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.

Example

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.
2. Pretty solid performance improvements (see benchmarks).
3. Returns generic iterables from TopologicalSorter.get_ready() and TopologicalSorter.static_order() instead of a tuple and generator respectively like the standard library does. Currently, these are both lists, but this should be considered an implementation detail.
4. Misc improvements, like working generics without postponed evaluateion (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:

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.
2. Run make init
3. Run make test
4. Make your changes
5. Push and open a pull request
6. 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).