SELECT  *
FROM    mytable mto
WHERE   EXISTS
        (
        SELECT  1
        FROM    mytable mti
        WHERE   mti.varchar_column = mto.varchar_column
        LIMIT 1, 1
        )
ORDER BY varchar_column

这个查询返回完整的记录，而不仅仅是不同的varchar_column。

这个查询不使用COUNT(*)。如果有很多重复项，COUNT(*)是昂贵的，并且不需要整个COUNT(*)，只需要知道是否有两行具有相同的值。

这是通过相关查询底部的LIMIT 1,1来实现的(本质上意味着“返回第二行”)。EXISTS只在前面提到的第二行存在时才返回true(即至少有两行具有相同的varchar_column值)。

当然，在varchar_column上建立索引将大大加快此查询的速度。

Select column_name, column_name1,column_name2, count(1) as temp from table_name group by column_name having temp > 1

对GROUP BY子句执行SELECT操作。假设name是你想要在其中找到重复项的列:

SELECT name, COUNT(*) c FROM table GROUP BY name HAVING c > 1;

这将返回一个在第一列中包含名称值的结果，以及该值在第二列中出现次数的计数。

SELECT * 
FROM `dps` 
WHERE pid IN (SELECT pid FROM `dps` GROUP BY pid HAVING COUNT(pid)>1)

一个非常晚的贡献…万一这能帮助到以后的任何人…我有一个任务是在一个银行应用程序中找到匹配的交易对(实际上是账户到账户转账的双方)，以识别每个账户间转账交易的“从”和“到”，所以我们最终得到了这个:

SELECT 
    LEAST(primaryid, secondaryid) AS transactionid1,
    GREATEST(primaryid, secondaryid) AS transactionid2
FROM (
    SELECT table1.transactionid AS primaryid, 
        table2.transactionid AS secondaryid
    FROM financial_transactions table1
    INNER JOIN financial_transactions table2 
    ON table1.accountid = table2.accountid
    AND table1.transactionid <> table2.transactionid 
    AND table1.transactiondate = table2.transactiondate
    AND table1.sourceref = table2.destinationref
    AND table1.amount = (0 - table2.amount)
) AS DuplicateResultsTable
GROUP BY transactionid1
ORDER BY transactionid1;

The result is that the DuplicateResultsTable provides rows containing matching (i.e. duplicate) transactions, but it also provides the same transaction id's in reverse the second time it matches the same pair, so the outer SELECT is there to group by the first transaction ID, which is done by using LEAST and GREATEST to make sure the two transactionid's are always in the same order in the results, which makes it safe to GROUP by the first one, thus eliminating all the duplicate matches. Ran through nearly a million records and identified 12,000+ matches in just under 2 seconds. Of course the transactionid is the primary index, which really helped.

SELECT varchar_col
FROM table
GROUP BY varchar_col
HAVING COUNT(*) > 1;