## Debugging with q

Currently a lot of my time is spent on writing services controlling models or generating reports. In these systems, the control flow moves from the user facing HTTP server to a task queue (think of celery) that handles the actual task. Depending on the setup the log output will be scattered over multiple files and has to be painstakingly pieced together - one reason, I found that debugging these kinds of applications becomes tiring quite fast.

From his talk, I gather Ka-Ping Yee had a similar problem when he created q. This small python package is now my first import for a quick and dirty debugging session. To use q, import it and call it like function:

import q as qq # qq is easier to search for
qq("foo", "bar")


When you use q, you may wonder where the output went? The beauty of q is that it writes its output to the file \$TMPDIR/q. Thereby the debugging output is collected in a single file and is not mixed with other output.

q can also easily be embedded into expressions, removing the need to rewrite the code when debugging. Depending on the chosen operator q prints the full expression or the next expression only:

foo(qq|1 + 2 + 3) # prints 5
foo(qq/1 + 2 + 3) # prints 1


Another feature of q is its ability to trace function calls. Used as a decorator, it prints out all arguments and the return value of function. With nested function calls it is particular helpful because it indents the output nicely. Unfortunately, the way q is implemented the renamed import breaks the @q decorator. However, using the trace function explicitly works just fine:

@qq.trace
def say_hello(name):
print("hello {}".format(name))


To sum up, q is an incredibly useful package when debugging and I highly recommend to check it out.