Frequently Asked Questions¶
Where Can I Report Bugs / Send Feedback / Request Features?¶
Can Python 2.7 Be Supported?¶
In theory, yes. However, it is considerable more effort than Python 3. And since Python 2.7 is being deprecated in 2020, in the project author’s opinion it isn’t worth the effort.
No python interpreter found of version 3.* Error When Building¶
This is due to a dependent crate insisting that a Python executable
exist on PATH. Set the PYTHON_SYS_EXECUTABLE environment
variable to the path of a Python 3.7 executable and try again. e.g.:
# UNIX
$ export PYTHON_SYS_EXECUTABLE=/usr/bin/python3.7
# Windows
$ SET PYTHON_SYS_EXECUTABLE=c:\python37\python.exe
Note
The pyoxidizer tool should take care of setting PYTHON_SYS_EXECUTABLE
and prevent this error. If you see this error and you are building with
pyoxidizer, it is a bug that should be reported.
Why Rust?¶
This is really 2 separate questions:
Why choose Rust for the run-time/embedding components?
Why choose Rust for the build-time components?
PyOxidizer binaries require a driver application to interface with
the Python C API and that driver application needs to compile to native
code in order to provide a native executable without requiring a run-time
on the machine it executes on. In the author’s opinion, the only appropriate
languages for this were C, Rust, and maybe C++.
Of those 3, the project’s author prefers to write new projects in Rust because it is a superior systems programming language that has built on lessons learned from decades working with its predecessors. The author prefers technologies that can detect and eliminate entire classes of bugs (like buffer overflow and use-after-free) at compile time. On a less-opinionated front, Rust’s built-in build system support means that we don’t have to spend considerable effort solving hard problems like cross-compiling. Implementing the embedding component in Rust also creates interesting opportunities to embed Python in Rust programs. This is largely an unexplored area in the Python ecosystem and the author hopes that PyOxidizer plays a part in more people embedding Python in Rust.
For the non-runtime packaging side of PyOxidizer, pretty much any
programming language would be appropriate. The project’s author initially
did prototyping in Python 3 but switched to Rust for synergy with the the
run-time driver and because Rust had working solutions for several systems-level
problems, such as parsing ELF, DWARF, etc executables, cross-compiling,
integrating custom memory allocators, etc. A minor factor was the author’s
desire to learn more about Rust by starting a real Rust project.
Why is the Rust Code… Not Great?¶
This is the project author’s first real Rust project. Suggestions to improve the Rust code would be very much appreciated!
Keep in mind that the pyoxidizer crate is a build-time only
crate and arguably doesn’t need to live up to quality standards as
crates containing run-time code. Things like aggressive .unwrap()
usage are arguably tolerable.
The run-time code that produced binaries run (pyembed) is held to
a higher standard and is largely panic! free.
What is the Magic Sauce That Makes PyOxidizer Special?¶
There are 2 technical achievements that make PyOxidizer special.
First, PyOxidizer consumes Python distributions that were specially
built with the aim of being used for standalone/distributable applications.
These custom-built Python distributions are compiled in such a way that
the resulting binaries have very few external dependencies and run on
nearly every target system. Other tools that produce standalone Python
binaries often rely on an existing Python distribution, which often
doesn’t have these characteristics.
Second is the ability to import .py/.pyc files from memory. Most
other self-contained Python applications rely on Python’s zipimporter
or do work at run-time to extract the standard library to a filesystem
(typically a temporary directory or a FUSE filesystem like SquashFS). What
PyOxidizer does is expose the .py/.pyc modules data to the
Python interpreter via a Python extension module built-in to the binary.
In addition, the importlib._bootstrap_external module (which is
frozen into libpython) is replaced by a modified version that
defines a custom module importer capable of loading Python modules
from the in-memory data structures exposed from the built-in extension
module.
The custom importlib_bootstrap_external frozen module trick is
probably the most novel technical achievement of PyOxidizer. Other
Python distribution tools are encouraged to steal this idea!
See the docs in the pyembed crate for an overview of how the
in-memory import machinery works.
Can Applications Import Python Modules from the Filesystem?¶
Yes. While the default is to import all Python modules from in-memory
data structures linked into the binary, it is possible to configure
sys.path to allow importing from additional filesystem paths.
Support for importing compiled extension modules is also possible.
What are the Implications of Static Linking?¶
Most Python distributions rely heavily on dynamic linking. In addition to
python frequently loading a dynamic libpython, many C extensions
are compiled as standalone shared libraries. This includes the modules
_ctypes, _json, _sqlite3, _ssl, and _uuid, which
provide the native code interfaces for the respective non-_ prefixed
modules which you may be familiar with.
These C extensions frequently link to other libraries, such as libffi,
libsqlite3, libssl, and libcrypto. And more often than not,
that linking is dynamic. And the libraries being linked to are provided
by the system/environment Python runs in. As a concrete example, on
Linux, the _ssl module can be provided by
_ssl.cpython-37m-x86_64-linux-gnu.so, which can have a shared library
dependency against libssl.so.1.1 and libcrypto.so.1.1, which
can be located in /usr/lib/x86_64-linux-gnu or a similar location
under /usr.
When Python extensions are statically linked into a binary, the Python extension code is part of the binary instead of in a standalone file.
If the extension code is linked against a static library, then the code for that dependency library is part of the extension/binary instead of dynamically loaded from a standalone file.
When PyOxidizer produces a fully statically linked binary, the code
for these 3rd party libraries is part of the produced binary and not
loaded from external files at load/import time.
There are a few important implications to this.
One is related to security and bug fixes. When 3rd party libraries are provided by an external source (typically the operating system) and are dynamically loaded, once the external library is updated, your binary can use the latest version of the code. When that external library is statically linked, you need to rebuild your binary to pick up the latest version of that 3rd party library. So if e.g. there is an important security update to OpenSSL, you would need to ship a new version of your application with the new OpenSSL in order for users of your application to be secure.
Another implication is code compatibility. If multiple consumers try to use different versions of the same library… TODO