Julien Danjou: Python bad practice, a concrete case
A lot of people read up on good Python practice, and there's plenty of
information about that on the Internet. Many tips are included in the book I
wrote this year,
The Hacker's Guide to Python. Today I'd
like to show a concrete case of code that I don't consider being the state of
the art.
In my last article
where I talked about my new project Gnocchi, I wrote about how I tested, hacked
and then ditched whisper out. Here I'm
going to explain part of my thought process and a few things that raised my
eyebrows when hacking this code.
Before I start, please don't get the spirit of this article wrong. It's in no
way a personal attack to the authors and contributors (who I don't know).
Furthermore, whisper is a piece of code that is in production in thousands of
installation, storing metrics for years. While I can argue that I consider the
code not to be following best practice, it definitely works well enough and is
worthy to a lot of people.
Tests
The first thing that I noticed when trying to hack on whisper, is the lack of
test. There's only one file containing tests, named test_whisper.py, and the
coverage it provides is pretty low. One can check that using the coverage
tool.
$ coverage run test_whisper.py
...........
----------------------------------------------------------------------
Ran 11 tests in 0.014s
OK
$ coverage report
Name Stmts Miss Cover
----------------------------------
test_whisper 134 4 97%
whisper 584 227 61%
----------------------------------
TOTAL 718 231 67%
While one would think that 61% is "not so bad", taking a quick peak at the actual test code shows that the tests are incomplete. Why I mean by incomplete is that they for example use the library to store values into a database, but they never check if the results can be fetched and if the fetched results are accurate. Here's a good reason one should never blindly trust the test cover percentage as a quality metric. When I tried to modify whisper, as the tests do not check the entire cycle of the values fed into the database, I ended up doing wrong changes but had the tests still pass. No PEP 8, no Python 3
The code doesn't respect PEP 8 . A run of
flake8 +
hacking shows 732 errors While it does
not impact the code itself, it's more painful to hack on it than it is on most
Python projects.
The hacking tool also shows that the code is not Python 3 ready as there is
usage of Python 2 only syntax.
A good way to fix that would be to set up tox
and adds a few targets for PEP 8 checks and Python 3 tests. Even if the test
suite is not complete, starting by having flake8 run without errors and the few
unit tests working with Python 3 should put the project in a better light.
Not using idiomatic Python
A lot of the code could be simplified by using idiomatic Python. Let's take a
simple example:
def fetch(path,fromTime,untilTime=None,now=None):
fh = None
try:
fh = open(path,'rb')
return file_fetch(fh, fromTime, untilTime, now)
finally:
if fh:
fh.close()
That piece of code could be easily rewritten as:
def fetch(path,fromTime,untilTime=None,now=None):
with open(path, 'rb') as fh:
return file_fetch(fh, fromTime, untilTime, now)
This way, the function looks actually so simple that one can even wonder why it should exists but why not. Usage of loops could also be made more Pythonic:
for i,archive in enumerate(archiveList):
if i == len(archiveList) - 1:
break
could be actually:
for i, archive in itertools.islice(archiveList, len(archiveList) - 1):
That reduce the code size and makes it easier to read through the code. Wrong abstraction level
Also, one thing that I noticed in whisper, is that it abstracts its features
at the wrong level.
Take the create() function, it's pretty obvious:
def create(path,archiveList,xFilesFactor=None,aggregationMethod=None,sparse=False,useFallocate=False):
# Set default params
if xFilesFactor is None:
xFilesFactor = 0.5
if aggregationMethod is None:
aggregationMethod = 'average'
#Validate archive configurations...
validateArchiveList(archiveList)
#Looks good, now we create the file and write the header
if os.path.exists(path):
raise InvalidConfiguration("File %s already exists!" % path)
fh = None
try:
fh = open(path,'wb')
if LOCK:
fcntl.flock( fh.fileno(), fcntl.LOCK_EX )
aggregationType = struct.pack( longFormat, aggregationMethodToType.get(aggregationMethod, 1) )
oldest = max([secondsPerPoint * points for secondsPerPoint,points in archiveList])
maxRetention = struct.pack( longFormat, oldest )
xFilesFactor = struct.pack( floatFormat, float(xFilesFactor) )
archiveCount = struct.pack(longFormat, len(archiveList))
packedMetadata = aggregationType + maxRetention + xFilesFactor + archiveCount
fh.write(packedMetadata)
headerSize = metadataSize + (archiveInfoSize * len(archiveList))
archiveOffsetPointer = headerSize
for secondsPerPoint,points in archiveList:
archiveInfo = struct.pack(archiveInfoFormat, archiveOffsetPointer, secondsPerPoint, points)
fh.write(archiveInfo)
archiveOffsetPointer += (points * pointSize)
#If configured to use fallocate and capable of fallocate use that, else
#attempt sparse if configure or zero pre-allocate if sparse isn't configured.
if CAN_FALLOCATE and useFallocate:
remaining = archiveOffsetPointer - headerSize
fallocate(fh, headerSize, remaining)
elif sparse:
fh.seek(archiveOffsetPointer - 1)
fh.write('\x00')
else:
remaining = archiveOffsetPointer - headerSize
chunksize = 16384
zeroes = '\x00' * chunksize
while remaining > chunksize:
fh.write(zeroes)
remaining -= chunksize
fh.write(zeroes[:remaining])
if AUTOFLUSH:
fh.flush()
os.fsync(fh.fileno())
finally:
if fh:
fh.close()
The function is doing everything: checking if the file doesn't exist already, opening it, building the structured data, writing this, building more structure, then writing that, etc. That means that the caller has to give a file path, even if it just wants a whipser data structure to store itself elsewhere.
StringIO() could be used
to fake a file handler, but it will fail if the call to fcntl.flock() is not
disabled and it is inefficient anyway.
There's a lot of other functions in the code, such as for example
setAggregationMethod(), that mixes the handling of the files even doing
things like os.fsync() while manipulating structured data. This is
definitely not a good design, especially for a library, as it turns out reusing
the function in different context is near impossible.
Race conditions
There are race conditions, for example in create() (see added comment):
if os.path.exists(path):
raise InvalidConfiguration("File %s already exists!" % path)
fh = None
try:
# TOO LATE I ALREADY CREATED THE FILE IN ANOTHER PROCESS YOU ARE GOING TO
# FAIL WITHOUT GIVING ANY USEFUL INFORMATION TO THE CALLER :-(
fh = open(path,'wb')
That code should be:
try:
fh = os.fdopen(os.open(path, os.O_WRONLY os.O_CREAT os.O_EXCL), 'wb')
except OSError as e:
if e.errno = errno.EEXIST:
raise InvalidConfiguration("File %s already exists!" % path)
to avoid any race condition. Unwanted optimization
We saw earlier the fetch() function that is barely useful, so let's take a
look at the file_fetch() function that it's calling.
def file_fetch(fh, fromTime, untilTime, now = None):
header = __readHeader(fh)
[...]
The first thing the function does is to read the header from the file handler. Let's take a look at that function:
def __readHeader(fh):
info = __headerCache.get(fh.name)
if info:
return info
originalOffset = fh.tell()
fh.seek(0)
packedMetadata = fh.read(metadataSize)
try:
(aggregationType,maxRetention,xff,archiveCount) = struct.unpack(metadataFormat,packedMetadata)
except:
raise CorruptWhisperFile("Unable to read header", fh.name)
[...]
The first thing the function does is to look into a cache. Why is there a cache? It actually caches the header based with an index based on the file path (
fh.name). Except that if one for example decide not to use file and cheat
using StringIO, then it does not have any name attribute. So this code path
will raise an AttributeError.
One has to set a fake name manually on the StringIO instance, and it must be
unique so nobody messes with the cache
import StringIO
packedMetadata = <some source>
fh = StringIO.StringIO(packedMetadata)
fh.name = "myfakename"
header = __readHeader(fh)
The cache may actually be useful when accessing files, but it's definitely useless when not using files. But it's not necessarily true that the complexity (even if small) that the cache adds is worth it. I doubt most of whisper based tools are long run processes, so the cache that is really used when accessing the files is the one handled by the operating system kernel, and this one is going to be much more efficient anyway, and shared between processed. There's also no expiry of that cache, which could end up of tons of memory used and wasted. Docstrings
None of the docstrings are written in a a parsable syntax like
Sphinx. This means you cannot generate any
documentation in a nice format that a developer using the library could read
easily.
The documentation is also not up to date:
def fetch(path,fromTime,untilTime=None,now=None):
"""fetch(path,fromTime,untilTime=None)
[...]
"""
def create(path,archiveList,xFilesFactor=None,aggregationMethod=None,sparse=False,useFallocate=False):
"""create(path,archiveList,xFilesFactor=0.5,aggregationMethod='average')
[...]
"""
This is something that could be avoided if a proper format was picked to write the docstring. A tool cool be used to be noticed when there's a diversion between the actual function signature and the documented one, like missing an argument. Duplicated code
Last but not least, there's a lot of code that is duplicated around in the
scripts provided by whisper in its bin directory. Theses scripts should be
very lightweight and be using the console_scripts facility of setuptools,
but they actually contains a lot of (untested) code. Furthermore, some of that
code is partially duplicated from the whisper.py library which is against
DRY.
Conclusion
There are a few more things that made me stop considering whisper, but these
are part of the whisper features, not necessarily code quality. One can also
point out that the code is very condensed and hard to read, and that's a more
general problem about how it is organized and abstracted.
A lot of these defects are actually points that made me start writing
The Hacker's Guide to Python a year ago.
Running into this kind of code makes me think it was a really good idea to write
a book on advice to write better Python code!
A book I wrote talking about designing Python applications, state of
the art, advice to apply when building your application, various
Python tips, etc. Interested?
Check it out.
A little more than a 
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