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

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 flamegraph.pl. 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 lobste.rs

If people are looking for a profiler, Austin looks pretty cool. Check it out!

-- Michael Kennedy on Python Bytes 180

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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 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 use together with the VS Code extension or combine it with FlameGraph or Speedscope. However, Austin's binary output can be piped into any other external or custom tools for further processing. Look, for instance, at the following Python TUI

Check out A Survey of Open-Source Python Profilers by Peter Norton for a general overview of Austin.

Keep reading for more tool ideas and examples!

💜
Austin is a free and open-source project. A lot of effort goes into its development to ensure the best performance and that it stays up-to-date with the latest Python releases. If you find it useful, consider sponsoring this project.
🙏

Austin is available to install from PyPI and from the major software repositories of the most popular platforms. Check out the latest release page for pre-compiled binaries and installation packages.

On all supported platforms and architectures, Austin can be installed from PyPI with pip or pipx via the commands

pip install austin-dist

or

pipx install austin-dist

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 repositories with apt. Anaconda users can install Austin from Conda Forge.

On Windows, Austin can be easily installed from the command line using either Chocolatey or Scoop. Alternatively, you can download the installer from the latest release page.

On macOS, Austin can be easily installed from the command line using Homebrew. Anaconda users can install Austin from Conda Forge.

For any other platforms, compiling Austin from sources is as easy as cloning the repository and running the C compiler. The Releases page has many pre-compiled binaries that are ready to be uncompressed and used.

Installing Austin using autotools amounts to the usual ./configure, make and make install finger gymnastic. The only dependency is the standard C library. Before proceeding with the steps below, make sure that the autotools are installed on your system. Refer to your distro's documentation for details on how to do so.

git clone --depth=1 https://github.com/P403n1x87/austin.git && cd austin
autoreconf --install
./configure
make
make install

NOTE Some Linux distributions, like Manjaro, might require the execution of automake --add-missing before ./configure.

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

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

sudo snap install austin --classic

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

sudo apt update -y && sudo apt install austin -y

Austin can be installed on macOS using Homebrew:

brew install austin

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

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

scoop install austin

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

scoop update

Anaconda users on Linux and macOS can install Austin from Conda Forge with the command

conda install -c conda-forge austin

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

git clone --depth=1 https://github.com/P403n1x87/austin.git

On Linux, one can then use the command

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

whereas on macOS it is enough to run

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

On Windows, the -lpsapi -lntdll switches are needed

gcc -O3 -Os -Wall -lpsapi -lntdll 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 is a frame stack sampler for CPython that is used to extract profiling
data out of a running Python process (and all its children, if required) that
requires no instrumentation and has practically no impact on the tracee.

  -c, --cpu                  Sample on-CPU stacks only.
  -C, --children             Attach to child processes.
  -f, --full                 Produce the full set of metrics (time +mem -mem).
  -g, --gc                   Sample the garbage collector state.
  -i, --interval=n_us        Sampling interval in microseconds (default is
                             100). Accepted units: s, ms, us.
  -m, --memory               Profile memory usage.
  -o, --output=FILE          Specify an output file for the collected samples.
  -p, --pid=PID              Attach to the process with the given PID.
  -P, --pipe                 Pipe mode. Use when piping Austin output.
  -t, --timeout=n_ms         Start up wait time in milliseconds (default is
                             3000). Accepted units: s, ms.
  -w, --where=PID            Dump the stacks of all the threads within the
                             process with the given PID.
  -x, --exposure=n_sec       Sample for n_sec seconds only.
  -?, --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 <https://github.com/P403n1x87/austin/issues>.

Austin generates binary output in the MOJO format. This is a compact binary representation of the collected data that can be processed by the mojo2austin tool that comes with the austin-python Python package to produce the more commonly used collapsed stack format. The MOJO format can also be converted to the Speedscope JSON format using the austin2speedscope tool that also comes with the austin-python Python package. If you use Visual Studio Code, you can use the [Austin VS Code extension] to visualise the profile data directly in the editor.

Some behaviour of Austin can be configured via environment variables.

Since Python 3.11, code objects carry finer-grained location information at the column level. When using the binary MOJO format, Austin can extract this extra location information when profiling code running with versions of the interpreter that expose this data.

Since Austin 3.5.0.

When profiling in memory mode with the -m or --memory switch, the metric value associated with each stack is the memory delta between samples, measured in bytes. In full mode (-f or --full switches), each sample will include both a time and memory metric, plus the information of whether the stack was on CPU. This is useful if you want to collect wall-time/CPU-time and memory profiles in a single run.

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.

Austin has support for Python applications that make use of sub-interpreters. This means that Austin will sample all the sub-interpreters that are running within each process making up the Python application.

Since Austin 3.6.0.

Austin can sample the Python garbage collector state for applications running with Python 3.7 and later versions. If the -g/--gc option is passed, Austin will append :GC: at the end of each collected frame stack whenever the garbage collector is collecting. This gives you a measure of how busy the Python GC is during a run.

Since Austin 3.1.0.

If you are only interested in what is currently happening inside a Python process, you can have a quick overview printed on the terminal with the -w/--where option. This takes the PID of the process whose threads you want to inspect, e.g.

sudo austin -w `pgrep -f my-running-python-app`

Below is an example of what the output looks like

This works with the -C/--children option too. The emojis to the left indicate whether the thread is active or sleeping and whether the process is a child or not.

Since Austin 3.3.0.

Austin can sample stacks very rapidly, guaranteeing high accuracy, as well as allowing to reach very high sampling rates, if required. This makes Austin suitable for profiling applications in production with almost no compromise between accuracy and performance.

If you want observability into the native frame stacks, you can use the austinp variant of austin which can be obtained by compiling the source with -DAUSTINP on Linux, or from the released binaries.

austinp makes use of ptrace to halt the application and grab a snapshot of the call stack with libunwind. If you are compiling austinp from sources make sure that you have the development version of the libunwind library available on your system, for example on Ubuntu,

sudo apt install libunwind-dev binutils-dev

and compile with

gcc -O3 -Os -Wall -pthread src/*.c -DAUSTINP -lunwind-ptrace -lunwind-generic -lbfd -o src/austinp

then use as per normal. The extra -k/--kernel option is available with austinp which allows sampling kernel call stacks as well.

The austinp-resolve tool from the austin-python Python package can be used to resolve the VM addresses to source and line numbers, provided that the referenced binaries have DWARF debug symbols. Internally, the tool uses addr2line(1) to determine the source name and line number given an address, when possible.

The where option is also available for the austinp variant and will show both native and Python frames. Highlighting helps tell frames apart. The -k option outputs Linux kernel frames too, as shown in this example

Austin uses syslog on Linux and macOS, 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.

Logging can be disabled using environment variables.

All the above Austin options and arguments are summarised in a cheat sheet that you can find in the doc folder in either the SVG, PDF or PNG format

Austin supports Python 3.9 through 3.14, and has been tested on the following platforms and architectures

x86_64	✓	✓	✓
i686	✓		✓
armv7	✓
arm64	✓		✓
ppc64le	✓

If you are looking for support for older versions of CPython, the following table summarises the compatibility of Austin with CPython versions

Because of platform-specific details, Austin usage may vary slightly. Below are further compatibility details to be aware of.

Austin requires the CAP_SYS_PTRACE capability to attach to an external process. This means that you will have to either use sudo when attaching to a running Python process or grant the CAP_SYS_PTRACE capability to the Austin binary with, e.g.

sudo setcap cap_sys_ptrace+ep `which austin`

To use Austin with Docker, the --cap-add SYS_PTRACE option needs to be passed when starting a container.

Due to the System Integrity Protection, introduced in MacOS with El Capitan, and the Hardened Runtime, introduced in Mojave, Austin cannot profile Python processes that use an executable located in the /bin folder, or code-signed, even with sudo. This is the case for the system-provided version of Python, and the one installed with the official installers from python.org. Other installation methods, like pyenv or Anaconda or Homebrew are known to work with Austin, out of the box.

To use Austin with Python from the official installer, you could remove the signature from the binaries with

codesign --remove-signature /Library/Frameworks/Python.framework/Versions/<M.m>/bin/python3
codesign --remove-signature /Library/Frameworks/Python.framework/Versions/<M.m>/Resources/Python.app/Contents/MacOS/Python

Alternatively, you could self-sign the Austin binary with the Debugging Tool Entitlement, as done for debugging tools like GDB. However, this method has not been tested.

Austin requires the use of sudo to work on MacOS. To avoid having to type the password every time you use Austin, consider adding a rule to the sudoers file, e.g.

yourusername  ALL = (root) NOPASSWD: /usr/local/bin/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 except for 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 alternatives that perform some aggregations at run-time.

• 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.

To profile a Python script, run Austin with the command

austin -o profile.mojo python myscript.py

To profile a running Python application, attach Austin to it with the -p/--pid option, e.g.

austin -o profile.mojo -p <pid>

where <pid> is the process ID of the running Python application you want to profile. If you also want to profile the child processes of the target application, add the -C/--children option to the command line:

austin -o profile.mojo -Cp <pid>

For example, to profile an Apache2 WSGI application, one can attach Austin to the web server with

austin -Cp `pgrep apache2 | head -n 1` > profile.mojo

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

It is easy to write your own extension for your favourite text editor. This, for example, is a demo of a Visual Studio Code extension that highlights the most hit lines of code straight into the editor

The Austin TUI is a text-based user interface for Austin that gives you a top-like view of what is currently running inside a Python application. It is most useful for scripts that have long-running procedures as you can see where execution is at without tracing instructions in your code. You can also save the collected data from within the TUI and feed it to Flame Graph for visualisation, or convert it to the pprof format.

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

pip install austin-tui --upgrade

and run it with

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

with the same command line as Austin. Please note that the austin binary should be available from within the PATH environment variable in order for the TUI to work.

Austin Web is a web application that wraps around Austin. At its core, Austin Web is based on d3-flame-graph to display a live flame graph in the browser, that refreshes every 3 seconds with newly collected samples. Austin Web can also be used for remote profiling by setting the --host and --port options.

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

pip install austin-web --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 localhost by default. When no explicit port is given, Austin Web will use an ephemeral one.

Please note that the austin binary should be available from within the PATH environment variable in order for Austin Web to work.

Austin output is now supported by Speedscope. However, the austin-python library comes with format conversion tools that allow converting the output from Austin to the Speedscope JSON format.

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

pip install austin-python --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.

Austin's format can also be converted to the Google pprof format using the austin2pprof utility that comes with austin-python. If you want to give it a go you can install it using pip with

pip install austin-python --upgrade

and run it with

austin2pprof [-h] [-V] input output

where input is a file containing the output from Austin and output is the name of the protobuf file to use to save the result of the conversion, ready to be used with Google's pprof tools.

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 the Austin by becoming a sponsor and/or by buying me a coffee on BMC or by chipping in a few pennies on PayPal.Me.

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