# Error Handling

Terms defined: YAML, assertion, catch (an exception), comma-separated values, defensive programming, exception, external error, internal error, logging, raise (an exception), rotating file.

When Mister Safety Catch Is Not On, Mister Crossbow Is Not Your Friend.

Terry Pratchett

We are imperfect people living in an imperfect world. People will misunderstand how to use our programs, and even if we test thoroughly as described in , those programs will probably still contain bugs. We should therefore plan from the start to detect and handle errors.

## Handling Errors

Internal errors, are mistakes in the program itself, such as calling a function with None instead of a list. External errors are usually caused by interactions between the program and the outside world: a user may mis-type a filename, the network might be down, and so on.

When an internal error occurs, the only thing we can do in most cases is report it and halt the program. If a function has been passed None instead of a valid list, for example, the odds are good that one of our data structures is corrupted. We can try to guess what the problem is and take corrective action, but our guess will often be wrong and our attempt to correct the problem might actually make things worse. When an external error occurs, on the other hand, we don't always want the program to stop: if a user mis-types her password, prompting her to try again is friendlier than halting and requiring her to restart the program.

Most modern programming languages use exceptions for error handling. In Python, for example, exceptions are handled using the keywords try and except. If nothing unexpected happens inside the try block, the except block isn't run, but if something does go wrong, the program jumps immediately to the body of the except.

We often want to know exactly what went wrong, so Python and other languages store information about the error in an object (which is also called an exception). We can catch an exception and inspect it as follows:

for denom in [-5, 0, 5]:
try:
result = 1/denom
print(f'1/{denom} == {result}')
except Exception as error:
print(f'{denom} has no reciprocal: {error}')

1/-5 == -0.2
0 has no reciprocal: division by zero
1/5 == 0.2


Most languages also allow us to specify what kind of exception we want to catch. For example, we can write code to handle out-of-range indexing and division by zero in Python separately:

numbers = [-5, 0, 5]
for i in [0, 1, 2, 3]:
try:
denom = numbers[i]
result = 1/denom
print(f'1/{denom} == {result}')
except IndexError as error:
print(f'index {i} out of range')
except ZeroDivisionError as error:
print(f'{denom} has no reciprocal: {error}')

1/-5 == -0.2
0 has no reciprocal: division by zero
1/5 == 0.2
index 3 out of range


So where do exceptions come from? The answer is that programmers can raise them explicitly:

for number in [1, 0, -1]:
try:
if number < 0:
raise ValueError(f'no negatives: {number}')
print(number)
except ValueError as error:
print(f'exception: {error}')

1
0
exception: no negatives: -1


We can define our own exception types, and many libraries do, but the built-in types are enough to cover common cases.

One final note is that exceptions don't have to be handled where they are raised: in fact, their greatest strength is that they allow long-range error handling. If an exception occurs inside a function and there is no except for it there, Python checks to see if whoever called the function is willing to handle the error. It keeps working its way up through the call stack until it finds a matching except. If there isn't one, Python takes care of the exception itself.

This behavior is designed to support a pattern called "throw low, catch high": write most of your code without exception handlers, since there's nothing useful you can do in the middle of a small utility function, but put a few handlers in the outermost functions of your program to catch and report all errors. This also makes libraries more reusable: different applications will want to handle errors in different ways, so the library should just report the problem.

### Kinds of errors

The "if then raise" approach is sometimes referred to as, "Look before you leap," while the try/except approach is, "It's easier to ask for forgiveness than permission." The first approach is more precise, but has the shortcoming that programmers can't anticipate everything that can go wrong when running a program, so there should always be an except somewhere to deal with unexpected cases.

The one rule we should always follow is to check for errors as early as possible so that we don't waste the user's time. Few things are as frustrating as being told at the end of an hour-long calculation that the program doesn't have permission to write to an output directory. It's a little extra work to check things like this up front, but the larger your program or the longer it runs, the more useful those checks will be.

## Defensive Programming

The first step in building confidence in our programs is to assume that mistakes will happen and guard against them. This is called defensive programming, and the most common way to do it is to add assertions to our code so that it checks itself as it runs. An assertion is a statement that something must be true at a certain point in a program. When the program runs, it checks the assertion's condition. If it's true, the program does nothing; if it's false, it halts and prints a user-defined error message. For example, this Python code halts as soon as the loop encounters a negative word frequency:

frequencies = [13, 10, 2, -4, 5, 6, 25]
total = 0.0
for freq in frequencies[:5]:
assert freq >= 0.0, 'Word frequencies must be non-negative'
total += freq
print('total frequency of first 5 words:', total)


Programs intended for widespread use are typically full of assertions: 10–20% of the code they contain is there to check that the other 80–90% is working correctly.

## Writing Useful Error Messages

This is not a useful error message:

OSError: Something went wrong, try again.


It doesn't provide any information on what went wrong, so it is difficult for the user to know what to change next time. A slightly better message is:

OSError: Unsupported file type.


This message tells us the problem is with the type of file we're trying to process, but it still doesn't tell us what file types are supported, which means we have to rely on guesswork or read the source code. Telling the user that a file isn't a CSV file makes it clear that the program only works with files of that type, but since we don't actually check the content of the file, this message could confuse someone who has comma-separated values saved in a .txt file. An even better message would therefore be:

OSError: File must end in .csv


This message tells us exactly what the criteria are to avoid the error.

Error messages are often the first thing people read about a piece of software, so they should be its most carefully written documentation. A web search for "writing good error messages" turns up hundreds of hits, but recommendations are often more like gripes than guidelines and are usually not backed up by evidence. What research there is gives us the following rules Becker2016:

Tell the user what they did, not what the program did.
Putting it another way, the message shouldn't state the effect of the error, it should state the cause.
Be spatially correct
I.e., point at the actual location of the error. Few things are as frustrating as being pointed at line 28 when the problem is on line 35.
Be as specific as possible.
For example, "file not found" is very different from "cannot open file", since the latter could mean "no permissions" or many other things.
Write for your audience's level of understanding.
For example, error messages should never use programming terms more advanced than those you would use to describe the code to the user.
Do not blame the user, and do not use words like fatal, illegal, etc.
The former can be frustrating—in many cases, "user error" actually isn't—and the latter can make people worry that the program has damaged their data, their computer, or their reputation.
Do not try to make the computer sound like a human being.
In particular, avoid humor; very few jokes are funny on the dozenth re-telling, and most users are going to see error messages at least that often.
Use a consistent vocabulary.
This rule can be hard to enforce when error messages are written by several different people, but putting them all in one module makes review easier.

That last suggestion deserves a little elaboration. Most people write error messages directly in their code:

if fname[-4:] != '.csv':
raise OSError(f'{fname}: File must end in .csv')


A better approach is to put all the error messages in a dictionary:

ERRORS = {
'not_csv_suffix' : '{fname}: File must end in .csv',
'config_corrupted' : '{config_name} corrupted',
# ...more error messages...
}


and then only use messages from that dictionary:

if fname[-4:] != '.csv':
raise OSError(ERRORS['not_csv_suffix'].format(fname=fname))


Doing this makes it much easier to ensure that messages are consistent. It also makes it much easier to give messages in the user's preferred language:

ERRORS = {
'en' : {
'not_csv_suffix' : '{fname}: File must end in .csv',
'config_corrupted' : '{config_name} corrupted',
# ...more error messages in English...
},
'fr' : {
'not_csv_suffix' : '{fname}: Doit se terminer par .csv',
'config_corrupted' : f'{config_name} corrompu',
# ...more error messages in French...
}
# ...other languages...
}


The error report is then looked up and formatted as:

ERRORS[user_language]['not_csv_suffix'].format(fname=fname)


where user_language is a two-letter code for the user's preferred language.

## Logging

Something else you can design into your system to make your life easier later on is logging. Instead of writing print statements like this:

def extrapolate(basis, case):
print "entering extrapolate..."
trials = count_basis_width(basis)
if not trials:
print "...no trials!"
raise InvalidDataException("no trials")
print "...running", len(trials), "trials"
result = run_trial(trials[0])
for t in range(1, len(trials)):
result = max(result, run_trial(trials[i]))
print "...exiting extrapolate with", result


you use your language's logging library:

import logging

def extrapolate(basis, case):
logging.debug("entering extrapolate...")
trials = count_basis_width(basis)
if not trials:
logging.warning("...no trials!")
raise InvalidDataException("no trials")
logging.debug(f"...running {len(trials)} trials")
result = run_trial(trials[0])
for t in range(1, len(trials)):
result = max(result, run_trial(trials[i]))
logging.debug(f"...exiting extrapolate with {result}")


At first glance this is just more verbose. The benefit, though, is that your messages are now divided into categories. If you want to get all the messages you put:

logging.basicConfig(level=logging.DEBUG)


somewhere near the start of your program. The DEBUG option identifies the lowest-level messages in your program—the ones you probably only want to see when you're trying to figure out what's gone wrong. In order, the more important levels in most logging libraries are INFO, WARNING, ERROR, and CRITICAL. If you only want messages at the WARNING level and above, you change the configuration to:

logging.basicConfig(level=logging.WARNING)


so that DEBUG and INFO messages aren't printed.

A logging library allows you to control how much your program tells you about its execution by making one change, in one place. It also means that you can leave these messages in your code, even when you release it—there's no more commenting out print statements only to add them back in later. (And no more embarrassment from inappropriately-worded messages that weren't commented out but should have been before your demo…)

Logging libraries also let you divide messages into groups by component, so that you can (for example) turn on debugging-level messages from the database interface but only see errors and above from the image processing functions. They also let you control where your messages are sent. By default, they go to the screen, but you can send them to a file instead simply by changing the configuration:

logging.basicConfig(level=logging.ERROR,
filename="/tmp/mylog.txt",
filemode="append")


This is handy if it takes your program a while to get to the point where the error occurs. It's also handy if you don't know whether your program contains an error or not: if you leave logging turned on every time you run it, then whenever it does something unexpected (like crashing), you will have at least some idea of what it was doing beforehand.

### Logging for security

One of the recommendations in was to log actions to help you find suspicious activity. When you do this, make sure the log records who, what, and when; in particular, make sure you have a record of every time permissions were changed or new accounts were created.

Most logging libraries also support rotating files, i.e., they will write to log.1 on the first day, log.2 on the second day, and so on until they reach (for example) log.7, then wrap around and overwrite log.1. Web servers and other long-lived programs are usually set up to do this so that they don't fill up the disk with log information. It's all straightforward to set up, and once it's in place, it gives you a lot more insight into what your program is actually doing.

### Parsers as a sign of bad design

You will sometimes inspect logs yourself, but you will also frequently want to search them for patterns. All of the logs you produce should therefore be in a machine-readable format like CSV, JSON, or YAML; you can easily write a small program to pretty-print the data you want for manual inspection.

This guideline is one instance of a more general design rule. The world has more data formats than it needs, so if anyone has to write a parser to analyze what your program produces, you should probably be producing something else.