Managing How Resources are Added¶
An important concept in PyOxidizer packaging is how to manage resources that are added to built applications.
A resource is some entity that will be packaged and distributed. Examples of resources include Python module source and bytecode, Python extension modules, and arbitrary files on the filesystem.
Resources are represented by a dedicated Starlark type for each resource flavor (see Resource Types).
During evaluation of PyOxidizer’s Starlark configuration files, resources are created and added to another Starlark type whose job is to collect all desired resources and then do something with them.
Classified Resources Versus Files¶
All resources in PyOxidizer are ultimately derived from or representable
by a file or a file-like primitive. For example, a
PythonModuleSource is derived from or could be
manifested as a
Various PyOxidizer functionality works by scanning existing files and turning those files into resources.
This file scanning functionality has two modes of operation: classified
and files. In files mode, PyOxidizer simply emits resources corresponding
to the raw files it encounters. In classified mode, PyOxidizer attempts to
classify a file as a particular resource and emit a strongly-typed
Classified mode is more powerful because PyOxidizer is able to build an index of typed resources at packaging time and make this index available to oxidized_importer Python Extension at run-time to facilitate faster loading of resources.
However, the main downside to classified mode is it relies on being able to identify files properly and this is unreliable. Python file layouts are under-specified and there are many edge cases where PyOxidizer fails to properly classify a file. See Debugging Resource Scanning and Identification with find-resources for how to identify problems here.
In files mode, PyOxidizer simply indexes and manages a named file
and its content. There is far less potential for PyOxidizer to make
mistakes about a file’s type and how it is handled. This means that
files mode often just works when classified mode doesn’t. The main
downside to files mode is that oxidized_importer Python Extension doesn’t have a
rich index embedded in the built binary, so you will have to rely on
Python’s default filesystem-based importer, which is slower than
Packaging Policies and Adding Resources¶
The exact mechanism by which resources are emitted and added to resource
collectors is influenced by a packaging policy (represented by the
PythonPackagingPolicy Starlark type) and attributes on
each resource object influencing how they are added.
When resources are created, the packaging policy determines whether
emitted resources are classified or simply files. And the packaging
policy is applied to each created resource to populate the initial values
for the various
add_* attributes on the Starlark resource types.
When a resource is added (e.g. by calling
PythonExecutable.add_python_resource()), these aforementioned
add_* attributes are consulted and used to influence exactly how that
resource is added/packaged.
For example, a
PythonModuleSource can set attributes
indicating to exclude source code and only generate bytecode at
a specific optimization level. Or a
can set attributes saying to prefer to compile it into the built
binary or materialize it as a standalone dynamic extension module
The following Starlark types represent individual resources:
Source code for a Python module. Roughly equivalent to a
This type can also be converted to Python bytecode (roughly equivalent to a
.pyc) when added to a resource collector.
A Python module defined through compiled, machine-native code. On Linux, these are typically encountered as
.sofiles. On Windows,
A non-module resource file loadable by Python resources APIs, such as those in
A non-module resource file defining metadata for a Python package. Typically accessed via
importlib.metadata. This is how files in
*.egg-infodirectories are represented.
Represents a filesystem path and its content.
Represents the content of a filesystem file.
This is different from
Filein that it only represents file content and doesn’t have an associated path. (It is likely these 2 types will be merged someday.)
There are also Starlark types that are logically containers for multiple resources:
Holds a mapping of relative filesystem paths to
starlark_tugger.FileContentinstances. This type effectively allows modeling a directory tree.
Holds a collection of Python resources of various types. (This type is often hidden away. e.g. inside a
Resources have the concept of a location. A resource’s location determines where the data for that resource is packaged and how that resource is loaded at run-time.
When a Python resource is placed in the in-memory location, the content behind the resource will be embedded in a built binary and loaded from there by the Python interpreter.
Python modules imported from memory do not have the
set. This can cause compatibility issues if Python code is relying on the
existence of this module. See __file__ and __cached__ Module Attributes for more.
When a Python resource is placed in the filesystem-relative location,
the resource will be materialized as a file next to the produced entity.
e.g. a filesystem-relative
PythonModuleSource for the
Python module added to a
PythonExecutable will be materialized as the
foo/bar/__init__.py in a directory next to the
Resources added to filesystem-relative locations should be materialized
under paths that preserve semantics with standard Python file layouts. For
e.g. Python source and bytecode modules, it should be possible to point
sys.path of any Python interpreter at the destination directory and
the modules will be loadable.
During packaging, PyOxidizer indexes all filesystem-relative resources and embeds metadata about them in the built binary. While the files on the filesystem may look like a standard Python install layout, loading them is serviced by PyOxidizer’s custom importer, not the standard importer that Python uses by default.
Customizing Python Packaging Policies¶
As described in Packaging Policies and Adding Resources, a
PythonPackagingPolicy Starlark type instance is bound to every
entity creating resource instances and this packaging policy is
used to derive the default
add_* attributes which influence
what happens when a resource is added to some entity.
PythonPackagingPolicy instances can be customized to influence
what the default values of the
add_* attributes are.
The primary mechanisms for doing this are:
Registering a function that will be called whenever a resource is created. This enables custom Starlark code to perform arbitrarily complex logic to influence settings and enables application developers to devise packaging strategies more advanced than what PyOxidizer provides out-of-the-box.
The following sections give examples of customized packaging policies.
Changing the Resource Handling Mode¶
As documented in Classified Resources Versus Files, PyOxidizer can operate on classified resources or files-based resources.
exists to change the operating mode of a
def make_exe(): dist = default_python_distribution() policy = dist.make_python_packaging_policy() # Set policy attributes to only operate on "classified" resource types. # (This is the default.) policy.set_resource_handling_mode("classify") # Set policy attributes to only operate on `File` resource types. policy.set_resource_handling_mode("files")
just a convenience method for manipulating a collection of attributes on
PythonPackagingPolicy instances. If you don’t like the behavior of
its pre-defined modes, feel free to adjust attributes to suit your needs.
You can even configure things to emit both classified and files
Customizing Default Resource Locations¶
PythonPackagingPolicy.resources_location_fallback attributes define
primary and fallback locations that resources should attempt to be added
to. These effectively define the default values for the
add_location_fallback attributes on individual resource objects.
The accepted values are:
Load resources from memory.
Load resources from the filesystem at a path relative to some entity (probably the binary being built).
PythonPackagingPolicy.resources_location_fallback can be
None to remove a fallback location.
And here is how you would manage these values in Starlark:
def make_exe(): dist = default_python_distribution() policy = dist.make_python_packaging_policy() policy.resources_location = "in-memory" policy.resources_location_fallback = None # Only allow resources to be added to the in-memory location. exe = dist.to_python_executable( name = "myapp", packaging_policy = policy, ) # Only allow resources to be added to the filesystem-relative location under # a "lib" directory. policy = dist.make_python_packaging_policy() policy.resources_location = "filesystem-relative:lib" policy.resources_location_fallback = None exe = dist.to_python_executable( name = "myapp", packaging_policy = policy, ) # Try to add resources to in-memory first. If that fails, add them to a # "lib" directory relative to the built executable. policy = dist.make_python_packaging_policy() policy.resources_location = "in-memory" policy.resources_location_fallback = "filesystem-relative:lib" exe = dist.to_python_executable( name = "myapp", packaging_policy = policy, ) return exe
Using Callbacks to Influence Resource Attributes¶
PythonPackagingPolicy.register_resource_callback() method will
register a function to be called when resources are created. This function
receives as arguments the active
PythonPackagingPolicy and the newly
Functions registered as resource callbacks are called after the
add_* attributes are derived for a resource but before the resource
is otherwise made available to other Starlark code. This means that
these callbacks provide a hook point where resources can be modified as
soon as they are created.
register_resource_callback() can be called multiple times to register
multiple callbacks. Registered functions will be called in order of
Functions can be leveraged to unify all resource packaging logic in a single place, making your Starlark configuration files easier to reason about.
Here’s an example showing how to route all resources belonging to
a single package to a
filesystem-relative location and everything
else to memory:
def resource_callback(policy, resource): if type(resource) in ("PythonModuleSource", "PythonPackageResource", "PythonPackageDistributionResource"): if resource.package == "my_package": resource.add_location = "filesystem-relative:lib" else: resource.add_location = "in-memory" def make_exe(): dist = default_python_distribution() policy = dist.make_python_packaging_policy() policy.register_resource_callback(resource_callback) exe = dist.to_python_executable( name = "myapp", packaging_policy = policy, ) exe.add_python_resources(exe.pip_install(["my_package"]))
PythonExtensionModule Location Compatibility¶
Many resources just work in any available location. This is not the case for
While there only exists a single
PythonExtensionModule type to represent
Python extension modules, Python extension modules come in various flavors.
Examples of flavors include:
A module that is part of a Python distribution and is compiled into
libpython(a builtin extension module).
A module that is part of a Python distribution that is compiled as a standalone shared library (e.g. a
A non-distribution module that is compiled as a standalone shared library.
A non-distribution module that is compiled as a static library.
Not all extension module flavors are compatible with all Python distributions. Furthermore, not all flavors are compatible with all build configurations.
Here are some of the rules governing extension modules and their locations:
A builtin extension module that’s part of a Python distribution will always be statically linked into
A Windows Python distribution with a statically linked
standalone_staticdistribution flavor) is not capable of loading extension modules defined as shared libraries and only supports loading builtin extension modules statically linked into the binary.
A Windows Python distribution with a dynamically linked
standalone_dynamicdistribution flavor) is capable of loading shared library backed extension modules from the in-memory location. Other operating systems do not support the in-memory location for loading shared library extension modules.
If the current build configuration targets Linux MUSL-libc, shared library extension modules are not supported and all extensions must be statically linked into the binary.
If the object files for the extension module are available, the extension module may be statically linked into the produced binary.
If loading extension modules from in-memory import is supported, the extension module will have its dynamic library embedded in the binary.
The extension module will be materialized as a file next to the produced binary and will be loaded from the filesystem. (This is how Python extension modules typically work.)
Extension module handling is one of the more nuanced aspects of PyOxidizer. There are likely many subtle bugs and room for improvement. If you experience problems handling extension modules, please consider filing an issue.