PostgreSQL™ has a rich set of native data types available to users. Users can add new types to PostgreSQL™ using the CREATE TYPE(7) command.
Table 8.1, “Data Types” shows all the built-in general-purpose data types. Most of the alternative names listed in the “Aliases” column are the names used internally by PostgreSQL™ for historical reasons. In addition, some internally used or deprecated types are available, but are not listed here.
Table 8.1. Data Types
Name | Aliases | Description |
---|---|---|
bigint | int8 | signed eight-byte integer |
bigserial | serial8 | autoincrementing eight-byte integer |
bit [ (n ) ] | fixed-length bit string | |
bit varying [ (n ) ] | varbit | variable-length bit string |
boolean | bool | logical Boolean (true/false) |
box | rectangular box on a plane | |
bytea | binary data (“byte array”) | |
character [ (n ) ] | char [ (n ) ] | fixed-length character string |
character varying [ (n ) ] | varchar [ (n ) ] | variable-length character string |
cidr | IPv4 or IPv6 network address | |
circle | circle on a plane | |
date | calendar date (year, month, day) | |
double precision | float8 | double precision floating-point number (8 bytes) |
inet | IPv4 or IPv6 host address | |
integer | int, int4 | signed four-byte integer |
interval [ fields ] [ (p ) ] | time span | |
json | textual JSON data | |
jsonb | binary JSON data, decomposed | |
line | infinite line on a plane | |
lseg | line segment on a plane | |
macaddr | MAC (Media Access Control) address | |
money | currency amount | |
numeric [ (p ,
s ) ] | decimal [ (p ,
s ) ] | exact numeric of selectable precision |
path | geometric path on a plane | |
pg_lsn | PostgreSQL™ Log Sequence Number | |
point | geometric point on a plane | |
polygon | closed geometric path on a plane | |
real | float4 | single precision floating-point number (4 bytes) |
smallint | int2 | signed two-byte integer |
smallserial | serial2 | autoincrementing two-byte integer |
serial | serial4 | autoincrementing four-byte integer |
text | variable-length character string | |
time [ (p ) ] [ without time zone ] | time of day (no time zone) | |
time [ (p ) ] with time zone | timetz | time of day, including time zone |
timestamp [ (p ) ] [ without time zone ] | date and time (no time zone) | |
timestamp [ (p ) ] with time zone | timestamptz | date and time, including time zone |
tsquery | text search query | |
tsvector | text search document | |
txid_snapshot | user-level transaction ID snapshot | |
uuid | universally unique identifier | |
xml | XML data |
The following types (or spellings thereof) are specified by SQL: bigint, bit, bit varying, boolean, char, character varying, character, varchar, date, double precision, integer, interval, numeric, decimal, real, smallint, time (with or without time zone), timestamp (with or without time zone), xml.
Each data type has an external representation determined by its input and output functions. Many of the built-in types have obvious external formats. However, several types are either unique to PostgreSQL™, such as geometric paths, or have several possible formats, such as the date and time types. Some of the input and output functions are not invertible, i.e., the result of an output function might lose accuracy when compared to the original input.
Numeric types consist of two-, four-, and eight-byte integers, four- and eight-byte floating-point numbers, and selectable-precision decimals. Table 8.2, “Numeric Types” lists the available types.
Table 8.2. Numeric Types
Name | Storage Size | Description | Range |
---|---|---|---|
smallint | 2 bytes | small-range integer | -32768 to +32767 |
integer | 4 bytes | typical choice for integer | -2147483648 to +2147483647 |
bigint | 8 bytes | large-range integer | -9223372036854775808 to +9223372036854775807 |
decimal | variable | user-specified precision, exact | up to 131072 digits before the decimal point; up to 16383 digits after the decimal point |
numeric | variable | user-specified precision, exact | up to 131072 digits before the decimal point; up to 16383 digits after the decimal point |
real | 4 bytes | variable-precision, inexact | 6 decimal digits precision |
double precision | 8 bytes | variable-precision, inexact | 15 decimal digits precision |
smallserial | 2 bytes | small autoincrementing integer | 1 to 32767 |
serial | 4 bytes | autoincrementing integer | 1 to 2147483647 |
bigserial | 8 bytes | large autoincrementing integer | 1 to 9223372036854775807 |
The syntax of constants for the numeric types is described in the section called “Constants”. The numeric types have a full set of corresponding arithmetic operators and functions. Refer to Chapter 9, Functions and Operators for more information. The following sections describe the types in detail.
The types smallint, integer, and bigint store whole numbers, that is, numbers without fractional components, of various ranges. Attempts to store values outside of the allowed range will result in an error.
The type integer is the common choice, as it offers the best balance between range, storage size, and performance. The smallint type is generally only used if disk space is at a premium. The bigint type is designed to be used when the range of the integer type is insufficient.
SQL only specifies the integer types integer (or int), smallint, and bigint. The type names int2, int4, and int8 are extensions, which are also used by some other SQL database systems.
The type numeric can store numbers with a very large number of digits and perform calculations exactly. It is especially recommended for storing monetary amounts and other quantities where exactness is required. However, arithmetic on numeric values is very slow compared to the integer types, or to the floating-point types described in the next section.
We use the following terms below: The scale of a numeric is the count of decimal digits in the fractional part, to the right of the decimal point. The precision of a numeric is the total count of significant digits in the whole number, that is, the number of digits to both sides of the decimal point. So the number 23.5141 has a precision of 6 and a scale of 4. Integers can be considered to have a scale of zero.
Both the maximum precision and the maximum scale of a numeric column can be configured. To declare a column of type numeric use the syntax:
NUMERIC(precision
,scale
)
The precision must be positive, the scale zero or positive. Alternatively:
NUMERIC(precision
)
selects a scale of 0. Specifying:
NUMERIC
without any precision or scale creates a column in which numeric values of any precision and scale can be stored, up to the implementation limit on precision. A column of this kind will not coerce input values to any particular scale, whereas numeric columns with a declared scale will coerce input values to that scale. (The SQL standard requires a default scale of 0, i.e., coercion to integer precision. We find this a bit useless. If you're concerned about portability, always specify the precision and scale explicitly.)
The maximum allowed precision when explicitly specified in the type declaration is 1000; NUMERIC without a specified precision is subject to the limits described in Table 8.2, “Numeric Types”.
If the scale of a value to be stored is greater than the declared scale of the column, the system will round the value to the specified number of fractional digits. Then, if the number of digits to the left of the decimal point exceeds the declared precision minus the declared scale, an error is raised.
Numeric values are physically stored without any extra leading or
trailing zeroes. Thus, the declared precision and scale of a column
are maximums, not fixed allocations. (In this sense the numeric
type is more akin to varchar(n
)
than to char(n
).) The actual storage
requirement is two bytes for each group of four decimal digits,
plus three to eight bytes overhead.
In addition to ordinary numeric values, the numeric
type allows the special value NaN
, meaning
“not-a-number”. Any operation on NaN
yields another NaN
. When writing this value
as a constant in an SQL command, you must put quotes around it,
for example UPDATE table SET x = 'NaN'
. On input,
the string NaN
is recognized in a case-insensitive manner.
In most implementations of the “not-a-number” concept,
NaN
is not considered equal to any other numeric
value (including NaN
). In order to allow
numeric values to be sorted and used in tree-based
indexes, PostgreSQL™ treats NaN
values as equal, and greater than all non-NaN
values.
The types decimal and numeric are equivalent. Both types are part of the SQL standard.
The data types real and double precision are inexact, variable-precision numeric types. In practice, these types are usually implementations of IEEE Standard 754 for Binary Floating-Point Arithmetic (single and double precision, respectively), to the extent that the underlying processor, operating system, and compiler support it.
Inexact means that some values cannot be converted exactly to the internal format and are stored as approximations, so that storing and retrieving a value might show slight discrepancies. Managing these errors and how they propagate through calculations is the subject of an entire branch of mathematics and computer science and will not be discussed here, except for the following points:
If you require exact storage and calculations (such as for monetary amounts), use the numeric type instead.
If you want to do complicated calculations with these types for anything important, especially if you rely on certain behavior in boundary cases (infinity, underflow), you should evaluate the implementation carefully.
Comparing two floating-point values for equality might not always work as expected.
On most platforms, the real type has a range of at least 1E-37 to 1E+37 with a precision of at least 6 decimal digits. The double precision type typically has a range of around 1E-307 to 1E+308 with a precision of at least 15 digits. Values that are too large or too small will cause an error. Rounding might take place if the precision of an input number is too high. Numbers too close to zero that are not representable as distinct from zero will cause an underflow error.
The extra_float_digits setting controls the
number of extra significant digits included when a floating point
value is converted to text for output. With the default value of
0
, the output is the same on every platform
supported by PostgreSQL. Increasing it will produce output that
more accurately represents the stored value, but may be unportable.
In addition to ordinary numeric values, the floating-point types have several special values:
Infinity
-Infinity
NaN
These represent the IEEE 754 special values
“infinity”, “negative infinity”, and
“not-a-number”, respectively. (On a machine whose
floating-point arithmetic does not follow IEEE 754, these values
will probably not work as expected.) When writing these values
as constants in an SQL command, you must put quotes around them,
for example UPDATE table SET x = 'Infinity'
. On input,
these strings are recognized in a case-insensitive manner.
IEEE754 specifies that NaN
should not compare equal
to any other floating-point value (including NaN
).
In order to allow floating-point values to be sorted and used
in tree-based indexes, PostgreSQL™ treats
NaN
values as equal, and greater than all
non-NaN
values.
PostgreSQL™ also supports the SQL-standard
notations float and
float(p
) for specifying
inexact numeric types. Here, p
specifies
the minimum acceptable precision in binary digits.
PostgreSQL™ accepts
float(1) to float(24) as selecting the
real type, while
float(25) to float(53) select
double precision. Values of p
outside the allowed range draw an error.
float with no precision specified is taken to mean
double precision.
The assumption that real and
double precision have exactly 24 and 53 bits in the
mantissa respectively is correct for IEEE-standard floating point
implementations. On non-IEEE platforms it might be off a little, but
for simplicity the same ranges of p
are used
on all platforms.
The data types smallserial, serial and
bigserial are not true types, but merely
a notational convenience for creating unique identifier columns
(similar to the AUTO_INCREMENT
property
supported by some other databases). In the current
implementation, specifying:
CREATE TABLEtablename
(colname
SERIAL );
is equivalent to specifying:
CREATE SEQUENCEtablename
_colname
_seq; CREATE TABLEtablename
(colname
integer NOT NULL DEFAULT nextval('tablename
_colname
_seq') ); ALTER SEQUENCEtablename
_colname
_seq OWNED BYtablename
.colname
;
Thus, we have created an integer column and arranged for its default
values to be assigned from a sequence generator. A NOT NULL
constraint is applied to ensure that a null value cannot be
inserted. (In most cases you would also want to attach a
UNIQUE
or PRIMARY KEY
constraint to prevent
duplicate values from being inserted by accident, but this is
not automatic.) Lastly, the sequence is marked as “owned by”
the column, so that it will be dropped if the column or table is dropped.
Because smallserial, serial and
bigserial are implemented using sequences, there may
be "holes" or gaps in the sequence of values which appears in the
column, even if no rows are ever deleted. A value allocated
from the sequence is still "used up" even if a row containing that
value is never successfully inserted into the table column. This
may happen, for example, if the inserting transaction rolls back.
See nextval()
in the section called “Sequence Manipulation Functions”
for details.
To insert the next value of the sequence into the serial
column, specify that the serial
column should be assigned its default value. This can be done
either by excluding the column from the list of columns in
the INSERT statement, or through the use of
the DEFAULT
key word.
The type names serial and serial4 are equivalent: both create integer columns. The type names bigserial and serial8 work the same way, except that they create a bigint column. bigserial should be used if you anticipate the use of more than 231 identifiers over the lifetime of the table. The type names smallserial and serial2 also work the same way, except that they create a smallint column.
The sequence created for a serial column is automatically dropped when the owning column is dropped. You can drop the sequence without dropping the column, but this will force removal of the column default expression.