This library is a reference implementation of Dragonbox in C++.

Dragonbox is a float-to-string conversion algorithm based on a beautiful algorithm Schubfach, developed by Raffaello Giulietti in 2017-2018. Dragonbox is further inspired by Grisu and Grisu-Exact.


Dragonbox generates a pair of integers from a floating-point number: the decimal significand and the decimal exponent of the input floating-point number. These integers can then be used for string generation of decimal representation of the input floating-point number, the procedure commonly called ftoa or dtoa.

The algorithm guarantees three things:

1) It has the roundtrip guarantee; that is, a correct parser interprets the generated output string as the original input floating-point number.

2) The output is of the shortest length; that is, no other output strings that are interpreted as the input number can contain less number of significand digits than the output of Dragonbox.

3) The output is correctly rounded: the number generated by Dragonbox is the closest to the actual value of the input number among possible outputs of minimum number of digits.

About the Name “Dragonbox”

The core idea of Schubfach, which Dragonbox is based on, is a continuous analogue of discrete pigeonhole principle. The name Schubfach is coming from the German name of the pigeonhole principle, Schubfachprinzip, meaning “drawer principle”. Since another name of the pigeonhole principle is Dirichlet’s box principle, I decided to call my algorithm “Dragonbox” to honor its origins: Schubfach (box) and Grisu (dragon).

How to Use

Although Drgonbox is intended for float-to-string conversion routines, the actual string generation is not officially a part of the algorithm. Dragonbox just outputs two integers (the decimal significand/exponent) that can be consumed by a string generation procedure. The header file include/dragonbox/dragonbox.h includes everything needed for this (it is header-only). Nevertheless, a string generation procedure is included in the library. There are two additional files needed for that: include/dragonbox/dragonbox_to_chars.h and source/dragonbox_to_chars.cpp. Since there are only three files, it should be not difficult to set up this library manually if you want, but you can also use it via CMake as explained below.

Installing Dragonbox

The following will install Dragonbox into your system:

git clone
cd dragonbox
mkdir build
cd build
cmake ..
cmake --install .

Of course you can specify things like --config or --prefix for configuring/installing if you wish. You can also specify the option -DDRAGONBOX_INSTALL_TO_CHARS=OFF if you only want dragonbox.h but not dragonbox_to_chars.h/.cpp.

Including Dragonbox into CMake project

The easiest way to include Dragonbox in a CMake project is to do the following:

target_link_libraries(my_target dragonbox::dragonbox) # or dragonbox::dragonbox_to_chars

Or, if you already have installed Dragonbox in your system, you can include it with:

target_link_libraries(my_target dragonbox::dragonbox) # or dragonbox::dragonbox_to_chars

Language Standard

The library is targetting C++17 and actively using its features (e.g., if constexpr).

Usage Examples

(Simple string generation from float/double)

#include "dragonbox/dragonbox_to_chars.h"
double x = 1.234;  // Also works for float
char buffer[31];   // Should be long enough

// Null-terminate the buffer and return the pointer to the null character
// Hence, the length of the string is (end_ptr - buffer)
// buffer is now { '1', '.', '2', '3', '4', 'E', '0', '\0', (garbages) }
char* end_ptr = jkj::dragonbox::to_chars(x, buffer);

// Does not null-terminate the buffer; returns the next-to-end pointer
// buffer is now { '1', '.', '2', '3', '4', 'E', '0', (garbages) }
// you can wrap the buffer with things like std::string_view
end_ptr = jkj::dragonbox::to_chars_n(x, buffer);

(Direct use of jkj::dragonbox::to_decimal)

#include "dragonbox/dragonbox.h"
double x = 1.234;   // Also works for float

// Here, x should be a nonzero finite number!
// The return value v is a struct with three members:
// significand : decimal significand (1234 in this case);
//               it is of type std::uint64_t for double, std::uint32_t for float
//    exponent : decimal exponent (-3 in this case); it is of type int
// is_negative : as the name suggests; it is of type bool
auto v = jkj::dragonbox::to_decimal(x);

By default, jkj::dragonbox::to_decimal returns a struct with three members (significand, exponent, and is_negative). But the return type and the return value can change if you specify policy paramters. See below.


Dragonbox provides several policies that the user can select. Most of the time the default policies will be sufficient, but for some situation this customizability might be useful. There are currently six different kinds of policies that you can specify: sign policy, trailing zero policy, rounding mode policy, correct rounding policy, cache policy, and input validation policy. Those policies are living in the namespace jkj::dragonbox::policy. You can provide the policies as additional parameters to jkj::dragonbox::to_decimal or jkj::dragonbox::to_chars or jkj::dragonbox::to_chars_n. Here is an example usage:

#include "dragonbox/dragonbox.h"
auto v = jkj::dragonbox::to_decimal(x,

In this example, the ignore sign policy and the compressed cache policy are specified. The return value will not include the member is_negative, and jkj::dragonbox::to_decimal will internally use compressed cache for the computation. There is no particular order for policy parameter; you can give them in any order. Default policies will be chosen if you do not explicitly specify any. In the above example, for instance, nearest_to_even rounding mode policy is chosen, which is the default rounding mode policy. If you provide two or more policies of the same kind, or if you provide an invalid policy parameter, then the compliation will fail.

Policy parameters (e.g., jkj::dragonbox::policy::sign::ignore in the above example) are of different types, so different combinations of policies generally result in separate template instantiation, which might cause binary bloat. (However, it is only the combination that does matter; giving the same parameter combination in a different order will usually not generate a separate binary.)

Sign policy

Determines whether or not if jkj::dragonbox::to_decimal will extract and return the sign of the input paramter.

  • jkj::dragonbox::policy::sign::ignore: The sign of the input is ignored, and there is no is_negative member in the returned struct. It seems that this can improve the parameter passing overhead thus resulting in a faster string generation. Of course, you need to take care of the sign yourself. jkj::dragonbox::to_chars and jkj::dragonbox::to_chars_n use this policy internally.

  • jkj::dragonbox::policy::sign::return_sign: This is the default policy. The sign of the input will be written in the is_negative member of the returned struct.

You cannot specify sign policy to jkj::dragonbox::to_chars/jkj::dragonbox::to_chars_n.

Trailing zero policy

Determines what jkj::dragonbox::to_decimal will do with possible trailing decimal zeros.

  • jkj::dragonbox::policy::trailing_zero::ignore: Do not care about trailing zeros; the output significand may contain trailing zeros.

  • jkj::dragonbox::policy::trailing_zero::remove: This is the default policy. Remove all trailing zeros in the output.

  • jkj::dragonbox::policy::trailing_zero::report: The output significand may contain trailing zeros, but such possibility will be reported in the additional member may_have_trailing_zeros of the returned struct. This member will be set to true if there might be trailing zeros, and it will be set to false if there should be no trailing zero.

You can also specify jkj::dragonbox::policy::trailing_zero::ignore/jkj::dragonbox::policy::trailing_zero::remove policies to jkj::dragonbox::to_chars/jkj::dragonbox::to_chars_n, but jkj::dragonbox::policy::trailing_zero::report cannot be used.

Rounding mode policy

jkj::dragonbox::to_decimal provides various rounding modes. Rounding mode is the rule that determines the interval represented by a single bit pattern.

  • jkj::dragonbox::policy::rounding_mode::nearest_to_even: This is the default policy. Use round-to-nearest, tie-to-even rounding mode.

  • jkj::dragonbox::policy::rounding_mode::nearest_to_odd: Use round-to-nearest, tie-to-odd rounding mode.

  • jkj::dragonbox::policy::rounding_mode::nearest_toward_plus_infinity: Use round-to-nearest, tie-toward-plus-infinity rounding mode.

  • jkj::dragonbox::policy::rounding_mode::nearest_toward_minus_infinity: Use round-to-nearest, tie-toward-minus-infinity rounding mode.

  • jkj::dragonbox::policy::rounding_mode::nearest_toward_zero: Use round-to-nearest, tie-toward-zero rounding mode. This will produce the fastest code among all round-to-nearest rounding modes.

  • jkj::dragonbox::policy::rounding_mode::nearest_away_from_zero: Use round-to-nearest, tie-away-from-zero rounding mode.

  • jkj::dragonbox::policy::rounding_mode::nearest_to_even_static_boundary: Use round-to-nearest, tie-to-even rounding mode, but there will be completely independent code paths for even inputs and odd inputs. This will produce a bigger binary, but might run faster than jkj::dragonbox::policy::rounding_mode::nearest_to_even for some situation.

  • jkj::dragonbox::policy::rounding_mode::nearest_to_odd_static_boundary: Use round-to-nearest, tie-to-odd rounding mode, but there will be completely independent code paths for even inputs and odd inputs. This will produce a bigger binary, but might run faster than jkj::dragonbox::policy::rounding_mode::nearest_to_odd for some situation.

  • jkj::dragonbox::policy::rounding_mode::nearest_toward_plus_infinity_static_boundary: Use round-to-nearest, tie-toward-plus-infinity rounding mode, but there will be completely independent code paths for positive inputs and negative inputs. This will produce a bigger binary, but might run faster than jkj::dragonbox::policy::rounding_mode::nearest_toward_plus_infinity for some situation.

  • jkj::dragonbox::policy::rounding_mode::nearest_toward_minus_infinity_static_boundary: Use round-to-nearest, tie-toward-plus-infinity rounding mode, but there will be completely independent code paths for positive inputs and negative inputs. This will produce a bigger binary, but might run faster than jkj::dragonbox::policy::rounding_mode::nearest_toward_minus_infinity for some situation.

  • jkj::dragonbox::policy::rounding_mode::toward_plus_infinity: Use round-toward-plus-infinity rounding mode.

  • jkj::dragonbox::policy::rounding_mode::toward_minus_infinity: Use round-toward-minus-infinity rounding mode.

  • jkj::dragonbox::policy::rounding_mode::toward_zero: Use round-toward-zero rounding mode.

  • jkj::dragonbox::policy::rounding_mode::away_from_zero: Use away-from-zero rounding mode.

All of these policies can be specified also to jkj::dragonbox::to_chars/jkj::dragonbox::to_chars_n.

Correct rounding policy

Determines what jkj::dragonbox::to_decimal will do when rounding tie occurs. This policy will be completely ignored if the specified rounding mode policy is not one of the round-to-nearest policies.

  • jkj::dragonbox::policy::correct_rounding::do_not_care: Do not care about correct rounding at all and just find any shortest output with the correct roundtrip. It will produce a faster code, but the performance difference will not be big.

  • jkj::dragonbox::policy::correct_rounding::to_even: This is the default policy. Choose the even number when rounding tie occurs.

  • jkj::dragonbox::policy::correct_rounding::to_odd: Choose the odd number when rounding tie occurs.

  • jkj::dragonbox::policy::correct_rounding::away_from_zero: Choose the number with the bigger absolute value when rounding tie occurs.

  • jkj::dragonbox::policy::correct_rounding::toward_zero: Choose the number with the smaller absolute value when rounding tie occurs.

All of these policies can be specified also to jkj::dragonbox::to_chars/jkj::dragonbox::to_chars_n.

Cache policy

Choose between the full cache table and the compressed one. Using the compressed cache will result in about 20% slower code, but it can significantly reduce the amount of required static data. It currently has no effect for binary32 (float) inputs. For binary64 (double) inputs, jkj::dragonbox::cache_policy::normal will cause jkj::dragonbox::to_decimal to use 24*16 + 619*16 = 10288 bytes of static data table, while the corresponding amount for jkj::dragonbox::cache_policy::compressed is 24*16 + 23*16 + 27*8 + 39*4 = 1124 bytes.

  • jkj::dragonbox::policy::cache::normal: This is the default policy. Use the full table.

  • jkj::dragonbox::policy::cache::compressed: Use the compressed table.

All of these policies can be specified also to jkj::dragonbox::to_chars/jkj::dragonbox::to_chars_n.

Input validation policy

jkj::dragonbox::to_decimal only works with finite floating-point inputs. This policy determines what jkj::dragonbox::to_decimal will do with invalid inputs.

  • jkj::dragonbox::policy::input_validation::assert_finite: This is the default policy. assert that the input is finite.

  • jkj::dragonbox::policy::input_validation::do_nothing: Do nothing even if the input is not finite. It might be sometimes useful for debugging.

You cannot specify input validation policy to jkj::dragonbox::to_chars/jkj::dragonbox::to_chars_n.


In my machine (Intel Core i7-7700HQ 2.80GHz, Windows 10), it defeats or is on par with other contemporary algorithms including Grisu-Exact and Ryu.

The following benchmark result is obtained using Milo’s dtoa benchmark framework ( The complete source code for the benchmark below is available here.


The red line at the bottom is the performance of Dragonbox with the full cache table, and the deep blue line above the purple line is the performance of Dragonbox with the compressed cache table.

There is also a benchmark done by myself (top: benchmark for float data, bottom: benchmark for double data; solid lines are the averages, dashed lines are the medians, and the shaded regions show 30%, 50%, and 70% percentiles):

(Clang) digits_benchmark_binary32 digits_benchmark_binary64

(MSVC) digits_benchmark_binary32 digits_benchmark_binary64

Here is another performance plot with uniformly randomly generated float(top) or double(bottom) data:

(Clang) uniform_benchmark_binary32 uniform_benchmark_binary64

(MSVC) uniform_benchmark_binary32 uniform_benchmark_binary64

Dragonbox seems to be also faster than Schubfach, but since the implementation of Schubfach I benchmarked against does not remove trailing decimal zeros, the version that does not care about trailing decimal zeros is used for the benchmarks below:

Digits benchmark (top: float, bottom: double):

(Clang) digits_benchmark_binary32 digits_benchmark_binary64

(MSVC) digits_benchmark_binary32 digits_benchmark_binary64

Uniform benchmark (top: float, bottom: double):

(Clang) uniform_benchmark_binary32 uniform_benchmark_binary64

(MSVC) uniform_benchmark_binary32 uniform_benchmark_binary64

Comprehensive Explanation of the Algorithm

Please see this paper.

How to Run Tests, Benchmark, and Others

There are four subprojects contained in this repository: 1. common: The subproject that other subprojects depend on. 2. benchmark: Runs benchmark. 3. test: Runs tests. 4. meta: Generates static data that the main library uses.

Build each subproject independently

All subprojects including tests and benchmark are standalone, which means that you can build and run each of them independently. For example, you can do the following to run tests:

git clone
cd dragonbox
mkdir -p build/subproject/test
cd build/subproject/test
cmake ../../../subproject/test
cmake --build .
ctest .

(You might need to pass the configuration option to cmake and ctest if you use multi-configuration generators like Visual Studio.)

Build all subprojects from the root directory

It is also possible to build all subprojects from the root directory by passing the option -DDRAGONBOX_ENABLE_SUBPROJECT=On to cmake:

git clone
cd dragonbox
mkdir build
cd build
cmake --build .

Notes on working directory

Some executable files require correct working directory to be set. For example, the executable for benchmark runs some MATLAB scripts provided in subproject/benchmark/matlab directory, which will be failed to be executed if the working directory is not set to subproject/benchmark. If you use the provided CMakeLists.txt files to generate Visual Studio solution, the debugger’s working directory is automatically set to the corresponding source directory. For example, the working directory is set to subproject/benchmark for the benchmark subproject. However, other generators of cmake is not able to set debugger’s working directory, so in that case you need to manually set the correct working directory when running the executables in order to make them work correctly.


Besides the uniformly random tests against Ryu, I also ran a joint test of Dragonbox with a binary-to-decimal floating-point conversion routine I developed, and confirmed correct roundtrip for all possible IEEE-754 binary32-encoded floating-point numbers (aka float) with the round-to-nearest, tie-to-even rounding mode. Therefore, I am currently pretty confident about the correctness of both of the algorithms. I will make a separate repository for the reverse algorithm in a near future.


All code, except for those belong to third-party libraries (code in subproject/3rdparty), is licensed under either of

  • Apache License Version 2.0 with LLVM Exceptions (LICENSE-Apache2-LLVM or or
  • Boost Software License Version 1.0 (LICENSE-Boost or

except for the file dragonbox_to_chars.cpp, which is licensed under either of

  • Apache License Version 2.0 (LICENSE-Apache2 or or
  • Boost Software License Version 1.0 (LICENSE-Boost or

Reference implementation of Dragonbox in C++

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