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 -- Sent via pgsql-general mailing list (pgsql-general@xxxxxxxxxxxxxx) To make changes to your subscription: http://www.postgresql.org/mailpref/pgsql-general
