• 雪花算法 Java 版


    雪花算法根据时间戳生成有序的 64 bit 的 Long 类型的唯一 ID

    各 bit 含义:

    • 1 bit: 符号位,0 是正数 1 是负数, ID 为正数,所以恒取 0
    • 41 bit: 时间差,我们可以选择一个参考点,用它来计算与当前时间的时间差 (毫秒数),41 bit 存储时间差,足够使用 69 年
    • 10 bit: 机器码,能编码 1024 台机器;可以手动指定含义,比如前5 bit 作为机器编号、后 5 bit 作为进程编号
    • 12 bit: 序列号,同一机器同一毫秒内产生不同的序列号,12 bit 可以支持 4096 个序列号

    优点:

    • 灵活配置:机器码可以根据需求灵活配置含义
    • 无需持久化:如果序号自增往往需要持久化,本算法不需要持久化
    • ID 有含义/可逆性:ID 可以反解出来,对 ID 进行统计分析,可以很简单的分析出整个系统的繁忙曲线,还可以定位到每个机器,在某段时间承担了多少工作,分析出负载均衡情况
    • 高性能:生成速度很快
    public class Snowflake {
    
        /**
         * 每一部分所占位数
         */
        private final long unusedBits = 1L;
        private final long timestampBits = 41L;
        private final long datacenterIdBits = 5L;
        private final long workerIdBits = 5L;
        private final long sequenceBits = 12L;
    
        /**
         * 向左的位移
         */
        private final long timestampShift = sequenceBits + datacenterIdBits + workerIdBits;
        private final long datacenterIdShift = sequenceBits + workerIdBits;
        private final long workerIdShift = sequenceBits;
    
        /**
         * 起始时间戳,初始化后不可修改
         */
        private final long epoch = 1451606400000L; // 2016-01-01
    
        /**
         * 数据中心编码,初始化后不可修改
         * 最大值: 2^5-1 取值范围: [0,31]
         */
        private final long datacenterId;
    
        /**
         * 机器或进程编码,初始化后不可修改
         * 最大值: 2^5-1 取值范围: [0,31]
         */
        private final long workerId;
    
        /**
         * 序列号
         * 最大值: 2^12-1 取值范围: [0,4095]
         */
        private long sequence = 0L;
    
        /** 上次执行生成 ID 方法的时间戳 */
        private long lastTimestamp = -1L;
    
        /*
         * 每一部分最大值
         */
        private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits); // 2^5-1
        private final long maxWorkerId = -1L ^ (-1L << workerIdBits); // 2^5-1
        private final long maxSequence = -1L ^ (-1L << sequenceBits); // 2^12-1
    
        /**
         * 生成序列号
         */
        public synchronized long nextId() {
            long currTimestamp = timestampGen();
    
            if (currTimestamp < lastTimestamp) {
                throw new IllegalStateException(
                        String.format("Clock moved backwards. Refusing to generate id for %d milliseconds",
                                lastTimestamp - currTimestamp));
            }
    
            if (currTimestamp == lastTimestamp) {
                sequence = (sequence + 1) & maxSequence;
                if (sequence == 0) { // overflow: greater than max sequence
                    currTimestamp = waitNextMillis(currTimestamp);
                }
    
            } else { // reset to 0 for next period/millisecond
                sequence = 0L;
            }
    
            // track and memo the time stamp last snowflake ID generated
            lastTimestamp = currTimestamp;
    
            return ((currTimestamp - epoch) << timestampShift) | //
                    (datacenterId << datacenterIdShift) | //
                    (workerId << workerIdShift) | // new line for nice looking
                    sequence;
        }
    
        public Snowflake(long datacenterId, long workerId) {
            if (datacenterId > maxDatacenterId || datacenterId < 0) {
                throw new IllegalArgumentException(
                        String.format("datacenter Id can't be greater than %d or less than 0", maxDatacenterId));
            }
            if (workerId > maxWorkerId || workerId < 0) {
                throw new IllegalArgumentException(
                        String.format("worker Id can't be greater than %d or less than 0", maxWorkerId));
            }
    
            this.datacenterId = datacenterId;
            this.workerId = workerId;
        }
    
        /**
         * 追踪调用 waitNextMillis 方法的次数
         */
        private final AtomicLong waitCount = new AtomicLong(0);
    
        public long getWaitCount() {
            return waitCount.get();
        }
    
        /**
         * 循环阻塞直到下一秒
         */
        protected long waitNextMillis(long currTimestamp) {
            waitCount.incrementAndGet();
            while (currTimestamp <= lastTimestamp) {
                currTimestamp = timestampGen();
            }
            return currTimestamp;
        }
    
        /**
         * 获取当前时间戳
         */
        public long timestampGen() {
            return System.currentTimeMillis();
        }
    }
    

    参考:snowflake ID 生成算法

    完整代码:GitHub

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  • 原文地址:https://www.cnblogs.com/victorbu/p/11101850.html
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