How big is too big for a PostgreSQL table?

Rows per a table won't be an issue on it's own.

So roughly speaking 1 million rows a day for 90 days is 90 million rows. I see no reason Postgres can't deal with that, without knowing all the details of what you are doing.

Depending on your data distribution you can use a mixture of indexes, filtered indexes, and table partitioning of some kind to speed thing up once you see what performance issues you may or may not have. Your problem will be the same on any other RDMS that I know of. If you only need 3 months worth of data design in a process to prune off the data you don't need any more. That way you will have a consistent volume of data on the table. Your lucky you know how much data will exist, test it for your volume and see what you get. Testing one table with 90 million rows may be as easy as:

select x,1 as c2,2 as c3
from generate_series(1,90000000) x;

https://wiki.postgresql.org/wiki/FAQ

Limit   Value
Maximum Database Size       Unlimited
Maximum Table Size          32 TB
Maximum Row Size            1.6 TB
Maximum Field Size          1 GB
Maximum Rows per Table      Unlimited
Maximum Columns per Table   250 - 1600 depending on column types
Maximum Indexes per Table   Unlimited

Another way to speed up your queries significantly on a table with > 100 million rows is to cluster the table on the index that is most often used in your queries. Do this in your database's "off" hours. We have a table with > 218 million rows and have found 30X improvements.

Also, for a very large table, it's a good idea to create an index on your foreign keys.

EXAMPLE:

1. Assume we have a table named investment in a database named ccbank.
2. Assume the index most used in our queries is (bankid,record_date)

Here are the steps to create and cluster an index:

1. psql -c "drop index investment_bankid_rec_dt_idx;" ccbank
2. psql -c "create index investment_bankid_rec_dt_idx on investment(bankid, record_date);"
3. psql -c "cluster investment_bankid_rec_dt_idx on investment;"
4. vacuumdb -d ccbank -z -v -t investment

In steps 1-2 we replace the old index with a new, optimized one. In step 3 we cluster the table: this basically puts the DB table in the physical order of the index, so that when PostgreSQL performs a query it caches the most likely next rows. In step 4 we vacuum the database to reset the statistics for the query planner.