Hi all
Has anyone any experience with very large tables?
I've been asked to store a grid of 1.5 million geographical locations,
fine. However, associated with each point are 288 months, and
associated with each month are 500 float values (a distribution
curve), i.e. 1,500,000 * 288 * 500 = 216 billion values :).
So a 216 billion row table is probably out of the question. I was
considering storing the 500 floats as bytea.
This means I'll need a table something like this:
grid_point_id | month_id | distribution_curve
(int4) | (int2) | (bytea?)
------------------+---------------+---------------
Any advice would be appreciated, especially on the storage of the 500 floats.
Another (somewhat far fetched) possibility was a custom data type,
which delegated it's data access to HDF5 somehow - perhaps by storing
a reference to a value location. The reason for this is that data will
be written using PyTables and HDF5. It is produced in 500 runs each
providing a value to the distribution curve for all points and months
-(500 updates of a 500 million row table...no thanks). Querying is the
opposite - we want the whole chunk of 500 values at a time. Is this a
fantasy?
Cheers
Will T
