterId);
String name = ManagementFactory.getRuntimeMXBean().getName();
if (!name.isEmpty()) {
/*
* GET jvmPid
*/
mpid.append(name.split("@")[0]);
}
/*
* MAC + PID 的 hashcode 获取16个低位
*/
return (mpid.toString().hashCode() & 0xffff) % (maxWorkerId + 1);
}
/**
* <p>
* 数据标识id部分
* </p>
*/
protected static long getDatacenterId(long maxDatacenterId) {
long id = 0L;
try {
InetAddress ip = InetAddress.getLocalHost();
NetworkInterface network = NetworkInterface.getByInetAddress(ip);
if (network == null) {
id = 1L;
} else {
byte[] mac = network.getHardwareAddress();
id = ((0x000000FF & (long) mac[mac.length - 1])
| (0x0000FF00 & (((long) mac[mac.length - 2]) << 8))) >> 6;
id = id % (maxDatacenterId + 1);
}
} catch (Exception e) {
System.out.println(" getDatacenterId: " + e.getMessage());
}
return id;
}
}
雪花算法的主键生成方案用在分布式场景下比较多，单机的话感觉就不太有必要，因为雪花算法生成的主键是固定位数的Long类型数据（比如上面代码生成的就是19位的主键），如果没有太大的数据量的 话，一方面浪费数据库资源，一方面前后端交互会有问题（js和java的序列化策略不一样，后端传long类型数据到前端会有精度丢失，只保留前17位数据）。所以如果不是很必要的话，还是用老实点的解决方案吧。