I observe that python library primarily use exception for error handling
rather than use error code.
In the article API Design Matters by Michi Henning
Communications of the ACM
Vol. 52 No. 5, Pages 46-56
10.1145/1506409.1506424
http://cacm.acm.org/magazines/2009/5/24646-api-design-matters/fulltext
It says "Another popular design flaw—namely, throwing exceptions for
expected outcomes—also causes inefficiencies because catching and
handling exceptions is almost always slower than testing a return
value."
This is very, very wrong.
Firstly, notice that the author doesn't compare the same thing. He
compares "catching AND HANDLING" the exception (emphasis added) with
*only* testing a return value. Of course it is faster to test a value and
do nothing, than it is to catch an exception and then handle the
exception. That's an unfair comparison, and that alone shows that the
author is biased against exceptions.
But it's also wrong. If you call a function one million times, and catch
an exception ONCE (because exceptions are rare) that is likely to be
much, much faster than testing a return code one million times.
Before you can talk about which strategy is faster, you need to
understand your problem. When exceptions are rare (in CPython, about one
in ten or rarer) then try...except is faster than testing each time. The
exact cut-off depends on how expensive the test is, and how much work
gets done before the exception is raised. Using exceptions is only slow
if they are common.
But the most important reason for preferring exceptions is that the
alternatives are error-prone! Testing error codes is the anti-pattern,
not catching exceptions.
See, for example:
http://c2.com/cgi/wiki?UseExceptionsInsteadOfErrorValues
http://c2.com/cgi/wiki?ExceptionsAreOurFriends
http://c2.com/cgi/wiki?AvoidExceptionsWheneverPossible
Despite the title of that last page, it has many excellent arguments for
why exceptions are better than the alternatives.
(Be warned: the c2 wiki is filled with Java and C++ programmers who
mistake the work-arounds for quirks of their language as general design
principles. For example, because exceptions in Java are evcen more
expensive and slow than in Python, you will find lots of Java coders
saying "don't use exceptions" instead of "don't use exceptions IN JAVA".)
There are many problems with using error codes:
* They complicate your code. Instead of returning the result you care
about, you have to return a status code and the return result you care
about. Even worse is to have a single global variable to hold the status
of the last function call!
* Nobody can agree whether the status code means the function call
failed, or the function call succeeded.
* If the function call failed, what do you return as the result code?
* You can't be sure that the caller will remember to check the status
code. In fact, you can be sure that the caller WILL forget sometimes!
(This is human nature.) This leads to the frequent problem that by the
time a caller checks the status code, the original error has been lost
and the program is working with garbage.
* Even if you remember to check the status code, it complicates the code,
makes it less readable, confuses the intent of the code, and often leads
to the Arrow Anti-pattern:
http://c2.com/cgi/wiki?ArrowAntiPattern
That last argument is critical. Exceptions exist to make writing correct
code easier to write, understand and maintain.
Python uses special result codes in at least two places:
str.find(s) returns -1 if s is not in the string
re.match() returns None is the regular expression fails
Both of these are error-prone. Consider a naive way of getting the
fractional part of a float string:
s = "234.567"
print s[s.find('.')+1:]
567
But see:
s = "234"
print s[s.find('.')+1:]
234
You need something like:
p = s.find('.')
if p == -1:
print ''
else:
print s[p+1:]
Similarly, we cannot safely do this in Python:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'NoneType' object has no attribute 'group'
You need to do this:
mo = re.match(r'\d+', '123abcd')
if mo is not None: # or just `if mo` will work
mo.group()
Exceptions are about making it easier to have correct code. They're also
about making it easier to have readable code. Which is easier to read,
easier to understand and easier to debug?
x = function(1, 2, 3)
if x != -1:
y = function(x, 1, 2)
if y != -1:
z = function(y, x, 1)
if z != -1:
print "result is", z
else:
print "an error occurred"
else:
print "an error occurred"
else:
print "an error occurred"
versus:
try:
x = function(1, 2, 3)
y = function(x, 1, 2)
print "result is", function(y, x, 1)
except ValueError:
print "an error occurred"
In Python, setting up the try...except block is very fast, about as fast
as a plain "pass" statement, but actually catching the exception is quite
slow. So let's compare string.find (which returns an error result) and
string.index (which raises an exception):
1.2237567901611328
There's hardly any difference at all, and in fact index is slightly
faster. But what about if there's an exceptional case?
.... try:
.... p = source.index('z')
.... except ValueError:
.... pass
.... """, setup).repeat())
3.5699808597564697.... p = source.find('z')
.... if p == -1:
.... pass
.... """, setup).repeat())
1.7874350070953369
So in Python, catching the exception is slower, in this case about twice
as slow. But remember that the "if p == -1" test is not free. It might be
cheap, but it does take time. If you call find() enough times, and every
single time you then test the result returned, that extra cost may be
more expensive than catching a rare exception.
The general rule in Python is:
* if the exceptional event is rare (say, on average, less than about one
time in ten) then use a try...except and catch the exception;
* but if it is very common (more than one time in ten) then it is faster
to do a test.
My observation is contradicted to the above statement by Henning. If my
observation is wrong, please just ignore my question below.
Otherwise, could some python expert explain to me why exception is
widely used for error handling in python? Is it because the efficiency
is not the primary goal of python?
Yes.
Python's aim is to be fast *enough*, without necessarily being as fast as
possible.
Python aims to be readable, and to be easy to write correct, bug-free
code.
