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
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