On 02/08/2010 3:20 AM, Hardik Belani wrote:
> We are using postgres as RDBMS for our product. There is a requirement
> coming for a feature which will require me to store data about various
> data points (mainly numbers) on a time scale. Data measurement is
> being taken every few secs/mins based and it is needed to be stored
> for statistical analysis. Now this requires numbers (integers/floats)
> to be stored at every mins.
> For this i can create a table with number and time (may be time offset
> instead of timestamp) as columns. But still it will require me to
> store huge number of rows in the order of few millions. Data is read
> only and only inserts can happen. But I need to perform all kinds of
> aggregation to get various statistics. for example: daily avg, monthly
> avg etc..
> We already are using postgres for our product so using postgres does
> not add any additional installation requirement and hence it is a bit
> easier.
> Would you recommand postgres for this kind of requirement and will be
> provide the performance. OR do you recommand any other database meant
> for such requirements. I am also searching for a good historian
> database if postgres doesn't suppport.
> Thanks,
> Hardik
Hi Hardik,
Data warehousing techniques could help you with your requirements of
aggregating large amounts of data. Have a look at "The Data Warehouse
Toolkit" by R. Kimball on how to design a star schema with aggregate
tables (these can be done as materialized views using PL/pgSQL and
triggers under postgres). You could also use an OLAP server (e.g.
Mondrian, which pretty nice and open source as well) on top of your
postgres DB, as it can use aggregate tables transparently when needed.
Etienne