本文主要介绍 uid-generator （一种全局ID服务实现）

uid-generator介绍

全局ID服务是分布式服务中的基础服务，需要保持全局唯一，高效，高可靠性。有些时候还可能要求保持单调，但也并非一定要严格递增或者递减

全局ID也可以通过数据库的自增主键来获取，但是如果要求QPS很高显然是不现实的

uid-generator是对Snowflake算法的改进，也引入了高性能队列disruptor中RingBuffer思想，进一步提升了效率

  +------+----------------------+----------------+-----------+
  | sign |     delta seconds    | worker node id | sequence  |
  +------+----------------------+----------------+-----------+
    1bit          28bits              22bits         13bits

• sign 符号位 保证为正数

• delta seconds 当前时间与约定时间的差值

• word node id机器码

• sequence 同一时刻支持并发数

上图就是snowflake算法生成的64位的长整型构成

uid-generator的work node id 使用了数据库自增主键的key，每次重启服务都需要刷新，这也保证了集群中全局ID的唯一性

worker node id字段处理

uid-generator使用数据库主键作为worker node id

这样看来这个worker node id其实可以有很丰富的扩展性，只要对表结构稍微修改，就可以记录使得worker node id 有更有意义的含义。

例如使用uid-generator生成的值作为表的主键ID，可以通过对WORKER_NODE表增加一列表名记录表，这样通过id就反向查找对应的表名

sequence字段的处理

uid-generator中实现了原生的snowflake以及缓存版的。这两个版本对于sequence字段的处理有所不同

DefaultUidGenerator.java

/**
* Get UID
*
* @return UID
* @throws UidGenerateException in the case: Clock moved backwards; Exceeds the max timestamp
*/
protected synchronized long nextId() {
    long currentSecond = getCurrentSecond();

    // Clock moved backwards, refuse to generate uid
    if (currentSecond < lastSecond) {
        long refusedSeconds = lastSecond - currentSecond;
        throw new UidGenerateException("Clock moved backwards. Refusing for %d seconds", refusedSeconds);
    }

    // At the same second, increase sequence
    if (currentSecond == lastSecond) {
        sequence = (sequence + 1) & bitsAllocator.getMaxSequence();
        // Exceed the max sequence, we wait the next second to generate uid
        if (sequence == 0) {
            currentSecond = getNextSecond(lastSecond);
        }

    // At the different second, sequence restart from zero
    } else {
        sequence = 0L;
    }

    lastSecond = currentSecond;

    // Allocate bits for UID
    return bitsAllocator.allocate(currentSecond - epochSeconds, workerId, sequence);
}

DefaultUidGenerator 并发通过 函数加锁控制；获取seq时通过时间判断是否需要调到下一秒

CachedUidGenerator.java

 /**
    * Padding buffer fill the slots until to catch the cursor
    */
public void paddingBuffer() {
    LOGGER.info("Ready to padding buffer lastSecond:{}. {}", lastSecond.get(), ringBuffer);

    // is still running
    if (!running.compareAndSet(false, true)) {
        LOGGER.info("Padding buffer is still running. {}", ringBuffer);
        return;
    }

    // fill the rest slots until to catch the cursor
    boolean isFullRingBuffer = false;
    while (!isFullRingBuffer) {
        List<Long> uidList = uidProvider.provide(lastSecond.incrementAndGet());
        for (Long uid : uidList) {
            isFullRingBuffer = !ringBuffer.put(uid);
            if (isFullRingBuffer) {
                break;
            }
        }
    }

    // not running now
    running.compareAndSet(true, false);
    LOGGER.info("End to padding buffer lastSecond:{}. {}", lastSecond.get(), ringBuffer);
}

CachedUidGenerator 加锁通过CAS操作；由于需要一次填充完缓存，所以选取了一次填充一秒内所有的seq，以此保证了seq在一秒内的唯一性。这样带来的一个小弊端是不能通过id看出来这个id生成的时间

CachedUidGenerator核心RingBuffer实现

RingBuffer是一个环形数组，通过两个指针，tail、cursor来实现复用槽

在这里需要介绍一下FalseShare陷阱，由于tail和cursor指针在高并发情况下变动频繁，如果tail，cursor处于同一个缓存中，将会频繁导致缓存失效，可以看到uid-generator已经考虑了这个问题

通过对PaddedAtomicLong进行填充，保证了每一个long值都在不同的缓存行中，解决了这个问题

RingBuffer基本都用位运算取代了乘除以及取模计算，提高了计算效率

/**
    * Put an UID in the ring & tail moved<br>
    * We use 'synchronized' to guarantee the UID fill in slot & publish new tail sequence as atomic operations<br>
    * 
    * <b>Note that: </b> It is recommended to put UID in a serialize way, cause we once batch generate a series UIDs and put
    * the one by one into the buffer, so it is unnecessary put in multi-threads
    *
    * @param uid
    * @return false means that the buffer is full, apply {@link RejectedPutBufferHandler}
    */
public synchronized boolean put(long uid) {
    long currentTail = tail.get();
    long currentCursor = cursor.get();

    // tail catches the cursor, means that you can't put any cause of RingBuffer is full
    long distance = currentTail - (currentCursor == START_POINT ? 0 : currentCursor);
    if (distance == bufferSize - 1) {
        rejectedPutHandler.rejectPutBuffer(this, uid);
        return false;
    }

    // 1. pre-check whether the flag is CAN_PUT_FLAG
    int nextTailIndex = calSlotIndex(currentTail + 1);
    if (flags[nextTailIndex].get() != CAN_PUT_FLAG) {
        rejectedPutHandler.rejectPutBuffer(this, uid);
        return false;
    }

    // 2. put UID in the next slot
    // 3. update next slot' flag to CAN_TAKE_FLAG
    // 4. publish tail with sequence increase by one
    slots[nextTailIndex] = uid;
    flags[nextTailIndex].set(CAN_TAKE_FLAG);
    tail.incrementAndGet();

    // The atomicity of operations above, guarantees by 'synchronized'. In another word,
    // the take operation can't consume the UID we just put, until the tail is published(tail.incrementAndGet())
    return true;
}

在RingBuffer的put方法中可以看到整个的流程，首先是函数加锁，加锁的原因在注释部分也解释了，由于是每次批量存入的，多线程put操作是没有必要的，之后第一步计算tail与cursor距离当前数组是否还可以继续填充。这里还有另外一个标识位用来判断当前槽是否可以做PUT以及TAKE操作，更像是双保险，防止上一个判断距离结束了之后tail位置有变动，导致槽位被覆盖

同样对于take操作

    /**
     * Take an UID of the ring at the next cursor, this is a lock free operation by using atomic cursor<p>
     * 
     * Before getting the UID, we also check whether reach the padding threshold, 
     * the padding buffer operation will be triggered in another thread<br>
     * If there is no more available UID to be taken, the specified {@link RejectedTakeBufferHandler} will be applied<br>
     * 
     * @return UID
     * @throws IllegalStateException if the cursor moved back
     */
    public long take() {
        // spin get next available cursor
        long currentCursor = cursor.get();
        long nextCursor = cursor.updateAndGet(old -> old == tail.get() ? old : old + 1);

        // check for safety consideration, it never occurs
        Assert.isTrue(nextCursor >= currentCursor, "Curosr can't move back");

        // trigger padding in an async-mode if reach the threshold
        long currentTail = tail.get();
        if (currentTail - nextCursor < paddingThreshold) {
            LOGGER.info("Reach the padding threshold:{}. tail:{}, cursor:{}, rest:{}", paddingThreshold, currentTail,
                    nextCursor, currentTail - nextCursor);
            bufferPaddingExecutor.asyncPadding();
        }

        // cursor catch the tail, means that there is no more available UID to take
        if (nextCursor == currentCursor) {
            rejectedTakeHandler.rejectTakeBuffer(this);
        }

        // 1. check next slot flag is CAN_TAKE_FLAG
        int nextCursorIndex = calSlotIndex(nextCursor);
        Assert.isTrue(flags[nextCursorIndex].get() == CAN_TAKE_FLAG, "Curosr not in can take status");

        // 2. get UID from next slot
        // 3. set next slot flag as CAN_PUT_FLAG.
        long uid = slots[nextCursorIndex];
        flags[nextCursorIndex].set(CAN_PUT_FLAG);

        // Note that: Step 2,3 can not swap. If we set flag before get value of slot, the producer may overwrite the
        // slot with a new UID, and this may cause the consumer take the UID twice after walk a round the ring
        return uid;
    }

正如注释中所说，take部分并没有并发限制，在剩余可用槽位小于一个阈值的时候，会触发一次填充操作

CachedUidGenerator 对于填充有两种处理，一个是低于阈值填充，一种是开启Schedule，定时填充，定时填充可选

uid-generator可靠性很高，除了workid依赖数据库之外基本不依赖外部中间件，全局ID在分布式服务中扮演关键角色，一旦服务出错，解决起来也很棘手。