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A Frame Stack Sampler for CPython


Travis CI Build Status Snap Status Debian package status Version 1.0.1 LICENSE

Synopsis • Installation • Usage • Compatibility • Why Austin • Examples • Contribute

Buy Me A Coffee

This is the nicest profiler I’ve found for Python. It’s cross-platform, doesn’t need me to change the code that’s being profiled, and its output can be piped directly into I just used it to pinpoint a gross misuse of SQLAlchemy at work that’s run in some code at the end of each day, and now I can go home earlier.

-- gthm on

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Austin is a Python frame stack sampler for CPython written in pure C. Samples are collected by reading the CPython interpreter virtual memory space in order to retrieve information about the currently running threads along with the stack of the frames that are being executed. Hence, one can use Austin to easily make powerful statistical profilers that have minimal impact on the target application and that don't require any instrumentation.

The key features of Austin are:

  • Zero instrumentation;
  • Minimal impact;
  • Fast and lightweight;
  • Time and memory profiling;
  • Built-in support for multi-process applications (e.g. mod_wsgi).

The simplest way to turn Austin into a full-fledged profiler is to combine it with FlameGraph. However, Austin's simple output format can be piped into any other external or custom tool for further processing. Look, for instance, at the following Python TUI

Keep reading for more tools ideas and examples!


Austin is available from the major software repositories of the most popular platforms.

On Linux, it can be installed using autotools or as a snap from the Snap Store. The latter will automatically perform the steps of the autotools method with a single command. On distributions derived from Debian, Austin can be installed from the official repositores with Aptitude.

On Windows, Austin can be easily installed from the command line from the Chocolatey repositories.

For any other platform, compiling Austin from sources is as easy as cloning the repository and running the C compiler.

With autotools

Installing Austin using autotools amounts to the usual ./configure, make and make install finger gymnastic. The only dependency is the standard C library.

git clone --depth=1
autoreconf --install
make install

Alternatively, sources can be compiled with just a C compiler (see below).

From the Snap Store

Austin can be installed on many major Linux distributions from the Snap Store with the following command

sudo snap install austin --classic

On Debian and Derivatives

On March 30 2019 Austin was accepted into the official Debian repositories and can therefore be installed with the apt utility.

From Chocolatey

To install Austin from Chocolatey, run the following command from the command line or from PowerShell

choco install austin

To upgrade run the following command from the command line or from PowerShell:

choco upgrade austin

From Sources

To install Austin from sources using the GNU C compiler, without autotools, clone the repository with

git clone --depth=1

On Linux one can then use the command

gcc -O3 -Wall -pthread src/*.c -o src/austin

whereas on Mac OS it is enough to run

gcc -O3 -Wall src/*.c -o src/austin

On Windows, the -lpsapi switch is needed

gcc -O3 -Wall -lpsapi src/*.c -o src/austin

Add -DDEBUG if you need a more verbose log. This is useful if you encounter a bug with Austin and you want to report it here.


Usage: austin [OPTION...] command [ARG...]
Austin -- A frame stack sampler for Python.

  -a, --alt-format           Alternative collapsed stack sample format.
  -C, --children             Attach to child processes.
  -e, --exclude-empty        Do not output samples of threads with no frame
  -f, --full                 Produce the full set of metrics (time +mem -mem).
  -i, --interval=n_us        Sampling interval (default is 500us).
  -m, --memory               Profile memory usage.
  -o, --output=FILE          Specify an output file for the collected samples.
  -p, --pid=PID              The the ID of the process to which Austin should
  -s, --sleepless            Suppress idle samples.
  -t, --timeout=n_ms         Approximate start up wait time. Increase on slow
                             machines (default is 100ms).
  -?, --help                 Give this help list
      --usage                Give a short usage message
  -V, --version              Print program version

Mandatory or optional arguments to long options are also mandatory or optional
for any corresponding short options.

Report bugs to <>.

The output is a sequence of frame stack samples, one on each line. The format is the collapsed one that is recognised by FlameGraph so that it can be piped straight to for a quick visualisation, or redirected to a file for some further processing.

By default, each line has the following structure:

[Process <pid>;]?Thread <tid>[;[frame]]* [metric]*

where the presence of the process ID, the structure of [frame] and the number and type of metrics on each line depend on the mode.

Normal Mode

When no special switch are passed to Austin from the command line, the process identifier is omitted and [frame] has the structure

[frame] := <function> (<module>);L<line number>

The reason for not including the line number in the ([module]) part, as one might have expected, is that this way the flame graph will show the total time spent in each function, plus the finer detail of the time spent on each line. A drawback of this format is that frame stacks double in height. If you prefer something more conventional, you can use the -a option to switch to the alternative format in which [frame] has the structure

[frame] := <function> (<module>:<line number>)

Each line then ends with a single [metric], i.e. the sampling time measured in microseconds.

Memory and Full Metrics

When profiling in memory mode with the -m or --memory switch, the metric value at the end of each line is the memory delta between samples, measured in KB. In full mode (-f or --full switches), each samples ends with three values: the time delta, any positive memory delta (memory allocations) or zero and any negative memory delta (memory releases) or zero.

Multi-process Applications

Austin can be told to profile multi-process applications with the -C or --children switch. This way Austin will look for new children of the parent process. In this case, each sample will contain the process identifier to help determine from which process the sample came from.


Austin uses syslog on Linux and Mac OS, and %TEMP%\austin.log on Windows for log messages, so make sure to watch these to get execution details and statistics. Bad frames are output together with the other frames. In general, entries for bad frames will not be visible in a flame graph as all tests show error rates below 1% on average.


Austin supports Python 2.3-2.7 and 3.3-3.8 and has been tested on the following platforms and architectures


Due to the System Integrity Protection introduced in MacOS with El Capitan, Austin cannot profile Python processes that use an executable located in the /bin folder, even with sudo. Hence, either run the interpreter from a virtual environment or use a Python interpreter that is installed in, e.g., /Applications or via brew with the default prefix (/usr/local). Even in these cases, though, the use of sudo is required.

NOTE Austin might work with other versions of Python on all the platforms and architectures above. So it is worth giving it a try even if your system is not listed below.

Why Austin

When there already are similar tools out there, it's normal to wonder why one should be interested in yet another one. So here is a list of features that currently distinguish Austin.

  • Written in pure C Austin is written in pure C code. There are no dependencies on third-party libraries with the exception of the standard C library and the API provided by the Operating System.

  • Just a sampler Austin is just a frame stack sampler. It looks into a running Python application at regular intervals of time and dumps whatever frame stack it finds. The samples can then be analysed at a later time so that Austin can sample at rates higher than other non-C alternative that also analyse the samples as they run.

  • Simple output, powerful tools Austin uses the collapsed stack format of FlameGraph that is easy to parse. You can then go and build your own tool to analyse Austin's output. You could even make a player that replays the application execution in slow motion, so that you can see what has happened in temporal order.

  • Small size Austin compiles to a single binary executable of just a bunch of KB.

  • Easy to maintain Occasionally, the Python C API changes and Austin will need to be adjusted to new releases. However, given that Austin, like CPython, is written in C, implementing the new changes is rather straight-forward.


The following flame graph has been obtained with the command

austin -i 50 ./ | ./ --countname=us > test.svg

where the sample script has the following content

import psutil

for i in range(1000):

To profile Apache2 WSGI application, one can attach Austin to the web server with

austin -Cp `pgrep apache2 | head -n 1`

Any child processes will be automatically detected as they are created and Austin will sample them too.

Austin TUI

The Python TUI that is currently included in this repository provides an example of how to use Austin to profile Python applications. You can use PageUp and PageDown to navigate the frame stack of each frame as the Python application runs.

If you want to give it a go you can install it using pip with

pip install git+ --upgrade

and run it with

austin-tui [OPTION...] command [ARG...]

with the same command line as Austin.

The TUI is based on python-curses. The version included with the standard Windows installations of Python is broken so it won't work out of the box. A solution is to install the the wheel of the port to Windows from this page. Wheel files can be installed directly with pip, as described in the linked page.

Web Austin

Web Austin is yet another example of how to use Austin to make a profiling tool. It makes use of d3-flame-graph to display a live flame graph in the web browser that refreshes every 3 seconds with newly collected samples. Web Austin can also be used for remote profiling by setting the WEBAUSTIN_HOST and WEBAUSTIN_PORT environment variables.

If you want to give it a go you can install it using pip with

pip install git+ --upgrade

and run it with

austin-web [OPTION...] command [ARG...]

with the same command line as Austin. This starts a simple HTTP server that serves on WEBAUSTIN_HOST if set or on localhost otherwise. The port can be controlled with the WEBAUSTIN_PORT environment variable. If it is not set, Web Austin will use an ephemeral port.


Austin output format can be converted easily into the Speedscope JSON format. You can find a sample utility along with the TUI and Austin Web.

If you want to give it a go you can install it using pip with

pip install git+ --upgrade

and run it with

austin2speedscope [-h] [--indent INDENT] [-V] input output

where input is a file containing the output from Austin and output is the name of the JSON file to use to save the result of the conversion, ready to be used on Speedscope.


If you like Austin and you find it useful, there are ways for you to contribute.

If you want to help with the development, then have a look at the open issues and have a look at the contributing guidelines before you open a pull request.

You can also contribute to the development of Austin by either becoming a Patron on Patreon

by buying me a coffee on BMC

Buy Me A Coffee

or by chipping in a few pennies on PayPal.Me.

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