Numeric types consist of two-, four-, and eight-byte integers, as well as eight-byte floating-point numbers and selectable-precision decimals:
|small-range integer: -32768 to +32767 (2 bytes)|
|typical choice for integer: -2147483648 to +2147483647 (4 bytes)|
|large-range integer: -9223372036854775808 to +9223372036854775807 (8 bytes)|
|exact, fixed-length representation of numbers with decimal point: up to decimal 38 digits|
|variable-precision, inexact: 15 decimal digits precision|
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.
numeric can store fixed-point numbers with up to 38 digits
without loss of precision. It is especially recommended for storing
monetary amounts and other quantities where exactness is required.
numeric values yield exact results where possible,
e.g. addition, subtraction, multiplication.
We use the following terms below: 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. The scale of a
numeric is the count of decimal digits in the fractional part, to the
right 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.
The maximum supported precision is 38. Internally,
numeric values are
stored as 64-bit values if the precision is smaller or equal to 18.
Precisions over 18 require 128-bit for internal storage. Processing
numeric values is often slower than processing 64-bit values,
so it is advisable to use a sensible precision for the use case at hand
instead of always using the maximum precision by default.
Both the maximum precision and the maximum scale of a
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.
NUMERIC(precision) selects a scale of 0.
NUMERIC selects the maximum precision of 38 and a scale of 0.
The type propagation rules for arithmetic operations with numerics never
decrease the scale and set the precision such that the biggest possible
result will fit into the result type. This may lead to undesired growth
of both scale and precision, especially when chaining multiple
arithmetic operations. Large scale might be undesirable because it takes
away from the digits in front of the decimal point, potentially leading
to overflow errors. Large precision might also be undesirable because
numeric values with precision over 18 internally use 128-bit which may
slow down processing. To avoid this, explicit casts to the desired scale
and precision can be added throughout a query.
Arithmetic operations between a
NUMERIC(p1,s1) and a
have the following results:
|+ or -||NUMERIC(precision, scale) with:|
scale = max(s1,s2)
precision = min(38, max((p1-s1),(p2-s2)) + 1 + scale)
|*||NUMERIC(precision, scale) with:|
scale = max(max(s1,s2), min(s1+s2, 38 - (p1-s1) - (p2-s2)))
precision = min(p1+p2, 38)
|/||NUMERIC(precision, scale) with:|
scale = max(s1,s2)
precision = min(38, ((p1-s1) + s2 + scale))
|%||NUMERIC(precision, scale) with:|
scale = max(s1,s2)
precision = min((p1-s1), (p2-s2)) + scale
When used in arithmetic operation together with
DOUBLE PRECISION operands will always give
DOUBLE PRECISION results,
SMALLINT behaves the same as
In the SQL standard, as well as in PostgreSQL and many other database
systems, the types
numeric are equivalent and both
support variable-length precision. This is unlike Hyper, where
has fixed-length precision and
decimal is not officially supported.
If you create an extract of a relational database in Tableau, the
extract will always use the Hyper
double precision type, so you only
get 15 digits of precision. However, you can create the extract file
using the Hyper API and specify the
numeric type to get up to 38
numeric value has a precision of over 18, 128-bit are used for
internal storage which can impact the performance of all subsequent
numeric columns with precision larger than 18 in Hyper files
is not supported, yet.
The data type
double precision is an inexact, variable-precision
numeric type. On all currently supported platforms, these types are
implementations of IEEE Standard 754 for Binary Floating-Point
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. This is not a limitation of Hyper but an inherent trade-off of using floating-point values. In particular, the following recommendations should be taken into account when using floating-point types:
If you require exact storage and calculations (such as for monetary amounts), use the
Aggregations such as
sum()on floating-point values may yield inconsistent results when executed repeatedly due to parallel computation of aggregates. If you require consistent results, consider using
Comparing two floating-point values for equality might not always work as expected. Using difference to a small epsilon value is recommended instead.
On all currently supported platforms, the
double precision type 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.
By default, floating point values are output in text form in their shortest precise decimal representation; the decimal value produced is closer to the true stored binary value than to any other value representable in the same binary precision. This value will use at most 17 significant decimal digits.
In addition to ordinary numeric values, the floating-point types have several special values:
These represent the IEEE 754 special values "infinity", "negative
infinity", and "not-a-number", respectively. When writing these values
as constants in an SQL command, you must put quotes around them, for
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 itself). In Hyper,
as equal to
NaN, though. This decision was made for compatibility
with PostgresQL and many other database systems.
Hyper also supports the SQL-standard notations
for specifying inexact numeric types. Here,
p specifies the minimum
acceptable precision in binary digits. However, the
p argument is
currently ignored and all
float(p) types are simply mapped to
float with no precision specified is also mapped