Learning Rust Part 5 - Data Structures

Introduction

Rust offers a versatile range of data structures that make working with various types of data efficient and safe. In this post, we’ll cover fundamental data structures like vectors, hash maps, and arrays, along with Rust’s powerful enums and structs for creating custom data types.

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Strings and String Slices

Rust provides two main string types:

• String: A growable, heap-allocated string.
• &str (string slice): An immutable view into a string, commonly used for read-only access.

String example:

let mut s = String::from("Hello");
s.push_str(", world!");
println!("{}", s);

String slice example:

let greeting = "Hello, world!";
let slice = &greeting[0..5]; // "Hello"

Collections

Vectors

Vectors (Vec<T>) are dynamic arrays that can grow or shrink in size, making them ideal for storing sequences of values when the size isn’t known at compile-time.

fn main() {
    let mut numbers = vec![1, 2, 3];
    numbers.push(4); // Add an element
    println!("{:?}", numbers);
}

LinkedList

A LinkedList<T> is a doubly linked list that allows fast insertion and deletion of elements at both ends of the list. It is less commonly used than Vec but can be useful when you need to insert and remove elements frequently from both the front and back of the collection.

 
use std::collections::LinkedList;

fn main() { 
    let mut list = LinkedList::new(); 

    list.push_back(1); 
    list.push_back(2); 
    list.push_front(0);

    for value in &list {
        println!("{}", value); // Prints 0, 1, 2
    }
} 

HashMap

A HashMap<K, V> stores key-value pairs, enabling efficient value retrieval based on keys.

use std::collections::HashMap;

fn main() {
    let mut scores = HashMap::new();
    scores.insert("Alice", 10);
    scores.insert("Bob", 20);

    println!("{:?}", scores.get("Alice")); // Some(&10)
}

BTreeMap

BTreeMap<K, V> is similar to HashMap but keeps keys sorted, making it useful for scenarios where sorted keys are necessary.

use std::collections::BTreeMap;

fn main() {
    let mut scores = BTreeMap::new();
    scores.insert("Alice", 10);
    scores.insert("Bob", 20);

    for (key, value) in &scores {
        println!("{}: {}", key, value);
    }
}

BinaryHeap

A BinaryHeap<T> is a priority queue implemented with a binary heap, where elements are always ordered so the largest (or smallest) element can be accessed quickly. By default, BinaryHeap maintains a max-heap, but it can be customized for min-heap operations.

 
use std::collections::BinaryHeap;

fn main() { 
    let mut heap = BinaryHeap::new(); 

    heap.push(1); 
    heap.push(5); 
    heap.push(3);

    while let Some(top) = heap.pop() {
        println!("{}", top); // Prints values in descending order: 5, 3, 1
    }
}

HashSet

A HashSet<T> is a collection of unique values, implemented as a hash table. It provides fast membership checking and is useful when you need to store non-duplicate items without any specific order.

 
use std::collections::HashSet;

fn main() { 
    let mut set = HashSet::new(); 

    set.insert("apple"); 
    set.insert("banana"); 
    set.insert("apple"); // Duplicate, ignored by the set

    println!("{:?}", set.contains("banana")); // true
    println!("{:?}", set); // {"apple", "banana"}
}

BTreeSet

A BTreeSet<T> is a sorted, balanced binary tree-based set. Like HashSet, it only stores unique values, but unlike HashSet, it maintains its items in sorted order. This makes it suitable for range queries and ordered data.

 
use std::collections::BTreeSet;

fn main() { 
    let mut set = BTreeSet::new(); 

    set.insert(10); 
    set.insert(20); 
    set.insert(15);

    for value in &set {
        println!("{}", value); // Prints 10, 15, 20 in sorted order
    }
}

VecDeque

A VecDeque<T> (double-ended queue) is a resizable, efficient data structure that supports adding and removing elements from both the front and back. It’s ideal for queue-like operations where both ends need to be accessible.

 
use std::collections::VecDeque;

fn main() { 
    let mut deque = VecDeque::new(); 

    deque.push_back(1); 
    deque.push_front(0);

    println!("{:?}", deque.pop_back()); // Some(1)
    println!("{:?}", deque.pop_front()); // Some(0)

} 

Option and Result Types

Rust’s Option and Result types are enums that enable safe handling of optional values and errors. We went over error handling in part 3 of this series.

Option

The Option<T> type represents an optional value: Some(T) for a value, or None if absent.

fn find_element(index: usize) -> Option<i32> {
    let numbers = vec![1, 2, 3];
    numbers.get(index).copied()
}

fn main() {
    match find_element(1) {
        Some(number) => println!("Found: {}", number),
        None => println!("Not found"),
    }
}

Result

The Result<T, E> type is used for functions that may succeed (Ok(T)) or fail (Err(E)), promoting explicit error handling.

fn divide(a: f64, b: f64) -> Result<f64, &'static str> {
    if b == 0.0 {
        Err("Cannot divide by zero")
    } else {
        Ok(a / b)
    }
}

Custom Data Types and Enums

Rust’s enums and structs allow you to create custom data types, essential for building complex and expressive programs.

Enums

Rust’s enums can hold different types of data within each variant, enabling versatile data representations.

enum Message {
    Text(String),
    Image { url: String, width: u32, height: u32 },
    Quit,
}

fn main() {
    let msg = Message::Text(String::from("Hello"));
    match msg {
        Message::Text(text) => println!("Text: {}", text),
        Message::Image { url, width, height } => println!("Image at {}, size: {}x{}", url, width, height),
        Message::Quit => println!("Quit message"),
    }
}

Structs and Tuple Structs

Structs allow for creating complex types with named fields.

struct Person {
    name: String,
    age: u8,
}

fn main() {
    let person = Person {
        name: String::from("Alice"),
        age: 30,
    };
    println!("Name: {}, Age: {}", person.name, person.age);
}

Tuple structs are useful for grouping values without naming fields, often for simpler data types.

struct Color(u8, u8, u8);

fn main() {
    let red = Color(255, 0, 0);
    println!("Red: {}, {}, {}", red.0, red.1, red.2);
}

Arrays, Slices, and Compile-Time Length Arrays

Arrays

Arrays in Rust are fixed-size collections of elements with known length at compile-time. They’re stack-allocated, offering efficiency and safety.

fn main() {
    let numbers: [i32; 3] = [1, 2, 3];
    println!("First element: {}", numbers[0]);
}

Slices

Slices provide a way to view sections of an array or vector, avoiding the need to copy data.

fn main() {
    let numbers = [1, 2, 3, 4];
    let slice = &numbers[1..3];
    println!("{:?}", slice); // [2, 3]
}

Slices work with both arrays and vectors and are typically used as function parameters to avoid copying large data structures.

Reference Counting

Rc<Vec<T>> and Arc<Vec<T>>

Rc (Reference Counting) and Arc (Atomic Reference Counting) are common wrappers around collections like Vec to allow multiple ownership. Rc is single-threaded, while Arc is thread-safe, and both are used frequently for sharing collections between parts of a program.

 
use std::rc::Rc; 
use std::sync::Arc;

fn main() { 
    let vec = vec![1, 2, 3]; 
    let shared_rc = Rc::new(vec.clone()); 
    let shared_arc = Arc::new(vec);

    println!("Rc count: {}", Rc::strong_count(&shared_rc)); // Rc count: 1
    println!("Arc count: {}", Arc::strong_count(&shared_arc)); // Arc count: 1

} 

Summary

Rust’s data structures—from collections like Vec and HashMap to custom types with struct and enum—enable flexible, efficient, and safe data handling. With tools like Option and Result, Rust enforces a safety-first approach without compromising on performance, making it an ideal language for robust application development.