Thread: Putting many related fields as an array
Hi, Currently doing some level of aggregrate tables for some data. These data will be used for slice/dice activity and we want to be able to play/manipulate the data such that I can get means and stddev data. Eg: For each Original Column eg: population_in_town : (I get derivatives) - mean # of ppl in each town - stddev # of ppl in each town (stdev calc already uses 2 extra columns for # of ppl squared and qty of ppl) - count of ppl - count of # of ppl is < 100 (to get a percentage of population) - count of # of ppl is < 500 Hence, I'm seeing a 1:5 column growth here if I put them as column based. eg: | sum of count | sum_of_count_squared | qty | qty < 100 | qty < 500 | I'm thinking of lumping them into 1 column via an array instead of into 5 different columns. Not sure how to go about this, hence the email to the list. something like {244,455,1234,43,23} query can be done like sum_of_count / qty = Ave (sum_of_count_squared * sum_qty ) / (qty * (qty-1)) = STDEV (sum_qty<100 / sum_qty) = % < 100 (sum_qty<500 / sum_qty) = % < 500 Then there's the issue of speed/responsiveness on doing it. Help would be appreciated in this.
On Tue, May 12, 2009 at 01:23:14PM +0800, Ow Mun Heng wrote: > | sum of count | sum_of_count_squared | qty | qty < 100 | qty < 500 | > > > I'm thinking of lumping them into 1 column via an array instead of into > 5 different columns. Not sure how to go about this, hence the email to > the list. The normal array constructor should work: SELECT ARRAY[MIN(v),MAX(v),AVG(v),STDEV(v)] FROM (VALUES (1),(3),(4)) x(v); Not sure why this is better than using separate columns though. Maybe a new datatype and a custom aggregate would be easier to work with? -- Sam http://samason.me.uk/
-----Original Message----- From: pgsql-general-owner@postgresql.org [mailto:pgsql-general- On Tue, May 12, 2009 at 01:23:14PM +0800, Ow Mun Heng wrote: >> | sum of count | sum_of_count_squared | qty | qty < 100 | qty < 500 | >> >> >> I'm thinking of lumping them into 1 column via an array instead of into >> 5 different columns. Not sure how to go about this, hence the email to >> the list. >The normal array constructor should work: > > SELECT ARRAY[MIN(v),MAX(v),AVG(v),STDEV(v)] > FROM (VALUES (1),(3),(4)) x(v); > >Not sure why this is better than using separate columns though. Maybe a >new datatype and a custom aggregate would be easier to work with? The issue here is the # of columns needed to populate the table. The table I'm summarizing has close to between 50 to 100+ columns, if the 1:5x is used as a yardstick, then the table will get awfully wide quickly. I need to know how to do it first, then test accordingly for performance and corner cases.
On Tue, May 12, 2009 at 08:06:25PM +0800, Ow Mun Heng wrote: > From: pgsql-general-owner@postgresql.org [mailto:pgsql-general- > On Tue, May 12, 2009 at 01:23:14PM +0800, Ow Mun Heng wrote: > >Not sure why this is better than using separate columns though. Maybe a > >new datatype and a custom aggregate would be easier to work with? > > The issue here is the # of columns needed to populate the table. > > The table I'm summarizing has close to between 50 to 100+ columns, if the > 1:5x is used as a yardstick, then the table will get awfully wide quickly. > > I need to know how to do it first, then test accordingly for performance and > corner cases. Yes, those are going to be pretty wide tables! Maybe if you can make the source tables a bit "narrower" it will help things; PG has to read entire rows from the table, so if your queries are only touching a few columns then it's going to need a lot more disk bandwidth to get a specific number of rows back from the table. -- Sam http://samason.me.uk/
On Tue, May 12, 2009 at 7:06 AM, Ow Mun Heng <ow.mun.heng@wdc.com> wrote:
I apologize for coming into this conversation late. I used to do analysis of a public use data flat file that had one row per patient and up to 24 diagnosis codes, each in a different column. Is this analogous to your situation? I found it was worth the effort to convert the flat file into a relational data model where the patients' diagnosis codes were in one column in a separate table. This model also makes more complex analysis easier.
Since there were several types of fields that needed to be combined into their own tables, I found it took less time to convert the flat file to the relational model using a script prior to importing the data into the database server. A Python script would read the original file and create 5 clean, tab-delimited files that were ready to be imported.
I hope this helps.
Andrew
-----Original Message-----
From: pgsql-general-owner@postgresql.org [mailto:pgsql-general-
On Tue, May 12, 2009 at 01:23:14PM +0800, Ow Mun Heng wrote:
>> | sum of count | sum_of_count_squared | qty | qty < 100 | qty < 500 |
>>
>>
>> I'm thinking of lumping them into 1 column via an array instead of into
>> 5 different columns. Not sure how to go about this, hence the email to
>> the list.
>The normal array constructor should work:
>
> SELECT ARRAY[MIN(v),MAX(v),AVG(v),STDEV(v)]
> FROM (VALUES (1),(3),(4)) x(v);
>
>Not sure why this is better than using separate columns though. Maybe a
>new datatype and a custom aggregate would be easier to work with?
The issue here is the # of columns needed to populate the table.
The table I'm summarizing has close to between 50 to 100+ columns, if the
1:5x is used as a yardstick, then the table will get awfully wide quickly.
I need to know how to do it first, then test accordingly for performance and
corner cases.
I apologize for coming into this conversation late. I used to do analysis of a public use data flat file that had one row per patient and up to 24 diagnosis codes, each in a different column. Is this analogous to your situation? I found it was worth the effort to convert the flat file into a relational data model where the patients' diagnosis codes were in one column in a separate table. This model also makes more complex analysis easier.
Since there were several types of fields that needed to be combined into their own tables, I found it took less time to convert the flat file to the relational model using a script prior to importing the data into the database server. A Python script would read the original file and create 5 clean, tab-delimited files that were ready to be imported.
I hope this helps.
Andrew