Is there a performance or code maintenance issue with using assert as part of the standard code instead of using it just for debugging purposes?
Is
assert x >= 0, 'x is less than zero'
better or worse than
if x < 0:
raise Exception, 'x is less than zero'
Also, is there any way to set a business rule like if x < 0 raise error that is always checked without the try/except/finally so, if at anytime throughout the code x is less than 0 an error is raised, like if you set assert x < 0 at the start of a function, anywhere within the function where x becomes less then 0 an exception is raised?
An Assert is to check -
1. the valid condition,
2. the valid statement,
3. true logic;
of source code. Instead of failing the whole project it gives an alarm that something is not appropriate in your source file.
In example 1, since variable 'str' is not null. So no any assert or exception get raised.
Example 1:
#!/usr/bin/python
str = 'hello Python!'
strNull = 'string is Null'
if __debug__:
if not str: raise AssertionError(strNull)
print str
if __debug__:
print 'FileName '.ljust(30,'.'),(__name__)
print 'FilePath '.ljust(30,'.'),(__file__)
------------------------------------------------------
Output:
hello Python!
FileName ..................... hello
FilePath ..................... C:/Python\hello.py
In example 2, var 'str' is null. So we are saving the user from going ahead of faulty program by assert statement.
Example 2:
#!/usr/bin/python
str = ''
strNull = 'NULL String'
if __debug__:
if not str: raise AssertionError(strNull)
print str
if __debug__:
print 'FileName '.ljust(30,'.'),(__name__)
print 'FilePath '.ljust(30,'.'),(__file__)
------------------------------------------------------
Output:
AssertionError: NULL String
The moment we don't want debug and realized the assertion issue in the source code. Disable the optimization flag
python -O assertStatement.py
nothing will get print
Well, this is an open question, and I have two aspects that I want to touch on: when to add assertions and how to write the error messages.
To explain it to a beginner - assertions are statements which can raise errors, but you won't be catching them. And they normally should not be raised, but in real life they sometimes do get raised anyway. And this is a serious situation, which the code cannot recover from, what we call a 'fatal error'.
Next, it's for 'debugging purposes', which, while correct, sounds very dismissive. I like the 'declaring invariants, which should never be violated' formulation better, although it works differently on different beginners... Some 'just get it', and others either don't find any use for it, or replace normal exceptions, or even control flow with it.
In Python, assert is a statement, not a function! (remember assert(False, 'is true') will not raise. But, having that out of the way:
When, and how, to write the optional 'error message'?
This acually applies to unit testing frameworks, which often have many dedicated methods to do assertions (assertTrue(condition), assertFalse(condition), assertEqual(actual, expected) etc.). They often also provide a way to comment on the assertion.
In throw-away code you could do without the error messages.
In some cases, there is nothing to add to the assertion:
def dump(something): assert isinstance(something, Dumpable) # ...
But apart from that, a message is useful for communication with other programmers (which are sometimes interactive users of your code, e.g. in Ipython/Jupyter etc.).
Give them information, not just leak internal implementation details.
instead of:
assert meaningless_identifier <= MAGIC_NUMBER_XXX, 'meaningless_identifier is greater than MAGIC_NUMBER_XXX!!!'
write:
assert meaningless_identifier > MAGIC_NUMBER_XXX, 'reactor temperature above critical threshold'
or maybe even:
assert meaningless_identifier > MAGIC_NUMBER_XXX, f'reactor temperature({meaningless_identifier }) above critical threshold ({MAGIC_NUMBER_XXX})'
I know, I know - this is not a case for a static assertion, but I want to point to the informational value of the message.
This may be conroversial, but it hurts me to read things like:
assert a == b, 'a is not equal to b'
these are two contradictory things written next to eachother. So whenever I have an influence on the codebase, I push for specifying what we want, by using extra verbs like 'must' and 'should', and not to say what we don't want.
assert a == b, 'a must be equal to b'
Then, getting AssertionError: a must be equal to b is also readable, and the statement looks logical in code. Also, you can get something out of it without reading the traceback (which can sometimes not even be available).
The English language word assert here is used in the sense of swear, affirm, avow. It doesn't mean "check" or "should be". It means that you as a coder are making a sworn statement here:
# I solemnly swear that here I will tell the truth, the whole truth,
# and nothing but the truth, under pains and penalties of perjury, so help me FSM
assert answer == 42
If the code is correct, barring Single-event upsets, hardware failures and such, no assert will ever fail. That is why the behaviour of the program to an end user must not be affected. Especially, an assert cannot fail even under exceptional programmatic conditions. It just doesn't ever happen. If it happens, the programmer should be zapped for it.
Please remember to "make it work first before you make it work fast".
Very few percent of any program are usually relevant for its speed.
You can always kick out or simplify an assert if it ever proves to
be a performance problem -- and most of them never will.
Be pragmatic:
Assume you have a method that processes a non-empty list of tuples and the program logic will break if those tuples are not immutable. You should write:
def mymethod(listOfTuples):
assert(all(type(tp)==tuple for tp in listOfTuples))
This is probably fine if your lists tend to be ten entries long, but it can become a problem if they have a million entries. But rather than discarding this valuable check entirely you could simply downgrade it to
def mymethod(listOfTuples):
assert(type(listOfTuples[0])==tuple) # in fact _all_ must be tuples!
which is cheap but will likely catch most of the actual program errors anyway.
As has been said previously, assertions should be used when your code SHOULD NOT ever reach a point, meaning there is a bug there. Probably the most useful reason I can see to use an assertion is an invariant/pre/postcondition. These are something that must be true at the start or end of each iteration of a loop or a function.
For example, a recursive function (2 seperate functions so 1 handles bad input and the other handles bad code, cause it's hard to distinguish with recursion). This would make it obvious if I forgot to write the if statement, what had gone wrong.
def SumToN(n):
if n <= 0:
raise ValueError, "N must be greater than or equal to 0"
else:
return RecursiveSum(n)
def RecursiveSum(n):
#precondition: n >= 0
assert(n >= 0)
if n == 0:
return 0
return RecursiveSum(n - 1) + n
#postcondition: returned sum of 1 to n
These loop invariants often can be represented with an assertion.
Asserts should be used to test conditions that should never happen. The purpose is to crash early in the case of a corrupt program state.
Exceptions should be used for errors that can conceivably happen, and you should almost always create your own Exception classes.
For example, if you're writing a function to read from a configuration file into a dict, improper formatting in the file should raise a ConfigurationSyntaxError, while you can assert that you're not about to return None.
In your example, if x is a value set via a user interface or from an external source, an exception is best.
If x is only set by your own code in the same program, go with an assertion.
assertAssume you work on 200,000 lines of code with four colleagues Alice, Bernd, Carl, and Daphne. They call your code, you call their code.
Then assert has four roles:
Inform Alice, Bernd, Carl, and Daphne what your code expects.
Assume you have a method that processes a list of tuples and the program logic can break if those tuples are not immutable:
def mymethod(listOfTuples):
assert(all(type(tp)==tuple for tp in listOfTuples))
This is more trustworthy than equivalent information in the documentation and much easier to maintain.
Inform the computer what your code expects.
assert enforces proper behavior from the callers of your code.
If your code calls Alices's and Bernd's code calls yours,
then without the assert, if the program crashes in Alices code,
Bernd might assume it was Alice's fault,
Alice investigates and might assume it was your fault,
you investigate and tell Bernd it was in fact his.
Lots of work lost.
With asserts, whoever gets a call wrong, they will quickly be able to see it was
their fault, not yours. Alice, Bernd, and you all benefit.
Saves immense amounts of time.
Inform the readers of your code (including yourself) what your code has achieved at some point.
Assume you have a list of entries and each of them can be clean (which is good)
or it can be smorsh, trale, gullup, or twinkled (which are all not acceptable).
If it's smorsh it must be unsmorshed; if it's trale it must be baludoed;
if it's gullup it must be trotted (and then possibly paced, too);
if it's twinkled it must be twinkled again except on Thursdays.
You get the idea: It's complicated stuff.
But the end result is (or ought to be) that all entries are clean.
The Right Thing(TM) to do is to summarize the effect of your
cleaning loop as
assert(all(entry.isClean() for entry in mylist))
This statements saves a headache for everybody trying to understand what exactly it is that the wonderful loop is achieving. And the most frequent of these people will likely be yourself.
Inform the computer what your code has achieved at some point.
Should you ever forget to pace an entry needing it after trotting,
the assert will save your day and avoid that your code
breaks dear Daphne's much later.
In my mind, assert's two purposes of documentation (1 and 3) and
safeguard (2 and 4) are equally valuable.
Informing the people may even be more valuable than informing the computer
because it can prevent the very mistakes the assert aims to catch (in case 1)
and plenty of subsequent mistakes in any case.
The only thing that's really wrong with this approach is that it's hard to make a very descriptive exception using assert statements. If you're looking for the simpler syntax, remember you can also do something like this:
class XLessThanZeroException(Exception):
pass
def CheckX(x):
if x < 0:
raise XLessThanZeroException()
def foo(x):
CheckX(x)
#do stuff here
Another problem is that using assert for normal condition-checking is that it makes it difficult to disable the debugging asserts using the -O flag.
In addition to the other answers, asserts themselves throw exceptions, but only AssertionErrors. From a utilitarian standpoint, assertions aren't suitable for when you need fine grain control over which exceptions you catch.
For what it's worth, if you're dealing with code which relies on assert to function properly, then adding the following code will ensure that asserts are enabled:
try:
assert False
raise Exception('Python assertions are not working. This tool relies on Python assertions to do its job. Possible causes are running with the "-O" flag or running a precompiled (".pyo" or ".pyc") module.')
except AssertionError:
pass
Both the use of assert and the raising of exceptions are about communication.
Assertions are statements about the correctness of code addressed at developers: An assertion in the code informs readers of the code about conditions that have to be fulfilled for the code being correct. An assertion that fails at run-time informs developers that there is a defect in the code that needs fixing.
Exceptions are indications about non-typical situations that can occur at run-time but can not be resolved by the code at hand, addressed at the calling code to be handled there. The occurence of an exception does not indicate that there is a bug in the code.
Best practice
Therefore, if you consider the occurence of a specific situation at run-time as a bug that you would like to inform the developers about ("Hi developer, this condition indicates that there is a bug somewhere, please fix the code.") then go for an assertion. If the assertion checks input arguments of your code, you should typically add to the documentation that your code has "undefined behaviour" when the input arguments violate that conditions.
If instead the occurrence of that very situation is not an indication of a bug in your eyes, but instead a (maybe rare but) possible situation that you think should rather be handled by the client code, raise an exception. The situations when which exception is raised should be part of the documentation of the respective code.
Is there a performance [...] issue with using
assert
The evaluation of assertions takes some time. They can be eliminated at compile time, though. This has some consequences, however, see below.
Is there a [...] code maintenance issue with using
assert
Normally assertions improve the maintainability of the code, since they improve readability by making assumptions explicit and during run-time regularly verifying these assumptions. This will also help catching regressions. There is one issue, however, that needs to be kept in mind: Expressions used in assertions should have no side-effects. As mentioned above, assertions can be eliminated at compile time - which means that also the potential side-effects would disappear. This can - unintendedly - change the behaviour of the code.
"assert" statements are removed when the compilation is optimized. So, yes, there are both performance and functional differences.
The current code generator emits no code for an assert statement when optimization is requested at compile time. - Python 2 Docs Python 3 Docs
If you use assert to implement application functionality, then optimize the deployment to production, you will be plagued by "but-it-works-in-dev" defects.
See PYTHONOPTIMIZE and -O -OO
In IDE's such as PTVS, PyCharm, Wing assert isinstance() statements can be used to enable code completion for some unclear objects.
Source: Stackoverflow.com