treeshaker 0.0.3

A tree-shaking tool for Python.

treeshaker

A tree-shaking tool for Python.

What it does

If you give treeshaker a root environment (specified by a requirements.txt file), a list of target packages target_packages (in this case mypkg and otherpkg), and a target module (in this case mypkg.target), treeshaker will create an output directory that contains

1. the subset of modules among all the modules in all the target_packages that are needed to import the target module, copied as .py files directly into the root of the output directory and
2. a new requirements.txt file containing the subset of the root requirements.txt lines needed to import all the modules in (1)

An end user can then open the folder, pip install -r requirements.txt, and import the target module, without installing or obtaining the complete code for any of the target_packages.

Quick start

1. Install treeshaker:

   $ pip install treeshaker
   

2. Define your root environment in a requirements.txt file and ensure the listed packages are available in your current Python environment. If you don't have a requirements.txt file yet, you can run

   $ pip install <package that provides target module>
   $ pip freeze > requirements.txt
   

   If you already have a requirements.txt file, you can instead run

   $ pip install -r requirements.txt
   

3. Create a configuration file (see the example config file for reference), specifying the location of your root requirements.txt file as well as the details of what targets should be built and how.

4. Run treeshaker:

   $ treeshaker
   

Demo

1. git clone https://github.com/sclabs/treeshaker.git
2. cd treeshaker
3. virtualenv venv
4. source venv/bin/activate
5. pip install . ./examples/mypkg ./examples/otherpkg ./examples/externaldep ./examples/otherdep
6. treeshaker --config examples/treeshaker.cfg

To clean up after the demo run

$ python examples/clean.py

Important features

Building multiple targets versus building one target

To build one specific target, run

$ treeshaker --target <target_module_name>

treeshaker will find the configuration file section [target:<target_module>] (which must exist) to determine the outdir (a required key in every [target:<target_module>] section) and all other parameters for the build.

To build a list of targets, add the desired targets to the [targets] section (as keys with no values) and run

$ treeshaker

Module renaming

The copied modules will be renamed if necessary to avoid name conflicts.

If one copied module is mypkg.dep and another is otherpkg.dep, these will be renamed to dep_mypkg.py and dep_otherpkg.py, respectively.

Import rewriting

During module renaming, treeshaker will attempt to rewrite import statements in the copied modules as necessary. The logic for doing this is quite rudimentary and is only expected to succeed when the import is of the form

import mypkg.mymod

or

from mypkg.mymod import not_a_module

In these cases, when mypkg.mymod is copied, it will be renamed and a simple find-and-replace will "fix" the import statement.

The following form of import is expected to fail:

from mypkg import mymod

Relative imports are generally also expected to fail, though they may work when importing a sibling module whose name has no conflicts.

Configuration file inheritance

If a key is missing from a [target:<target_module>] section, treeshaker will fall back to looking for the key in the [target] section.

The example configuration file defines many keys in the [target] section, but all of these can be overridden on a per-target basis by adding the key to the corresponding [target:<target_module>] section.

<outdir> interpolation

You can use the special string <outdir> in the [target] section or any [target:<target_module>] section and it will be replaced with the actual outdir at runtime.

Automatic documentation

Functions and/or classes listed in a functions key and python-fire components listed in a fire_components key in the configuration file will be documented in <outdir>/README.md, using their docstring or fire help page, respectively.

You can also manually write a README section that will be included in <outdir>/README.md by specifying a filename in a readme key. If this file does not exist on the disk, it will not be included.

Custom configuration file name

To run treeshaker using a specific configuration file, run

$ treeshaker --config path/to/customconfig.cfg

How does it work?

Dependency graph construction

Python provides modulefinder in the standard library, but it is not quite powerful of flexible enough to accomplish what we want. Instead, we use the modulegraph library, which is both performant (thanks to the useful excludes argument) and flexible (it creates a dependency graph that we can analyze in detail to classify imported modules).

Matching PyPI package names to root module names

How can treeshaker determine that if you import a module from sklearn, the corresponding requirements.txt line is e.g., scikit-learn==0.22.1?

pipreqs has compiled a mapping of PyPI names to root module names for many PyPI packages; we use this list to perform this matching. We considered adding a dependency on pipreqs, but we decided to vendor it ourselves to avoid an additional seven dependent package installs (we only use the single file that contains the mapping).

For consistency, we choose to use the actual module name (the one used in imports and the one that reflects the location of the package on-disk after install) to specify the target_packages.

Caveats

While treeshaking is a cool concept, there's rarely any real need to perform treeshaking on Python code. This library was created for a highly specific use case: automatically minimizing the amount of code revealed from a monolithic private codebase when providing code for one specific module (or more specifically, the task that module performs).

In a monolithic library design, it's possible to have a very large number of modules and a large number of tasks that the library can accomplish. It might be better to apply the "single responsibility principle" and design a set of libraries that each do one task, but what happens when the monolith already exists and a redesign is not feasible? In most situations, there is no harm in distributing the entire library even if the person requesting the code wants to accomplish just one task. However, when the library code is private, it may be desirable to minimize the total amount of code "exposed" or "revealed" to the requestor.