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PEP:410
Title:Use decimal.Decimal type for timestamps
Version:369cd3f69cd2
Last-Modified:2012-02-29 18:58:50 +0100 (Wed, 29 Feb 2012)
Author:Victor Stinner <victor.stinner at gmail.com>
Status:Rejected
Type:Standards Track
Content-Type:text/x-rst
Created:01-February-2012
Python-Version:3.3
Resolution:http://mail.python.org/pipermail/python-dev/2012-February/116837.html

Abstract

Decimal becomes the official type for high-resolution timestamps to make Python support new functions using a nanosecond resolution without loss of precision.

Rationale

Python 2.3 introduced float timestamps to support sub-second resolutions. os.stat() uses float timestamps by default since Python 2.5. Python 3.3 introduced functions supporting nanosecond resolutions:

  • os module: futimens(), utimensat()
  • time module: clock_gettime(), clock_getres(), monotonic(), wallclock()

os.stat() reads nanosecond timestamps but returns timestamps as float.

The Python float type uses binary64 format of the IEEE 754 standard. With a resolution of one nanosecond (10-9), float timestamps lose precision for values bigger than 224 seconds (194 days: 1970-07-14 for an Epoch timestamp).

Nanosecond resolution is required to set the exact modification time on filesystems supporting nanosecond timestamps (e.g. ext4, btrfs, NTFS, ...). It helps also to compare the modification time to check if a file is newer than another file. Use cases: copy the modification time of a file using shutil.copystat(), create a TAR archive with the tarfile module, manage a mailbox with the mailbox module, etc.

An arbitrary resolution is preferred over a fixed resolution (like nanosecond) to not have to change the API when a better resolution is required. For example, the NTP protocol uses fractions of 232 seconds (approximatively 2.3 × 10-10 second), whereas the NTP protocol version 4 uses fractions of 264 seconds (5.4 × 10-20 second).

Note

With a resolution of 1 microsecond (10-6), float timestamps lose precision for values bigger than 233 seconds (272 years: 2242-03-16 for an Epoch timestamp). With a resolution of 100 nanoseconds (10-7, resolution used on Windows), float timestamps lose precision for values bigger than 229 seconds (17 years: 1987-01-05 for an Epoch timestamp).

Specification

Add decimal.Decimal as a new type for timestamps. Decimal supports any timestamp resolution, support arithmetic operations and is comparable. It is possible to coerce a Decimal to float, even if the conversion may lose precision. The clock resolution can also be stored in a Decimal object.

Add an optional timestamp argument to:

  • os module: fstat(), fstatat(), lstat(), stat() (st_atime, st_ctime and st_mtime fields of the stat structure), sched_rr_get_interval(), times(), wait3() and wait4()
  • resource module: ru_utime and ru_stime fields of getrusage()
  • signal module: getitimer(), setitimer()
  • time module: clock(), clock_gettime(), clock_getres(), monotonic(), time() and wallclock()

The timestamp argument value can be float or Decimal, float is still the default for backward compatibility. The following functions support Decimal as input:

  • datetime module: date.fromtimestamp(), datetime.fromtimestamp() and datetime.utcfromtimestamp()
  • os module: futimes(), futimesat(), lutimes(), utime()
  • select module: epoll.poll(), kqueue.control(), select()
  • signal module: setitimer(), sigtimedwait()
  • time module: ctime(), gmtime(), localtime(), sleep()

The os.stat_float_times() function is deprecated: use an explicit cast using int() instead.

Note

The decimal module is implemented in Python and is slower than float, but there is a new C implementation which is almost ready for inclusion in CPython.

Backwards Compatibility

The default timestamp type (float) is unchanged, so there is no impact on backward compatibility nor on performances. The new timestamp type, decimal.Decimal, is only returned when requested explicitly.

Objection: clocks accuracy

Computer clocks and operating systems are inaccurate and fail to provide nanosecond accuracy in practice. A nanosecond is what it takes to execute a couple of CPU instructions. Even on a real-time operating system, a nanosecond-precise measurement is already obsolete when it starts being processed by the higher-level application. A single cache miss in the CPU will make the precision worthless.

Note

Linux actually is able to measure time in nanosecond precision, even though it is not able to keep its clock synchronized to UTC with a nanosecond accuracy.

Alternatives: Timestamp types

To support timestamps with an arbitrary or nanosecond resolution, the following types have been considered:

  • decimal.Decimal
  • number of nanoseconds
  • 128-bits float
  • datetime.datetime
  • datetime.timedelta
  • tuple of integers
  • timespec structure

Criteria:

  • Doing arithmetic on timestamps must be possible
  • Timestamps must be comparable
  • An arbitrary resolution, or at least a resolution of one nanosecond without losing precision
  • It should be possible to coerce the new timestamp to float for backward compatibility

A resolution of one nanosecond is enough to support all current C functions.

The best resolution used by operating systems is one nanosecond. In practice, most clock accuracy is closer to microseconds than nanoseconds. So it sounds reasonable to use a fixed resolution of one nanosecond.

Number of nanoseconds (int)

A nanosecond resolution is enough for all current C functions and so a timestamp can simply be a number of nanoseconds, an integer, not a float.

The number of nanoseconds format has been rejected because it would require to add new specialized functions for this format because it not possible to differentiate a number of nanoseconds and a number of seconds just by checking the object type.

128-bits float

Add a new IEEE 754-2008 quad-precision binary float type. The IEEE 754-2008 quad precision float has 1 sign bit, 15 bits of exponent and 112 bits of mantissa. 128-bits float is supported by GCC (4.3), Clang and ICC compilers.

Python must be portable and so cannot rely on a type only available on some platforms. For example, Visual C++ 2008 doesn't support 128-bits float, whereas it is used to build the official Windows executables. Another example: GCC 4.3 does not support __float128 in 32-bit mode on x86 (but GCC 4.4 does).

There is also a license issue: GCC uses the MPFR library for 128-bits float, library distributed under the GNU LGPL license. This license is not compatible with the Python license.

Note

The x87 floating point unit of Intel CPU supports 80-bit floats. This format is not supported by the SSE instruction set, which is now preferred over float, especially on x86_64. Other CPU vendors don't support 80-bit float.

datetime.datetime

The datetime.datetime type is the natural choice for a timestamp because it is clear that this type contains a timestamp, whereas int, float and Decimal are raw numbers. It is an absolute timestamp and so is well defined. It gives direct access to the year, month, day, hours, minutes and seconds. It has methods related to time like methods to format the timestamp as string (e.g. datetime.datetime.strftime).

The major issue is that except os.stat(), time.time() and time.clock_gettime(time.CLOCK_GETTIME), all time functions have an unspecified starting point and no timezone information, and so cannot be converted to datetime.datetime.

datetime.datetime has also issues with timezone. For example, a datetime object without timezone (unaware) and a datetime with a timezone (aware) cannot be compared. There is also an ordering issues with daylight saving time (DST) in the duplicate hour of switching from DST to normal time.

datetime.datetime has been rejected because it cannot be used for functions using an unspecified starting point like os.times() or time.clock().

For time.time() and time.clock_gettime(time.CLOCK_GETTIME): it is already possible to get the current time as a datetime.datetime object using:

datetime.datetime.now(datetime.timezone.utc)

For os.stat(), it is simple to create a datetime.datetime object from a decimal.Decimal timestamp in the UTC timezone:

datetime.datetime.fromtimestamp(value, datetime.timezone.utc)

Note

datetime.datetime only supports microsecond resolution, but can be enhanced to support nanosecond.

datetime.timedelta

datetime.timedelta is the natural choice for a relative timestamp because it is clear that this type contains a timestamp, whereas int, float and Decimal are raw numbers. It can be used with datetime.datetime to get an absolute timestamp when the starting point is known.

datetime.timedelta has been rejected because it cannot be coerced to float and has a fixed resolution. One new standard timestamp type is enough, Decimal is preferred over datetime.timedelta. Converting a datetime.timedelta to float requires an explicit call to the datetime.timedelta.total_seconds() method.

Note

datetime.timedelta only supports microsecond resolution, but can be enhanced to support nanosecond.

Tuple of integers

To expose C functions in Python, a tuple of integers is the natural choice to store a timestamp because the C language uses structures with integers fields (e.g. timeval and timespec structures). Using only integers avoids the loss of precision (Python supports integers of arbitrary length). Creating and parsing a tuple of integers is simple and fast.

Depending of the exact format of the tuple, the precision can be arbitrary or fixed. The precision can be choose as the loss of precision is smaller than an arbitrary limit like one nanosecond.

Different formats have been proposed:

  • A: (numerator, denominator)
    • value = numerator / denominator
    • resolution = 1 / denominator
    • denominator > 0
  • B: (seconds, numerator, denominator)
    • value = seconds + numerator / denominator
    • resolution = 1 / denominator
    • 0 <= numerator < denominator
    • denominator > 0
  • C: (intpart, floatpart, base, exponent)
    • value = intpart + floatpart / baseexponent
    • resolution = 1 / base exponent
    • 0 <= floatpart < base exponent
    • base > 0
    • exponent >= 0
  • D: (intpart, floatpart, exponent)
    • value = intpart + floatpart / 10exponent
    • resolution = 1 / 10 exponent
    • 0 <= floatpart < 10 exponent
    • exponent >= 0
  • E: (sec, nsec)
    • value = sec + nsec × 10-9
    • resolution = 10 -9 (nanosecond)
    • 0 <= nsec < 10 9

All formats support an arbitrary resolution, except of the format (E).

The format (D) may not be able to store the exact value (may loss of precision) if the clock frequency is arbitrary and cannot be expressed as a power of 10. The format (C) has a similar issue, but in such case, it is possible to use base=frequency and exponent=1.

The formats (C), (D) and (E) allow optimization for conversion to float if the base is 2 and to decimal.Decimal if the base is 10.

The format (A) is a simple fraction. It supports arbitrary precision, is simple (only two fields), only requires a simple division to get the floating point value, and is already used by float.as_integer_ratio().

To simplify the implementation (especially the C implementation to avoid integer overflow), a numerator bigger than the denominator can be accepted. The tuple may be normalized later.

Tuple of integers have been rejected because they don't support arithmetic operations.

Note

On Windows, the QueryPerformanceCounter() clock uses the frequency of the processor which is an arbitrary number and so may not be a power or 2 or 10. The frequency can be read using QueryPerformanceFrequency().

timespec structure

timespec is the C structure used to store timestamp with a nanosecond resolution. Python can use a type with the same structure: (seconds, nanoseconds). For convenience, arithmetic operations on timespec are supported.

Example of an incomplete timespec type supporting addition, subtraction and coercion to float:

class timespec(tuple):
    def __new__(cls, sec, nsec):
        if not isinstance(sec, int):
            raise TypeError
        if not isinstance(nsec, int):
            raise TypeError
        asec, nsec = divmod(nsec, 10 ** 9)
        sec += asec
        obj = tuple.__new__(cls, (sec, nsec))
        obj.sec = sec
        obj.nsec = nsec
        return obj

    def __float__(self):
        return self.sec + self.nsec * 1e-9

    def total_nanoseconds(self):
        return self.sec * 10 ** 9 + self.nsec

    def __add__(self, other):
        if not isinstance(other, timespec):
            raise TypeError
        ns_sum = self.total_nanoseconds() + other.total_nanoseconds()
        return timespec(*divmod(ns_sum, 10 ** 9))

    def __sub__(self, other):
        if not isinstance(other, timespec):
            raise TypeError
        ns_diff = self.total_nanoseconds() - other.total_nanoseconds()
        return timespec(*divmod(ns_diff, 10 ** 9))

    def __str__(self):
        if self.sec < 0 and self.nsec:
            sec = abs(1 + self.sec)
            nsec = 10**9 - self.nsec
            return '-%i.%09u' % (sec, nsec)
        else:
            return '%i.%09u' % (self.sec, self.nsec)

    def __repr__(self):
        return '<timespec(%s, %s)>' % (self.sec, self.nsec)

The timespec type is similar to the format (E) of tuples of integer, except that it supports arithmetic and coercion to float.

The timespec type was rejected because it only supports nanosecond resolution and requires to implement each arithmetic operation, whereas the Decimal type is already implemented and well tested.

Alternatives: API design

Add a string argument to specify the return type

Add an string argument to function returning timestamps, example: time.time(format="datetime"). A string is more extensible than a type: it is possible to request a format that has no type, like a tuple of integers.

This API was rejected because it was necessary to import implicitly modules to instantiate objects (e.g. import datetime to create datetime.datetime). Importing a module may raise an exception and may be slow, such behaviour is unexpected and surprising.

Add a global flag to change the timestamp type

A global flag like os.stat_decimal_times(), similar to os.stat_float_times(), can be added to set globally the timestamp type.

A global flag may cause issues with libraries and applications expecting float instead of Decimal. Decimal is not fully compatible with float. float+Decimal raises a TypeError for example. The os.stat_float_times() case is different because an int can be coerced to float and int+float gives float.

Add a protocol to create a timestamp

Instead of hard coding how timestamps are created, a new protocol can be added to create a timestamp from a fraction.

For example, time.time(timestamp=type) would call the class method type.__fromfraction__(numerator, denominator) to create a timestamp object of the specified type. If the type doesn't support the protocol, a fallback is used: type(numerator) / type(denominator).

A variant is to use a "converter" callback to create a timestamp. Example creating a float timestamp:

def timestamp_to_float(numerator, denominator):
return float(numerator) / float(denominator)

Common converters can be provided by time, datetime and other modules, or maybe a specific "hires" module. Users can define their own converters.

Such protocol has a limitation: the timestamp structure has to be decided once and cannot be changed later. For example, adding a timezone or the absolute start of the timestamp would break the API.

The protocol proposition was as being excessive given the requirements, but that the specific syntax proposed (time.time(timestamp=type)) allows this to be introduced later if compelling use cases are discovered.

Note

Other formats may be used instead of a fraction: see the tuple of integers section for example.

Add new fields to os.stat

To get the creation, modification and access time of a file with a nanosecond resolution, three fields can be added to os.stat() structure.

The new fields can be timestamps with nanosecond resolution (e.g. Decimal) or the nanosecond part of each timestamp (int).

If the new fields are timestamps with nanosecond resolution, populating the extra fields would be time consuming. Any call to os.stat() would be slower, even if os.stat() is only called to check if a file exists. A parameter can be added to os.stat() to make these fields optional, the structure would have a variable number of fields.

If the new fields only contain the fractional part (nanoseconds), os.stat() would be efficient. These fields would always be present and so set to zero if the operating system does not support sub-second resolution. Splitting a timestamp in two parts, seconds and nanoseconds, is similar to the timespec type and tuple of integers, and so have the same drawbacks.

Adding new fields to the os.stat() structure does not solve the nanosecond issue in other modules (e.g. the time module).

Add a boolean argument

Because we only need one new type (Decimal), a simple boolean flag can be added. Example: time.time(decimal=True) or time.time(hires=True).

Such flag would require to do an hidden import which is considered as a bad practice.

The boolean argument API was rejected because it is not "pythonic". Changing the return type with a parameter value is preferred over a boolean parameter (a flag).

Add new functions

Add new functions for each type, examples:

  • time.clock_decimal()
  • time.time_decimal()
  • os.stat_decimal()
  • os.stat_timespec()
  • etc.

Adding a new function for each function creating timestamps duplicate a lot of code and would be a pain to maintain.

Add a new hires module

Add a new module called "hires" with the same API than the time module, except that it would return timestamp with high resolution, e.g. decimal.Decimal. Adding a new module avoids to link low-level modules like time or os to the decimal module.

This idea was rejected because it requires to duplicate most of the code of the time module, would be a pain to maintain, and timestamps are used modules other than the time module. Examples: signal.sigtimedwait(), select.select(), resource.getrusage(), os.stat(), etc. Duplicate the code of each module is not acceptable.