On 19/11/2020 02:13, Loris Bennett wrote:
dn <pythonl...@danceswithmice.info> writes:
Firsty, thanks for taking the time to write such a detailed reply.
Bitte!
I have a method for manipulating the membership of groups such as:
def execute(self, operation, users, group):
"""
Perform the given operation on the users with respect to the
group
"""
action = {
'get': self.get,
'add': self.add,
'delete': self.delete,
}
return action.get(operation)(users, group)
The 'get' action would return, say, a dict of users attribute, whereas
the 'add/delete' actions would return, say, nothing, and all actions
could raise an exception if something goes wrong.
The method which calls 'execute' has to print something to the terminal,
such as the attributes in the case of 'get' and 'OK' in the cases of
'add/delete' (assuming no exception occurred).
Is there a canonical way of dealing with a method which returns different
types of data, or should I just make all actions return the same data
structure so that I can generate a generic response?
Is the problem caused by coding the first step before thinking of the overall
task? Try diagramming or pseudo-coding the complete solution (with multiple
approaches), ie the operations AND the printing and exception-handling.
You could have a point, although I do have a reasonable idea of what the
task is and coming from a Perl background, Python always feels a bit
like pseudocode anyway (which is one of the things I like about Python).
+1 the ease of Python, but can this be seductive?
Per the comment about Perl/Python experience, the operative part is the
"thinking", not the tool - as revealed in responses below...
Sometimes we design one 'solution' to a problem, and forget (or 'brainwash'
ourselves into thinking) that there might be 'another way'.
It may/not apply in this case, but adjusting from a diagram-first methodology,
to the habit of 'jumping straight into code' exhibited by many colleagues,
before readjusting back to (hopefully) a better balance; I felt that
coding-first often caused me to 'paint myself into a corner' with some
'solutions, by being too-close to the code and not 'stepping back' to take a
wider view of the design - but enough about me...
Might it be more appropriate to complete not only the get but also its
reporting, as a unit. Similarly the add and whatever happens after that; and the
delete, likewise.
Currently I am already obtaining the result and doing the reporting in
one method, but that makes it difficult to write tests, since it
violates the idea that one method should, in general, just do one thing.
That separation would seem appropriate here, since testing whether a
data set is correctly retrieved from a database seems to be
significantly different to testing whether the
reporting of an action is correctly laid out and free of typos.
SRP = design thinking! +1
I knew the idea, but I didn't now the TLA for it ;-)
Yes, there are plenty of those!
You may be interested in reading about "Clean Code", instigated (IIRC)
by "Uncle Bob" (Robert Martin). NB Python may/not be used for
book-examples. Just the other day I came across "Clean Code in Python",
Mariano Anaya, PacktPub, 2018. I have yet to read it, but the contents
page seemed to 'tick all the boxes'. The book is two years old, and IIRC
he presented at EuroPython a few years before that (YouTube videos
on-line - in case you prefer that medium, or want to gain a flavor
before spending money...). All of these TLAs, and others comprising the
"SOLID Principles" appear in the ToC, along with plenty of others, eg
YAGNI and EAFP; plus some specific to Python, eg MRO.
TDD = early testing! +1
Agreed: The tasks are definitely separate. The first is data-related. The second
is about presentation.
In keeping with the SRP philosophy, keep the split of execution-flow into the
three (or more) functional-tasks by data-process, but turn each of those tasks
into two steps/routines. (once the reporting routine following "add" has been
coded, and it comes time to implement "delete", it may become possible to repeat
the pattern, and thus 're-use' the second-half...)
Putting it more formally: as the second-half is effectively 'chosen' at the same
time as the first, is the reporting-routine "dependent" upon the data-processor?
function get( self, ... )
self.get_data()
self.present_data()
function add( self, ... )
self.add_data()
self.report_success_fail()
...
Thus, the functional task can be tested independently of any reporting follow-up
(for example in "get"); whilst maintaining/multiplying SRP...
The above approach appeals to me a lot. Slight downsides are that
such 'metafunctions' by necessity non-SRP functions and that, as there
would be no point writing tests for such functions, some tools which try
to determine test coverage might moan.
First comes (Python) 'duty': the word "meta", perhaps more in the
context of "meta-classes" has particular meaning in Python, that may not
align with expectations generated by understanding the term "meta" in
other contexts!
Second, we return to earlier comments about up-front design. Consider
"Stepwise Decomposition"
(https://en.wikipedia.org/wiki/Top-down_and_bottom-up_design) and how
solving a 'large problem' is likened to pealing an onion, ie one 'layer'
at a time. Thus there is a sub-problem, eg report on xyz; this divides
into smaller problems: (i) from where do I find the data on xyz, and
(ii) how do I present this.
If you code top-down, then it may be that there are three subroutines
(functions in Python) which implement the three of these. Further, that
only the two "smaller" routines appear to be doing any 'work'. However,
remember that the function names both document the solution and
reproduce the specification. Thus three well-chosen names will add value
and ease understanding for you/us, six months later...
If you code bottom-up and have TDD-built the two "smaller" functions,
then adding the 'higher' function as an 'umbrella' will tie them
together - for the reasons/results mentioned above.
There are different types of testing. Unit testing is straightforward
with pytest or similar. This takes care of tests such as 'did "get"
realise the correct data?' and 'after "delete" does this data exist?'.
These are likely tests of functions at the lowest and/or lower levels of
the implementation - hence the name.
When it comes to reporting, life becomes more complicated. Whereas
pytest will capture and allow testing of sysout, when we move to Qt,
gtk, or some other UI took-kit, we need to find a compatible testing
tool. If presentation is HTML, then web-page testing is accomplished
with the likes of Selenium.
If we are talking UX (User Experience) testing, then the
information-presented is almost-irrelevant. If you have a user on the
dev.team (see also Agile teams), then (s)he will perform such 'testing'
manually (and give rapid feedback). Thus, no tool required, as such.
NB If you are concerned about the actual information being presented,
surely that will have already been tested as accurate by the unit test
mentioned earlier?
Regarding the comment about "moan[ing]" tools. Who's in-charge here?
When it is helping you it is a "tool". What is something that is getting
in your way, causing you frustration, or otherwise interfering with your
happiness and productivity?
Pointy-headed managers [a reference to the Dilbert cartoons] have often
tried to create/impose 'rules' on developers. One of my favorites is:
"there will be at least one comment for every ten lines of code". Do you
need to strain-the-brain to know what happens?
# this code has a comment
...
# add one to x
x += 1
I'm afraid the idea of 100% code-coverage is a nirvana that is probably
not worth seeking. See also @Ned's comments (about his own coverage.py
tool)
https://nedbatchelder.com/blog/200710/flaws_in_coverage_measurement.html
The car's speedo might tell you that it can motor-along at some
incredible speed, but using the information sensibly might attract less
attention from the Highway Patrol!
Otherwise the code must first decide which action-handler, and later,
which result-handler - but aren't they effectively the same decision?
Thus, is the reporting integral to the get (even if they are in
separate routines)?
I think you are right here. Perhaps I should just ditch the dispatch
table. Maybe that only really makes sense if the methods being
dispatched are indeed more similar. Since I don't anticipate having
more than half a dozen actions, if that, so an if-elif-else chain
wouldn't be too clunky.
An if...elif...else 'ladder' is logically-easy to read, but with many choices it
may become logistically-complicated - even too long to display at-once on a
single screen.
Whereas, the table is a more complex solution (see 'Zen of Python') that only
becomes 'simple' with practice.
So, now we must balance the 'level(s)' of the team likely to maintain the
program(me) against the evaluation of simple~complex. Someone with a ComSc
background will have no trouble coping with the table - and once Python's
concepts of dictionaries and functions as 'first-class objects' are understood,
will take to it like the proverbial "duck to water". Whereas, someone else may
end-up scratching his/her head trying to cope with 'all the above'.
The team? L'équipe, c'est moi :-) Having said that I do try to program
not only with my fictitious replacement in mind, should I be hit by the
proverbial bus, but also my future self, and so tend to err on the side
of 'simple'.
+1 "simple"
+1 "ego-less programming"
German, English, and now French?
Given that Python does not (yet) have a switch/case construct, does the table
idea assume a greater importance? Could it be (reasonably) expected that
pythonista will understand such more readily?
IMHO the table is easier to maintain - particularly 'six months later', but
likely 'appears' as a 'natural effect' of re-factoring*, once I've implemented
the beginnings of an if-ladder and 'found' some of those common follow-up
functions.
* although, like you, I may well 'see' it at the design-stage, particularly if
there are a number (more) cases to implement!
Is functional "similar"[ity] (as above) the most-appropriate metric? What about
the number of decision-points in the code? (ie please re-consider "effectively
the same decision")
# which data-function to execute?
if action==get
do get_data
elif action == add
do add_data
elif ...
...
# now 'the work' has been done, what is the follow-through?
if action=get
do present_data
elif action == add
report success/fail
...
In my current case this is there is a one-to-one relationship between
the 'work' and the 'follow-through', so this approach doesn't seem that
appealing to me. However I have other cases in which the data to be
displayed comes from multiple sources where the structure above might
be a good fit.
I hope to have addressed this above.
To help (I hope), consider if, in the proverbial six-months time, you
are asked to add logging to each of these various actions. Now, there
are three tasks: 'work', 'follow-through', and 'logging'; to be done for
each of the n-action choices.
Would an 'umbrella function' which acts as both the single destination
for an action-choice, and as a 'distributor' for the various specific
tasks that must be executed, start to appear more viable?
Having said that, I do prefer the idea of having a single jumping off
point, be it a dispatch table or a single if-then-else ladder, which
reflects the actions which the user can take and where the unpleasant
details of, say, how the data are gathered are deferred to a lower level
of the code.
+1
Back to the comment about maintainability - is there a risk that an extension
requested in six months' time will tempt the coding of a new "do" function AND
induce failure to notice that there must be a corresponding additional function
in the second 'ladder'?
This becomes worse if we re-factor to re-use/share some of the follow-throughs,
eg
...
elif action in [ add, delete, update]
report success/fail
...
because, at first glance, the second 'ladder' appears to be quite dissimilar -
is a different length, doesn't have the condition-clause symmetry of the first,
etc! So, our fictional maintainer can ignore the second, correct???
Consider SRP again, and add DRY: should the "despatch" decision be made once, or
twice, or... ?
With my non-fictional-maintainer-cum-six-month-older-self hat on I think
you have made a good case for the dispatch table, which is my latent
preference anyway, especially in connection with the 'do/display'
metafunctions and the fact that in my current case DRY implies that the
dispatch decision should only be made once.
+1 Definitely!
See also @Wulfraed's point about OOP (Object-Oriented Programming)! If
we were talking about people, then I'd expect to find a class Person, or
similar, in the code. That means that "get" and "delete" might refer to
database transactions. Hence, they should be part of the Person class,
rather than functions 'in the wild'. Thus, where we have used the term
"function" (or even "subroutine"), we should have said "method". A class
is a (very good) way to 'collect' related functionality and keep it
'together'!
Another aspect, following-on from UI comments (above). If you are using
a framework, the presentation code will largely fit within those
objects. Therefore, logically-separate from manipulating the
source-object. Another consideration (maybe) for how to structure and
relate the routines...
As a general rule, I keep print() out of functions which 'calculate' -
even, out of the Person class. This facilitates re-use, where the next
use may want to present the results differently, or merely to use the
calculation as 'input' and not present 'it' at all!
Thanks again for the input!
It is good that you are reviewing your code and considering alternate
approaches! Many others 'here' will have benefited from considering your
points/questions...
You may like to pass some information about the Free University:
- is Python the primary teaching language
- is Python widely used within various schools/departments
- is instruction in English, or...
- what does "Free" mean
- is it also $free
- is it open to (non-German) foreigners
Tschüss!
--
Regards =dn
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