A Comprehensive Guide to Handling Exceptions in Python

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Nobody desires their code throwing errors, however exceptions in Python can have an entire number of use instances and are critically essential to writing good code.
This text describes some examples of what to do and never do when writing exceptions. Hopefully, it’ll instill in you a deeper understanding of exception dealing with broadly as effectively present some helpful Python ideas.


Do Write Plenty of Exceptions

  • AttemptedToAccessCoreDatabasePriorToDatabaseTransactionInitiatedException
  • JavascriptMillisecondTimeFormatBufferOverflowException
  • CannotDeleteUserAccountThatDoesntExistException

Exceptions, exceptions, exceptions in all places. These names look difficult to learn and are lengthy to sort, however are they actually so scary?
These exceptions present essentially the most priceless operate exceptionally effectively — they’re particular, informative, and to the purpose.
OK, the final one might most likely be renamed to CannotDeleteNonExistentUser, certain, however the level is that they’re clear. You realize the place to go to search out the difficulty, and you realize precisely what to strive catch in case you’re OK with making an attempt to delete a consumer that’s already been deleted.
There was as soon as a ti
me earlier than Atom, Visible Studio, and IntelliJ, within the land of Vi and Nano, the place variable identify size really affected developer productiveness.
I learnt to code in C, utilizing Vim, with no autocomplete. And, sure, I used to call issues int num or char* s1. Positive, I used to be lazy. Positive, I used to be a younger scholar. But additionally, I needed to sort all three of these characters in num. Lately, I’ll be fortunate to sort greater than two earlier than my IDE realises what I’m attempting to do and finishes the job.
My IDE autocompletes lengthy exceptions after just 1 character
Lengthy names now not decelerate improvement, however they do considerably profit anybody debugging or studying the code. That’s proper — exceptions aren’t only for debugging or dealing with. In addition they assist folks perceive what’s happening. Take the next instance.

Confusing API responses being translated into a clear exception

Some suppliers simply need to watch the entire world burn.
We might undoubtedly refactor this to make the response codes an enum or a equally expressive format, however there’s no confusion on this code that no matter these random response codes are, they imply that we don’t have 2-factor authentication enabled and we have to configure this setting in our instance supplier to get this to work.

Rule of thumb: Each time you’ve details about a particular, replicable edge case, use it.

However why trouble with exceptions in any respect? We use asserts in testing on a regular basis, they usually do an important job. Why not simply use them as an alternative of exceptions?


Don’t ‘assert’ Except You’re in a Take a look at

assert and increase seem to operate equally. Each cease the management movement, each can terminate this system, and each can log/print a message explaining why.

At first, it may appear tempting to make use of asserts to substantiate every part is in a legitimate state, however that is thought of unhealthy observe in enterprise Python improvement. There are a number of explanation why, they usually could possibly be an article of their very own.

However to maintain it quick: You possibly can customise exception dealing with and exception particulars, and exception raises won’t ever be “optimised” out of your code.

Rule of thumb: You need to solely assert not possible situations, similar to asserting {that a} worth you’ve simply squared isn’t adverse (assuming you’re not modelling quantum physics or one thing equally wild). If there’s even a distant chance the assertion would possibly fail, then it ought to be changed by an exception.

A traditional instance the place folks typically make this error is when coping with third-party suppliers.

Say you might be calling a Yahoo Climate API inside your app, and also you’ve determined so as to add in an assert that response is just not None. You selected an assert as a result of Yahoo at all times returns the climate (although the predictions may not at all times be appropriate).
However what occurs when Yahoo’s Climate API experiences a disruption? All of the sudden, all your providers are failing, and all it’s a must to work with is AssertionError: response is None.

Now I’m certain Yahoo Climate might be very dependable, and also you most likely have line numbers in your logs. However that’s no cause to not spend the one minute of additional improvement time and create a WeatherProviderUnresponsiveException (“naming issues is tough … [give this writer a break]” — Phil Karlton).

OK, so we should always use numerous exceptions, however which exceptions ought to we use? Earlier than we dive into that, we have to perceive an important idea in exception dealing with (and programming usually).


Fast Tangent — What’s Sort Inheritance?

Let’s begin with the fundamentals right here as a result of there’s so much to unpack.

Firstly, inheritance. When a category in Python inherits from one other class, it takes on all of the strategies and attributes, but it surely additionally takes on the kind of the father or mother class — that’s, on the planet of Python, I’m myself, however I’m additionally my father and my mom. Even in case you override strategies or attributes of father or mother courses, you’ll nonetheless preserve the sort inheritance. Let’s have a look at some code to elucidate.

Only one observe earlier than diving in: Courses aren’t precisely varieties, but they kind of are. OK, now that you just’re extra confused than ever, let’s have a look at some code:

A script to explain type inheritance

We’ve outlined a couple of customized varieties right here, all inheriting from the inbuilt dict class. Take RedDict, for instance, which has the sort RedDict but additionally dict.

isinstance(red_map, dict) # True
isinstance(red_map, RedDict) # True

Nevertheless, the reverse isn’t true — in that RedDict isn’t a PurpleDict.

isinstance(red_map, PurpleDict) # False

Run the above snippet to see the outcomes for all of the courses above.

object_diagram.png
You possibly can objectify something you need in Python since every part is an object

You possibly can objectify something you need in Python since every part is an object
OK, now let’s get again to the place we have been …

strive:
segway_about_types()
catch OnATangentError, Exception as e:
smooth_transition(e)

Don’t Catch ‘Exception as e’

Catching all exceptions and throwing them away is the second only solution to have bug-free code (the first is to remove all code). So certainly which means it’s good, proper?

Catching every exception in the program to make it bug free

The problem with catching Exception is expounded to sort inheritance (therefore, the interlude) as a result of we received’t simply be catching all customized exceptions in your utility however an entire heap of Python inbuilt exceptions as effectively, together with some you may not need to be throwing away.

Python inbuilt exceptions have a fancy inheritance construction. Right here’s the newest record from the docs, the place every indentation means inheritance.

Official python3 inbuild exceptions
Don’t you would like Python3 docs had fairly diagrams?

This hierarchy is for a superb cause so you need to use sort inheritance to be intelligent about the way you catch exceptions (see the subsequent level).

What we simply learnt about sort inheritance tells us this implies a TypeError can also be an Exception (its father or mother), so TypeErrors will get caught once we catch Exception. That is most likely positive.

However what about ModuleNotFoundError? Do you really need your program to maintain working if it’s lacking a dependency fully? What about MemoryError? Certainly, you don’t need to flip your again when Python is suffocating in your reminiscence card.

Not solely will you be catching all these wild and fantastic inbuilt exceptions but additionally each customized exception (sure … besides people who derive from BaseException somewhat than Exception).

Is that basically what you wished to do? Maybe, the answer is to catch a number of, particular customized exceptions (which you do by way of tuples) like so:

catch (FileNotFoundError, IsADirectoryError, PermissionError) as e:

This can safely catch a FileNotFoundError however received’t catch a extra harmful OSError, similar to ChildProcessError.

After all, there are situations the place catching all exceptions is what you need to do, however they’re few and much between. It’s additionally essential to notice that the way you deal with the exception is essential right here. In the event you catch all exceptions however then increase that exception once more or use logger.exception(), this isn’t a difficulty.

Some examples if you would possibly need to catch all exceptions:
-In pulling from a queue and dealing with one message at a time, you would possibly use logger.exception() to log the difficulty with out breaking the movement
-As a part of chaos-engineering practices on the service stage, significantly for async providers, you would possibly catch all exceptions, safely shut every part, after which increase or log the exceptions
-Internet scraping or crawling hyperlinks is a unclean process, and infrequently throws all types of errors — in some instances, this requires very broad exception dealing with.

Rule of thumb: Catch as particular of an error as you may. Don’t catch Exception as e until you realize precisely what you’re doing.

I did point out the inbuilt exception hierarchy is helpful. Let’s check out how exceptions can work for us, not towards us.


Do Use Inbuilt Exceptions When It’s Smart

This will likely sound like a counterargument to the sooner do about writing numerous exceptions, however typically it is sensible to make use of a easy inbuilt Python exception like ValueError or TypeError.

Let’s say you’re making a automotive that may be both electric-powered, petrol-powered, or hybrid, and also you need to cross two booleans to create a automotive that specifies the engine sort.

A python electric car class, with two default boolean variables raising a ValueError

This can be a good instance the place ValueError is appropriate. You possibly can’t outline the rule that no less than certainly one of electrical or petrols have to be true in your operate definition (RIP overriding features), so that you’ll have to test this manually.

Notice: This can be a contrived instance that I’ve created to show a degree — don’t use boolean flags like this.

This can be a nice alternative to make use of the inbuilt ValueError, as many packages might strive catch this error and use it. For instance, a program to robotically run parametric exams would possibly run by way of each mixture of true, false, and catch and skip any that return ValueErrors.

Nevertheless, I prefer to go one step additional. We will use sort inheritance in our favour so as to add readability to our code by inheriting our exceptions from a extra particular exception like ValueError. That method, any program that catches ValueError will even catch our exception, however we will add customized code and names.

The same example as above but with a custom exception defined

Should … write … extra … exceptions.

Rule of thumb: All the time inherit from as particular of an inbuild exception as you may. In the event you don’t have any extra info so as to add, simply use the inbuilt exception.

For the complete record of inbuilt Python exceptions and their hierarchy, see the official docs.


Don’t Put Delicate Information in Exception Messages

I need a safe system, so it is sensible I increase CommonPasswordException(f"password: {password} is just too frequent"). (Uncertain what that f means? Check out my article on it).

It’s a superb exception — it’s clear what the exception means, I’ve supplied enough info with it, and it’s particular sufficient it could possibly be safely wrapped in a try-catch and dealt with in a different way if, for instance, we have been extra relaxed with admin passwords than customers.

The problem right here comes all the way down to delicate information. I’ve used an unfair instance right here, so I hope it’s blindingly apparent to you that this isn’t a superb exception. This exception can be spewing raw-text passwords by way of your logs, in responses, into your monitoring software program, and, maybe, into unsavoury arms.

Exceptions for inner use (see observe on the backside about client-facing exceptions) can include technical particulars, similar to user_ids or particular information that triggered the crash, but it surely’s essential to recollect when an exception happens, these messages can be unfold far and huge, by way of logging, reporting, and monitoring software program — and, in case you’re not cautious, probably to your customers.

After all, this all comes all the way down to good software program design, however in a world the place regulation round private information is continually getting stricter, you may by no means be too cautious, and it’s nearly at all times potential to offer priceless error messages with out compromising buyer privateness.

Rule of thumb: Don’t use delicate info in exception messages.

It’s not solely buyer privateness you could fear about, although. Dangerous actors are in all places.

Conspiracy meme from It's Always Sunny in Philidelphia
attempting to safe your prospects information, shield your servers from hackers all whereas constructing options on time

Let’s say you run an internet site the place customers provide a big quantity, and also you calculate the components of this quantity. Let’s name your web site FactoriseMe.com.

You’re about to lift your seed funding, however a competitor pops up out there, NumbersWithinNumbers.com. You’ve examined their product, and whereas it really works, it’s not as quick as yours. And the client expertise is much worse.

One in every of your builders notices that for very massive numbers, your back-end service struggles to compute the components — in actual fact, for very, very massive numbers, the service spends the complete 180 seconds quantity crunching and simply instances out.

You resolve to proceed to enhance your buyer expertise by responding with a pleasant error for the client: “Calculator timed out whereas computing components. Sorry, these ones are exhausting.”

That is nice. Now the client is aware of why the web site failed to search out any components. You stroll into your investor to provide a demo, and abruptly your web site is down. What occurred?

After trying by way of the logs, you discover that between 9 a.m. and 10 a.m., you acquired 1,000 requests with large numbers from an IP simply down the highway, not removed from the NumbersWithinNumbers HQ. These requests overloaded your back-end providers and crashed your web site. How?

You revealed your weak spot. By no means reveal your weak spot. Or no less than by no means reveal weaknesses within the inner workings of your software program to customers. Hackers, opponents, trolls, and the web at massive is full of people that need to break what you’ve constructed. Sure, it’s best to give your customers suggestions about what’s happening, however by no means, ever inform them in regards to the inner workings of your software program.

Rule of thumb: Inform the consumer about what they’ll do, not what occurred.

You’ll see that is frequent in lots of functions: “Please strive once more later.” “If this occurs once more please contact assist.” “Unknown error — we’re sorry, we’re trying into it.”

However this complete factor is getting off subject now, Consumer-focused exceptions are an entire new can of worms. Just remember to take a leaf out of the knight from Monty Python’s book, and pretend that everything is OK.


Don’t Be Inconsistent With the Strictness of Your Code

All these dos and don’t’s are nice in concept, however in the true world, it’s a must to take care of different groups writing providers with all types of exceptions.

Say Crew A is the innovation workforce — they’re mavericks, untamable by your by-the-books VP of engineering. They resolve their code has a radical exception coverage of throwing exceptions on the slightest change (within the subsequent level, you’ll see why this isn’t so radical in any respect). In Crew B, alternatively, you inherit all exceptions from a TeamBException and use them scarcely and solely to indicate critical points.

Ultimately, it’s a must to work on a venture utilizing a library constructed by Crew A and discover that you may’t a lot as depend on a easy print operate with out all types of unusual exceptions shutting down your service. You’re on a deadline, and also you realise the one method you may end your process is to wrap all interfaces with the library with broad try-catch Exception as eclauses and hope for the very best. Not preferrred.

This is the reason it’s essential that your complete codebase accommodates constant exception use and dealing with. Your VP of engineering and tech leads (and everybody) ought to be pushing for constant ideas and practices in relation to exception dealing with as a result of inconsistencies can rapidly construct to grow to be vital technical debt.

Rule of thumb: Be an evangelist for good exception dealing with inside your organization.
If your organization has a coverage on exceptions, comply with it. If it doesn’t, why not write one primarily based on this text?


Do Catch Plenty of Particular Exceptions

Python is a fantastic language — it’s extremely well-liked, has a ton of nice assets for studying, has libraries for every part, and might do something from machine studying to hardware to net improvement.

Maybe for this reason you selected to be taught Python, however for a lot of, we selected Python as a result of we imagine within the values of Python. If that is sounding culty, that’s as a result of it’s. Go to your terminal, and run Python. Then sort import this. You’ll see the manifesto of Python, the 19 (or 20) aphorisms of the Python language, as written by its creator, Tim Peters.

The 19 aphorisms in the manifesto of Python – “The Zen of Python” by Tim Peters

This does have worth past leisure, because it gives an perception into the mission behind Python and provides us tips about the way it was meant for use. The essential line on this case is:

“Errors ought to by no means cross silently. Except explicitly silenced.”

I hope this has been made clear within the above factors. I learn this as: “Don’t catch all exceptions until you actually imply to, and catch specific exceptions typically.”
One other Pythonic idiom in the neighborhood is:

“Apologize, not permission.”

That is the place Python differs dramatically to the normal paradigms of C, Java, and different conventional languages. The Python solution to deal with processes with exceptions is to only go for it and deal with the exceptions if they arrive up.

In C, you would possibly write strains and features of code to test for all preconditions earlier than writing to a file — similar to, does the file exist, does the method have write permissions to the file, and so forth. It’d be unhealthy observe in C to ignore all these instances and simply write on to the file.

In Python, it’s fairly the other. Actually, it’s significantly extra environment friendly in some instances to strive first and deal with exceptions later. Within the above instance, whereas it’s fully potential the file permissions are incorrect (or another challenge), it’s an edge case. A majority of the time, you’ll be writing to a file that exists and you’ve got permission to entry, so why waste priceless processing energy checking for file permissions?

The Pythonic solution to deal with that is to wrap the write in a try-catch and catch solely the particular exceptions/edge instances which can be more likely to come up — and to deal with them appropriately. Typically, the write will succeed, and any logic within the catch block can be missed fully. Within the case the place one thing does go incorrect, we nonetheless deal with it.

Take a look at this Stack Overflow thread for a couple of extra examples.

Rule of thumb: Don’t spend extreme quantities of time checking for preconditions when you may predict and catch particular exceptions as an alternative (i.e., apologize, not permission).


However What About Shopper-Going through Exceptions?

Shopper exceptions are nice — however very completely different.

Half 2 of this information will cowl how client-facing exceptions differ from inner exceptions and can embody some tips about architecting a easy and efficient exception construction. Comply with me on codementor, medium or subscribe to my substack to remain tuned for half 2.

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