https://github.com/adriangb/graphlib2
A rust port of Python stdlib's graphlib
https://github.com/adriangb/graphlib2
Last synced: 6 days ago
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A rust port of Python stdlib's graphlib
- Host: GitHub
- URL: https://github.com/adriangb/graphlib2
- Owner: adriangb
- License: mit
- Created: 2021-11-27T10:21:36.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-15T22:06:32.000Z (about 2 years ago)
- Last Synced: 2025-04-16T15:13:45.675Z (10 days ago)
- Language: Jupyter Notebook
- Size: 3.58 MB
- Stars: 26
- Watchers: 2
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# graphlib2
This is a Rust port of Python's stdlib [graphlib].
It passes all of the standard libraries tests and is a drop in replacement.
This also happens to be Python 3.7 compatible, so it can be used as a backport.
Since usage is exactly the same as the standard libraries, please refer to their documentation for usage details.
See this project on [GitHub](https://github.com/adriangb/graphlib2).
## Example
```python
from graphlib2 import TopologicalSorter
graph = {0: [1], 1: [2]} # 0 depends on 1, 1 depends on 2
ts = TopologicalSorter(graph)
ts.prepare()
while ts.is_active():
ready_nodes = ts.get_ready()
ts.done(*ready_nodes) # all at a time or one by one
```
## Motivation
This was primarily written for [di] and for me to learn Rust.
In other words: please vet the code yourself before using this.
## Differences with the stdlib implementation
1. Added `TopologicalSorter.copy()` which copies a prepared or unprepared graph so that it can be executed multiple times.
1. Pretty solid performance improvements (see [benchmarks]).
1. Misc improvements, like working generics without postponed evaluation (`ToplologicalSorter[int]` works at runtime).
## Performance
The implementation was designed for the specific use case of adding all nodes, calling `prepare()` then copying and executing in a loop:
```python
from graphlib2 import TopologicalSorter
graph = {0: [1], 1: [2]}
ts = TopologicalSorter(graph)
ts.prepare()
while True: # hot loop
t = ts.copy()
while t.is_active():
ready_nodes = t.get_ready()
t.done(*ready_nodes)
```
This means that the focus is on the performance of `TopologicalSorter.get_ready()` and `TopologicalSorter.done()`, and only minimal effort was put into other methods (`prepare()`, `add()` and `get_static_order()`), although these are still quite performant.
## Contributing
1. Clone the repo.
1. Run `make init`
1. Run `make test`
1. Make your changes
1. Push and open a pull request
1. Wait for CI to run.
If your pull request gets approved and merged, it will automatically be relased to PyPi (every commit to `main` is released).
[di]: https://github.com/adriangb/di
[graphlib]: https://docs.python.org/3/library/graphlib.html
[benchmarks]: https://nbviewer.org/github/adriangb/graphlib2/blob/main/bench.ipynb