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What is the best practice for handling unexpected errors in an AWS Step Function using Python Lambdas?

Step Functions are AWS structures that control the flow of lambdas (or other events). All my lambdas use Python (but Lambdas can use most major languages). Throughout the process my step function sends status updates back to the client (the client triggered it via API). Let's say it progresses through these updates: Started -> In Progress -> Finishing -> Done. For handled errors it will send an 'Error' status back to the client. So the client could see a timeline like this: Started -> In Progress -> Errored. This is ideal - so the user knows the process has stopped.
But when there are unexpected/unhandled errors the client never really knows and the timeline might sit at 'In Progress' indefinitely - the user doesn't know what happened. So I started looking into the built-in Step Function error handling. I like this option because I can create a 'Catch' function for each lambda or event where I can communicate back to the client if there is an error. The downside to this was that it really made the step function template/design messy see the before/after screenshots below.
BEFORE---------------
AFTER---------------
The template code that generates these graphs doesn't look much better. So I considered an alternative which seems similarly messy. I could add a single try/except block within each lambda for the entire lambda - to catch any/all errors. For example:
def lambda_handler(event, context):
try:
#Execute function tasks
except:
#Communicate back to client that there was an error
Similar to the step function 'Catch' functions this would ensure that I catch and communicate any error. But this seems like a bad idea just because of what it is (adding blanket/blind try/except).
So right now I'm stuck between messy/repeated code and try/except-ing everything. Am I implementing step function 'Catch' incorrectly? Am I missing a better way to handle unknown Python errors? Is there another approach entirely?

As #stijndepestel pointed out, having a catch-all error check is a good idea.
What I do in my Python Lambda functions is this: I have a custom router class, which besides route managing, it handles all errors. If the error inherits from a base error class that I've created, then it's custom error that I threw, and those are assigned special info when I created them that automatically gets formatted when they are converted into strings. The router sends that back to the client if possible.
But if the error is some unknown/unexpected one, then the router prints it with as much detail as possible to CloudWatch Logs, and then returns a generic "500 Internal Server Error" message to the client.
I'd probably set it up in the future to notify me by email or something like that when such errors occur, so that I can take action quickly.

I don't see why having a try-catch system for the entirety of your lambda is such a bad idea. It just ensures that you're always in control of how errors are communicated to the caller of the lambda function.
Imagine for example a lambda that serves as a back-end for an HTTP API, it would be better practice to have an try-catch for everything, so you can communicate to your clients what the problem was, or at least provide a generic HTTP 500 type error. In this case, the functions will be called by AWS Step Functions, which means you're error messages don't have to be user friendly, but the fact you might want to be in control of how unexpected exceptions are handled, is still the same in my book.

What are the possible exits of a python script and how to catch them?

I want to write a python script which ensures in any case that a database connection will be closed. (Please note that I'm not sure if I used the correct terms for everything described below.)
I could think of the following situations to end the script:
The script runs without any problem to its end.
The script is stopped by an raised exception.
The script is stopped while receiving a SIGTERM.
The script is stopped while receiving a SIGKILL.
What would be the best method to ensure that the database connection will be closed in any case. It would be nice if you could point out where the strengths and boundaries of the with and finally statements are.
As this question has a more theoretical interest no minimal code example is given. Please also node that it doesn't have to be a database connection I'm generally interested in the possibilities.
Thank you in advance.
Best,
Christian

One possibility is the atexit module. But it is cleaner to use try:/finally:, or even better make a context manager so that your connection object can be used in a with: statement.
By the way, another way an exit can happen is that the sys.exit() function is called. Internally, even sys.exit() works by raising an exception of type SystemExit, so with: statements and finally: handlers will still be called.
As the atexit documentation points out, none of these will be called if the program is exited with os._exit().

What is the proper way to handle (in python) IOError: [Errno 4] Interrupted system call, raised by multiprocessing.Queue.get

When I use multiprocessing.Queue.get I sometimes get an exception due to EINTR.
I know definitely that sometimes this happens for no good reason (I open another pane in a tmux buffr), and in such a case I would want to continue working and retry the operation.
I can imagine that in some other cases The error would be due to a good reason and I should stop running or fix some error.
How can I distinguish the two?
Thanks in advance

The EINTR error can be returned from many system calls when the application receives a signal while waiting for other input. Typically these signals can be quite benign and already handled by Python, but the underlying system call still ends up being interrupted. When doing C/C++ coding this is one reason why you can't entirely rely on functions like sleep(). The Python libraries sometimes handle this error code internally, but obviously in this case they're not.
You might be interested to read this thread which discusses this problem.
The general approach to EINTR is to simply handle the error and retry the operation again - this should be a safe thing to do with the get() method on the queue. Something like this could be used, passing the queue as a parameter and replacing the use of the get() method on the queue:
import errno
def my_queue_get(queue, block=True, timeout=None):
while True:
try:
return queue.get(block, timeout)
except IOError, e:
if e.errno != errno.EINTR:
raise
# Now replace instances of queue.get() with my_queue_get(queue), with other
# parameters passed as usual.
Typically you shouldn't need to worry about EINTR in a Python program unless you know you're waiting for a particular signal (for example SIGHUP) and you've installed a signal handler which sets a flag and relies on the main body of the code to pick up the flag. In this case, you might need to break out of your loop and check the signal flag if you receive EINTR.
However, if you're not using any signal handling then you should be able to just ignore EINTR and repeat your operation - if Python itself needs to do something with the signal it should have already dealt with it in the signal handler.

Old question, modern solution: as of Python 3.5, the wonderful PEP 475 - Retry system calls failing with EINTR has been implemented and solves the problem for you. Here is the abstract:
System call wrappers provided in the standard library should be retried automatically when they fail with EINTR , to relieve application code from the burden of doing so.
By system calls, we mean the functions exposed by the standard C library pertaining to I/O or handling of other system resources.
Basically, the system will catch and retry for you a piece of code that failed with EINTR so you don't have to handle it anymore. If you are targeting an older release, the while True loop still is the way to go. Note however that if you are using Python 3.3 or 3.4, you can catch the dedicated exception InterruptedError instead of catching IOError and checking for EINTR.

How to handle exceptions with google app engine

I've been using GAE for more than a year now, and one of the most difficult things for me to deal with is the fact that my otherwise well written code may occasionally raise an exception because of a GAE hiccup.
I already have a decent procedure for unhandled exceptions. My custom request handler presents a nice error page and administrators get an email. This, however, is a bad user experience.
What I want to do is to handle exceptions so I can immediately take the appropriate action and prevent some generic error page.
My questions are:
What exceptions should I catch?
Where should I catch them?
I realize that a full answer to this is not practical, but I'm looking for some best practices for the most common situations.
For exceptions that I should catch, I sometimes see exceptions that are not on the official list. For example, I've received an UnknownError.
For where to catch exceptions, I wonder if I should catch them in each get/post method. Something like this:
def get(self):
try:
# normal get processing
except SomeException:
# redirect to the same page to try again and fix any data if necessary
I'm surprised there is not more information out there about this as this is an important aspect of any GAE app. There are some good articles here and here, but these don't answer my questions.

What exceptions should I catch?
That depends upon what level of error catching you're going for. From my experience catching the errors in the official list and linked articles will get you a very high level of error catching. If you need to go above and beyond that putting in a generic except would be easier than trying to predict unknown errors.
Where should I catch them?
The most likely place(s) for GAE errors is when interacting with the db, so setting some try-except blocks around there if you haven't will give you a good return on your effort for dealing with GAE-issue error handling.
Besides the advice of your linked articles you can also think about putting the failed operations into a task queue. Each task will automatically retry 5 times before failing which can give you some ability to ride out datastore switches or other service interruptions if you don't need immediate feedback from the operation.

Python Daemons - Program Structure and Exception Control

I've been doing amateur coding in Python for a while now and feel quite comfortable with it. Recently though I've been writing my first Daemon and am trying to come to terms with how my programs should flow.
With my past programs, exceptions could be handled by simply aborting the program, perhaps after some minor cleaning up. The only consideration I had to give to program structure was the effective handling of non-exception input. In effect, "Garbage In, Nothing Out".
In my Daemon, there is an outside loop that effectively never ends and a sleep statement within it to control the interval at which things happen. Processing of valid input data is easy but I'm struggling to understand the best practice for dealing with exceptions. Sometimes the exception may occur within several levels of nested functions and each needs to return something to its parent, which must, in turn, return something to its parent until control returns to the outer-most loop. Each function must be capable of handling any exception condition, not only for itself but also for all its subordinates.
I apologise for the vagueness of my question but I'm wondering if anyone could offer me some general pointers into how these exceptions should be handled. Should I be looking at spawning sub-processes that can be terminated without impact to the parent? A (remote) possibility is that I'm doing things correctly and actually do need all that nested handling. Another very real possibility is that I haven't got a clue what I'm talking about. :)
Steve

Exceptions are designed for the purpose of (potentially) not being caught immediately-- that's how they differ from when a function returns a value that means "error". Each exception can be caught at the level where you want to (and can) do something about it.
At a minimum, you could start by catching all exceptions at the main loop and logging a message. This is simple and ensures that your daemon won't die. At the main loop it's probably too late to fix most problems, so you can catch specific exceptions sooner. E.g. if a file has the wrong format, catch the exception in the routine that opens and tries to use the file, not deep in the parsing code where the problem is discovered; perhaps you can try another format. Basically if there's a place where you could recover from a particular error condition, catch it there and do so.

The answer will be "it depends".
If an exception occurs in some low-level function, it may be appropriate to catch it there if there is enough information available at this level to let the function complete successfully in spite of the exception. E.g. when reading triangles from an .stl file, the normal vector of the triangle it both explicitly given and implicitly given by the sequence of the three points that make up the triangle. So if the normal vector is given as (0,0,0), which is a 0-length vector and should trigger an exception in the constructor of a Normal vector class, that can be safely caught in the constructor of a Triangle class, because it can still be calculated by other means.
If there is not enough information available to handle an exception, it should trickle upwards to a level where it can be handled. E.g. if you are writing a module to read and interpret a file format, it should raise an exception if the file it was given doesn't match the file format. In this case it is probably the top level of the program using that module that should handle the exception and communicate with the user. (Or in case of a daemon, log the error and carry on.)