I have a dataset I'm not sure how to store. The structure is simple: 30 numeric attributes identified by about 15 billion x, y, and t values. We're expecting ~17k t values and maybe 90k x/y combinations, though it could be some combination that gives us 20 million records in the end.
The processing involves retrieving 1-10 columns for each x and y pair and storing various calculated numeric values. Are we nearing/passing the limit of fast response times for Postgres with this many rows?
The processing is all done in-house by one person and shouldn't need to happen more than a couple dozen times as we settle on what summaries we want. So we're not worried about a high number of writes or connections, strict security, making updates to existing records, table relations, losing data because of network connection issues. Basically, we're not concerned about the kinds of things I understand the ACID part of a RDBMS like Postgres brings to the table. But we also don't need to replicate or distribute the data, high availability, change the schema on the fly, or manage an unusual number of writes (say a dozen for each of the 90k x/y pairs)- the kinds of things I understand NoSQL DBs offer.
So I guess the real issue is read-speed out of Postgres for a table of this size and the real question is whether there's a better storage mechanism for what we need to accomplish. Does the answer change if we have 40 billion records? 60?