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Numeric types consist of two-, four-, and eight-byte integers, four- and eight-byte floating-point numbers, and selectable-precision decimals. Table 8-2 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 Section 4.1.2. The numeric types have a full set of corresponding arithmetic operators and functions. Refer to Chapter 9 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. It is especially recommended for
storing monetary amounts and other quantities where exactness is
required. Calculations with `numeric` values yield exact
results where possible, e.g. addition, subtraction, multiplication.
However, calculations on `numeric` values are 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.)

Note:The maximum allowed precision when explicitly specified in the type declaration is 1000;NUMERICwithout a specified precision is subject to the limits described in Table 8-2.

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

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.

Note:In most implementations of the "not-a-number" concept,NaNis not considered equal to any other numeric value (includingNaN). In order to allownumericvalues to be sorted and used in tree-based indexes, PostgreSQL treatsNaNvalues as equal, and greater than all non-NaNvalues.

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.

Note: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 of0, 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`

Note:IEEE754 specifies thatNaNshould not compare equal to any other floating-point value (includingNaN). In order to allow floating-point values to be sorted and used in tree-based indexes, PostgreSQL treatsNaNvalues as equal, and greater than all non-NaNvalues.

PostgreSQL also supports the SQL-standard
notations `float` and
`float(p)` for specifying
inexact numeric types. Here,

Note:The assumption thatrealanddouble precisionhave 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 ofare used on all platforms.p

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 TABLE(tablenameSERIAL );colname

is equivalent to specifying:

CREATE SEQUENCE_tablename_seq; CREATE TABLEcolname(tablenameinteger NOT NULL DEFAULT nextval('colname_tablename_seq') ); ALTER SEQUENCEcolname_tablename_seq OWNED BYcolname.tablename;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.

Note:Becausesmallserial,serialandbigserialare 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. Seenextval()in Section 9.16 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 2^{31} 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.