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|#12||fixed||PyYAML is slow||xi||edemaine@…|
Here are two simple wall-clock timings comparing PyYAML to PySyck on a Pentium 4 2.8GHz with 1MB cache and 1GB RAM:
$ wc file1.yaml 2036 8767 59154 file1 $ test.py file1.yaml 0:00:00.001419 to read the YAML via Syck 0:00:04.029627 to read the YAML via PyYAML $ wc file2.yaml 8949 35105 317342 file2 $ test.py file2.yaml 0:00:00.001564 to read the YAML via Syck 0:00:19.288912 to read the YAML via PyYAML
I do not expect PyYAML to be terribly competitive with Syck: the language barrier is big, and PyYAML is written with a higher level of abstraction. But I was surprised to see a factor of 12,000 difference. I wonder if a bit of profiling and tuning might reduce this gap to just a couple of orders of magnitude (100x) instead of four? Personally, 19 seconds to read a 0.3 meg file is too slow for my application, so I'll have to switch back to Syck for now, unfortunately. Just food for thought...
|#13||fixed||Windows install broken||xi||anonymous|
None of the source files install except the EGG-INFO directory.
C:\>easy_install -Z PyYAML Searching for PyYAML Reading http://www.python.org/pypi/PyYAML/ Reading http://pyyaml.org/wiki/PyYAML Best match: PyYAML 3.01 Downloading http://pyyaml.org/download/pyyaml/PyYAML-3.01.win32.exe Processing PyYAML-3.01.win32.exe WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/resolver.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/parser.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/scanner.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/error.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/composer.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/loader.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/events.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/dumper.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/tokens.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/serializer.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/constructor.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/representer.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/__init__.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/nodes.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/reader.py WARNING: can't process home/xi/lib/python2.4/site-packages/yaml/emitter.py creating 'c:\docume~1\user\locals~1\temp\easy_install-wfs0iv\PyYAML-3.01-py2.4-w in32.egg' and adding 'c:\docume~1\user\locals~1\temp\easy_install-wfs0iv\PyYAML- 3.01-py2.4-win32.egg.tmp' to it creating c:\python24\lib\site-packages\PyYAML-3.01-py2.4-win32.egg Extracting PyYAML-3.01-py2.4-win32.egg to c:\python24\lib\site-packages Adding PyYAML 3.01 to easy-install.pth file Installed c:\python24\lib\site-packages\pyyaml-3.01-py2.4-win32.egg Processing dependencies for PyYAML C:\>
|#14||fixed||Inf and NaN handling needs re-vamp||xi||Scott David Daniels <Scott.Daniels@…>|
Trying to import YAML fails in Python 2.5. Even simple patches fail, because the root cause is that NaNs and INFs cannot be marshalled/unmarshalled. Marshalling is used to save and restore compiled python modules, so a tested module can work initially, but later fail to load (when not from source).
When handling INFs and NaNs, you need to be careful. 1e300000 is not a safe way to represent infinity, and fails to pickle/unpickle safely from manifest constants. Different C runtimes represent the text for INFs and NaNs differently. Since Python 2.5 folds constants, a simple expression won't solve the problem.
The following changes should allow yaml to work on python 2.5a2 on Win2000 (and I think for 64-bit machines as well):
=============== constructor.py: ===============
*** 231,239 **** else: return sign*int(value) - inf_value = 1e300000 - nan_value = inf_value/inf_value - def construct_yaml_float(self, node): value = str(self.construct_scalar(node)) value = value.replace('_', '') --- 231,236 ---- *************** *************** *** 242,251 **** sign = -1 if value in '+-': value = value[1:] ! if value.lower() == '.inf': ! return sign*self.inf_value ! elif value.lower() == '.nan': ! return self.nan_value elif ':' in value: digits = [float(part) for part in value.split(':')] digits.reverse() --- 239,253 ---- sign = -1 if value in '+-': value = value[1:] ! if value.lower() in ('.inf', '.nan'): ! big = 1e300 ! bigger = big * big ! while bigger > big and bigger == bigger: ! big = bigger ! bigger = big * big ! if value.lower() == '.nan': ! return bigger / bigger ! return sign * bigger elif ':' in value: digits = [float(part) for part in value.split(':')] digits.reverse()
=============== representer.py: ===============
*** 192,200 **** def represent_long(self, data): return self.represent_scalar(u'tag:yaml.org,2002:int', unicode(data)) ! repr_pos_inf = repr(1e300000) ! repr_neg_inf = repr(-1e300000) ! repr_nan = repr(1e300000/1e300000) def represent_float(self, data): repr_data = repr(data) --- 192,206 ---- def represent_long(self, data): return self.represent_scalar(u'tag:yaml.org,2002:int', unicode(data)) ! big = 1e300 ! bigger = big * big ! while bigger > big and bigger == bigger: ! big = bigger ! bigger = big * big ! repr_pos_inf = repr(bigger) ! repr_neg_inf = repr(-bigger) ! repr_nan = repr(bigger / bigger) ! del big, bigger def represent_float(self, data): repr_data = repr(data)