I often run into situations where an error occurs, an exception is caught, and some action is taken before the exception is re-raised so that higher levels of the program can deal with it. Exactly how the exception is re-raised can have a big impact on how much information is available to someone trying to debug a problem. I got this wrong as a newbie, and I’ve seen even experienced python programmers get tripped up by these subtleties so I thought I would try to post a clear example. Consider the following program:

def bar():
    raise Exception("Oops!")

def foo():
    bar()

def dinkum():
    foo()

def fair():
    try:
        dinkum()
    except Exception as e:
        print "Something failed!"
        raise e

fair()

As you can see I’ve created a few methods in order to have a stack trace to look at, and raised an exception in the last one to be called, foo(). That exception is caught in fair() and some information is printed before the exception is re-raised by the statement “raise e.” This syntax looks pretty reasonable, so let’s run it and see what happens:

mark@viking:~/workspace$ python raise_test1.py
 Something failed!
 Traceback (most recent call last):
 File "raise_test.py", line 21, in 
 fair()
 File "raise_test.py", line 19, in fair
 raise e
 Exception: Oops!

Hmm. Looks like we’re missing some useful information. The exception was raised on line 6 but the stack trace begins on line 19. Lets change the program slightly and run it again:

def bar():
    raise Exception("Oops!")

# ... snip for brevity ...

def fair():
    try:
        dinkum()
    except Exception as e:
        print "Something failed!"
        raise

fair()

The only change here is that now we’re simply using “raise” rather than re-raising the exception by name with “raise e.” The output is quite different:

mark@viking:~/workspace$ python raise_test2.py
Something failed!
Traceback (most recent call last):
  File "raise_test.py", line 21, in 
    fair()
  File "raise_test.py", line 16, in fair
    dinkum()
  File "raise_test.py", line 12, in dinkum
    foo()
  File "raise_test.py", line 9, in foo
    bar()
  File "raise_test.py", line 6, in bar
    raise Exception("Oops!")
Exception: Oops!

That’s more like it. Now we can see the whole stack trace and see that the error originated on line 6 in the call to bar(). This would be helpful stuff to have in a debugging situation. So what’s going on here?

In some languages and runtimes exception objects carry their own stack trace information, but in python the interpreter is responsible for capturing the state of the call stack when an exception occurs. To access this information a program can call the sys.exc_info() method, which returns a tuple with the last traceback in item 3. We can use this method to get a better look at the state of the call stack when we catch an exception:

import sys
import traceback

def bar():
    raise Exception("Oops!")

# ... snip for brevity ...

def fair():
    try:
        dinkum()
    except Exception as e:
        print "Something failed!"
        traceback.print_tb(sys.exc_info()[2])
        raise
try:
    fair()
except Exception as e:
    print "It sure looks like it!"
    traceback.print_tb(sys.exc_info()[2])

The print_tb() method defined in the traceback module is a handy way to get a prettyprint of the traceback object. When run this code produces the following:

mark@viking:~/workspace$ python raise_test3.py
Something failed!
File "raise_test.py", line 15, in fair
dinkum()
File "raise_test.py", line 11, in dinkum
foo()
File "raise_test.py", line 8, in foo
bar()
File "raise_test.py", line 5, in bar
raise Exception("Oops!")
It sure looks like it!
File "raise_test.py", line 21, in
fair()
File "raise_test.py", line 15, in fair
dinkum()
File "raise_test.py", line 11, in dinkum
foo()
File "raise_test.py", line 8, in foo
bar()
File "raise_test.py", line 5, in bar
raise Exception("Oops!")

The interpreter creates a new traceback when the exception is initially raised on line 5. As the stack is unwound back to the first “except” block each call point (method name and line number) is recorded on the traceback. As you can see above when we first catch the exception there are four call points on the traceback. When we catch it a second time after using “raise” to re-raise it, a fifth call point has been added. The crucial thing is that “raise” did not prompt the interpreter to start a new stack trace. What happens when instead of “raise” we use “raise e” to re-raise the exception?

Something failed!
File "raise_test.py", line 15, in fair
dinkum()
File "raise_test.py", line 11, in dinkum
foo()
File "raise_test.py", line 8, in foo
bar()
File "raise_test.py", line 5, in bar
raise Exception("Oops!")
It sure looks like it!
File "raise_test.py", line 21, in
fair()
File "raise_test.py", line 19, in fair
raise e

In this case the interpreter started a new stack trace when it saw the “raise e” statement. In fact this is the exact same behavior you’d get by just raising a whole new exception with the statement “raise Exception(“Sorry, no traceback for you”).”

So when would you actually want to use “raise e?” To the extent that ‘e’ is an exception that you’ve just caught, never. Doing so will always lose the traceback information from the previous frames on the stack, and obfuscate the point of origin of the original error. However, it can sometimes make sense to translate one exception into another. Something like the following isn’t an unknown pattern:

try:
    some_vulnerable_op()
except LowLevelException as e:
    very_important_cleanup()
    raise HighLevelException("Low level stuff failed")

While this is a valid pattern I’m not a huge fan of it unless information specific to the inner exception is logged or otherwise made available. There are a number of ways you could accomplish this goal, but we can leave that for another time. For now, suffice it to say that exception handling complicates reasoning about the control flow and behavior of a program, so it makes sense to keep your exception handling patterns as simple as you can.

Many python programmers are familiar with Celery due to its integration with django, a hugely popular web framework. A colleague recommended it to me recently when the need to schedule pruning of old logstash indices came up during a development discussion. As I soon discovered, Celery is a fast and powerful way to turn any python callable into a scheduled task, or message-processing worker. We’re now using it to clip the aforementioned indices, as well as perform other tasks such as injecting work payloads into a distributed queue.

Celery is a python package, so the easiest way to get it into your virtualenv (or Docker container, or vagrant env) is simply:

pip install celery

The gzipped tarball is only 1.3 megs, and while there are a few other dependencies (billiard, kombu, pytz) the installation takes less than a minute. Once it is complete you’re ready to create a task. Let’s start with a simple script that downloads a web page and saves it to a file. We’ll create the script and set it up to run every ten minutes. For downloading the page we’ll use the awesome requests package. First, the script itself.

from datetime import timedelta
from celery import Celery
import requests

app = Celery('page_saver')
app.conf.update(
    BROKER_URL='redis://localhost',
    CELERY_TASK_SERIALIZER='json',
    CELERY_ACCEPT_CONTENT=['json'],
    CELERYBEAT_SCHEDULE={
        'save_page':{
            'task':'page_saver.save_page',
            'schedule': timedelta(minutes=10)
        }
    }
)

@app.task(name='page_saver.save_page')
def save_page(url, file_name):
    response = requests.get(url)
    if response.status_code == requests.codes.ok:
        f = open(file_name, 'wb')
        f.write(response.text)
        f.close()

After importing Celery and the requests package the first thing this script does is create the app object and initialize it. The Celery app object marks this module as a Celery app, and the only parameter we’re passing here is the name, which must match the name of the module. In this example the script would be saved on the file system as “page_saver.py”.

The call to app.conf.update is one way of passing in configuration data to the Celery object. There are several others, and in general most of the configuration options and settings are well beyond the scope of this post. You can find a good intro and links to more information here.

The first setting, ‘BROKER_URL’, specifies the pipeline that Celery will use for passing messages between clients and workers. I’m using redis here because I always have it lying around, but you can also use RabbitMQ, or a database, although that isn’t recommended in production.

The next two settings, ‘CELERY_TASK_SERIALIZER’, and ‘CELERY_ACCEPT_CONTENT’ instruct Celery to use json encoding when talking to the task as a client, and when accepting messages in the task as a server. Without these settings Celery will also allow pickle (and warn on startup), and nobody should receive pickle from a network port unless it is with a pair of tweezers. In any event, pickle is deprecated so json is the way to go for that reason as well.

The last setting contains the schedule on which our task will be executed. This requires some explanation, and it is easiest to do it in tandem with an explanation of the actual task method. As you can see in the script we define a method named save_page, and decorate it with “@app.task()”, passing in a name for the task. The naming seems pretty much arbitrary, but I like the suggested convention of “appname.taskname.”

The decorator turns this callable into the entry point for a Celery task with the given name. The script could contain many more tasks, each being individually callable, schedulable, etc., but for this example one will suffice, and I think I like a 1:1 mapping between app and task anyway. The actual implementation of the save_page method is self-explanatory, and contains no error handling or retry logic for brevity’s sake.

With the task defined the script above constitutes a complete Celery worker, and it can be run at any time using Celery to activate it and send it a message. For example, save the script to a folder and then cd into that folder and do this:

celery -A page_saver worker --loglevel=INFO

You should see a bunch of output indicating that celery is starting the task. Once it is up and running open up another terminal and start the python interpreter. Enter the following statements:

>>>from page_saver import save_page
>>>save_page.delay('https://news.ycombinator.com/','hn.txt')

The task object, save_page, serves as a proxy to call itself in the worker process running in the other terminal session. The delay method executes the call asynchronously, and Celery provides additional methods to access the results when they become available. In this case the only result is the file output.

So, that brings us back to the schedule. To have a scheduled task we need a scheduler – a client to wake up and essentially call delay() on our task when a timer expires. Fortunately Celery includes celery-beat, which does exactly that. You can run celery-beat as a stand-alone service and use the same schedule configuration schema, but you can also run it in tandem with celery using the -B command line switch:

celery -A page_saver worker -B --loglevel=INFO

The schedule configuration in the example script establishes a single schedule named “save_page” and tells it to run the task named “page_saver.save_page” every ten minutes using a timedelta. You can also set the ‘schedule’ field to a crontab object and use all of the same options available to a cron job, so there is a lot of flexibility.

And that’s about it for this simple example, which incidentally is complete enough to handle a lot of system maintenance tasks. Add some error-handling, logging and notification and you’re ready to go. If your needs go beyond simple repeatable tasks you’ll find Celery has a lot of options for executing distributed workloads and returning results asynchronously. Have fun!