I'm making a GUI and I'm finding myself to be using a lot of try except statements. My question is, should I be redesigning my program to use less try except statements or is try except a good practice to be using in python programs? I like them because they're informative and make debugging, for me, easier. Just wondering what real developers think about it.
Thanks
One of Python's idioms is: It's easier to ask for forgiveness than for permission. (Python Glossary, have a look at EAFP).
So it's perfectly acceptable to structure program flow with exception handling (and reasonably fast too, compared to other languages). It fits Python's dynamic nature nicely imho.
One large consideration when deciding whether to catch an exception is what legitimate errors you could be hiding.
For example, consider this code:
try:
name = person['name']
except KeyError:
name = '<none provided>'
This is reasonable if person is known to be a dict… But if person can possibly be something more complex, for example:
class Person(object):
def __getitem__(self, key):
return do_something(key)
You run the risk of accidentally catching an exception which was the result of a legitimate bug (for example, a bug in do_something).
And I feel the need to mention: you should never, ever (except under a couple of very specific circumstances) use a "naked" except:.
My personal preference is to avoid catching exceptions when ever possible (for example, using name = person.get('name', '<none provided>')), both because I find it cleaner and I dislike the look of try/catch blocks.
It's hard to give a general answer on whether you should use less exception handling... you can definitely do too much and too little. It's almost certainly wrong to be catching every possible exception and also almost certainly wrong to be doing no exception handling.
Here are some things to think about:
It's usually a good idea to catch the exception if you can programmatically do something about the error condition. E.g. your code is trying to make a web request and if it fails, you want to retry. In that situation you want to catch the exception and then do the retry.
Think carefully about where to catch an exception. In some low-level function, can you reasonably do something about the error? E.g. let's say you have a function that writes out a file and it fails with a permissions error. Probably not much you can do about it there but maybe at a higher level you can catch the exception and display a message to the user instructing them to try to save the file somewhere else.
It almost never makes sense to catch "fatal" types of errors e.g. out of memory, stack overflow etc. At least not low down in your code - it might make sense to have a top-level handler that tries to gracefully exit.
Don't "swallow" exceptions that really should bubble up i.e. don't have an except clause that doesn't re-raise the exception if your calling function should really see it. This can hide serious bugs.
For more, do a Google search for "exception handling guidelines". Many of the results you see will be for other languages/environments, but the concepts apply just as well.
Related
This question already has answers here:
How can I know which exceptions might be thrown from a method call?
(8 answers)
Closed 6 years ago.
We are working on a medium-sized commercial Python project and have one reccuring problem when using functions from the standard library.
The documentation of the standard library often does not list all (or even any) exceptions that a function can throw, so we try all the error cases that we can come up with, have a look through the source of the library and then catch whatever is plausible. But quite often we miss that one random error that can still happen, but that we didn't come up with. For example, we missed, that json.loads() can raise a ValueError, if any of the built-in constants are spelled the wrong way (e.g. True instead of true).
In other cases, we tried to just catch Exception, because that part of the code is so critical, that it should never break on an Exception, but should rather try again. The problem here is, that it even caught the KeyboardInterrupt.
So, is there any way to find all exceptions that a function can raise, even if the documentation does not say anything about that?
Are there any tools that can determine what exceptions can be raised?
There is no real way to do this other than reading all of the possible code paths that can be taken in that function and looking to see what exceptions can be raised there. I suppose some sort of automated tool could be written to do this, but even that is pretty tricky because due to python's dynamic nature, just about any exception could be raised from anywhere (If I really wanted to, I can always patch a dependency function with a different function that raises something else entirely).
Monkey Patching aside, To actually get it right, you'd need a really good type inferencer (maybe astroid could help?) to infer various TypeError or AttributeError that could be raised from accessing non-existent members or calling functions with the wrong arguments, etc. ValueError is particularly tricky because it can still get raised when you pass something of the correct type.
In other cases, we tried to just catch Exception, because that part of the code is so critical, that it should never break on an Exception, but should rather try again. The problem here is, that it even caught the KeyboardInterrupt.
This feels like a bad idea to me. For one, retrying code should only be done for exceptions that might give you a different result if you retry it (weird connectivity issues, etc.). For your ValueError case, you'll just raise the ValueError again. The best case scenario here is that the ValueError is allowed to propagate out of the exception handler on the second call -- The worst case is that you end up in an infinite loop (or RecursionError) that you don't really get much information to help debug.
Catching Exception should be a last resort (and it shouldn't catch KeyboardInterrupt or SystemExit since those don't inherit from Exception) and should probably only format some sort of error message that somebody can use to track down the issue and fix it.
In the Java world, we know that the exceptions are classified into checked vs runtime and whenever something throws a checked exception, the caller of that something will be forced to handle that exception, one way or another. Thus the caller would be well aware of the fact that there is an exception and be prepared/coded to handle that.
But coming to Python, given there is no concept of checked exceptions (I hope that is correct), how does the caller of something know if that something would throw an exception or not? Given this "lack of knowledge that an exception could be thrown", how does the caller ever know that it could have handled an exception until it is too late?
There are no checked exceptions in Python.
Read the module docs.
Read the source.
Discover during testing.
Catch a wide range of exception types if necessary (see below).
For example, if you need to be safe:
try:
...
except Exception:
...
Avoid using a bare except clause, as it will even catch things like a KeyboardInterrupt.
As far as I know Python (6 years) there isn't anything similar to Java's throws keyword in Python.
how does the caller of something know if that something would throw an exception or not?
By reading the documentation for that something.
Design Principle of Python: it's easier to ask forgiveness than permission
EAFP
Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and
catches exceptions if the assumption proves false. This clean and fast
style is characterized by the presence of many try and except
statements. The technique contrasts with the LBYL style common to many
other languages such as C.
Basics of Unix Philosophy: Rule of Repair
Repair what you can — but when you must fail, fail noisily and as soon
as possible.
The essence of both is to use error handling that allows you to find your bugs quickly and wind up with a much more robust program over the long run.
The practical lesson is to learn what errors you should look for as you develop, and only attempt to catch those in your modules, and only use generic Exception handling as a wrapper.
I've been told time and again that exception handling for operations like determining type is bad form since exceptions are always computationally expensive. Nevertheless, I've seen posts (especially Python-related ones, such as the to reply of this one) that advise using exception handling for exactly that purpose.
I was wondering, then, if throwing and catching exceptions is to be avoided universally, because it is always computationally expensive, or whether some languages, such as Python, handle exceptions better and it is permissible to use exception handling more liberally.
You cannot give general advice such as "exceptions are expensive and therefore they should be avoided" for all programming languages.
As you suspected, in Python, Exceptions are used more liberally than in other languages such as C++. Instead of raw performance, Python puts emphasis on code readability. There is an idiom "It's easier to ask for forgiveness than for permission", meaning: It's easier to just attempt what you want to achieve and catch an exception than check for compatibility first.
Forgiveness:
try:
do_something_with(dict["key"])
except (KeyError, TypeError):
# Oh well, there is no "key" in dict, or it has the wrong type
Permission:
if hasattr(dict, "__getitem__") and "key" in dict:
do_something_with(dict["key"])
else:
# Oh well
Actually, in Python, iteration with for loops is implemented with exceptions under the hood: The iterable raises a StopIteration exception when the end is reached. So even if you try to avoid exceptions, you will use them anyway all the time.
I think a lot of it comes down to specific use cases.
In the example you posted, the poster explicitly refers to the "duck-typing" aspect of Python. Essentially, you use the exceptions generated to determine if a variable has a particular capability or set of capabilities instead of manually checking (since Python allows a lot of dynamic operations, a class might access "split" through __getattr__, which makes it impossible to check using a standard if statement, so you try to use split, then if it can't do it, we go to plan B).
In a lot of Python applications, also, we tend not to worry a lot about some of the performance details that might matter in other applications, so any overhead from exceptions is "trivial."
In coding my module tco, I encountered this question. In the version 1.0.1alpha, I emplemented three versions of the same class. The module is intended for computational purpose; thus I think I may give some answer to your question.
Computing quick operations by embedding them in the class working without exceptions was twice as quick as with the two classes working with exception. But you have to know that such a test may be meaningless if you think that computing interesting things between the exceptions will make the difference become very tiny. Nobody will seriously care about the difference of time between an empty loop and an empty system raising and catching exceptions!
For that reason, I decided to remove the first system when releasing the 1.1 version of my module. Though a little slower, I found that the system relying on exceptions was more robust and I focused on it.
I find myself handling exceptions without specifying an exception type when I call code that interacts with system libraries like shutil, http, etc, all of which can throw if the system is in an unexpected state (e.g. a file locked, network unavailable, etc)
try:
# call something
except:
print("OK, so it went wrong.")
This bothers me because it also catches SyntaxError and other exceptions based in programmer error, and I've seen recommendations to avoid such open-ended exception handlers.
Is there a convention that all runtime errors derive from some common exception base class that I can use here? Or anything that doesn't involve syntax errors, module import failures, etc.? Even KeyError I would consider a bug, because I tend to use dict.get() if I'm not 100% sure the key will be there.
I'd hate to have to list every single conceivable exception type, especially since I'm calling a lot of supporting code I have no control over.
UPDATE: OK, the answers made me realize I'm asking the wrong question -- what I'm really wondering is if there's a Python convention or explicit recommendation for library writers to use specific base classes for their exceptions, so as to separate them from the more mundane SyntaxError & friends.
Because if there's a convention for library writers, I, as a library consumer, can make general assumptions about what might be thrown, even if specific cases may vary. Not sure if that makes more sense?
UPDATE AGAIN: Sven's answer finally led me to understand that instead of giving up and catching everything at the top level, I can handle and refine exceptions at the lower levels, so the top level only needs to worry about the specific exception type from the level below.
Thanks!
Always make the try block as small as possible.
Only catch the exceptions you want to handle. Look in the documentation of the functions you are dealing with.
This ensures that you think about what exceptions may occur, and you think about what to do if they occur. If something happens you never thought about, chances are your exception handling code won't be able to correctly deal with that case anyway, so it would be better the exception gets propagated.
You said you'd "hate to have to list every single conceivable exception type", but usually it's not that bad. Opening a file? Catch IOError. Dealing with some library code? They often have their own exception hierarchies with a specific top-level exception -- just catch this one if you want to catch any of the library-specific exceptions. Be as specific as possible, otherwise errors will pass unnoticed sooner or later.
As for convention about user-defined exceptions in Python: They usually should be derived from Exception. This is also what most user-defined exceptions in the standard library derive from, so the least you should do is use
except Exception:
instead of a bare except clause, which also catches KeyboardInterrupt and SystemExit. As you have noted yourself, this would still catch a lot of exceptions you don't want to catch.
Check out this list of built-in exceptions in the Python docs. It sounds like what you mostly want is to catch StandardError although this also includes KeyError. There are also other base classes, such as ArithmeticError and EnvironmentError, that you may find useful sometimes.
I find third-party libraries do derive their custom exceptions from Exception as documented here. Programmers also tend to raise standard Python exceptions such as TypeError, ValueError, etc. in appropriate situations. Unfortunately, this makes it difficult to consistently catch library errors separately from other errors derived from Exception. I wish Python defined e.g. a UserException base class. Some libraries do declare a base class for their exceptions, but you'd have to know what these are, import the module, and catch them explicitly.
Of course, if you want to catch everything except KeyError and, say, IndexError, along with the script-stopper exceptions, you could do this:
try:
doitnow()
except (StopIteration, GeneratorExit, KeyboardInterrupt, SystemExit):
raise # these stop the script
except (KeyError, IndexError):
raise # we don't want to handle these ones
except Exception as e:
handleError(e)
Admittedly, this becomes a hassle to write each time.
How about RuntimeError: http://docs.python.org/library/exceptions.html#exceptions.RuntimeError
If that isn't what you want (and it may well not be), look at the list of exceptions on that page. If you're confused by how the hierarchy fits together, I suggest you spend ten minutes examining the __bases__ property of the exceptions you're interested in, to see what base classes they share. (Note that __bases__ isn't closed over the whole hierarchy - you may need to examine superclass bases also).
This particular example relates to Django in Python, but should apply to any language supporting exceptions:
try:
object = ModelClass.objects.get(search=value)
except DoesNotExist:
pass
if object:
# do stuff
The Django model class provides a simple method get which allows me to search for one and only one object from the database, if it finds more or less it raises an exception. If can find zero or more with an alternative filter method, which returns a list:
objects = ModelClass.objects.filter(search=value)
if len(objects) == 1:
object = objects[0]
# do stuff
Am I overly averse to exceptions? To me the exception seems a little wasteful, at a guess, a quarter-to-a-half of the time will be 'exceptional'. I'd much prefer a function that returns None on failure. Would I be better to use Django's filter method and process the list myself?
Believe it or not, this actually is an issue that is a bit different in each language. In Python, exceptions are regularly thrown for events that aren't exceptional by the language itself. Thus I think that the "you should only throw exceptions under exceptional circumstances" rule doesn't quite apply. I think the results you'll get on this forum will be slanted towards that point of view though, considering the high number of .Net programmers (see this question) for more info on that).
At a very minimum, I'd better not catch anyone who sticks to that rule ever using a generator or a for loop in Python (both of which involve throwing exceptions for non-exceptional circumstances).
There's a big schism in programming languages around the use of exceptions.
The majority view is that exceptions should be exceptional. In most languages with exceptions, transfer of control by exception is considerably more expensive than by procedure return, for example.
There is a strong minority view that exceptions are just another control-flow construct, and they should be cheap. The Standard ML of New Jersey and Objective Caml compilers subscribe to that view. If you have cheap exceptions you can code some fancy backtracking algorithms in ways that are more difficult to code cleanly using other mechanisms.
I've seen this debate repeated many times for new language designs, and almost always, the winner decides that exceptions should be expensive and rare. When you care about performanced, you'd be wise to program with this in mind.
The clue is in the name - exceptions should be exceptional.
If you always expect the item will exist then use get, but if you expect it not to exist a reasonable proportion of the time (i.e. it not existing is an expected result rather than an exceptional result) then I'd suggest using filter.
So, seeing as you indicated that between 1 in 2 and 1 in 4 are expected not to exist, I'd definitely write a wrapper around filter, as that's definitely not an exceptional case.
I agree with the other answer but I wanted to add that exception passing like this is going to give you a very noticeable performance hit. Highly recommended that you check to see if the result exists (if that's what filter does) instead of passing on exceptions.
Edit:
In response to request for numbers on this, I ran this simple test...
import time
def timethis(func, list, num):
st=time.time()
for i in xrange(0,1000000):
try:
func(list,num)
except:
pass
et = time.time()
print "Took %gs" % (et-st)
def check(list, num):
if num < len(list):
return list[num]
else:
return None
a=[1]
timethis(check, a, 1)
timethis(lambda x,y:x[y], a, 1)
And the output was..
Took 0.772558s
Took 3.4512s
HTH.
The answer will depend on the intent of the code. (I'm not sure what your code sample was meant to do, the pass in the exceptional case is confusing, what will the rest of the code do with object variable to work with?)
Whether to use exceptions or to use a method which treat the case as non-exceptional is a matter of taste in many cases. Certainly if the real code in the except clause is as complicated as the filter method you'd have to use to avoid the exception, then use the filter method. Simpler code is better code.
Aversion to excpetions is a matter of opinion - however, if there's reason to believe that a function or method is going to be called many times or called rapidly, exceptions will cause a significant slowdown. I learned this from my previous question, where I was previously relying on a thrown exception to return a default value rather than doing parameter checking to return that default.
Of course, exceptions can still exist for any reason, and you shouldn't be afraid to use or throw one if necessary - especially ones that could potentially break the normal flow of the calling function.
I disagree with the above comments that an exception is inefficient in this instance, especially since it's being used in an I/O bound operation.
Here's a more realistic example using Django with an in-memory sqlite database. Each of a 100 different queries was run, then averaged for each of a 100 runs. Although I doubt if it would matter, I also changed the order of execution.
With ObjectDoesNotExist... 0.102783939838
Without exception ........ 0.105322141647
With ObjectDoesNotExist... 0.102762134075
Without exception ........ 0.101523952484
With ObjectDoesNotExist... 0.100004930496
Without exception ........ 0.107946784496
You can instrument this in your own Django environment, but I doubt if your time is well spent avoiding this exception.