This is an overview of the tools and practices I've used for debugging or profiling purposes. This is not necessarily complete, there are so many tools so I'm listing only what I think is best or relevant. If you know better tools or have other preferences, please comment below.
Yes, really. Can't stress enough how important it is to have adequate logging in your application. You should log important stuff. If your logging is good enough, you can figure out the problem just from the logs. Lots of time saved right there.
If you do ever litter your code with print statements stop now. Use logging.debug instead. You'll be able to reuse that later, disable it altogether and so on ...
Sometimes it's better to see what gets executed. You could run step-by-step using some IDE's debugger but you would need to know what you're looking for, otherwise the process will be very slow.
python -mtrace --trace script.py
This will make lots of output (every line executed will be printed so you might want to pipe it through grep to only see the interesting modules). Eg:
python -mtrace --trace script.py | egrep '^(mod1.py|mod2.py)'
If you're feeling adventurous then you could try smiley - it shows you the variables and you can use it to trace programs remotely.
Alternativelly, if you want very selective tracing you can use aspectlib.debug.log to make existing or 3rd party code emit traces.
Very basic intro, everyone should know this by now:
import pdb pdb.set_trace() # opens up pdb prompt
try: code that fails except: import pdb pdb.pm() # or pdb.post_mortem()
Or (press c to start the script):
python -mpdb script.py
Once in the REPL do:
- c or continue
- q or quit
- l or list, shows source at the current frame
- w or where, shows the traceback
- d or down, goes down 1 frame on the traceback
- u or up, goes up 1 frame on the traceback
- <enter>, repeats last command
- ! <stuff>, evaluates <stuff> as python code on the current frame
- everything else, evaluates as python code if it's not a PDB command
Drop in replacements for pdb:
- ipdb (pip install ipdb) - like ipython (autocomplete, colors etc).
- pudb (pip install pudb) - curses based (gui-like), good at browsing sourcecode.
- pdb++ (pip install pdbpp) - autocomplete, colors, extra commands etc.
sudo apt-get install winpdb
instead of pdb.set_trace() do:
import rpdb2 rpdb2.start_embedded_debugger("secretpassword")
Now run winpdb and go to File > Attach with the password.
Don't like Winpdb ? Use PDB over TCP*
Get remote-pdb and then, to open a remote PDB on first available port, use:
from remote_pdb import set_trace set_trace() # you'll see the port number in the logs
To use some specific host/port:
from remote_pdb import RemotePdb RemotePdb(host='0.0.0.0', port=4444).set_trace()
To connect just run something like telnet 192.168.12.34 4444 ...
Standard Linux tools*
I'm always surprised of how underused they are. You can figure out a wide range of problems with these: from performance problems (too many syscalls, memory allocations etc) to deadlocks, network issues, disk issues etc
The most useful is downright strace, just run sudo strace -p 12345 or strace -f command (-f means strace forked processes too) and you're set. Output is generally very large so you might want to redirect it to a file (just add &> somefile) for more analysis.
Then there's ltrace, it's just like strace but but library calls. Arguments are mostly the same.
And lsof for figuring out what the handler numbers you see in ltrace / strace are for. Eg: lsof -p 12345
It's so easy to use and can do so many things - everyone should have htop installed !
sudo apt-get install htop sudo htop
Now find the process you want, and press:
- s for system call trace (strace)
- L for library call trace (ltrace)
- l for lsof
There's no replacement for good, continuous server monitoring but if you ever find yourself in that weird spot scrambling to find out why everything is slow and where are the resources going ... don't bother with iotop, iftop, htop, iostat, vmstat etc just yet, start with dstat instead ! It can do most of the aforementioned tools do and maybe better !
It will show you data continuously, in a compact, color-coded fashion (unlike iostat, vmstat) and you can always see past data (unlike iftop, iotop, htop).
Just run this:
dstat --cpu --io --mem --net --load --fs --vm --disk-util --disk-tps --freespace --swap --top-io --top-bio-adv
There's probably a shorter way to write it but then again, shell history or aliases.
This one is a rather complicated and powerful tool, but I'm only covering the basic stuff (setup and basic commands).
sudo apt-get install gdb python-dbg zcat /usr/share/doc/python2.7/gdbinit.gz > ~/.gdbinit run app with python2.7-dbg sudo gdb -p 12345
- bt - stacktrace (C level)
- pystack - python stacktrace, you need to have ~/.gdbinit and use python-dbg unfortunately
- c (continue)
Having segfaults ? faulthandler*
Rather awesome addition from Python 3.3, backported to Python 2.x
Just do this and you'll get at least an idea of what's causing the segmentation fault. Just add this in some module that's always imported:
import faulthandler faulthandler.enable()
Make sure the path is correct - otherwise it won't have any effect (e.g.: run locate libSegFault.so).
Quick stacktrace on a signal ? faulthandler*
Add this in some module that's always imported:
import faulthandler import signal faulthandler.register(signal.SIGUSR2, all_threads=True)
Then run kill -USR2 <pid> to get a stacktrace for all threads on the process's stderr.
Well, there's are plenty of tools here, some specialized on WSGI applications like Dozer but my favorite is definitely objgraph. It's so convenient and easy to use it's amazing. It's doesn't have any integration with WSGI or anything so you need to find yourself a way to run code like:
>>> import objgraph >>> objgraph.show_most_common_types() # try to find objects to investigate Request 119105 function 7413 dict 2492 tuple 2396 wrapper_descriptor 1324 weakref 1291 list 1234 cell 1011 >>> objs = objgraph.by_type("Request")[:15] # select few Request objects >>> objgraph.show_backrefs(objs, max_depth=15, highlight=lambda v: v in objs, filename="/tmp/graph.png") # and plot them Graph written to /tmp/objgraph-zbdM4z.dot (107 nodes) Image generated as /tmp/graph.png
Sometimes you want to use less memory. Less allocations usually make applications faster and well, users like them lean and mean :)
There are lots of tools  but the best one in my opinion is pytracemalloc - it has very little overhead (doesn't need to rely on the speed crippling sys.settrace) compared to other tools and it's output is very detailed. It's a pain to setup because you need to recompile python but apt makes it very easy to do so. In fact, it is so good that it got included in Python 3.4. See PEP-454 for details.
Just run these commands and go grab lunch or something:
apt-get source python2.7 cd python2.7-* wget https://github.com/wyplay/pytracemalloc/raw/master/python2.7_track_free_list.patch patch -p1 < python2.7_track_free_list.patch debuild -us -uc cd .. sudo dpkg -i python2.7-minimal_2.7*.deb python2.7-dev_*.deb
And install pytracemalloc (note that if you're doing this in a virtualenv, you need to recreate it after the python re-install - just run virtualenv myenv):
pip install pytracemalloc
Now wrap your application in code like this:
import tracemalloc, time tracemalloc.enable() top = tracemalloc.DisplayTop( 5000, # log the top 5000 locations file=open('/tmp/memory-profile-%s' % time.time(), "w") ) top.show_lineno = True try: # code that needs to be traced finally: top.display()
And output is like this:
2013-05-31 18:05:07: Top 5000 allocations per file and line #1: .../site-packages/billiard/_connection.py:198: size=1288 KiB, count=70 (+0), average=18 KiB #2: .../site-packages/billiard/_connection.py:199: size=1288 KiB, count=70 (+0), average=18 KiB #3: .../python2.7/importlib/__init__.py:37: size=459 KiB, count=5958 (+0), average=78 B #4: .../site-packages/amqp/transport.py:232: size=217 KiB, count=6960 (+0), average=32 B #5: .../site-packages/amqp/transport.py:231: size=206 KiB, count=8798 (+0), average=24 B #6: .../site-packages/amqp/serialization.py:210: size=199 KiB, count=822 (+0), average=248 B #7: .../lib/python2.7/socket.py:224: size=179 KiB, count=5947 (+0), average=30 B #8: .../celery/utils/term.py:89: size=172 KiB, count=1953 (+0), average=90 B #9: .../site-packages/kombu/connection.py:281: size=153 KiB, count=2400 (+0), average=65 B #10: .../site-packages/amqp/serialization.py:462: size=147 KiB, count=4704 (+0), average=32 B ...
EDIT: More about profiling here.