I am testing a software component and want that software to throw an Exception in certain situations.
I want to reproduce these situations by using the robot framework.
The testcase shall succeed if I catch a specific Exception (which I am expecting, because I am deliberately creating an error-state in my component under test)
The testcase shall fail if I do not receive the specific Exception (i.e. my component under test did not fail(throw an exception) in an error situation)
What I am looking for is something like this:
prepareErrorInTestEnvironment
try
executeComponentWhichThrowsException
except
pass
fail
Treatment of "expected exception" are a bit specific in Robot Framework as usually exception will fail the keyword and hence the test.
The keyword you are looking for is Run Keyword and Expect Error.
Your test would look like
*** Test Cases ***
my test
prepareErrorInTestEnvironment
Run Keyword and Expect Error TheExceptionYouExpect executeComponentWhichThrowsException
This will success if you get the proper exception, and fail otherwise
I believe try/else is what you want
prepareErrorInTestEnvironment
try:
executeComponentWhichThrowsException
except:
pass
else:
fail
Also you can return on except so fail will not execute:
prepareErrorInTestEnvironment
try:
executeComponentWhichThrowsException
except:
*dosomething*
return
fail
Related
I want to know what is the usefulness of tdie comparing to Java exceptions in talend knowing that when an exception occur tdie exit the job and is passing the error to tlogcatcher .The same thing can be done by java exceptions (they also can be received by tlogcatcher and they exit the job ).
So why java exceptions are not enough for logging so we use tdie?/what is the limits of java exceptions.
I don't use tDie after a component exception (like Component-->oncomponenterror-->tDie . As you stated, the java exception is caught : if you put a tDie, you'll only have 2 lines logged instead of just one.
I use tDie to put an end to a job if a condition is not met : for example , I can test the number of lines inserted in a DB, if it is 0 , I call tDie to end the job (with tDBOutput--if-->tDie , with a test on number of lines inserted inside the if condition).
This is more like a functional error than a technical one that I want to catch in this case.
I have a use case for joblib's Parallel, delayed. I have included some feature that terminates a worker under certain conditions. However, when I do that, I am randomly yielding JoblibWebdriverException, Multiprocessing exception, JoblibURLerror, or just error.
To my great amusement, I don't find any section on how to (define?)/catch Exceptions in the docs.
When I do:
try:
Parallel(delayed(function))
except (JoblibWebdriverException | error | 'Multiprocessing exception'):
# written with separate excepts in original code
log_errors()
I yield name JoblibWebdriverException not defined followed by:
---------
Sub-process traceback
---------
Multiprocessing exception:
(trace stack)
How to catch undefined joblib Exceptions when using Parallel in python?
I would recommend using concurrent.Futures which has robust support for Exception handling. joblib is notorious for not being able to raise Exceptions from child processes to the main thread due to way the multi-processing is set up.
I'm making a custom plugin to query a database for user info to aide customer support. My backend is slack.
Everytime I start the bot command I'm greeted with:
Computer says nooo. See logs for details:
catching classes that do not inherit from BaseException is not allowed
I'm not sure if this is warning me that I'm attempting to catch an exception that isn't a BaseClass in my code or if an unknown exception was raised and caught elsewhere outside of my plugin.
To debug I tried:
try:
do_the_thing()
except (TypeError, ValueError) as e:
return('Something went wrong.')
I also tried:
try:
do_the_thing()
except Exception as e:
return('Something went wrong.')
And I still get the errbot admonition. Note that the command still runs and does the right thing where there is no exception raised by do_the_thing().
It means that:
Somewhere in your code you have an except ... statement where the exception ... (or one of the exceptions in the sequence ...) is not a subclass of BaseException, and
An exception is being thrown that is caught by that except ... statement.
The TypeError can be raised only when an exception is actually thrown because the names you give to except ... must be evaluated for their current values at that time; just because TypeError referenced a particular class at one point in the program's execution doesn't mean it won't be changed later to reference another object (though that would be admittedly perverse).
The Python interpreter should be giving you a full traceback of the exception; the first thing you need to do is find this. It could be occurring in one of two situations. (This is for single-threaded programs; I'm assuming your program is not multithreaded.)
During the execution of your program, in which case the program will be terminated by the exception, or
During finalization of objects (in their __del__(self) functions) in which case the error will be printed to stderr.
In both cases there should be a stack trace, not just the error message; I've confirmed that at least on Python ≥3.4 a stack trace is printed out for case 2.
You then need to follow this stack trace to see where the problem lies. Remember that the names you give to except ... are variables (even things like TypeError) that can be reassigned, so that you could conceivably be dealing with a (perverse) situation like:
TypeError = False
try:
...
except TypeError:
...
But more likely it will be something obvious such as:
class MyException: # Doesn't inherit from Exception
...
try:
...
except MyException:
...
There is one special case you need to be aware of: if you are seeing messages to stderr (case "2. During finalization," above) printed out as your program exits that means that the exception was thrown during cleanup as the interpreter shuts down, where random variables throughout the program may have already been set to None as part of the cleanup process. But in this case your program should still exit successfully.
I'm trying to find a good way to express exceptions in dynamic-typed languages (e.g. Python, although the same code can be used with e.g. enums in static-typed languages). In my application, the exception is not going to be displayed to the user. Which one would be best? (or you can propose better)
def parseData(data):
length = unpack('!L', data[0:4])
if 4 + len(data) != length:
Option 1:
raise Exception("Invalid length")
Option 2:
return -1
# Some code later...
parseResult = parseData(data)
if validationResult == -1:
# Do something with the error.
The point is that when user doesn't see the exception, is it worth the hassle of making custom exception types instead of coming the easy path and returning integer values? (this is often done in functions like .indexOf(...)).
I can only comment on Python, but I would only extremely rarely raise Exception, as it makes error handling much harder; except Exception would catch many legitimate errors I would much rather hear about.
Instead, I would raise something more meaningful from the built-in exceptions:
raise ValueError("Invalid length.")
Most of Python's built-in classes and functions would raise an exception rather than returning some flag value. The only exception I can immediately think of is str.find, which will return -1 if the sub-string can't be found (its partner str.index will raise ValueError; I find this preferable, as -1 is a valid index).
There may be the occasional case where a single function could raise one of a range of exceptions, depending on what exactly has happened, but this is unusual - if your response will depend on what went wrong, that logic should probably be inside the function. You can still stick to the built-ins, for example:
def divide(n, lst, index):
return n / lst[index]
could raise TypeError, IndexError or ZeroDivisionError, and I can deal with that accordingly:
try:
divide(1, {3: 4, 5: 6}, 2)
except TypeError:
...
except IndexError:
...
except ZeroDivisionError:
...
except Exception as e:
print("Didn't see that coming: {0}".format(repr(e)))
You can even inspect the message in the exception to differentiate, if necessary:
def test():
test()
try:
test()
except RuntimeError as e:
if "recursion" in e.args[0]
print("Ran out of stack.")
else:
print("That was unexpected: {0}".format(repr(e)))
I have been doing some work with python-couchdb and desktopcouch. In one of the patches I submitted I wrapped the db.update function from couchdb. For anyone that is not familiar with python-couchdb the function is the following:
def update(self, documents, **options):
"""Perform a bulk update or insertion of the given documents using a
single HTTP request.
>>> server = Server('http://localhost:5984/')
>>> db = server.create('python-tests')
>>> for doc in db.update([
... Document(type='Person', name='John Doe'),
... Document(type='Person', name='Mary Jane'),
... Document(type='City', name='Gotham City')
... ]):
... print repr(doc) #doctest: +ELLIPSIS
(True, '...', '...')
(True, '...', '...')
(True, '...', '...')
>>> del server['python-tests']
The return value of this method is a list containing a tuple for every
element in the `documents` sequence. Each tuple is of the form
``(success, docid, rev_or_exc)``, where ``success`` is a boolean
indicating whether the update succeeded, ``docid`` is the ID of the
document, and ``rev_or_exc`` is either the new document revision, or
an exception instance (e.g. `ResourceConflict`) if the update failed.
If an object in the documents list is not a dictionary, this method
looks for an ``items()`` method that can be used to convert the object
to a dictionary. Effectively this means you can also use this method
with `schema.Document` objects.
:param documents: a sequence of dictionaries or `Document` objects, or
objects providing a ``items()`` method that can be
used to convert them to a dictionary
:return: an iterable over the resulting documents
:rtype: ``list``
:since: version 0.2
"""
As you can see, this function does not raise the exceptions that have been raised by the couchdb server but it rather returns them in a tuple with the id of the document that we wanted to update.
One of the reviewers went to #python on irc to ask about the matter. In #python they recommended to use sentinel values rather than exceptions. As you can imaging just an approach is not practical since there are lots of possible exceptions that can be received. My questions is, what are the cons of using Exceptions over sentinel values besides that using exceptions is uglier?
I think it is ok to return the exceptions in this case, because some parts of the update function may succeed and some may fail. When you raise the exception, the API user has no control over what succeeded already.
Raising an Exception is a notification that something that was expected to work did not work. It breaks the program flow, and should only be done if whatever is going on now is flawed in a way that the program doesn't know how to handle.
But sometimes you want to raise a little error flag without breaking program flow. You can do this by returning special values, and these values can very well be exceptions.
Python does this internally in one case. When you compare two values like foo < bar, the actual call is foo.__lt__(bar). If this method raises an exception, program flow will be broken, as expected. But if it returns NotImplemented, Python will then try bar.__ge__(foo) instead. So in this case returning the exception rather than raising it is used to flag that it didn't work, but in an expected way.
It's really the difference between an expected error and an unexpected one, IMO.
exceptions intended to be raised. It helps with debugging, handling causes of the errors and it's clear and well-established practise of other developers.
I think looking at the interface of the programme, it's not clear what am I supposed to do with returned exception. raise it? from outside of the chain that actually caused it? it seems a bit convoluted.
I'd suggest, returning docid, new_rev_doc tuple on success and propagating/raising exception as it is. Your approach duplicates success and type of 3rd returned value too.
Exceptions cause the normal program flow to break; then exceptions go up the call stack until they're intercept, or they may reach the top if they aren't. Hence they're employed to mark a really special condition that should be handled by the caller. Raising an exception is useful since the program won't continue if a necessary condition has not been met.
In languages that don't support exceptions (like C) you're often forced to check return values of functions to verify everything went on correctly; otherwise the program may misbehave.
By the way the update() is a bit different:
it takes multiple arguments; some may fail, some may succeed, hence it needs a way to communicate results for each arg.
a previous failure has no relation with operations coming next, e.g. it is not a permanent error
In that situation raising an exception would NOT be usueful in an API. On the other hand, if the connection to the db drops while executing the query, then an exception is the way to go (since it's a permament error and would impact all operations coming next).
By the way if your business logic requires all operations to complete successfully and you don't know what to do when an update fails (i.e. your design says it should never happen), feel free to raise an exception in your own code.