37.17. columns

The view columns contains information about all table columns (or view columns) in the database. System columns (ctid, 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 37.15. columns Columns

Column 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 PostgreSQL (see datetime_precision for the fractional seconds precision of interval type columns)
character_set_catalog sql_identifier Applies to a feature not available in PostgreSQL
character_set_schema sql_identifier Applies to a feature not available in PostgreSQL
character_set_name sql_identifier Applies to a feature not available in PostgreSQL
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 PostgreSQL
scope_schema sql_identifier Applies to a feature not available in PostgreSQL
scope_name sql_identifier Applies to a feature not available in PostgreSQL
maximum_cardinality cardinal_number Always null, because arrays always have unlimited maximum cardinality in PostgreSQL
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 PostgreSQL
is_identity yes_or_no If the column is an identity column, then YES, else NO.
identity_generation character_data If the column is an identity column, then ALWAYS or BY DEFAULT, reflecting the definition of the column.
identity_start character_data If the column is an identity column, then the start value of the internal sequence, else null.
identity_increment character_data If the column is an identity column, then the increment of the internal sequence, else null.
identity_maximum character_data If the column is an identity column, then the maximum value of the internal sequence, else null.
identity_minimum character_data If the column is an identity column, then the minimum value of the internal sequence, else null.
identity_cycle yes_or_no If the column is an identity column, then YES if the internal sequence cycles or NO if it does not; otherwise null.
is_generated character_data If the column is a generated column, then ALWAYS, else NEVER.
generation_expression character_data If the column is a generated column, then the generation expression, else null.
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 PostgreSQL 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 PostgreSQL, 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 PostgreSQL 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.