35.16. columns
The view columns contains information about all table columns (or view columns) in the database. System columns (oid, etc.) are not included. Only those columns are shown that the current user has access to (by way of being the owner or having some privilege).
Table 35.14. columns Columns
| Name | Data Type | Description |
|---|---|---|
table_catalog | sql_identifier | Name of the database containing the table (always the current database) |
table_schema | sql_identifier | Name of the schema containing the table |
table_name | sql_identifier | Name of the table |
column_name | sql_identifier | Name of the column |
ordinal_position | cardinal_number | Ordinal position of the column within the table (count starts at 1) |
column_default | character_data | Default expression of the column |
is_nullable | yes_or_no | YES if the column is possibly nullable, NO if it is known not nullable. A not-null constraint is one way a column can be known not nullable, but there can be others. |
data_type | character_data | Data type of the column, if it is a built-in type, or ARRAY if it is some array (in that case, see the view element_types), else USER-DEFINED (in that case, the type is identified in udt_name and associated columns). If the column is based on a domain, this column refers to the type underlying the domain (and the domain is identified in domain_name and associated columns). |
character_maximum_length | cardinal_number | If data_type identifies a character or bit string type, the declared maximum length; null for all other data types or if no maximum length was declared. |
character_octet_length | cardinal_number | If data_type identifies a character type, the maximum possible length in octets (bytes) of a datum; null for all other data types. The maximum octet length depends on the declared character maximum length (see above) and the server encoding. |
numeric_precision | cardinal_number | If data_type identifies a numeric type, this column contains the (declared or implicit) precision of the type for this column. The precision indicates the number of significant digits. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null. |
numeric_precision_radix | cardinal_number | If data_type identifies a numeric type, this column indicates in which base the values in the columns numeric_precision and numeric_scale are expressed. The value is either 2 or 10. For all other data types, this column is null. |
numeric_scale | cardinal_number | If data_type identifies an exact numeric type, this column contains the (declared or implicit) scale of the type for this column. The scale indicates the number of significant digits to the right of the decimal point. It can be expressed in decimal (base 10) or binary (base 2) terms, as specified in the column numeric_precision_radix. For all other data types, this column is null. |
datetime_precision | cardinal_number | If data_type identifies a date, time, timestamp, or interval type, this column contains the (declared or implicit) fractional seconds precision of the type for this column, that is, the number of decimal digits maintained following the decimal point in the seconds value. For all other data types, this column is null. |
interval_type | character_data | If data_type identifies an interval type, this column contains the specification which fields the intervals include for this column, e.g., YEAR TO MONTH, DAY TO SECOND, etc. If no field restrictions were specified (that is, the interval accepts all fields), and for all other data types, this field is null. |
interval_precision | cardinal_number | Applies to a feature not available in Postgres Pro (see datetime_precision for the fractional seconds precision of interval type columns) |
character_set_catalog | sql_identifier | Applies to a feature not available in Postgres Pro |
character_set_schema | sql_identifier | Applies to a feature not available in Postgres Pro |
character_set_name | sql_identifier | Applies to a feature not available in Postgres Pro |
collation_catalog | sql_identifier | Name of the database containing the collation of the column (always the current database), null if default or the data type of the column is not collatable |
collation_schema | sql_identifier | Name of the schema containing the collation of the column, null if default or the data type of the column is not collatable |
collation_name | sql_identifier | Name of the collation of the column, null if default or the data type of the column is not collatable |
domain_catalog | sql_identifier | If the column has a domain type, the name of the database that the domain is defined in (always the current database), else null. |
domain_schema | sql_identifier | If the column has a domain type, the name of the schema that the domain is defined in, else null. |
domain_name | sql_identifier | If the column has a domain type, the name of the domain, else null. |
udt_catalog | sql_identifier | Name of the database that the column data type (the underlying type of the domain, if applicable) is defined in (always the current database) |
udt_schema | sql_identifier | Name of the schema that the column data type (the underlying type of the domain, if applicable) is defined in |
udt_name | sql_identifier | Name of the column data type (the underlying type of the domain, if applicable) |
scope_catalog | sql_identifier | Applies to a feature not available in Postgres Pro |
scope_schema | sql_identifier | Applies to a feature not available in Postgres Pro |
scope_name | sql_identifier | Applies to a feature not available in Postgres Pro |
maximum_cardinality | cardinal_number | Always null, because arrays always have unlimited maximum cardinality in Postgres Pro |
dtd_identifier | sql_identifier | An identifier of the data type descriptor of the column, unique among the data type descriptors pertaining to the table. This is mainly useful for joining with other instances of such identifiers. (The specific format of the identifier is not defined and not guaranteed to remain the same in future versions.) |
is_self_referencing | yes_or_no | Applies to a feature not available in Postgres Pro |
is_identity | yes_or_no | Applies to a feature not available in Postgres Pro |
identity_generation | character_data | Applies to a feature not available in Postgres Pro |
identity_start | character_data | Applies to a feature not available in Postgres Pro |
identity_increment | character_data | Applies to a feature not available in Postgres Pro |
identity_maximum | character_data | Applies to a feature not available in Postgres Pro |
identity_minimum | character_data | Applies to a feature not available in Postgres Pro |
identity_cycle | yes_or_no | Applies to a feature not available in Postgres Pro |
is_generated | character_data | Applies to a feature not available in Postgres Pro |
generation_expression | character_data | Applies to a feature not available in Postgres Pro |
is_updatable | yes_or_no | YES if the column is updatable, NO if not (Columns in base tables are always updatable, columns in views not necessarily) |
Since data types can be defined in a variety of ways in SQL, and Postgres Pro contains additional ways to define data types, their representation in the information schema can be somewhat difficult. The column data_type is supposed to identify the underlying built-in type of the column. In Postgres Pro, this means that the type is defined in the system catalog schema pg_catalog. This column might be useful if the application can handle the well-known built-in types specially (for example, format the numeric types differently or use the data in the precision columns). The columns udt_name, udt_schema, and udt_catalog always identify the underlying data type of the column, even if the column is based on a domain. (Since Postgres Pro treats built-in types like user-defined types, built-in types appear here as well. This is an extension of the SQL standard.) These columns should be used if an application wants to process data differently according to the type, because in that case it wouldn't matter if the column is really based on a domain. If the column is based on a domain, the identity of the domain is stored in the columns domain_name, domain_schema, and domain_catalog. If you want to pair up columns with their associated data types and treat domains as separate types, you could write coalesce(domain_name, udt_name), etc.