describes various hard limits of PostgreSQL. However, practical limits, such as performance limitations or available disk space may apply before absolute hard limits are reached. Item Upper Limit Comment database size unlimited number of databases 4,294,950,911 relations per database 1,431,650,303 relation size 32 TB with the default BLCKSZ of 8192 bytes rows per table limited by the number of tuples that can fit onto 4,294,967,295 pages columns per table 1,600 further limited by tuple size fitting on a single page; see note below columns in a result set 1,664 field size 1 GB indexes per table unlimited constrained by maximum relations per database columns per index 32 can be increased by recompiling PostgreSQL partition keys 32 can be increased by recompiling PostgreSQL identifier length 63 bytes can be increased by recompiling PostgreSQL function arguments 100 can be increased by recompiling PostgreSQL query parameters 65,535 The maximum number of columns for a table is further reduced as the tuple being stored must fit in a single 8192-byte heap page. For example, excluding the tuple header, a tuple made up of 1,600 int columns would consume 6400 bytes and could be stored in a heap page, but a tuple of 1,600 bigint columns would consume 12800 bytes and would therefore not fit inside a heap page. Variable-length fields of types such as text, varchar, and char can have their values stored out of line in the table's TOAST table when the values are large enough to require it. Only an 18-byte pointer must remain inside the tuple in the table's heap. For shorter length variable-length fields, either a 4-byte or 1-byte field header is used and the value is stored inside the heap tuple. Columns that have been dropped from the table also contribute to the maximum column limit. Moreover, although the dropped column values for newly created tuples are internally marked as null in the tuple's null bitmap, the null bitmap also occupies space. Each table can store a theoretical maximum of 2^32 out-of-line values; see for a detailed discussion of out-of-line storage. This limit arises from the use of a 32-bit OID to identify each such value. The practical limit is significantly less than the theoretical limit, because as the OID space fills up, finding an OID that is still free can become expensive, in turn slowing down INSERT/UPDATE statements. Typically, this is only an issue for tables containing many terabytes of data; partitioning is a possible workaround.