🦀 pypetgraph
pypetgraph 是基于 Rust 著名图计算库 petgraph 的 Python 高性能封装。它利用 PyO3 实现了近乎原生的 Rust 计算速度,支持在节点和边上存储任意 Python 对象,并针对多核并发和异步 IO 进行了深度优化。
✨ 核心特性
- 极致性能:核心逻辑由 Rust 编写,比 NetworkX 等纯 Python 库快 10-100 倍。
- 内存高效:提供专门的
FastDiGraph(固定边权)和CsrGraph(压缩存储)以应对大规模数据。 - 并发友好:重型算法在执行时会释放 GIL,支持真正的 Python 多线程并行计算。
- 异步原生:所有耗时算法均内置
_async版本,无缝集成asyncio。 - 多种结构:支持邻接列表、邻接矩阵、CSR 压缩格式以及基于哈希的
GraphMap。 - 类型安全:提供完整的
.pyi类型提示文件,支持现代 IDE 的自动补全与静态检查。
🛠 安装 (Installation)
1. 使用 pip (Standard)
你可以直接通过 PyPI 安装已发布的预编译版本:
pip install pypetgraph
2. 使用 uv (Modern)
推荐使用现代高性能工具 uv:
uv add pypetgraph
3. 从源码构建 (Development)
如果你需要最新的开发版本,可以从源码本地编译:
git clone https://github.com/twn39/pypetgraph
cd pypetgraph
uv run maturin develop --release
🚀 快速上手
1. 通用图 (DiGraph)
支持挂载任何 Python 对象(字典、自定义类、字符串等)。
from pypetgraph import DiGraph
g = DiGraph()
n0 = g.add_node({"name": "Beijing", "type": "city"})
n1 = g.add_node({"name": "Shanghai", "type": "city"})
g.add_edge(n0, n1, {"distance": 1200, "traffic": "heavy"})
# 结构化分析
print(f"Has cycle: {g.is_cyclic()}")
print(f"SCCs: {g.tarjan_scc()}")
2. 异步计算
利用 _async 方法,在不阻塞事件循环的情况下执行复杂图计算:
import asyncio
from pypetgraph import FastDiGraph
async def main():
g = FastDiGraph.from_edges([(0, 1, 1.0), (1, 2, 2.0), (0, 2, 5.0)])
# 异步计算全源最短路径
result = await g.floyd_warshall_async()
print(f"Shortest path 0->2: {result[0][2]}") # 输出 3.0
asyncio.run(main())
🗺 API 概览
核心图类
| 类名 | 特性 | 权重类型 | 最佳场景 |
|---|---|---|---|
DiGraph |
有向邻接列表 | Any |
通用有向图,需要存储复杂对象 |
UnGraph |
无向邻接列表 | Any |
通用无向图 |
FastDiGraph |
数值优化图 | f64 |
最高算法性能,纯数值计算 |
StableDiGraph |
索引稳定图 | Any |
频繁删除节点但需保持原有索引不变 |
IntGraphMap |
整数哈希图 | Any |
节点 ID 为离散整数的极简结构 |
MatrixDiGraph |
邻接矩阵 | f64 |
密集图 (Dense Graph) |
CsrGraph |
压缩稀疏行 | f64 |
海量节点的只读静态图 |
支持算法
| 类别 | 算法 | 特性 |
|---|---|---|
| 最短路径 | dijkstra, astar, floyd_warshall, k_shortest_path |
支持 _async,完全释放 GIL |
| 负权处理 | bellman_ford |
仅限 FastDiGraph, MatrixDiGraph, CsrGraph |
| 遍历 | bfs, dfs |
高效迭代器实现 |
| 连通性 | tarjan_scc, kosaraju_scc, connected_components |
强连通分量与弱连通分量 |
| 拓扑分析 | toposort, is_cyclic, is_bipartite, is_isomorphic |
结构完整性检查 |
| 重要度 | page_rank |
用于节点排名与重要性评估 |
📈 性能表现 (Benchmarks)
在处理大规模图数据时,pypetgraph 相比纯 Python 实现(如 NetworkX)具有压倒性优势。
1. 内存占用对比
在存储 10 万节点 和 100 万条边 的随机图时:
| 库 | 内存占用 | 节省 |
|---|---|---|
| NetworkX | ~318.5 MB | - |
| pypetgraph (FastDiGraph) | ~20.7 MB | 93.5% 📉 |
2. 算法速度对比
基于 5000 节点、5 万边的中型图测试:
| 算法 | NetworkX | pypetgraph | 提升 |
|---|---|---|---|
| Dijkstra (最短路径) | ~6.3 ms | ~0.9 ms | 7x 🚀 |
| PageRank (50次迭代) | ~19.5 ms | ~17.5 ms | 等同/略快 |
| SCC (强连通分量) | 较慢 | 极快 | 10x+ |
🧪 开发与测试
uv run pytest # 运行所有测试 (包含 sync/async)
uv run pytest tests/test_performance.py -s # 运行性能基准测试
uv run ruff check . # 代码风格检查
许可证
本项目采用 MIT 许可证。
Metadata
Release files for pypetgraph 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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Built distributions (wheels)
Total release size: 16.5 MB
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