charm-guide/doc/source/coding-guidelines.rst

Coding Guidelines

Introduction

We write code for OpenStack charms. Mostly in Python. They say that code is read roughly 20 times more than writing it, and that’s just the process of writing code. Reviewing code and modifying it means that it will be read many, many times. Let’s make it as easy as possible. We’re lucky(!) with Python as the syntax ensures that it roughly always looks the same.

As OpenStack charms are for OpenStack it’s a good idea to adhere to the OpenStack Python coding standard. So first things first:

• Read the OpenStack Coding standard.
• Read PEP8 (again).

Topics

Multiple roots -- symlinks

Multiple roots with symlinks create issues in charms. This is where, for example, charmhelpers is symlinked into both a hooks and actions subdirectory. This creates a situation where the same modules are loaded into the Python interpreter's memory twice at two different module paths. This creates problems with testing as depending on the load time ordering it might not be clear which particular module path you're trying to mock out and which one is first in the module path map.

So only every have ONE root for your python code in a charm. e.g. put it in /lib and add that to path by sys.path.append(‘lib’).

Incidentally, if you are mocking out code in charmhelpers in you charms, it's probably not a good idea. Only mock code in the target object file, rather than an included module.

Install-time vs Load-time vs Runtime code

The hooks in charms are effectively short-term running scripts. However, despite being short-lived, the code invoked is often complex with multiple modules being imported which also import other modules.

It’s important to be clear on what is load time code and _runtime code. Although there is no actual distinction in Python, it’s useful to think of runtime starting when the following code is reached:

if __name__ == ‘__main__’:
    do_something()

I.e. the code execution of do_something() is runtime, with everything preceding being loadtime.

So why is the distinction useful? Put simply, it’s much harder to test load-time code in comparison to runtime code with respect to mocking. Consider these two fragments:

Bad:

import a.something

OUR_CONFIG = {
    ‘some_thing’: a.something.config(‘a-value’),
}

Good:

import a.something


def get_our_config():
    return {
        ‘some_thing’: a.something.config(‘a-value’),
    }

If performance is an issue (i.e. multiple calls to config() are expensive) then either use a @caching type decorator, or just doing it manually. e.g.

_our_config = None

def get_our_config():
    if _our_config is None:
        _our_config = {
            'some_thing': a.something.config('a-value'),
        }
    return _our_config

In the bad example, in order to mock out the config module we have to do something like:

with patch(‘a.something.config’) as mock_config:
    import a.something.config

This also relies on this being the _first time that module has been imported. Otherwise, the module is already cached and config can’t be mocked out.

Compare this with the good example.

def test_out_config(self):
    with patch(‘module.a.something.config’) as mock_config:
        mock_config.return_value = ‘thing’
        x = model.get_out_config()

This brings us to:

CONSTANTS should be simple

In the bad example above, the constant OUR_CONFIG is defined as load-time by calling a.something.config(). Thus, in reality, the constant is being defined at load-time using a runtime function that returns a value - it's dynamic.

Don’t:

CONFIG = {
    ‘some_key’: config(‘something’),
}

This is actually a function in disguise.

Prefer:

def get_config():
    return {
        ‘some_key’: config(‘something’),
    }

Why?

So that you can mock out get_config() or config() at the test run time, rather than before the module loads. This makes testing easier, more predictable, and also makes it obvious that it’s not really a constant, but actually a function which returns a structure that is dynamically generated from configuration.

And definitely don’t do this at the top level in a file:

CONFIGS = register_configs()

You’ve just created a load time test problem _and created a CONSTANT that isn’t really one. Just use register_configs() directly in the code and write register_configs() to be @cached if performance is an issue.

Decorators

There shouldn't be much need to write a decorator. They definitely should not be used instead of function application or instead of context managers. When they are used it's preferable that they are orthogonal to the function they are decorating, and don't change the nature of the function.

functools.wraps(f)

If they are used, then they should definitely make use of functools.wraps to preserve the function name of the original function and it's docstring. This makes stacktraces more readable. e.g.:

def my_decorator(f):
    functools.wraps(f):
    def decoration(*args, **kwargs):
        # do soemthing before the function call?
        r = f(*args, **kwargs)
        # do soemthing after the function call?
        return r

    return decoration

Mocking out decorators

If the decorator's functionality is orthogonal to the function, then mocking out the decorator shouldn't be necessary. However, if it isn't then tweaking how the decorator is written can make it easier to mock out the decorator.

Consider the following code:

@a_decorator("Hello")
def some_function():
    pass

def a_decorator(name):
    def outer(f):
        @functools.wraps(f)
        def inner(*args, **kwargs):
            # do something before the function
            r = f(*args, **kwargs)
            # do something after the function
            return r

        return inner

    return outer

It's very difficult to test some_function without invoking the decorator, and equally, it's difficult to stop the decorator from being applied to the function without mocking out @a_decorator before importing the module under test.

However, with a little tweaking of the decorator we can mock out the decorator without having to jump through hoops:

def a_decorator(name):
    def outer(f):
        @functools.wraps(f)
        def inner(*args, **kwargs):
            return _inner(name, args, kwargs)
        return inner
    return outer

def _inner(name, args, kwargs):
    # do something before the function
    r = f(*args, **kwargs)
    # do something afterwards
    return r

Now, we can easily mock _inner() after the module has been loaded, thus changing the function of the decorator _after it has been applied.

Import ordering and style

Let's be consistent and ensure that we have the same import ordering and style across all of the charms (and other code) that we release.

Use absolute imports

Use absolute imports. In Python 2 code this means also that we should force absolute imports:

from __future__ import absolute_import

We should use absolute imports so that we don't run into module name clashes across our own modules, nor with system and 3rd party packages. See https://www.python.org/dev/peps/pep-0328/#id8 for more details.

Import ordering

• Core Python system packages
• Third party modules
• Local modules

They should be be alphabetical order, with a single space between them, and preferably in alphabetical order. If load order is important (and it shouldn’t be!) then that’s the only reason they shouldn’t be in alpha order.

Import Style

It's preferable to import a module rather than an object, class, function or instance from a module.

Prefer:

import module

module.function()

over:

from module import function

function()

However, if there are good reasons to import from a module, and there is more than one item, then the style is:

from module import (
    one_import_per_line,
)

Why?

Using import module; module.function() rather than from module import function is preferable because:

• with multiple imports, more symbols are being brought into the importing modules namespace.
• It's clearer in the code when an external function is being used, as it is always prefixed by the external module name. This is useful as it makes it more obvious what is happening in the code.

Only patch mocks in the file/module under test

A unit test often needs to mock out functions, classes or instances in the file under test. The mocks should _only be applied to the file that contains the item that is being tested.

Don't:

# object.py
import something

def function_under_test(x):
    return something.doing(x)

In the unit test file: test_unit.py:

# test_unit.py
def unit_test():
    with patch('something.doing') as y:
        y.return_value = 5
        assert function_under_test(3) == 5

Prefer:

# object.py
import something

def function_under_test(x):
    return something.doing(x)

In the unit test file: test_unit.py:

# test_unit.py
def unit_test():
    with patch('object.something.doing') as y:
        y.return_value = 5
        assert function_under_test(3) == 5

i.e. the thing that is patched is in object.py not in the library file 'something.py'

Don't use _underscore_methods outside of the class

Underscore methods are supposed to be, by convention, private to the enclosing scope, be that a module or a class. They are used to signal that the method is _private even though the privacy can't be enforced.

Thus don't do this:

class A():
    def _private_method():
        pass

x = A()
x._private_method()

Simply rename the method without the underscore. Otherwise you break the convention and people will not understand how you are using private methods.

Equally, don't use them in derived classes _either. A private method is supposed to be private to the class, and not used in derived classes.

Only use list comprehensions when you want the list

Don’t:

[do_something_with(thing) for thing in mylist]

Prefer:

for thing in mylist:
    do_something_with(thing)

Why?

You just created a list and then threw it away. And it’s actually less clear what you are doing. Do use list comprehensions when you actually want a list to do something with.

Avoid C-style dictionary access in loops

Don’t:

for key in dictionary:
    do_something_with(key, dictionary[key])

Prefer:

for key, value in dictionary.items():
    do_something_with(key, value)

Why?

Using a list of keys to access a dictionary is less efficient and less obvious as to what’s happening. key, value could actually be config_name and config_item which means the code is more self-documenting.

Also remember that dictionary.keys() & dictionary.values() exist if you want to explicitly iterate just over the keys or values of a dictionary. Also, it’s preferable to iterate of dictionary.keys() rather than dictionary because, whilst they do the same thing, it’s not as obvious what is happening.

If performance is an issue (Python2) then iterkeys() and itervalues() for generators, which is the default on Python3.

Prefer tuples to lists

Tuples are non malleable lists, and should be used where the list isn’t going to change. They have (slight) performance advantages, but come with a guarantee that the list won’t change - note the objects within the tuple could change, just not their position or reference.

Thus don’t:

if x in [‘hello’, ‘there’]:
    do_something()

Prefer:

if x in (‘hello’, ‘there’):
    do_something()

However, remember the caveat. A single item tuple literal has to have a trailing comma:

my_tuple = (‘item’, )

Prefer CONSTANTS to string literals or numbers

This is the “No magic numbers” rule. In a lot of the OS charms there is code like:

db = kv()
previous_thing = db.get('thing_key', thing)

Prefer:

THING_KEY = ‘thing_key’

db = kv()
previous_thing = db.get(THING_KEY, thing)

Why?

String literals introduce a vector for mistakes. We can’t use the language to help prevent spelling mistakes, nor our tools to do autocompletion, nor use lint to find ‘undefined’ variables. This also means that if you use the same number or string literal more than once in code you should create a constant for that value and use that in code. This includes fixed array accesses, offsets, etc.

Don’t abuse __call__()

__call__() is a method that is invoked when () is invoked on an object --() on a class invokes __call__ on the metaclass for the class.

A good example of abuse of __call__ is the class HookData() which, to access the context manager, is invoked as:

with HookData()() as hd:
    hd.kv.set(...)

The sequence ()() is almost certainly a code smell. There is hidden behaviour that requires you to go to the class to see what is actually happening. It would have been more obvious if that method was just called cm() or context():

with HookData().context() as hd:
    hd.kv.set(...)

Don’t use old style string interpolation

action_fail("Cannot remove service: %s" % service.host)

Prefer:

action_fail("Cannot remove service: {}".format(service.host))

Why?

It’s the new style, and the old style is deprecated; eventually it will be removed. Plus the new style is way more powerful: keywords, dictionary support, to name but a few.

Docstrings and comments

Every function exported by a module should have a docstring. Generally, this means all functions mentioned in __ALL__ or implicitly those that do not start with an _.

The preferred format for documenting parameters and return values is ReStructuredText (reST) as described: http://docutils.sourceforge.net/rst.html

The field lists are described here: http://www.sphinx-doc.org/en/stable/domains.html#info-field-lists

An example of an acceptable function docstring is:

def mult(a, b):
    """Multiple a * b and return the result.

    :param a: Number
    :param b: Number
    :returns Number: a * b
    :raises: ValueError, TypeError if the params are not numbers
    """
    return a * b

Other comments should be used to support the code, but not just re-say what the code is doing.

Ensure there's a comma on the last item of a dictionary

This helps when the developer adds an item to a dictionary literal, in that they don't have to edit the previous line to add a comma. It also means that the review doesn't indicate that the previous line has changed (due to the addition of a comma).

Prefer:

a_dict = {
    'one': 1,
    'two': 2,
}

over:

a_dict = {
    'one': 1,
    'two': 2
}

Avoid dynamic default arguments in functions

Don't use a dynamic assignment to a default argument. e.g.

def a(b=[]):
    b.append('hello')
    print b

In [2]: a()
['hello']

In [3]: a()
['hello', 'hello']

As you can see, the list is only assigned the first time, and thereafter it 'remember' the previous values.

Also avoid other default, dynamic, assignments:

def f():
    return ['Hello']


def a(b=f()):
    b.append('there')
    print b


In [3]: a()
['Hello', 'there']

In [4]: a()
['Hello', 'there', 'there']

Instead, prefer:

def a(b=None):
    if b is None:
        b = f()
    b.append('there')
    print b


In [6]: a()
['Hello', 'there']

In [7]: a()
['Hello', 'there']

Why?

Although it can be a handy side-effect for allowing a function to remember previous values, due to a quirk in the interpreter in only assigning the reference once, it may be changed in the future and it hides the intention of the code.

Avoid side effects in Adapters and Contexts

Adapters (reactive charms) and Contexts should not alter the unit they are running, i.e. should not have unexpected side effects. Some environment altering side effects do exist in older contexts, however this should not be taken as an indicator that it is acceptable to add more.

Why?

Adapters and Contexts are regulary called via the update status hook to assess whether a charm is ready. If calling the Context or Adapter has unexpected side effects it could interrupt service. See Bug #1605184 for an example of this issue.