vmprof is a statistical profiler: it gathers information about your code by continuously taking samples of the call stack of the running program, at a given frequency. This is similar to tools like vtune or gperftools: the main difference is that those tools target C and C-like languages and are not very helpful to profile higher-level languages which run on top of a virtual machine, while vmprof is designed specifically for them. vmprof is also thread safe and will correctly display the information regardless of usage of threads.
There are three primary modes. The recommended one is to use our server infrastructure for a web-based visualization of the result:
python -m vmprof --web <program.py> <program parameters>
If you prefer a barebone terminal-based visualization, which will display only some basic statistics:
python -m vmprof <program.py> <program parameters>
To display a terminal-based tree of calls:
python -m vmprof -o output.log <program.py> <program parameters> vmprofshow output.log
To upload an already saved profile log to the vmprof web server:
python -m vmprof.upload output.log
For more advanced use cases, vmprof can be invoked and controlled from within the program using the given API.
VMProf runs on x86_64 and x86. It supports Linux, Mac OS X and Windows running CPython 2.7, 3.4, 3.5 and PyPy 4.1+.
vmprof is performed with a simple command:
pip install vmprof
PyPi ships wheels with libunwind shared objects (this means you need a recent version of pip).
If you build VMProf from source you need to compile C code:
sudo apt-get install python-dev
We strongly suggest using the
--web option that will display you a much
nicer web interface hosted on
If you prefer to host your own vmprof visualization server, you need the vmprof-server package.
-m vmprof you can specify some options:
--web- Use the web-based visualization. By default, the result can be viewed on our server.
--web-url- the URL to upload the profiling info as JSON. The default is
--web-auth- auth token for user name support in the server.
-p period- float that gives you how often the profiling happens (the max is about 300 Hz, rather don’t touch it).
-n- enable all C frames, only useful if you have a debug build of PyPy or CPython.
--lines- enable line profiling mode. This mode adds some overhead to profiling, but in addition to function calls it marks the execution of the specific lines inside functions.
-o file- save logs for later
--help- display help
--config- a ini format config file with all options presented above. When passing a config file along with command line arguments, the command line arguments will take precedence and override the config file values.
Example config.ini file:
web-url = vmprof.com web-auth = ffb7d4bee2d6436bbe97e4d191bf7d23f85dfeb2 period = 0.1
There is also an API that can bring more details to the table, but consider it unstable. The current API usage is as follows:
Module level functions¶
vmprof.enable(fileno, period=0.00099, memory=False)- enable writing
vmprofdata to a file described by a fileno file descriptor. Period is in float seconds. The minimal available resolution is around 1ms, we’re working on improving that (note the default is 0.99ms). Passing
memory=Truewill provide additional data in the form of total RSS of the process memory interspersed with tracebacks.
vmprof.disable()- finish writing vmprof data, disable the signal handler
vmprof.read_profile(filename)- read vmprof data from
start/stop_sampling()- Disables or starts the sampling of vmprof. This is useful to remove certain program parts from the profile. Be aware that those program parts still can be in the profile if that code is reached from another point in your program. In addition note that unix and windows implementation behave differntly. Unix will increment/decrement a counter, whereas windows has only two states for the counter (0 and 1). This may change in future.
Stats object gives you an overview of data:
stats.get_tree()- Gives you a tree of objects
Tree is made of Nodes, each node supports at least the following interface:
node[key]- a fuzzy search of keys (first match)
repr(node)- basic details
node.flatten()- returns a new tree that flattens all the metadata (gc, blackhole etc.)
node.walk(callback)- call a callable of form
callback(root)that will be invoked on each node
Why a new profiler?¶
There is a variety of python profilers on the market: CProfile is the one bundled with CPython, which together with lsprofcalltree.py provides good info and decent visualization; plop is an example of statistical profiler.
We wanted a profiler with the following characteristics:
- Minimal overhead, small enough that enabling the profiler in production is a viable option. Ideally the overhead should be in the range 1-5%, with the possibility to tune it for more accurate measurments
- Ability to display a full stack of calls, so it can show how much time was spent in a function, including all its children
- Good integration with PyPy: in particular, it must be aware of the underlying JIT, and be able to show how much time is spent inside JITted code, Garbage collector and normal intepretation.
None of the existing solutions satisfied our requirements, hence we decided to create our own profiler. In particular, cProfile is slow on PyPy, does not understand the JITted code very well and is shown in the JIT traces.
How does it work?¶
As most statistical profilers, the core idea is to have a signal handler which periodically inspects and dumps the stack of the running program: the most frequently executed parts of the code will be dumped more often, and the post-processing and visualization tools have the chance to show the end user usueful info about the behavior of the profiled program. This is the very same approach used e.g. by gperftools.
However, when profiling an interpreter such as CPython, inspecting the C stack
is not enough, because most of the time will always be spent inside the opcode
dispatching loop of the virtual machine (e.g.,
PyEval_EvalFrameEx in case
of CPython). To be able to display useful information, we need to know which
Python-level function correspond to each C-level
This is done by reading the stack of Python frames instead of C stack.
Additionally, when on top of PyPy the C stack contains also stack frames which belong to the JITted code: the vmprof signal handler is able to recognize and extract the relevant info from those as well.
Once we have gathered all the low-level info, we can post-process and
visualize them in various ways: for example, we can decide to filter out the
places where we are inside the
select() syscall, etc.
The machinery to gather the information has been the focus of the initial phase of vmprof development and now it is working well: we are currently focusing on the frontend to make sure we can process and display the info in useful ways.