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 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!