Skip to content

Number Handling

This document describes how the library is handling numbers.

Background

This section briefly summarizes how the JSON specification describes how numbers should be handled.

JSON number syntax

JSON defines the syntax of numbers as follows:

RFC 8259, Section 6

The representation of numbers is similar to that used in most programming languages. A number is represented in base 10 using decimal digits. It contains an integer component that may be prefixed with an optional minus sign, which may be followed by a fraction part and/or an exponent part. Leading zeros are not allowed.

A fraction part is a decimal point followed by one or more digits.

An exponent part begins with the letter E in uppercase or lowercase, which may be followed by a plus or minus sign. The E and optional sign are followed by one or more digits.

The following railroad diagram from json.org visualizes the number syntax:

Syntax for JSON numbers

Number interoperability

On number interoperability, the following remarks are made:

RFC 8259, Section 6

This specification allows implementations to set limits on the range and precision of numbers accepted. Since software that implements IEEE 754 binary64 (double precision) numbers [IEEE754] is generally available and widely used, good interoperability can be achieved by implementations that expect no more precision or range than these provide, in the sense that implementations will approximate JSON numbers within the expected precision. A JSON number such as 1E400 or 3.141592653589793238462643383279 may indicate potential interoperability problems, since it suggests that the software that created it expects receiving software to have greater capabilities for numeric magnitude and precision than is widely available.

Note that when such software is used, numbers that are integers and are in the range [-2^{53}+1, 2^{53}-1] are interoperable in the sense that implementations will agree exactly on their numeric values.

Library implementation

This section describes how the above number specification is implemented by this library.

Number storage

In the default json type, numbers are stored as std::uint64_t, std::int64_t, and double, respectively. Thereby, std::uint64_t and std::int64_t are used only if they can store the number without loss of precision. If this is impossible (e.g., if the number is too large), the number is stored as double.

Notes

Examples

  • Integer -12345678912345789123456789 is smaller than INT64_MIN and will be stored as floating-point number -1.2345678912345788e+25.
  • Integer 1E3 will be stored as floating-point number 1000.0.

Number limits

  • Any 64 bit signed or unsigned integer can be stored without loss of precision.
  • Numbers exceeding the limits of double (i.e., numbers that after conversion via std::strtod are not satisfying std::isfinite such as 1E400) will throw exception json.exception.out_of_range.406 during parsing.
  • Floating-point numbers are rounded to the next number representable as double. For instance 3.141592653589793238462643383279 is stored as 0x400921fb54442d18. This is the same behavior as the code double x = 3.141592653589793238462643383279;.

Interoperability

  • The library interoperable with respect to the specification, because its supported range [-2^{63}, 2^{64}-1] is larger than the described range [-2^{53}+1, 2^{53}-1].
  • All integers outside the range [-2^{63}, 2^{64}-1], as well as floating-point numbers are stored as double. This also concurs with the specification above.

Number serialization

  • Integer numbers are serialized as is; that is, no scientific notation is used.
  • Floating-point numbers are serialized as specified by the %g printf modifier with std::numeric_limits<double>::max_digits10 significant digits). The rationale is to use the shortest representation while still allow round-tripping.

Notes regarding precision of floating-point numbers

As described above, floating-point numbers are rounded to the nearest double and serialized with the shortest representation to allow round-tripping. This can yield confusing examples:

  • The serialization can have fewer decimal places than the input: 2555.5599999999999 will be serialized as 2555.56. The reverse can also be true.
  • The serialization can be in scientific notation even if the input is not: 0.0000972439793401814 will be serialized as 9.72439793401814e-05. The reverse can also be true: 12345E-5 will be serialized as 0.12345.
  • Conversions from float to double can also introduce rouding errors:
    float f = 0.3;
    json j = f;
    std::cout << j << '\n';
    
    yields 0.30000001192092896.

All examples here can be reproduced by passing the original double value to

std::printf("%.*g\n", std::numeric_limits<double>::max_digits10, double_value);

NaN handling

NaN (not-a-number) cannot be expressed with the number syntax described above and are in fact explicitly excluded:

RFC 8259, Section 6

Numeric values that cannot be represented in the grammar below (such as Infinity and NaN) are not permitted.

That is, there is no way to parse a NaN value. However, NaN values can be stored in a JSON value by assignment.

This library serializes NaN values as null. This corresponds to the behavior of JavaScript's JSON.stringify function.

Example

The following example shows how a NaN value is stored in a json value.

int main()
{
    double val = std::numeric_limits<double>::quiet_NaN();
    std::cout << "val=" << val << std::endl;
    json j = val;
    std::cout << "j=" << j.dump() << std::endl;
    val = j;
    std::cout << "val=" << val << std::endl;
}

output:

val=nan
j=null
val=nan

Number comparison

Floating-point inside JSON values numbers are compared with json::number_float_t::operator== which is double::operator== by default.

Alternative comparison functions

To compare floating-point while respecting an epsilon, an alternative comparison function could be used, for instance

template<typename T, typename = typename std::enable_if<std::is_floating_point<T>::value, T>::type>
inline bool is_same(T a, T b, T epsilon = std::numeric_limits<T>::epsilon()) noexcept
{
    return std::abs(a - b) <= epsilon;
}
Or you can self-define an operator equal function like this:

bool my_equal(const_reference lhs, const_reference rhs)
{
    const auto lhs_type lhs.type();
    const auto rhs_type rhs.type();
    if (lhs_type == rhs_type)
    {
        switch(lhs_type)
        {
            // self_defined case
            case value_t::number_float:
                return std::abs(lhs - rhs) <= std::numeric_limits<float>::epsilon();

            // other cases remain the same with the original
            ...
        }
    }
    ...
}

(see #703 for more information.)

Note

NaN values never compare equal to themselves or to other NaN values. See #514.

Number conversion

Just like the C++ language itself, the get family of functions allows conversions between unsigned and signed integers, and between integers and floating-point values to integers. This behavior may be surprising.

Unconditional number conversions

double d = 42.3;                          // non-integer double value 42.3
json jd = d;                              // stores double value 42.3
std::int64_t i = jd.get<std::int64_t>();  // now i==42; no warning or error is produced

Note the last line with throw a json.exception.type_error.302 exception if jd is not a numerical type, for instance a string.

The rationale is twofold:

  1. JSON does not define a number type or precision (see #json-specification).
  2. C++ also allows to silently convert between number types.

Conditional number conversion

The code above can be solved by explicitly checking the nature of the value with members such as is_number_integer() or is_number_unsigned():

// check if jd is really integer-valued
if (jd.is_number_integer())
{
    // if so, do the conversion and use i
    std::int64_t i = jd.get<std::int64_t>();
    // ...
}
else
{
    // otherwise, take appropriate action
    // ...
}

Note this approach also has the advantage that it can react on non-numerical JSON value types such as strings.

(Example taken from #777.)

Determine number types

As the example in Number conversion shows, there are different functions to determine the type of the stored number:

function unsigned integer signed integer floating-point string
is_number() true true true false
is_number_integer() true true false false
is_number_unsigned() true false false false
is_number_float() false false true false
type_name() "number" "number" "number" "string"
type() number_unsigned number_integer number_float string

Template number types

The number types can be changed with template parameters.

position number type default type possible values
5 signed integers std::int64_t std::int32_t, std::int16_t, etc.
6 unsigned integers std::uint64_t std::uint32_t, std::uint16_t, etc.
7 floating-point double float, long double

Constraints on number types

  • The type for signed integers must be convertible from long long. The type for floating-point numbers is used in case of overflow.
  • The type for unsigned integers must be convertible from unsigned long long. The type for floating-point numbers is used in case of overflow.
  • The types for signed and unsigned integers must be distinct, see #2573.
  • Only double, float, and long double are supported for floating-point numbers.

Example

A basic_json type that uses long double as floating-point type.

using json_ld = nlohmann::basic_json<std::map, std::vector, std::string, bool,
                                     std::int64_t, std::uint64_t, long double>;

Note values should then be parsed with json_ld::parse rather than json::parse as the latter would parse floating-point values to double before then converting them to long double.