[Python-Dev] PEP 410 (Decimal timestamp): the implementation is ready for a review (original) (raw)

Stefan Krah stefan at bytereef.org
Fri Feb 17 14:03:10 CET 2012

Victor Stinner <victor.stinner at gmail.com> wrote:

Can't we improve the compatibility between Decimal and float, e.g. by allowing Decimal+float? Decimal (base 10) + float (base 2) may loss precision and this issue matters in some use cases. So we still need a way to warn the user on loss of precision.

I think this should be discussed in a separate thread. It's getting slightly difficult to follow all the issues raised here.

decimal and cdecimal (decimal) have the same performance issue, don't expect them to become the standard float type.

Well, _decimal in tight loops is about 2 times slower than float.

There are areas where _decimal is actually faster than float, e.g in the cdecimal repository printing and formatting seems to be significantly faster:

$ cat format.py import time from decimal import Decimal

d = Decimal("7.928137192") f = 7.928137192

out = open("/dev/null", "w")

start = time.time() for i in range(1000000): out.write("%s\n" % d) end = time.time() print("Decimal: ", end-start)

start = time.time() for i in range(1000000): out.write("%s\n" % f) end = time.time() print("float: ", end-start)

start = time.time() for i in range(1000000): out.write("{:020,.30}\n".format(d)) end = time.time() print("Decimal: ", end-start)

start = time.time() for i in range(1000000): out.write("{:020,.30}\n".format(f)) end = time.time() print("float: ", end-start)

$ ./python format.py Decimal: 0.8835508823394775 float: 1.3872010707855225 Decimal: 2.1346139907836914 float: 3.154278039932251

So it would make sense to profile the exact application in order to determine the suitability of _decimal for timestamps.

There are also IEEE 754 for floating point types in base 10: decimal floating point (DFP), in 32, 64 and 128 bits. IBM System z9, System z10 and POWER6 CPU support these types in hardware. We may support this format in a specific module, or maybe use it to speedup the Python decimal module.

Apart from the rarity of these systems, decimal.py is arbitrary precision. If I restricted _decimal to DECIMAL64, I could probably speed it up further.

All that said, personally I wouldn't have problems with a chunked representation that includes nanoseconds, thus avoiding the decimal/float discusion entirely. I'm also a happy user of:

http://cr.yp.to/libtai/tai64.html#tai64n

Stefan Krah

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