5 ms·

Is this ok ? Python 3.13.3 (main, May 21 2025, 07:49:52) [GCC 14.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >

Is this ok ? Python 3.13.3 (main, May 21 2025, 07:49:52) [GCC 14.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import json >>> json.loads('47234762761726473624762746721647624764380000000000000000000000000000000000000000000') 47234762761726473624762746721647624764380000000000000000000000000000000000000000000

jazzyjackson 1y ago
yes, python falls into the sane language category with arbitrary-precision arithmetic
- faresahmed 1y ago
  Not so much, >>> s="1"+"0"*4300 >>> json.loads(s) ... ValueError: Exceeds the limit (4300 digits) for integer string conversion: value has 4301 digits; use sys.set_int_max_str_digits() to increase the limit This was done to prevent DoS attacks 3 years ago and have been backported to at least CPython 3.9 as it was considered a CVE. Relevant discussion: https://news.ycombinator.com/item?id=32753235 https://news.ycombinator.com/item?id=32753235 Your sibling comment suggests using decimal.Decimal which handles parsing >4300 digit numbers (by default).
  - lifthrasiir 1y ago
    This should be interpreted as a stop-gap measure before a subquadratic algorithm can be adopted. Take a look at _pylong.py in new enough CPython.
teddyh 1y ago
I prefer >> import json, decimal >> j = "47234762761726473624762746721647624764380000000000000000000000000000000000000000000" >> json.loads(j, parse_float=decimal.Decimal, parse_int=decimal.Decimal) Decimal('47234762761726473624762746721647624764380000000000000000000000000000000000000000000') This way you avoid this problem: >> import json >> j = "0.47234762761726473624762746721647624764380000000000000000000000000000000000000000000" >> json.loads(j) 0.47234762761726473 And instead can get: >> import json, decimal >> j = "0.47234762761726473624762746721647624764380000000000000000000000000000000000000000000" >> json.loads(j, parse_float=decimal.Decimal, parse_int=decimal.Decimal) Decimal('0.47234762761726473624762746721647624764380000000000000000000000000000000000000000000')
sevensor 1y ago
Just cross your fingers and hope for the best if your data is at any point decoded by a json library that doesn’t support bigints? Python’s ability to handle them is beside the point of they get mangled into ieee754 doubles along the way.

jazzyjackson 1y ago

yes, python falls into the sane language category with arbitrary-precision arithmetic

faresahmed 1y ago
Not so much, >>> s="1"+"0"*4300 >>> json.loads(s) ... ValueError: Exceeds the limit (4300 digits) for integer string conversion: value has 4301 digits; use sys.set_int_max_str_digits() to increase the limit This was done to prevent DoS attacks 3 years ago and have been backported to at least CPython 3.9 as it was considered a CVE. Relevant discussion: https://news.ycombinator.com/item?id=32753235 https://news.ycombinator.com/item?id=32753235 Your sibling comment suggests using decimal.Decimal which handles parsing >4300 digit numbers (by default).
- lifthrasiir 1y ago
  This should be interpreted as a stop-gap measure before a subquadratic algorithm can be adopted. Take a look at _pylong.py in new enough CPython.

teddyh 1y ago

I prefer >> import json, decimal >> j = "47234762761726473624762746721647624764380000000000000000000000000000000000000000000" >> json.loads(j, parse_float=decimal.Decimal, parse_int=decimal.Decimal) Decimal('47234762761726473624762746721647624764380000000000000000000000000000000000000000000') This way you avoid this problem: >> import json >> j = "0.47234762761726473624762746721647624764380000000000000000000000000000000000000000000" >> json.loads(j) 0.47234762761726473 And instead can get: >> import json, decimal >> j = "0.47234762761726473624762746721647624764380000000000000000000000000000000000000000000" >> json.loads(j, parse_float=decimal.Decimal, parse_int=decimal.Decimal) Decimal('0.47234762761726473624762746721647624764380000000000000000000000000000000000000000000')

sevensor 1y ago

Just cross your fingers and hope for the best if your data is at any point decoded by a json library that doesn’t support bigints? Python’s ability to handle them is beside the point of they get mangled into ieee754 doubles along the way.