MySQL中RAND()的分布 [英] Distribution of RAND() in MySQL

问题描述

我无法在 MySQL 文档中的任何地方找到 RAND() 函数是否使用均匀分布.有人知道吗?

I can't find anywhere in the documentation of MySQL whether RAND() function uses a uniform distribution or not. Does anyone know?

推荐答案

一直没能快速找到函数的源代码；看到它可能有助于获得更好的答案.也就是说，它看起来像是不……但有点接近".

I haven't been able to find the source code for the function quickly; seeing it might help get a better answer. That said, it looks like a 'no.. but kinda close'.

从文档中我可以看到它试图是随机的(并且您可以选择播种它)；但是有两个警告意味着当您期望它的第一个问题是在它的文档中时，它可能并不总是统一的:

From documentation I can see it tries to be random (and optionally you can seed it); but there are two caveats that mean it might not always be uniform when you expect it to the first issue is in it's documentation:

RAND() is not meant to be a perfect random generator. It is a fast way to generate random numbers on demand that is portable between platforms for the same MySQL version. 

即它的代码作为一个真正的随机数生成器是有问题的——它对于大多数用途来说已经足够好了，但不能被信任用于加密，我敢打赌，即使是统一的也不能被信任正如您所料.

i.e. the code for it is questionable in being a true random number generator - it's good enough for most purposes, but can't be trusted for use in, say, encryption, and I'd wager can't be trusted to even be as uniform as you may expect.

他们声称它并不完美意味着它几乎肯定会失败至少其中一些测试；如何仍然不清楚，但如果它不是完美的 RNG，您将不会看到完美均匀的分布；我希望更多/不常见的范围的差距或峰值.http://www.stat.fsu.edu/pub/diehard/cdrom/pscript/monkey.ps(此后还有其他工作，但这仍然是验证 RNG 的一些关键工作).

Their claim that it's not perfect means it will almost certainly fail at least some of these tests; the how is still unclear, but if it's not a perfect RNG, you won't see a perfectly uniform distribution; I'd expect either gaps or peaks of ranges that are more/less common. http://www.stat.fsu.edu/pub/diehard/cdrom/pscript/monkey.ps (there's been other work since but this is still some of the key work in validating RNG's).

打赌它背后的代码肯定不试图保证均匀分布，因为这样做(跟踪已经出现的历史记录)会浪费大量内存.在任何情况下，如果您已经使用完美的 RNG 生成了很多值，则无法保证如果到目前为止您的值都很低，那么下一个值会很高.它总是一样随机..

It'd wager that code behind it certainly does not try to guarantee a uniform distribution, since to do so (track history of what's come up already) would waste masses of memory. In any case, if you've already generated a lot of values using a perfect RNG, nothing guarantees that if your values so far were all low the next one will be high. It will always be just as random..

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