INT类型

8.1. Numeric Types

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 Description Range
Size

---

smallint 2 bytes small-range integer -32768 to +32767

integer 4 bytes typical choice for -2147483648 to +2147483647
integer

bigint 8 bytes large-range integer -9223372036854775808 to
9223372036854775807

decimal variable user-specified up to 131072 digits before the
precision, exact decimal point; up to 16383
digits after the decimal point

numeric variable user-specified up to 131072 digits before the
precision, exact decimal point; up to 16383
digits after the decimal point

real 4 bytes variable-precision, 6 decimal digits precision
inexact

double 8 bytes variable-precision, 15 decimal digits precision
precision inexact

serial 4 bytes autoincrementing 1 to 2147483647
integer

bigserial 8 bytes large 1 to 9223372036854775807
autoincrementing
integer

---

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.

8.1.1. Integer Types

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
should only be used if the range of the integer type is insufficient,
because the latter is definitely faster.

On very minimal operating systems the bigint type might not function
correctly, because it relies on compiler support for eight-byte
integers. On such machines, bigint acts the same as integer, but still
takes up eight bytes of storage. (We are not aware of any modern
platform where this is the case.)

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.

8.1.2. Arbitrary Precision Numbers

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.)

Note: 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.

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.

Note: 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.

8.1.3. Floating-Point Types

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
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.

Note: 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.

Note: Prior to PostgreSQL 7.4, the precision in float(p) was
taken to mean so many decimal digits. This has been corrected to match
the SQL standard, which specifies that the precision is measured in
binary digits. 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.

8.1.4. Serial Types

The data types 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 tablename (

colname SERIAL

);

is equivalent to specifying:

CREATE SEQUENCE tablename_colname_seq;

CREATE TABLE tablename (

colname integer NOT NULL DEFAULT
nextval(‘tablename_colname_seq’)

);

ALTER SEQUENCE tablename_colname_seq OWNED BY
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: 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 Section
9.15 for
details.

Note: Prior to PostgreSQL 7.3, serial implied UNIQUE. This is no
longer automatic. If you wish a serial column to have a unique
constraint or be a primary key, it must now be specified, just like any
other data type.

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 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.