使用SQL Server 2012+，您可以使用OFFSET FETCH查询对单个随机行执行此操作

select  * from MyTable ORDER BY id OFFSET n ROW FETCH NEXT 1 ROWS ONLY

其中id是一个标识列，n是您想要的行—计算为表的0到count()-1之间的随机数(偏移量0毕竟是第一行)

这适用于表数据中的漏洞，只要您有一个用于ORDER BY子句的索引。它对随机性也很有好处——因为你可以自己计算出来传递进来，但其他方法中的琐碎问题是不存在的。此外，性能非常好，在一个较小的数据集上，它表现得很好，尽管我没有尝试过针对数百万行进行认真的性能测试。

sql中的随机函数可能会有所帮助。此外，如果您想限制为一行，只需在最后添加它。

SELECT column FROM table
ORDER BY RAND()
LIMIT 1

我不知道这有多有效，但我以前用过:

SELECT TOP 1 * FROM MyTable ORDER BY newid()

因为guid是非常随机的，所以顺序意味着您得到的是随机行。

对于SQL Server和需要“单个随机行”..

如果不需要真采样，生成一个随机值[0,max_rows)并使用ORDER BY..OFFSET..从SQL Server 2012+获取。

如果COUNT和ORDER BY在适当的索引上，这是非常快的——这样数据就已经沿着查询行“排序”了。如果涵盖了这些操作，那么它就是一个快速请求，并且不会受到使用ORDER BY NEWID()或类似方法的可怕可伸缩性的影响。显然，这种方法在非索引的HEAP表上不能很好地伸缩。

declare @rows int
select @rows = count(1) from t

-- Other issues if row counts in the bigint range..
-- This is also not 'true random', although such is likely not required.
declare @skip int = convert(int, @rows * rand())

select t.*
from t
order by t.id -- Make sure this is clustered PK or IX/UCL axis!
offset (@skip) rows
fetch first 1 row only

确保使用了适当的事务隔离级别和/或考虑0结果。

对于SQL Server，需要一个“一般行样本”的方法..

注意:这是一个在SQL Server上找到的关于获取行样本的特定问题的答案的改编。它是根据上下文量身定制的。

虽然这里应该谨慎使用一般抽样方法，但对于其他答案(以及关于非伸缩和/或有问题的实现的重复建议)，它仍然是潜在的有用信息。如果目标是找到“单个随机行”，那么这种抽样方法的效率低于所示的第一个代码，并且容易出错。

这是一个更新和改进的对行百分比进行抽样的形式。它基于与其他一些使用CHECKSUM / BINARY_CHECKSUM和modulus的答案相同的概念。

It is relatively fast over huge data sets and can be efficiently used in/with derived queries. Millions of pre-filtered rows can be sampled in seconds with no tempdb usage and, if aligned with the rest of the query, the overhead is often minimal. Does not suffer from CHECKSUM(*) / BINARY_CHECKSUM(*) issues with runs of data. When using the CHECKSUM(*) approach, the rows can be selected in "chunks" and not "random" at all! This is because CHECKSUM prefers speed over distribution. Results in a stable/repeatable row selection and can be trivially changed to produce different rows on subsequent query executions. Approaches that use NEWID() can never be stable/repeatable. Does not use ORDER BY NEWID() of the entire input set, as ordering can become a significant bottleneck with large input sets. Avoiding unnecessary sorting also reduces memory and tempdb usage. Does not use TABLESAMPLE and thus works with a WHERE pre-filter.

这是要点。有关更多细节和注意事项，请参阅这个答案。

Naï亿一下:

declare @sample_percent decimal(7, 4)
-- Looking at this value should be an indicator of why a
-- general sampling approach can be error-prone to select 1 row.
select @sample_percent = 100.0 / count(1) from t

-- BAD!
-- When choosing appropriate sample percent of "approximately 1 row"
-- it is very reasonable to expect 0 rows, which definitely fails the ask!
-- If choosing a larger sample size the distribution is heavily skewed forward,
-- and is very much NOT 'true random'.
select top 1
    t.*
from t
where 1=1
    and ( -- sample
        @sample_percent = 100
        or abs(
            convert(bigint, hashbytes('SHA1', convert(varbinary(32), t.rowguid)))
        ) % (1000 * 100) < (1000 * @sample_percent)
    )

这可以在很大程度上通过混合抽样和ORDER by从小得多的样本集中选择的混合查询来补救。这将排序操作限制为样本大小，而不是原始表的大小。

-- Sample "approximately 1000 rows" from the table,
-- dealing with some edge-cases.
declare @rows int
select @rows = count(1) from t

declare @sample_size int = 1000
declare @sample_percent decimal(7, 4) = case
    when @rows <= 1000 then 100                              -- not enough rows
    when (100.0 * @sample_size / @rows) < 0.0001 then 0.0001 -- min sample percent
    else 100.0 * @sample_size / @rows                        -- everything else
    end

-- There is a statistical "guarantee" of having sampled a limited-yet-non-zero number of rows.
-- The limited rows are then sorted randomly before the first is selected.
select top 1
    t.*
from t
where 1=1
    and ( -- sample
        @sample_percent = 100
        or abs(
            convert(bigint, hashbytes('SHA1', convert(varbinary(32), t.rowguid)))
        ) % (1000 * 100) < (1000 * @sample_percent)
    )
-- ONLY the sampled rows are ordered, which improves scalability.
order by newid()

正如@BillKarwin对@cnu回答的评论所指出的那样…

当与LIMIT结合使用时，我发现随机排序的JOIN比直接对实际行进行排序执行得更好(至少在PostgreSQL 9.1中)。 SELECT * FROM tbl_post AS t 加入…… JOIN (SELECT id, CAST(-2147483648 * RANDOM() AS integer) AS rand 从tbl_post WHERE create_time >= 1349928000 ) r ON r.id = t.d id WHERE create_time >= 1349928000 AND… ORDER BY r.r rand 限制100

只要确保“r”为与它连接的复杂查询中的每个可能的键值生成一个“rand”值，但仍然尽可能限制“r”的行数。

CAST as Integer对于PostgreSQL 9.2特别有用，它对整型和单精度浮点类型有特定的排序优化。

而不是使用RAND()，因为它是不鼓励的，你可以简单地得到max ID (= max):

SELECT MAX(ID) FROM TABLE;

在1..Max (= My_Generated_Random)

My_Generated_Random = rand_in_your_programming_lang_function(1..Max);

然后运行SQL:

SELECT ID FROM TABLE WHERE ID >= My_Generated_Random ORDER BY ID LIMIT 1

注意，它将检查id等于或高于所选值的任何行。 也可以在表中寻找行，并获得一个等于或低于My_Generated_Random的ID，然后修改查询如下:

SELECT ID FROM TABLE WHERE ID <= My_Generated_Random ORDER BY ID DESC LIMIT 1