这是一个雪花算法。我自己执行。使用generate生成唯一的ID。
// start time
private final long startTimestamp = ZonedDateTime.of(2015, 1, 1, 0, 0, 0, 0, ZoneId.systemDefault()).toInstant().toEpochMilli();
// shift value
private final long workerDataLeftShiftValue;
// mask
private final long sequenceMask = 4095L;
// sequence from 0 - 4095
private long sequence = 0;
// last timestamp
private long lastTimestamp = System.currentTimeMillis();
public Snowflake(int dataCenterId, int workerId) {
// shifts
int workerLeftShift = 12;
int dataCenterLeftShift = 17;
this.workerDataLeftShiftValue = workerId << workerLeftShift | dataCenterId << dataCenterLeftShift;
// max id
int maxId = 32;
if (!(dataCenterId < maxId) || !(workerId < maxId)) {
throw new IllegalStateException("not a valid id, snowflake is not working");
}
}
@Override
public synchronized long generate() {
long curTimestamp = System.currentTimeMillis();
if (lastTimestamp > curTimestamp) {
throw new IllegalStateException("last timestamp > current timestamp, clock error when using snowflake");
}
if (lastTimestamp == curTimestamp) {
sequence = (sequence + 1) & sequenceMask;
if (sequence == 0) {
while (curTimestamp == System.currentTimeMillis()) {
curTimestamp = System.currentTimeMillis();
}
}
} else {
sequence = 0;
}
lastTimestamp = curTimestamp;
return sequence | workerDataLeftShiftValue | (curTimestamp - startTimestamp) << 22L;
}
public static void main(String[] args) {
Snowflake snowflake = new Snowflake(1, 1);
System.out.println(snowflake.generate());
}
这是Twitter实施的雪花。使用nextId生成唯一的ID。
private final long twepoch = 1420041600000L;
private final long workerIdBits = 5L;
private final long datacenterIdBits = 5L;
private final long maxWorkerId = -1L ^ (-1L << workerIdBits);
private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);
private final long sequenceBits = 12L;
private final long workerIdShift = sequenceBits;
private final long datacenterIdShift = sequenceBits + workerIdBits;
private final long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;
private final long sequenceMask = -1L ^ (-1L << sequenceBits);
private long workerId;
private long datacenterId;
private long sequence = 0L;
private long lastTimestamp = -1L;
public SnowflakeTwitter(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;
}
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) {
sequence = (sequence + 1) & sequenceMask;
if (sequence == 0) {
timestamp = tilNextMillis(lastTimestamp);
}
}
else {
sequence = 0L;
}
lastTimestamp = timestamp;
return ((timestamp - twepoch) << timestampLeftShift) //
| (datacenterId << datacenterIdShift) //
| (workerId << workerIdShift) //
| sequence;
}
protected long tilNextMillis(long lastTimestamp) {
long timestamp = timeGen();
while (timestamp <= lastTimestamp) {
timestamp = timeGen();
}
return timestamp;
}
protected long timeGen() {
return System.currentTimeMillis();
}
public static void main(String[] args) {
SnowflakeTwitter idWorker = new SnowflakeTwitter(0, 0);
System.out.println(idWorker.nextId());
}
因此,我对它们进行了一些测试。使用10个线程生成ID。 1个线程,用于观察id的总数。
public static void main(String[] args) throws InterruptedException {
Snowflake snowflake = new Snowflake(1, 1);
SnowflakeTwitter snowflakeTwitter = new SnowflakeTwitter(1, 1);
AtomicInteger sf = new AtomicInteger(0);
AtomicInteger sft = new AtomicInteger(0);
int tSize = 10;
for (int i = 0; i < tSize; i++) {
new Thread(() -> {
while (true) {
snowflakeTwitter.nextId();
sft.incrementAndGet();
}
}).start();
new Thread(() -> {
while (true) {
snowflake.generate();
sf.incrementAndGet();
}
}).start();
}
new Thread(() -> {
while (true) {
System.out.println("sft: " + sft.get() + ", sf: " + sf.get());
try {
TimeUnit.SECONDS.sleep(1);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}).start();
TimeUnit.SECONDS.sleep(100);
}
结果是:
sft: 6752, sf: 5663sft: 477792, sf: 194125sft: 1279909, sf: 661183sft: 2320410, sf: 1206993sft: 3374425, sf: 1712642sft: 4255803, sf: 2157003sft: 4661517, sf: 2338219sft: 5115752, sf: 2551146
所以我的执行速度很慢。我发现原因是:
// my impl return sequence | workerDataLeftShiftValue | (curTimestamp - startTimestamp) << 22L; // twitter impl return ((timestamp - twepoch) << timestampLeftShift) // | (datacenterId << datacenterIdShift) // | (workerId << workerIdShift) // | sequence;
为什么造成速度差异?谢谢。
最佳答案
显然(作为1 XOR x ==〜x,其中~
是一个人的补码,位取反):
-1L ^ x
很简单
~x
这样一来,便可以使用固定的最终int进行移位。
final int workerLeftShift = 12;
final int dataCenterLeftShift = 17;
我没有检查生成的代码,但这似乎与您提到的表达式有关。