Managing Resources and Their Locations

An important concept in PyOxidizer packaging is how to manage resources and their locations.

A resource is some entity that will be packaged or distributed. Examples of resources include Python module bytecode, Python extension modules, and arbitrary files on the filesystem.

A location is where that resource will be placed. Examples of locations included embedded in the built binary and in a file next to the built binary.

Resources are typically represented by a dedicated Starlark type. Locations are typically expressed through a function name.

Resource Types

The following Starlark types represent individual resources:

PythonSourceModule

Source code for a Python module. Roughly equivalent to a .py file.

PythonBytecodeModule

Bytecode for a Python module. Roughly equivalent to a .pyc file.

PythonExtensionModule

A Python module defined through compiled, machine-native code. On Linux, these are typically encountered as .so files. On Windows, .pyd files.

PythonPackageResource

A non-module resource file loadable by Python resources APIs, such as those in importlib.resources.

PythonPackageDistributionResource

A non-module resource file defining metadata for a Python package. Typically accessed via importlib.metadata. This is how files in *.dist-info or *.egg-info directories are represented.

FileContent

Represents the content of a filesystem file.

There are also Starlark types that are logically containers for multiple resources:

FileManifest

Holds a mapping of relative filesystem paths to FileContent instances. This type effectively allows modeling a directory tree.

PythonEmbeddedResources

Holds a collection of Python resources of various types. (This type is often hidden away. e.g. inside a PythonExecutable instance.)

Python Resource Locations

The PythonEmbeddedResources type represents a collection of Python resources of varying resource types and locations. When adding a Python resource to this type, you have the choice of multiple locations for the resource.

In-Memory

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 __file__ attribute 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.

Filesystem-Relative

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 PythonSourceModule for the foo.bar Python module added to a PythonExecutable will be materialized as the file foo/bar.py or foo/bar/__init__.py in a directory next to the built executable.

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.

Python Resource Location Policies

When constructing a Starlark type that represents a collection of Python resources, the caller can specify a policy for what locations are allowed and how to handle a resource if no explicit location is specified. See Python Resources Policy for the full documentation.

Here are some examples of how policies are used:

def make_exe():
    dist = default_python_distribution()

    # Only allow resources to be added to the in-memory location.
    exe = dist.to_python_executable(
        name="myapp",
        resources_policy="in-memory-only",
    )

    # Only allow resources to be added to the filesystem-relative location under
    # a "lib" directory.
    exe = dist.to_python_executable(
        name="myapp",
        resources_policy="filesystem-relative-only:lib",
    )

    # Try to add resources to in-memory first. If that fails, add them to a
    # "lib" directory relative to the built executable.
    exe = dist.to_python_executable(
        name="myapp",
        resources_policy="prefer-in-memory-fallback-filesystem-relative:lib"
    )

    return exe

Routing Python Resources to Locations

Python resource collections have various APIs for adding resources to them. For example, to add a PythonSourceModule to a PythonExecutable:

def make_exe():
    dist = default_python_distribution()

    exe = dist.to_python_executable(
        name="myapp",
        resources_policy="prefer-in-memory-fallback-filesystem-relative:lib",
    )

    for resource in dist.pip_install(["my-package"]):
        if type(resource) == "PythonSourceModule":
            exe.add_in_memory_module_source(resource)
            exe.add_filesystem_relative_module_source("site-packages", resource)

These resource addition APIs are either location-aware or location-agnostic.

Location-aware APIs route a resource to a specific location, such as in-memory or filesystem-relative. Examples of these APIs include PythonExecutable.add_module_source(module) and PythonExecutable.add_filesystem_relative_python_resource(prefix, ...).

Location-agnostic APIs route a resource to an appropriate location given the resource location policy for the container. e.g. if in-memory-only is in use, resources will be routed to the in-memory location. Examples of these APIs include PythonExecutable.add_module_bytecode(module, optimize_level=0) and PythonExecutable.add_python_resources(...).

Resource addition APIs are either type-aware or type-agnostic.

Type-aware APIs require that the resource being passed in be a specific type or an error occurs. Examples of type-aware APIs include PythonExecutable.add_filesystem_relative_module_source(prefix, module) and PythonExecutable.add_in_memory_package_resource(resource).

Type-agnostic APIs operate on any instance of an allowed type. It is safe to call these APIs with any accepted type. Examples of type-agnostic APIs include PythonExecutable.add_python_resource(...) and PythonExecutable.add_in_memory_python_resources(...).

PythonExtensionModule Location Compatibility

Many resources just work in any available location. This is not the case for PythonExtensionModule instances!

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 .so or .pyd file).
• 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 libpython.
• A Windows Python distribution with a statically linked libpython (e.g. the standalone_static distribution 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 libpython (e.g. the standalone_dynamic distribution 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.

The location-agnostic addition APIs will generally try to route a resource to an intelligent location based on the policy. And these APIs are a bit smarter about their actions than what is available in Starlark. For example, these APIs can see that both a static and shared library is available for an extension module and take a course of action that won’t result in a build failure.

Note

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.