Question

Possible Duplicate:
SQL: Find the max record per group

I have a table with four columns as such:

name   major    minor  revision
p1     0        4      3
p1     1        0      0
p1     1        1      4
p2     1        1      1
p2     2        5      0
p3     3        4      4

This is basically ca table containing records for each version of a program. I want to do a select to get all of the programs and their latest version so the results would look like this:

name   major    minor  revision
p1     1        1      4
p2     2        5      0
p3     3        4      4

I can’t just group by the name and get the max of each column because then i would just end up with the highest number from each column, but not the specific row with the highest version. How can I set this up?

Answer 1

The way I try to solve SQL problems is to take things step by step.

• You want the maximum revision for the maximum minor version corresponding to the maximum major version for each product.

The maximum major number for each product is given by:

SELECT Name, MAX(major) AS Major FROM CA GROUP BY Name;

The maximum minor number corresponding to the maximum major number for each product is therefore given by:

SELECT CA.Name, CA.Major, MAX(CA.Minor) AS Minor
  FROM CA
  JOIN (SELECT Name, MAX(Major) AS Major
          FROM CA
         GROUP BY Name
       ) AS CB
    ON CA.Name = CB.Name AND CA.Major = CB.Major
 GROUP BY CA.Name, CA.Major;

And the maximum revision (for the maximum minor version number corresponding to the maximum major number for each product), therefore, is given by:

SELECT CA.Name, CA.Major, CA.Minor, MAX(CA.Revision) AS Revision
  FROM CA
  JOIN (SELECT CA.Name, CA.Major, MAX(CA.Minor) AS Minor
          FROM CA
          JOIN (SELECT Name, MAX(Major) AS Major
                  FROM CA
                 GROUP BY Name
               ) AS CB
            ON CA.Name = CB.Name AND CA.Major = CB.Major
         GROUP BY CA.Name, CA.Major
       ) AS CC
    ON CA.Name = CC.Name AND CA.Major = CC.Major AND CA.Minor = CC.Minor
 GROUP BY CA.Name, CA.Major, CA.Minor;

Tested – it works and produces the same answer as Andomar‘s query does.

---

Performance

I created a bigger volume of data (11616 rows of data), and ran a benchmark timing of Andomar’s query against mine – target DBMS was IBM Informix Dynamic Server (IDS) version 11.70.FC2 running on MacOS X 10.7.2. I used the first of Andomar’s two queries since IDS does not support the comparison notation in the second one. I loaded the data, updated statistics, and ran the queries both with mine followed by Andomar’s and with Andomar’s followed by mine. I also recorded the basic costs reported by the IDS optimizer. The result data from both queries were the same (so the queries are both accurate – or equally inaccurate).

Table unindexed:

Andomar's query                           Jonathan's query
Time: 22.074129                           Time: 0.085803
Estimated Cost: 2468070                   Estimated Cost: 22673
Estimated # of Rows Returned: 5808        Estimated # of Rows Returned: 132
Temporary Files Required For: Order By    Temporary Files Required For: Group By

Table with unique index on (name, major, minor, revision):

Andomar's query                           Jonathan's query
Time: 0.768309                            Time: 0.060380
Estimated Cost: 31754                     Estimated Cost: 2329
Estimated # of Rows Returned: 5808        Estimated # of Rows Returned: 139
                                          Temporary Files Required For: Group By

As you can seen, the index dramatically improves the performance of Andomar’s query, but it still seems to be more expensive on this system than my query. The index gives a 25% time saving for my query. I’d be curious to see comparable figures for the two versions of Andomar’s query on comparable volumes of data, with and without the index. (My test data can be supplied if you need it; there were 132 products – the 3 listed in the question and 129 new ones; each new product had (the same) 90 version entries.)

The reason for the discrepancy is that the sub-query in Andomar’s query is a correlated sub-query, which is a relatively expensive process (dramatically so when the index is missing).