I am not aware of any dictionary table. However, if it is me, I will write a script which will loop through a table, reading fields and (also composite if need be) and then redirect the output to a file preferably and not table. In this way, an automation script can read for all databases and tables required and provide outputs for all fields or composite fields if required.

Raja K Thaw

My wiki: http://en.wikipedia.org/wiki/User:Kt_raj1

Street Children suffer not by their fault. We can help them if we want.

Hi Steven,

how do you define the skew factor?

I use this for calculating the percent deviation from average:

SELECT HASHAMP(HASHBUCKET(HASHROW(col))) AS vproc, COUNT(*) AS cnt, 100 * (cnt - AVG(cnt) OVER ()) / AVG(cnt) OVER () (DEC(8,2)) AS deviation FROM tab GROUP BY 1

And based on the count per AMP you can do the skew calclation:

SELECT SUM(cnt) AS RowCount ,MAX(SkewedAMP) AS SkewedAMP -- skew factor, 1 = even distribution, 1.1 = max AMP needs 10% more space than the average AMP ,MAX(cnt) / NULLIF(AVG(cnt),0) (DEC(5,2)) AS SkewFactor -- skew factor, between 0 and 99. Same calculation as WinDDI/ TD Administrator ,(100 - (AVG(cnt) / NULLIF(MAX(cnt),0) * 100)) (DEC(3,0)) AS SkewFactor_WINDDI FROM ( SELECT HASHAMP(HASHBUCKET(HASHROW(col))) AS vproc, COUNT(*) AS cnt, 100 * (cnt - AVG(cnt) OVER ()) / AVG(cnt) OVER () (DEC(8,2)) AS deviation, CASE WHEN cnt = MAX(cnt) OVER () THEN vproc END AS SkewedAMP FROM tab GROUP BY 1 ) AS dt

And for big tables you might better use a SAMPLE of a few percent instead of aggregating all rows

Dieter

and this works too...:)

select

sum(tallyset.rowtally)

,min(tallyset.rowtally)

,max(tallyset.rowtally)

,avg(tallyset.rowtally)

,100 - (avg(tallyset.rowtally)/max(tallyset.rowtally) *100) as skewfactor

from

(select

hashamp(hashbucket(hashrow(<candidate columns>))) as hashedamp

,count(*) as rowtally

from

<dbname.tablename>

group by 1) as tallyset

Some drink from the fountain of knowledge, others just gargle.

Here is a different approach in case this helps you.

For a given PI of a table the skew factor is based on the count of the values of the records across the amps. Just determine it based on the unique value. SEL PI COLUMNS, count(*) from <databasename>.<tablename> group by PI columns. usually if the count(*) is not distributed evenly then the table is skewed i.e., if the count of a particular value is in the order of some thousands and the minimum of another PI value is in the order of few, then the table is heavily skewed.

Take the new PI columns you wanted to check for, and check the distribution as explained above. if it is even distribution of the counts across the various values, then you can consider it for changing it to the new PI.

Hi

I was wondering if there was a way to calculate the skew of an existing table with a different choice of PI. I am aware of the hashing functions, such that the following query will show the distribution of rows across the amps based on the new PI, however with a a system with hundreds of AMPs, it would be nice to determine the skew factor value:

SELECT

hashamp(hashbucket(hashrow( new_PI_column_list ))) as ampnum

,count(*)

from <database>.<tablename>

group by 1

order by 2

Cheers

Steven