基于Snowflake生成全局唯一ID

背景

使用雪花算法生成的主键，二进制表示形式包含4部分，从高位到低位分表为：1bit符号位、41bit时间戳位、10bit工作进程位（也可以区分5bit数据中心、5bit机器ID）以及12bit序列号位。

• 符号位(1bit)
  预留的符号位，恒为零。

• 时间戳位(41bit)
  41位的时间戳可以容纳的毫秒数是2的41次方减1，一年所使用的毫秒数是：365 * 24 * 60 * 60 * 1000。通过计算可知：
  (Math.pow(2, 41) -1) / (365 * 24 * 60 * 60 * 1000L);
  结果约等于69年。Sharding-Sphere的雪花算法的时间纪元从2016年11月1日零点开始，可以使用到2085年，相信能满足绝大部分系统的要求。

• 工作进程位(10bit)
  该标志在Java进程内是唯一的，如果是分布式应用部署应保证每个工作进程的id是不同的。该值默认为0，可通过调用静态方法DefaultKeyGenerator.setWorkerId(“xxxx”)设置。

• 序列号位(12bit)
  该序列是用来在同一个毫秒内生成不同的ID。如果在这个毫秒内生成的数量超过4096(2的12次方)，那么生成器会等待到下个毫秒继续生成。

代码样例

public final class DefaultKeyGenerator {
    public static final long EPOCH;

    private static final long SEQUENCE_BITS = 12L;

    private static final long WORKER_ID_BITS = 10L;

    private static final long SEQUENCE_MASK = (1 << SEQUENCE_BITS) - 1;

    private static final long WORKER_ID_LEFT_SHIFT_BITS = SEQUENCE_BITS;

    private static final long TIMESTAMP_LEFT_SHIFT_BITS = WORKER_ID_LEFT_SHIFT_BITS + WORKER_ID_BITS;

    private static final long WORKER_ID_MAX_VALUE = 1L << WORKER_ID_BITS;

    private static long workerId;

    private static int maxTolerateTimeDifferenceMilliseconds = 10;

    static {
        //也可指定开始年份
        Calendar calendar = Calendar.getInstance();
        calendar.set(2016, Calendar.NOVEMBER, 1);
        calendar.set(Calendar.HOUR_OF_DAY, 0);
        calendar.set(Calendar.MINUTE, 0);
        calendar.set(Calendar.SECOND, 0);
        calendar.set(Calendar.MILLISECOND, 0);
        EPOCH = calendar.getTimeInMillis();
    }

    private byte sequenceOffset;

    private long sequence;

    private long lastMilliseconds;

    /**
     * Set work process id.
     *
     * @param workerId work process id
     */
    public static void setWorkerId(final long workerId) {
        Preconditions.checkArgument(workerId >= 0L && workerId < WORKER_ID_MAX_VALUE);
        DefaultKeyGenerator.workerId = workerId;
    }

    /**
     * Set max tolerate time difference milliseconds.
     *
     * @param maxTolerateTimeDifferenceMilliseconds max tolerate time difference milliseconds
     */
    public static void setMaxTolerateTimeDifferenceMilliseconds(final int maxTolerateTimeDifferenceMilliseconds) {
        DefaultKeyGenerator.maxTolerateTimeDifferenceMilliseconds = maxTolerateTimeDifferenceMilliseconds;
    }

    /**
     * Generate key.
     *
     * @return key type is @{@link Long}.
     */
    public synchronized Number generateKey() {
        long currentMilliseconds = System.currentTimeMillis();
        if (waitTolerateTimeDifferenceIfNeed(currentMilliseconds)) {
            currentMilliseconds = System.currentTimeMillis();
        }
        if (lastMilliseconds == currentMilliseconds) {
            //如果在这个毫秒内生成的数量超过4096(2的12次方)，那么生成器会等待到下个毫秒继续生成
            if (0L == (sequence = (sequence + 1) & SEQUENCE_MASK)) {
                currentMilliseconds = waitUntilNextTime(currentMilliseconds);
            }
        } else {
            //下面方法的sequence只能为0或1，也可以随机一个值如sequence = RandomUtils.nextLong(0L, 60L) * 60L;
            vibrateSequenceOffset();
            sequence = sequenceOffset;
        }
        lastMilliseconds = currentMilliseconds;
        return ((currentMilliseconds - EPOCH) << TIMESTAMP_LEFT_SHIFT_BITS) | (workerId << WORKER_ID_LEFT_SHIFT_BITS) | sequence;
    }

    @SneakyThrows
    private boolean waitTolerateTimeDifferenceIfNeed(final long currentMilliseconds) {
        if (lastMilliseconds <= currentMilliseconds) {
            return false;
        }
        long timeDifferenceMilliseconds = lastMilliseconds - currentMilliseconds;
        Preconditions.checkState(timeDifferenceMilliseconds < maxTolerateTimeDifferenceMilliseconds,
                "Clock is moving backwards, last time is %d milliseconds, current time is %d milliseconds", lastMilliseconds, currentMilliseconds);
        Thread.sleep(timeDifferenceMilliseconds);
        return true;
    }

    private long waitUntilNextTime(final long lastTime) {
        long result = System.currentTimeMillis();
        while (result <= lastTime) {
            result = System.currentTimeMillis();
        }
        return result;
    }

    private void vibrateSequenceOffset() {
        sequenceOffset = (byte) (~sequenceOffset & 1);
    }
}

单元测试

public class KeyGeneratorTest {
    @Test
    public void test() {
        //工作进程位10位 取值1-1024 默认0
        DefaultKeyGenerator.setWorkerId(1000);
        //时钟回拨，最大允许容忍差异毫秒数，超过这个时间将返回异常，默认10ms
        DefaultKeyGenerator.setMaxTolerateTimeDifferenceMilliseconds(10);
        DefaultKeyGenerator keyGenerator = new DefaultKeyGenerator();
        for(int i=0;i<1000;i++){
            System.out.println(keyGenerator.generateKey());
        }
    }
}

优化

为避免需要手动设置workerId，可通过使用IP地址计算得出workId

public class IPSectionKeyGenerator {
    private final SnowflakeKeyGenerator snowflakeKeyGenerator = new SnowflakeKeyGenerator();

    static {
        InetAddress address;
        try {
            address = InetAddress.getLocalHost();
        } catch (UnknownHostException var8) {
            throw new IllegalStateException("Cannot get LocalHost InetAddress, please check your network!");
        }

        long workerId = 0L;
        byte[] ipAddressByteArray = address.getAddress();
        int ipLength = ipAddressByteArray.length;
        int count = 0;

        flag:
        switch(ipLength) {
            case 4:
                while(true) {
                    if (count >= ipLength) {
                        break flag;
                    }
                    byte byteNum = ipAddressByteArray[count];
                    workerId += byteNum & 255;
                    ++count;
                }
            case 16:
                while(true) {
                    if (count >= ipLength) {
                        break flag;
                    }
                    byte byteNum = ipAddressByteArray[count];
                    workerId += byteNum & 63;
                    ++count;
                }
            default:
                throw new IllegalStateException("Bad LocalHost InetAddress, please check your network!");
        }
        SnowflakeKeyGenerator.setWorkerId(workerId);
    }

    public long generateKey() {
        return this.snowflakeKeyGenerator.generateKey();
    }
}

总结

雪花算法是由Twitter公布的分布式主键生成算法，它能够保证不同进程主键的不重复性，以及相同进程主键的有序性。
在同一个进程中，它首先是通过时间位保证不重复，如果时间相同则是通过序列位保证。
同时由于时间位是单调递增的，且各个服务器如果大体做了时间同步，那么生成的主键在分布式环境可以认为是总体有序的。同时代码对于时钟回拨问题也做了相应的处理。