wiki:CodingStandards

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Version 2 (modified by zooko, at 2009-05-17T00:29:39Z) (diff)
edit it down, remove some bits that are not likely to come up for most programmers

coding standards

Here are some Python code style guidelines. We also include official Python guidelines by reference (see the section on "official Python standards" below). This document overrides the official standards whenever there is a conflict.

basic standards

compatibility

Tahoe requires Python v2.4.2 or greater. No effort should be made to offer compatibility with versions of Python older than 2.4.2. Effort should be made to work with the most recent release of Python v2.x, and with every release between v2.4.2 and the most recent 2.x.

naming and layout

Use underscore_separated_names for functions, CamelCapNames for classes, alllowercasenames for modules, and ALL_CAPS_NAMES for constants. Use all lower-case variable names (e.g. variable_name or variablename). Prepend a leading underscore to private names.
Always put parenthesis around tuples. Write (spam, eggs) = fetch_breakfast() instead of spam, eggs = fetch_breakfast().

comments, idioms, miscellany, license

Here is a useful header for starting new Python files:

# Copyright (c) 2009 Allmydata, Inc.
__copyright__ = "Copyright (c) 2009 Allmydata, Inc."

"""
doc string describing the module here
"""

# import Python Standard Library modules here

from amdlib.util.assertutil import _assert, precondition, postcondition

# import from other libraries, with a blank line between each library

# your code here

Files should begin with a copyright notice then a docstring about that module.

truths and falsehoods

Don't use the literals True or False in conditional expressions -- instead just write the expression which will evaluate to true or false. For example, write if expr: instead of if expr == True: and if not expr: instead of if expr == False:.
Use the fact that empty sequences, empty strings, empty dicts, 0, and None all evaluate to false. Write if not items: instead of if len(items) == 0:.
But if your intent is to test for None instead of to test for "any false thing", then write it out as if thing is None:.

advanced idioms

preconditions and assertions

basic preconditions and assertions

Make sure you have from allmydata.util.assertutil import _assert, precondition, postcondition in your imports (as shown in the template above). Now design preconditions for your methods and functions, and assert them like this:

def oaep(m, emLen, p=""):
    precondition(emLen >= (2 * SIZE_OF_UNIQS) + 1, "emLen is required to be big enough.", emLen=emLen, SIZE_OF_UNIQS=SIZE_OF_UNIQS)
    ...

Notice how you pass in any values that ought to be printed out in the error message if the assertion fails. In the example, we pass the values emLen and SIZE_OF_UNIQS. You can pass these as normal args or keyword args. If you use keyword args then the name of the argument will also appear in the error message, which can be helpful. For example, if the assertion above fails, then a debug message will appear at the end of the stack trace, like this:

>>> oaep("some secret thingie", 20)
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
  File "<stdin>", line 2, in oaep
  File "/home/zooko/playground/pyutil/pyutil/assertutil.py", line 47, in precondition
    raise preconditionfailureexception
AssertionError: precondition: emLen is required to be big enough. -- emLen: 20 <type 'int'>, 'SIZE_OF_UNIQS': 20 <type 'int'>

The "error message" that will accompany a failed expression should be a statement of what is required for correct operation. Don't write something like "Spam isn't firm.", because that is ambiguous: the error could be that the spam is supposed to be firm and it isn't, or the error could be that spam isn't supposed to be firm and it is! The same ambiguity can apply to the sentence "Spam must be firm.". It helps to use the words "required to" in your message, for example "Spam is required to be firm.".

class invariants

If your class has internal state which is complicated enough that a bug in the class's implementation could lead to garbled internal state, then you should have a class invariant. A class invariant is a method like this (an actual example from BlockWrangler?, but truncated for space):

def _assert_consistency(self):
    # All of the keys in all of these dicts are required to be ids.
    for d in (self.bId2chunkobj, self.bId2peers, self.Idsofwantedblocks, self.Idsoflocatedblocks,):
        _assert(not [key for key in d.keys() if not idlib.is_id(key)], "All of the keys in these dicts are required to be ids.", listofnonIds=[key for key in d.keys() if not idlib.is_id(key)])

    # For each (peer, blockId,) tuple in peerclaimedblock, if the peer *has*
    # claimed the block, then the blockId is required to appear in bId2peers[blockId],
    # and if the peer has claimed *not* to have the block then the blockId
    # is required *not* to appear in bId2peers[blockId].
    for ((peer, blockId,), claim,) in self.peerclaimedblock.items():
        _assert((claim == "yes") == (peer in self.bId2peers.get(blockId, ())), "The blockId must appear in bId2peers if and only if the peer has claimed the block.", claim=claim, peer=peer, bId2peersentry=self.bId2peers.get(blockId, ()))

Now you can put assert self._assert_consistency() everywhere in your class where the class ought to be in an internally consistent state. For example, at the beginning of every externally-callable method. This technique can be very valuable in developing a complex class -- it catches bugs early, it isolates bugs into specific code paths, and it clarifies the internal structure of the class so that other developers can hack on it without subtle misunderstandings.

assertion policy

One axis of interest is how time-consuming the checks are. Many precondition checks can cause typical runtime to explode to O(n²) or O(n³), for example SortedList.__contains__ called _assert_invariants which took O(n log n) each time, when __contains__ ought to be O(log n). A caller who was expecting if b in list to take O(log n) could easily wind up turning their O(n log n) routine into O(n²) or worse.

Another axis is "who could cause it to fail": some checks are looking only at internal state. For example, if SortedList._assert_invariants fails, it indicates a problem in some SortedList method. Other checks are enforcing the external API, like those which do typechecks on input arguments. Even after the SortedList developer has gained confidence in the code and decides that internal checks are no longer necessary, it may be useful to retain the external checks to isolate usage problems that exist in callers.

The general rule is that nodes must be functional for light traffic even when the assertions are turned on. When assertions are turned off (-O), nodes must be functional for heavy traffic.

Time-consuming internal checks: once the code is working properly, consider removing them, but they may be left in place as long as they use assert (the form which gets turned off when -O is used).

Cheap internal checks: once the code is working properly, consider removing them, but it is less of a concern than the time-consuming ones. If they really are cheap, use _assert (the unconditional form that gets used even with -O).

Time-consuming external checks: maybe leave them in place, but always use assert so they will not be used with -O.

Cheap external checks: leave them in place, using the unconditional _assert

Production grids could run with -O (in practice, the allmydata.com production grid runs without -O, because there are no expensive checks in the current codebase).

Testing grids might run without -O in order to detect more bugs.

Local developer tests will probably not use -O, and developers should be prepared to experience the same CPU load problems if they subject their nodes to real traffic levels. Developers can use -O to turn off everyone else's checks, use _assert on their own code to enable their own assertions, and then subject their nodes to heavy traffic, as long as they are sure to change their checks to use assert (or remove them altogether) before committing.

configuration

minimizing configuration

Do not implement configuration files for modules or libraries -- code that is going to be used by other code. Only applications -- code that is going to be used by humans -- have configuration files. Modules and libraries get "configured" by the code that calls them, for example by passing arguments to their constructors.
If there are constant values which end-users do not need to modify, then do not make them configurable, but put them in all-caps variables at the beginning of the Python file in which they are used.
Design algorithms so that they have as few "voodoo constants" and "tweakable parameters" as possible.

how to implement configuration

Whether in application code or in library code, never pass configuration values via a configuration object. Instead use Python parameters. For example -- here's another real-life example -- do not write

class BlockStore:
    def __init__(self, confdict={}, recoverdb=True, name='*unnamed*'):
        if confdict.has_key('MAX_MEGABYTES'):
            self.maxspace = (2**20) * int(confdict.get('MAX_MEGABYTES'))
        else:
            self.maxspace = None
        self.basepath = os.path.abspath(confdict.get("PATH", ""))
        self.maintainertype = confdict.get("MAINTAINER", "rnd").lower()
        self.backendtype = confdict.get("BACKEND", "flat").lower()

, but instead write

class BlockStore:
    def __init__(self, maxspace=None, path="", maintainertype="rnd", backendtype="flat", recoverdb=True, name='*unnamed*'):
        self.basepath = os.path.abspath(path)
        self.maintainertype = maintainertype
        self.backendtype = backendtype

official Python standards

These are listed in decreasing order of priority, so if a point in one of the latter guidelines contradicts a point in one of the earlier ones, then go with the earlier. The Allmydata-specific guidelines above override all else, of course.