#tree-traversal #tree #traversal #node-tree #hierarchical #depth-first

easy-tree

A simple and efficient tree structure library for Rust with recursive traversal

4 releases

0.1.3 Nov 18, 2024
0.1.2 Jun 19, 2024
0.1.1 Jun 3, 2024
0.1.0 Jun 3, 2024

#433 in Data structures

Download history 10/week @ 2024-09-16 1/week @ 2024-09-23 14/week @ 2024-09-30 3/week @ 2024-11-04 166/week @ 2024-11-18

169 downloads per month

MIT license

29KB
219 lines

easy-tree

Crates.io Documentation Build and test

easy-tree is a lightweight Rust library for managing and traversing hierarchical data structures. It provides a simple interface for creating trees and supports recursive depth-first traversal with pre- and post-processing callbacks.

Key Features

  • Depth-first traversal: Easily process nodes with callbacks before and after their subtrees.
  • Simple API: Add, modify, and retrieve nodes effortlessly.
  • Customizable traversal logic: Use callbacks to handle specific traversal behaviors.
  • Optional parallel iteration: Boost performance with rayon.

Why Use easy-tree?

  1. Designed for Hierarchical Data: Ideal for file systems, DOM structures, organizational charts, and more.
  2. Traverse with Precision: Depth-first traversal with pre- and post-order processing in one simple call.
  3. Memory Efficient: Minimal overhead with direct references between nodes.
  4. Extensible: Integrates easily into larger systems and workflows.

Installation

Add easy-tree to your Cargo.toml:

[dependencies]
easy-tree = "0.1"

To enable parallel iteration:

[dependencies]
easy-tree = { version = "0.1", features = ["rayon"] }

How It Works

Tree Structure

Each node in the tree has:

  • A data payload (your custom type)
  • A single parent (or None for the root)
  • A list of child indices

Traversal Flow

Here’s an illustration of a depth-first traversal order:

Root
├── Child 1
│   └── Grandchild 1
├── Child 2
└── Child 3

Traversal output:

  1. Visiting Child 1
  2. Visiting Grandchild 1
  3. Leaving Grandchild 1
  4. Leaving Child 1
  5. ...

Examples

1. Basic Tree Creation

use easy_tree::Tree;

fn main() {
    let mut tree = Tree::new();
    let root = tree.add_node("root");
    let child = tree.add_child(root, "child");
    let grandchild = tree.add_child(child, "grandchild");

    assert_eq!(tree.get(root), Some(&"root"));
    assert_eq!(tree.get(grandchild), Some(&"grandchild"));
}

2. Depth-First Traversal

use easy_tree::Tree;

fn main() {
    let mut tree = Tree::new();
    let root = tree.add_node("root");
    let child1 = tree.add_child(root, "child1");
    let grandchild1 = tree.add_child(child1, "grandchild1");
    let child2 = tree.add_child(root, "child2");

    let mut log = vec![];
    tree.traverse(
        |idx, data, log| log.push(format!("Visiting node {}: {}", idx, data)),
        |idx, data, log| log.push(format!("Finished node {}: {}", idx, data)),
        &mut log,
    );

    println!("{:?}", log);
}

3. Parallel Iteration (Optional)

use easy_tree::Tree;

fn main() {
    let mut tree = Tree::new();
    let root = tree.add_node(0);
    let _child1 = tree.add_child(root, 1);
    let _child2 = tree.add_child(root, 2);

    #[cfg(feature = "rayon")]
    {
        tree.par_iter().for_each(|(idx, data)| {
            println!("Processing node {}: {}", idx, data);
        });
    }
}

Use Cases

Represent a File System

use easy_tree::Tree;

fn main() {
    let mut fs = Tree::new();
    let root = fs.add_node("root/");
    let home = fs.add_child(root, "home/");
    let user = fs.add_child(home, "user/");
    let file = fs.add_child(user, "file.txt");

    println!("Tree structure:");
    fs.traverse(
        |_, data, _| println!("Entering {}", data),
        |_, data, _| println!("Leaving {}", data),
        &mut (),
    );
}

Performance

  • Low Memory Overhead: Nodes are stored contiguously in a vector.
  • Efficient Traversal: Iterative depth-first traversal minimizes recursion overhead.
  • Parallel Ready: Enable the rayon feature for concurrent processing.

Advanced Features

  1. Custom Traversal Logic: Use pre- and post-processing callbacks for fine-grained control.
  2. Flexible Node Access: Retrieve, update, or delete nodes efficiently.

Dependencies

~0–265KB