Effective PyCharm Transcripts
Chapter: Performance and profiling
Lecture: Concept: Profiling
Login or purchase this course to watch this video and the rest of the course contents.
0:00 Let's review the core concepts around profiling with PyCharm and Python. Once you have a run configuration setup in PyCharm,
0:10 you just go over and hit the profile buttons, you can right click on the thing instead of saying run in this case program you
0:16 can say profile program or you can hit that little clock stopwatch looking thing next to the play button and debug button that you're familiar with.
0:25 Once you click profile you'll see that PyCharm automatically starts up what's called C profile This is a profiler within Python, that is a C based one.
0:35 It's pretty low overhead and works really well for understanding where our program is spending its
0:40 time once it's done running once you exit the program or if it's some kind of program that just runs and then stops,
0:49 you'll get this 'pstat' file opened up here and it opens up to the
0:54 statistics page where you can basically sort through and look at the different elements. It starts out by showing your own time.
1:01 I find that not to be very useful. I would focus in on the overall time spent in any given part of our program
1:08 for our program, we saw compute analytics learn run query search, get records. All of these were the things that were interesting for us to look
1:17 at. My personal favorite way to get a quick overview of where we're spending our
1:22 time is actually go over to the call graph and have this visual representation. I love how the colors in the flow,
1:29 show your where you're spending your time, for example, we're calling. Go and go is calling Get records and get records
1:35 Calling. Run query that right now is the path that we really need to be worried about when you're back over in that spreadsheet looking thing.
1:42 You don't see that nearly as obviously as you do here. So we are called program it starts up program calls main, main calls, go go calls,
1:51 three functions, compute analytics. Get records and search. And again the color matters when we see green.
2:00 Well that means that this is fast enough when we see yellow relatively speaking,
2:05 it's not horrible necessarily compared to the other parts of the program, but it's something you should pay attention to when it's red.
2:12 You definitely should, you know, focus in on that and see what's going on. Look at this graph, The colors and the flow are actually and really,
2:19 really good for you to be able to just jump right in and figure out what's going on. We can also navigate from both the statistics and call graph view.
2:29 If we go to the statistics one, we can right click and say show on the call graph and jump right into that
2:34 picture where we should be or we can say navigate over to the source or even double clicking get records right here.
2:41 We'll just take us there when we're over in the call graph we can select and
2:46 then right click one of these boxes and say navigate to source. In a final word of warning as we close out this section.
2:53 This chapter on profiling, profilers and debuggers. But profilers maybe even more so can have significant effects that actually alter how our program
3:04 behaves in quantum mechanics. We have this really weird principle that if you observe something
3:09 you may actually change it. The act of observing it may be changing it and
3:14 profilers have this sort of quantum mechanics feel to them there are certain parts of your
3:20 program that when run outside of a profiler it could be medium slow but if you
3:25 run it in the profiler it could actually be a lot slower and the profiler disabled This is really slow and you know,
3:31 in practice that may not always be true. One of those scenarios is where you're doing many,
3:36 many, many loops in Python code versus calling something external like a database query or
3:42 calling requests. The overhead of calling requests or a database query once is nearly zero
3:47 But if you're calling a loop thousands and thousands of times there's a little bit
3:52 of overhead for each function call from the profiler and that can add up. So just be aware that this might not be 100%.
4:00 The truth of how much time is being spent by each function but it's still really valuable information help you. Dive in and speed up your app.