Going In With Rust: The Interview Prep Guide for the Brave (or the Mad)

Why Rust? (No, Seriously, Why?)

Choosing Rust as your language for a coding interview is, at best, bold — and at worst, borderline reckless. Rust is not known for being ergonomic under time pressure. Unlike Python or Java, it doesn’t hold your hand when you’re fumbling through edge cases and messy input. The compiler is famously strict, and while that’s great for production code, it can feel like an enemy in a 45-minute interview. Forget about “just trying something” — Rust will make you justify every move. Want to mutate a value inside a loop? Better understand borrowing rules. Need a linked list? Be ready to bring in Rc, RefCell, and a prayer. Want to pass a slice around without cloning everything? Ownership will make you earn it. Many LeetCode problems assume a fast, flexible language — Rust is neither of those, out of the box. There’s no REPL. You can’t “just print stuff” — not without fighting the borrow checker and formatting macros. Even simple operations, like splitting a string, can require ceremony (split_whitespace, collect::<Vec<_>>(), and more). Most interviewers don’t expect Rust, and they won’t help you debug compile errors. Worse, many LeetCode-style platforms don’t have great Rust support or tooling. Even getting the signature right (fn something(nums: Vec<i32>) -> i32) can be weirdly non-obvious at first. You’ll spend time fighting syntax instead of solving the problem — unless you're extremely prepared. Most coding interviews reward speed, intuition, and fast feedback — Rust doesn’t offer that naturally. While others are cranking out brute-force solutions in Python in under 10 minutes, you’re still fixing lifetime issues. The mental tax of tracking ownership, lifetimes, and mutability adds up fast under pressure.
Simply put: Rust was built for correctness and safety — not speed and comfort.
And yet… if you master it, it will be your sword and your shield — and that’s what the rest of this guide is for.

Why the Hell Would You Use Rust Anyway?

Sure, Rust in an interview can feel like walking into a boxing match with a sword no one asked for. But there are legitimate reasons to bring it, as long as you know exactly what you're doing. Here’s when choosing Rust actually makes sense — or at least isn’t totally insane:

The company uses Rust in production (and wants you to, too). If you're interviewing at a company where Rust is part of the tech stack — say, a systems-level project, embedded software, or infrastructure tooling — using Rust in the interview can send a strong signal. It shows you're fluent in the company’s preferred language, and not just as a side project. Even if it's not required, demonstrating proficiency in Rust might earn bonus points from Rust-loving engineers on the panel. More importantly, it helps them evaluate how you'd actually write production code in their ecosystem. This is one of the only times using Rust in an interview isn’t an uphill battle — it's just smart alignment.
You’re applying to a role that demands control over performance, safety, and memory. For jobs that deal with performance-critical paths, memory management, or low-level optimization, Rust isn't just accepted — it’s respected. In such domains, your ability to write zero-cost abstractions, avoid data races, and manage memory without garbage collection becomes a competitive advantage. If your interviewers know these constraints exist in their production environment, seeing you code in Rust might be exactly what they want. Plus, it gives you the opportunity to discuss real trade-offs in safety and performance — something that scripting languages simply abstract away. So if the role is close to the metal, Rust can help you stand out.
You’re so comfortable with Rust that it’s your fastest and cleanest language. Let’s be honest — this one is rare. But if you’ve written thousands of lines of Rust, and you think in iterators, lifetimes, and pattern matching, then using Rust might actually reduce your cognitive overhead. That means you’ll write safer code, make fewer mistakes, and stay in the flow — even under time pressure. This is especially true if other languages now feel clunky or verbose to you by comparison. But this reason only holds up if you’re truly fluent, not just enthusiastic — otherwise the compiler will eat you alive.
You want to stand out (in a good way). In a sea of Python and Java solutions, a clean, efficient Rust implementation can draw attention. It shows you're not afraid to pick the harder path if it leads to better code — and that you care deeply about correctness and performance. Some interviewers will respect the boldness, and if they know Rust themselves, they might even enjoy it. But beware: standing out only helps if your code is solid. If you're constantly fixing borrow checker issues, you won’t seem clever — you'll seem underprepared.
You’re optimizing for long-term growth, not just short-term wins. If your goal is to use this interview process to build serious Rust interview muscle — perhaps because you plan to target Rust-heavy companies long-term — then the pain might be worth it. You’ll be forced to master Rust's most practical features, not just tinker with toy projects. Interviewing in Rust is one of the fastest ways to expose the gaps in your knowledge. Yes, it’s brutal. But it’s also effective. And if you're playing the long game, that pressure might forge real Rust fluency.

Back to the Primitives: Rust’s Building Blocks

Before you implement a Trie with smart pointers and interior mutability, you’ll be pushing numbers around and toggling booleans. Most LeetCode problems boil down to working with primitive types, and in Rust, it pays to know exactly how they behave — especially under pressure. This section covers all the essential primitive types, their behavior, conversions, and the little gotchas that can cost you precious minutes in an interview.

Integer Types: `i32`, `i64`, `u32`, `usize`, etc.

Use i32 by default unless you know the input size demands something else — it's the most commonly expected integer type on LeetCode.
Use usize for indexing arrays and slices. Many methods (like .len()) return usize, and trying to use i32 without conversion will cause type mismatches.

let nums: Vec<i32> = vec![1, 2, 3];
for i in 0..nums.len() {
    println!("{}", nums[i]); // i is usize, nums[i] works
}

Conversion between integer types:

let a: i32 = 42;
let b: usize = a as usize;

Watch out for overflow — Rust panics in debug mode but silently wraps in release unless you use checked_*, wrapping_*, or overflowing_* methods.

let x: u8 = 255;
// let y = x + 1; // panic in debug!
let y = x.wrapping_add(1); // y = 0

Floating Point Numbers: `f32` and `f64`

Use f64 unless constrained otherwise — it's the default floating point type and avoids precision issues.
Use .abs(), .sqrt(), .powi(), .powf() for mathematical operations.

let x: f64 = -2.5;
println!("{}", x.abs()); // 2.5

Avoid comparing floats directly due to precision issues:

let a = 0.1 + 0.2;
let b = 0.3;
println!("{}", (a - b).abs() < 1e-9); // true

Boolean: `bool`

The bool type is true or false, nothing fancy.
Logical operators: !, &&, ||

let a = true;
let b = false;
println!("{}", a && !b); // true

Often used in DP arrays and visited flags for graphs, so get comfortable with vec![false; n].

Character: `char`

Unlike many languages, Rust’s char is 4 bytes and represents a Unicode scalar value.
Use .is_digit(), .is_alphabetic(), .is_uppercase() etc. for classification.

let c = 'a';
println!("{}", c.is_alphabetic()); // true

Convert a char to u8 or usize for indexing:

let idx = (c as u8 - b'a') as usize;

Copy Trait (and why it matters)

All primitive types implement the Copy trait.
You don’t need to worry about ownership when passing them around — they’re cheap and copyable.

fn add(a: i32, b: i32) -> i32 {
    a + b // no need to borrow or move
}

Useful Standard Methods

i32::MAX, i64::MIN — handy when simulating integer bounds.
.abs(), .signum() — quick sign logic.
.count_ones(), .leading_zeros() — useful in bit manipulation problems.
.to_string() and str::parse() for converting between strings and primitives.

let n = 42;
let s = n.to_string();
let m: i32 = s.parse().unwrap();

When Types Don’t Match: Fixing Quickly

.clone() is rarely needed with primitives — just cast or convert directly.
Most type mismatches are between usize and i32, especially in loops.
Use as for fast casting:

let i: usize = 0;
let j: i32 = i as i32;

Finding `min` / `max` Between Numbers

Use std::cmp::min and std::cmp::max for comparing two values:

use std::cmp;

let a = 10;
let b = 42;
let smaller = cmp::min(a, b);
let larger = cmp::max(a, b);

You can also use the .min() and .max() methods when chaining or comparing more than two values:

let result = a.max(b).max(c); // find max of three numbers

These are pure functions, work for any type that implements Ord, and are great for clean, readable comparisons.
Avoid writing your own if-else blocks for simple min/max logic — these are faster, cleaner, and more idiomatic.

String Theory (The LeetCode Edition)

Strings are everywhere in LeetCode — from classic problems like “Reverse Words in a String” to more nuanced ones like “Valid Palindrome” or “Implement strStr()”. Rust strings are fast, safe, and Unicode-correct — but they’re also more explicit and strict than in most languages. If you don’t know how to handle String and &str properly, your time will be devoured by type mismatches, lifetime errors, and panics on invalid indexing. This section gives you everything you need to treat strings as tools, not traps.

String vs. &str (Heap vs. Stack Storage)

Rust has two main string types: String and &str.
A String is an owned, heap-allocated growable string. It’s like Vec<u8> under the hood with UTF-8 semantics. You use it when you need to build or mutate strings.
On the other hand, &str is a string slice — a borrowed view into a string. It’s usually what you get from string literals ("hello") or when slicing another string.
Most standard library functions accept &str, so you often borrow a String rather than clone or convert it.
Knowing when to use each (and how to switch between them) is key to avoiding unnecessary allocations or compiler complaints.

Converting Between `String` and `&str`

To convert a String to a &str, just borrow it with &. This is zero-cost and preferred:

let s: String = String::from("hello");
let slice: &str = &s;

To go the other way — from &str to String — you allocate with .to_string() or String::from():

let s: &str = "hello";
let owned: String = s.to_string();

Use to_owned() as a shortcut for to_string() when cloning a &str.
Conversions like these are common when passing data to functions or returning owned strings from algorithms.

Splitting a String (`split()`, `split_whitespace()`)

Splitting strings is a core operation for problems like “Reverse Words in a String” or token parsing.
Use .split() when you need to divide based on a specific delimiter (like commas or semicolons):

let s = "a,b,c";
let parts: Vec<&str> = s.split(',').collect();

Use .split_whitespace() when breaking on arbitrary spaces, tabs, or line breaks:

let input = " hello   world\tfrom rust ";
let words: Vec<&str> = input.split_whitespace().collect(); // ["hello", "world", "from", "rust"]

These methods are lazy iterators, so you can filter, map, or count tokens without collecting if needed.
Always remember that split returns borrowed &str slices, not owned Strings.

Reversing a String (`chars().rev().collect()`)

Rust strings can’t be reversed by indexing due to UTF-8 encoding.
Instead, use the .chars() iterator, which yields Unicode chars (not bytes), then .rev() and .collect() to build a new string:

let reversed: String = "rust".chars().rev().collect(); // "tsur"

This is useful for palindrome problems or any reverse-based manipulation.
Be cautious — reversing graphemes (emoji, accents) is not the same as reversing chars, but for LeetCode purposes, chars() usually does the job.
If performance matters and the string is ASCII-only, you might choose as_bytes() instead for raw byte reversal.

Converting a String to Bytes (`as_bytes()`)

Use .as_bytes() to get a byte slice (&[u8]) from a String or &str:

let s = "abc";
let b = s.as_bytes(); // &[97, 98, 99]

This is essential in problems that involve hashing, XOR, or byte-level operations.
Since strings in Rust are UTF-8 encoded, each char may take multiple bytes, so be careful when working at this level.
You can safely slice bytes only at valid UTF-8 boundaries — invalid slicing will panic at runtime.
Use .bytes() iterator if you want to iterate over each byte instead of getting a slice.

Efficient String Searching (`find()`, `contains()`)

Use .find() to locate the first index of a substring:

let s = "rustacean";
if let Some(pos) = s.find("ace") {
    println!("Found at index: {}", pos);
}

Use .contains() to simply check presence:

if s.contains("ace") {
    println!("It’s in there!");
}

Both are fast and safe — great for problems like implementing strStr() or substring search.
These functions operate on &str and are Unicode-safe.
Keep in mind that positions returned by .find() refer to byte offsets, not character indices.

Concatenation (`push()`, `push_str()`)

To build strings incrementally, use .push(char) and .push_str(&str):

let mut result = String::new();
result.push('a');
result.push_str("bc");

This is efficient and avoids unnecessary allocations, especially in string-building problems like "Zigzag Conversion" or "Longest Common Prefix".
Don’t use + unless you know what you’re doing — it consumes the left-hand String, which may not be what you want:

let s = String::from("a") + "b"; // s is now "ab", but the original left-hand `String` is moved

If you need to combine many strings, consider format!() or collect from an iterator.

Pattern Matching with Strings (`starts_with()`, `ends_with()`)

For prefix/suffix problems (e.g., “Longest Common Prefix”), use:

let s = "rustacean";
s.starts_with("rust"); // true
s.ends_with("cean");   // true

These are more readable and safer than manually slicing strings.
They also avoid panic risks from invalid index slicing.
You can even match against char or pattern tuples (like a list of prefixes).
These functions work with any &str — no need to convert or allocate.

Converting String to Numbers

Use .parse::<T>() to convert strings to numbers. You must annotate the type, or use turbofish (::<i32>):

let s = "42";
let n: i32 = s.parse().unwrap();

This is critical for reading input when numbers are space-separated strings.
For bulk conversion, map over a split iterator:

let input = "1 2 3";
let nums: Vec<i32> = input.split_whitespace().map(|s| s.parse().unwrap()).collect();

Always handle parsing carefully — unwrap() will panic if the input is malformed.

Formatting Strings

Use format!() to create a new string with embedded values:

let name = "Rust";
let s = format!("Hello, {}!", name);

This is like println!() but returns a String instead of printing.
It’s great for cleanly building strings, especially in problems where you need to return a custom message or formatted output.
Supports {} for default display, and {:?} for debug output.
Also allows width, alignment, and precision settings.

Creating a String from Bytes

You can build a String from bytes using String::from_utf8() — this is fallible, because invalid UTF-8 is possible:

let bytes = vec![104, 101, 108, 108, 111];
let s = String::from_utf8(bytes).unwrap(); // "hello"

Use this when you're manipulating data at the byte level (e.g., decoding, binary protocols).
If you're certain the bytes are valid UTF-8, from_utf8_unchecked() can skip checks (unsafe).
Never blindly convert arbitrary bytes — malformed UTF-8 will cause runtime panics.
For ASCII-only input, this method is safe and convenient.

Removing Whitespace (`trim()`, `trim_start()`, `trim_end()`)

Whitespace cleanup is essential in problems like “Reverse Words in a String”, or any case where you're normalizing input.

.trim() removes leading and trailing whitespace (spaces, tabs, newlines, etc.):

let raw = "   hello world   ";
let cleaned = raw.trim(); // "hello world"

Use .trim_start() or .trim_end() to target just one side:

let s = "   hello ";
let left = s.trim_start(); // "hello "
let right = s.trim_end();   // "   hello"

These methods return &str, so they don’t allocate — use them inline or convert to String if needed:

let owned = s.trim().to_string();

This is critical when parsing input strings or validating formats — always sanitize before processing.

Changing Case (`to_lowercase()`, `to_uppercase()`)

Case conversion is common in string normalization, anagram problems, and input validation.

Use .to_lowercase() or .to_uppercase() on String or &str:

let s = "RustLang";
let lower = s.to_lowercase(); // "rustlang"
let upper = s.to_uppercase(); // "RUSTLANG"

These return new String values — they allocate, so don’t overuse in tight loops.
Works properly with Unicode (so “ß”.to_uppercase() becomes “SS”).
For individual chars, you can also use:

let c = 'R';
let lc: String = c.to_lowercase().collect();

Case conversion is especially useful in problems like “Valid Anagram”, “Palindrome”, and case-insensitive matching.

Iterating Over Characters (`.chars()`)

Use .chars() when you want to process each Unicode scalar value (e.g. letter, digit, emoji, etc.).

let s = "hello";
for c in s.chars() {
    println!("{}", c); // 'h', 'e', 'l', 'l', 'o'
}

Returns char values — not bytes.
Good for checking characters one-by-one (is_digit, is_alphabetic, palindrome checks, etc.).
Safe for Unicode, but cannot be indexed directly — .chars().nth(i) is O(n), not constant-time.

Iterating Over Bytes (`.bytes()`)

Use .bytes() when you're only dealing with ASCII strings or need raw byte access.

let s = "abc";
for b in s.bytes() {
    println!("{}", b); // 97, 98, 99
}

Each item is a u8.
Perfect for problems that assume lowercase letters (a to z), binary strings, or ASCII-only content.
Much faster than chars() for these cases, and allows random access via .as_bytes().

Accessing an Arbitrary Character

⚠️ Rust does not allow direct indexing of String or &str like s[3]. This would panic or be a compile error, because Rust strings are UTF-8 and each character can take variable bytes.

Slow (but safe): `.chars().nth(i)`

let c = s.chars().nth(2).unwrap();

This is O(n) time, so avoid it in performance-critical loops.

Faster Alternative: Convert to `Vec<char>`

let chars: Vec<char> = s.chars().collect();
let c = chars[2];

Allows fast random access (O(1)), at the cost of allocation and memory.
Best used when you need to access the same string multiple times by index (e.g. in a palindrome loop).

Fastest (ASCII-only): Use `.as_bytes()`

let s = "abc";
let b = s.as_bytes();
let c = b[1] as char; // 'b'

Works only if you're guaranteed the string is ASCII (LeetCode often makes this assumption).
Super efficient and indexable.
Use for problems involving lowercase letters, digits, etc.

Arrays and Slices: Rust’s Core Data Structures

In LeetCode challenges, efficient data manipulation is crucial, and Rust's arrays and slices are fundamental tools in your arsenal. Understanding their nuances, from initialization to iteration, can significantly enhance your problem-solving efficiency. This section delves into the essential operations and methods associated with arrays and slices in Rust, ensuring you're well-prepared for any related challenges.

Understanding Arrays and Slices in Rust

Arrays are fixed-size collections of elements of the same type, stored contiguously in memory. Their size is known at compile time, making them stack-allocated and highly efficient for scenarios where the number of elements is constant.
```
let arr: [i32; 5] = [1, 2, 3, 4, 5];
```
Slices are dynamically-sized views into arrays or other sequences. They allow for referencing a contiguous sequence of elements without owning them, making them versatile for various operations where the size isn't known at compile time.
```
let slice: &[i32] = &arr[1..4]; // Contains elements [2, 3, 4]
```

Understanding when to use arrays versus slices is pivotal. Arrays offer performance benefits due to their fixed size, while slices provide flexibility, especially when dealing with subsets of data or when interfacing with functions that operate on sequences of varying lengths.

Initialization and Access

Initializing Arrays:

With Specific Values:
```
let arr = [1, 2, 3, 4, 5];
```
With Default Values:
```
let zeros = [0; 5]; // [0, 0, 0, 0, 0]
```

Accessing Elements:
- By Index:
```
let first = arr[0]; // Accesses the first element
```
  Rust performs bounds checking at runtime, ensuring safety by preventing out-of-bounds access, which would result in a panic.
- Slicing:
```
let slice = &arr[1..4]; // Contains elements [2, 3, 4]
```
  Slices provide a view into a portion of the array without copying data, which is efficient for operations requiring subsets of data.

Iterating Over Arrays and Slices

Using for Loops:
```
for &item in &arr {
    println!("{}", item);
}
```
Borrowing the array or slice allows for iteration without transferring ownership, maintaining the integrity of the original data.
Enumerating Indices and Values:
```
for (index, &value) in arr.iter().enumerate() {
    println!("Index: {}, Value: {}", index, value);
}
```
This approach is particularly useful when the position of each element is significant, such as in searching or sorting algorithms.

Common Operations and Methods

Finding Elements:
- Checking for Existence:
```
if arr.contains(&target) {
    println!("Element found!");
}
```
  The contains method provides a concise way to verify the presence of an element.
- Finding Position:
```
if let Some(pos) = arr.iter().position(|&x| x == target) {
    println!("Element found at position {}", pos);
}
```
  Determining the index of an element is essential in scenarios where the position dictates subsequent operations.
Sorting:
- In-Place Sorting:
```
let mut arr = [3, 1, 4, 1, 5];
arr.sort();
```
  Sorting is a common operation in problems requiring ordered data, such as merging intervals or finding medians.
- Custom Sorting:
```
arr.sort_by(|a, b| b.cmp(a)); // Sorts in descending order
```
  Custom sorting allows for flexibility in ordering criteria, accommodating problem-specific requirements.
Reversing:
- In-Place Reversal:
```
arr.reverse();
```
  Reversing an array or slice is often utilized in problems involving palindrome checks or reversing sequences.
Splitting:
- Based on a Predicate:
```
let parts: Vec<&[i32]> = arr.split(|&x| x == delimiter).collect();
```
  Splitting is useful in partitioning data based on specific conditions, aiding in problems that require segmenting input.

Conversion Between Arrays, Slices, and Vectors

From Array to Slice:
```
let slice: &[i32] = &arr;
```
Slices provide a view into arrays without ownership, facilitating functions that operate on sequences without requiring ownership transfer.
From Slice to Vector:
```
let vec: Vec<i32> = slice.to_vec();
```
Converting slices to vectors is beneficial when dynamic resizing or ownership of the data is needed.
From Vector to Slice:
```
let slice: &[i32] = &vec;
```
Borrowing a vector as a slice allows for read-only access to its elements, useful in contexts where mutation isn't required.

Safety and Performance Considerations

Bounds Checking: Rust ensures safety by performing bounds checking during element access. While this prevents common errors like buffer overflows, it's essential to be mindful of potential performance implications in performance-critical sections.
Mutability: To modify elements within an array or slice, ensure they are declared as mutable:
```
let mut arr = [1, 2, 3];
arr[0] = 10;
```
Mutability allows for in-place modifications, which is efficient in scenarios requiring frequent updates to the data.
Borrowing vs. Owning: Understanding when to borrow (&[T]) versus when to own (Vec<T>) is crucial. Borrowing is efficient and avoids unnecessary allocations, while owning is necessary when the data needs to be modified or stored beyond the current scope.

Getting the Length of an Array or Slice

Fixed-Size Arrays

You can use .len() to get the number of elements in a fixed-size array:

let arr = [10, 20, 30];
println!("{}", arr.len()); // Output: 3

arr.len() returns a usize.
This works even though the size is known at compile time.
Safe, fast, and constant time.

Slices

Slices (&[T]) also use .len() to return their length:

let slice = &arr[1..];
println!("{}", slice.len()); // Output: 2

.len() on slices returns how many elements are visible through the slice.
Very useful when iterating, partitioning, or checking edge cases.

When to Use `.len()` in LeetCode Problems

Looping:

for i in 0..arr.len() {
    // safe, bounds-checked access
}

Early Returns:
```
if arr.len() < 2 {
    return false;
}
```

Slice validation:

if &arr[..3].len() == 3 {
    // do something
}

Vec: The Workhorse of Rust

When it comes to solving LeetCode problems in Rust, Vec<T> is your best friend. It’s the standard growable, heap-allocated array type — essentially what Python and Java call a list or an ArrayList. Almost every single LeetCode problem involving dynamic data structures will use Vec. If you master it, you’ll write cleaner, safer, and more performant solutions. This section covers everything you need to know about Vec in an interview setting — no fluff, no missed essentials.

Creating Vectors

let empty: Vec<i32> = Vec::new();              // Explicit type
let filled = vec![1, 2, 3];                    // Macro, preferred
let zeros = vec![0; 5];                        // [0, 0, 0, 0, 0]

Use vec![...] macro for clarity and brevity.
Use vec![value; n] to initialize with repeated values — great for DP tables, visited arrays, and zero-initialized input buffers.

Accessing Elements

let v = vec![1, 2, 3];
let first = v[0];              // Panics on out-of-bounds
let maybe = v.get(5);          // Returns Option<&T>

Use v.get(i) in edge-case-prone code to avoid panics.
Indexing (v[i]) is okay when you’ve guaranteed bounds.
For mutable access: v[i] = new_value;

Modifying Vectors

let mut v = vec![1, 2, 3];
v.push(4);                     // Append
v.pop();                       // Remove last (returns Option<T>)
v.insert(1, 99);               // Insert at index
v.remove(0);                   // Remove and shift (O(n))
v.swap_remove(0);              // Remove and swap with last (O(1))
v.clear();                     // Empty the vector

Use swap_remove() when order doesn’t matter — it’s much faster.
Avoid insert/remove in the middle of the vector unless necessary — costly in large vectors.
push() and pop() are constant-time and safe to use often.

Iterating and Enumerating

for val in &v { ... }                   // Immutable borrow
for val in &mut v { *val += 1; }        // Mutable borrow
for (i, val) in v.iter().enumerate() { ... } // With index

Iterating with enumerate() is perfect for index-based logic.
Iterators avoid unnecessary cloning and are more idiomatic than traditional for i in 0..v.len() loops (but both are fine).

Transforming and Collecting

let squares: Vec<_> = v.iter().map(|&x| x * x).collect();

Chain .map(), .filter(), .collect() for fast transformations.
When mapping by value (x * x), make sure to dereference or use copied().

Searching and Filtering

v.contains(&3);                             // true/false
v.iter().position(|&x| x == target);        // Some(index) or None
v.iter().any(|&x| x % 2 == 0);              // true if any match
v.iter().all(|&x| x > 0);                   // true if all match
v.iter().find(|&&x| x > 5);                 // Returns Option<&T>

These methods are ideal for interview problems like “contains duplicates”, “first missing positive”, or “search insert position”.

Sorting and Reversing

v.sort();                                   // Ascending
v.sort_by(|a, b| b.cmp(a));                 // Descending
v.reverse();                                // In-place reversal

.sort() is stable and fast — use it for most problems that involve ordering.
For descending order, use .sort_by() with cmp().
.reverse() is often used in conjunction with two-pointer techniques or reversing segments.

Deduplication and Retention

v.dedup();                                  // Removes consecutive duplicates
v.retain(|&x| x % 2 == 0);                  // Keeps even numbers

dedup() only removes adjacent duplicates — sort first if needed.
retain() is a powerful way to filter in-place, especially in array cleanup problems.

Capacity and Optimization

v.reserve(100);                             // Pre-allocates capacity
v.shrink_to_fit();                          // Reduce memory usage

Rarely needed in interviews, but useful for optimizing performance in large input problems.

Binary Search (on sorted vectors)

let idx = v.binary_search(&target); // Ok(index) or Err(insert_pos)

Only works on sorted vectors.
Returns Result<usize, usize>, which gives you powerful insert-point logic.

Cloning and Copying

let copy = v.clone();                       // Deep clone
let slice_copy = v[..3].to_vec();           // Clone a slice into Vec

Use clone() when passing the vector by value and retaining the original.
Avoid cloning inside hot loops unless absolutely necessary — prefer references or slices when possible.

Multi-dimensional Vectors

let grid: Vec<Vec<i32>> = vec![vec![0; cols]; rows];

Common in matrix problems like “Number of Islands” or “Unique Paths”.
You can also mutate them like: grid[i][j] = 1;

Splitting, Chunking, and Windowing

v.windows(3);       // Overlapping sliding window of 3 elements
v.chunks(2);        // Non-overlapping chunks of 2
v.split(|&x| x == 0); // Split on condition

.windows(n) is vital for sliding window problems.
.chunks(n) can help in decoding/encoding problems (e.g., grouping).
.split() is helpful in partitioning logic — it returns slices.

HashMap and HashSet: Rust’s Secret Weapons

If Vec is your everyday hammer, then HashMap and HashSet are your lockpick and crowbar. These tools unlock elegant, fast solutions to some of the trickiest LeetCode problems — like frequency counting, duplicate detection, memoization, and mapping relationships. In Rust, they live in std::collections, and they come with quirks and power you need to know cold. Let’s break down what makes them essential, and how to wield them like a pro.

🔑 Import First

use std::collections::{HashMap, HashSet};

Always bring them in — you’ll use one or both in at least half of real-world LeetCode problems.

🧠 HashMap: The Brain Behind Counting and Mapping

Creating and Inserting

let mut map = HashMap::new();
map.insert("apple", 3);
map.insert("banana", 2);

Keys and values can be any types that implement Eq and Hash.
Common for counting occurrences (i32, &str, char) or mapping values (index → value).

Accessing Values

if let Some(count) = map.get("apple") {
    println!("Count: {}", count);
}

.get() returns an Option<&V> — be ready to unwrap() or pattern-match.
Use contains_key() to check for presence.

Counting Frequencies

let mut freq = HashMap::new();
for &num in &[1, 2, 1, 3, 1] {
    *freq.entry(num).or_insert(0) += 1;
}

This is the idiomatic way to count elements — the entry() API is incredibly powerful.
or_insert(default) inserts if missing and gives you a mutable reference.
You’ll use this pattern in problems like:
- Top K Frequent Elements
- Group Anagrams
- Majority Element

Iterating Over a HashMap

for (key, val) in &map {
    println!("{}: {}", key, val);
}

The order is not guaranteed — if you need ordering, use BTreeMap.

Removing Entries

map.remove("banana");

Returns Some(value) if it existed.

🧱 HashSet: Unique Elements, Fast Lookups

Creating and Inserting

let mut set = HashSet::new();
set.insert("rust");
set.insert("go");

Like HashMap<K, ()> under the hood.
Use for de-duplication, visited-tracking, and uniqueness checks.

Checking for Existence

if set.contains("rust") {
    println!("Found!");
}

Super fast O(1) average-case lookup.

Removing Elements

set.remove("go");

Works exactly like .insert() — simple and efficient.

Converting from Vec to Set

let nums = vec![1, 2, 2, 3];
let uniq: HashSet<_> = nums.into_iter().collect();

Instantly remove duplicates from a vector.
Useful for:
- Contains Duplicate
- Intersection of Two Arrays
- Happy Number
- Longest Consecutive Sequence

⚠️ Pitfalls to Avoid

Type inference issues — sometimes you’ll need to explicitly declare the HashMap<i32, i32> or HashSet<char> type.
Borrow checker — if you mutate while iterating, use .clone() or .into_iter().
Don’t forget mut — you need mut to insert, remove, or modify.
Order matters? Use BTreeMap instead — HashMap is unordered.

✨ Bonus: Set Intersection & Union

let a: HashSet<_> = vec![1, 2, 3].into_iter().collect();
let b: HashSet<_> = vec![2, 3, 4].into_iter().collect();

let intersection: HashSet<_> = a.intersection(&b).cloned().collect();
let union: HashSet<_> = a.union(&b).cloned().collect();

Useful in problems that involve merging or comparing element groups.

Sorting and Searching: Rust’s Power Tools for Algorithmic Problems

Sorting and binary search are at the heart of hundreds of LeetCode problems. Whether you're merging intervals, finding medians, or solving two-pointer problems, you’ll be sorting and searching all the time. Rust gives you efficient, expressive, and safe tools for this — but they come with a few sharp edges. This section covers everything you need to confidently sort, search, and slice your way through LeetCode challenges using the standard library.

Sorting a Vector (`sort()`, `sort_by()`)

Rust’s .sort() method works on mutable vectors and slices. It uses an efficient, stable sort (TimSort).

let mut nums = vec![3, 1, 4, 2];
nums.sort();
println!("{:?}", nums); // [1, 2, 3, 4]

Always requires a mutable vector or slice.
Only works if the type implements the Ord trait (integers, strings, tuples, etc.).
Panics if you try to call it on an immutable vector — don’t forget mut.

Custom Sorting with `sort_by()`

When you need full control over sorting behavior, use .sort_by() with a comparison function.

let mut nums = vec![1, 3, 2];
nums.sort_by(|a, b| b.cmp(a)); // Descending

Use cmp() to compare values.
You can sort by any custom logic: length, frequency, absolute value, etc.
Great for problems like:
- Sort Characters by Frequency
- Reorder Data in Log Files
- Meeting Rooms

Custom Key Sorting with `sort_by_key()`

A simpler way to sort by some derived value — great for sorting structs, tuples, or based on a single field.

let mut words = vec!["apple", "banana", "kiwi"];
words.sort_by_key(|s| s.len());
println!("{:?}", words); // ["kiwi", "apple", "banana"]

Cleaner than sort_by() when sorting by a single attribute.
Very useful for LeetCode problems where you're sorting objects, strings, or coordinates.

Binary Search with `binary_search()`

Rust’s built-in binary search is fast, easy to use, and doesn’t require external libraries — but it assumes the vector is sorted.

let nums = vec![1, 3, 5, 7, 9];
match nums.binary_search(&5) {
    Ok(i) => println!("Found at index {}", i),
    Err(pos) => println!("Not found, insert at {}", pos),
}

Returns Result<usize, usize>:
- Ok(index) if found
- Err(insert_position) if not found
Excellent for:
- Search Insert Position
- Find Minimum in Rotated Sorted Array
- Kth Missing Positive Number
.binary_search() uses full equality. For more flexible logic, you can use .partition_point().

Partial Searching with `partition_point()`

partition_point() finds the first index in a sorted slice where a predicate becomes true. This is powerful for range-based problems.

let nums = vec![1, 3, 5, 7, 9];
let idx = nums.partition_point(|&x| x < 6);
println!("{}", idx); // 3 → 7 is the first not < 6

Think of it as a custom binary search over a boolean condition.
It's perfect for questions like:
- “Find the first element greater than X”
- “Find how many elements are less than X”
- “Find insert position for a predicate”
Available in Rust 1.52+.

Finding Min and Max (`min()`, `max()`)

Rust gives you two clean ways to find the smallest or largest element in a collection.

Compare Two Values

use std::cmp::{min, max};

let a = 3;
let b = 7;
println!("{}", min(a, b)); // 3

Find in a Collection

let nums = vec![2, 4, 1, 9];
let min_val = nums.iter().min(); // Option<&i32>
let max_val = nums.iter().max();

Returns an Option<&T> — be sure to handle None in edge cases.
Useful for problems like:
- Find Peak Element
- Minimum Window Substring
- Maximum Subarray

💡 Pro Tips

Sort before using two pointers or sliding window techniques.
Use .clone() if you need to preserve the original vector.
Use .as_slice() if a function expects a slice (&[T]) instead of a vector.
Avoid sorting inside loops — sort once and reuse.
.binary_search() and .partition_point() are zero-allocation and fast — use them instead of writing custom search code.

Pattern Matching: Rust’s Swiss Army Knife

Pattern matching in Rust isn’t just syntax sugar — it’s a core part of how the language thinks. It’s used in everything from destructuring input, handling Option and Result, matching tuples, branching over enums, and simplifying control flow. While it's not required to solve LeetCode problems, it makes your code cleaner, safer, and more expressive, especially when you’re dealing with nested data, edge cases, or structured input.

If you want to write Rust that feels natural (and not like C in disguise), you need to embrace pattern matching.

The `match` Statement

Rust’s match is exhaustive — you must handle all possible cases. This avoids runtime surprises and makes intent explicit.

Matching Values

let x = 3;
match x {
    0 => println!("zero"),
    1..=4 => println!("between 1 and 4"),
    _ => println!("something else"),
}

Use ranges (1..=4), exact matches, and the wildcard _.
No fallthrough like switch in C — each arm is independent.

Matching `Option<T>`

This is where pattern matching shines in everyday Rust.

let maybe = Some(42);
match maybe {
    Some(val) => println!("Got {}", val),
    None => println!("Nothing there"),
}

Much cleaner than unwrapping or panicking. Essential in problems involving optional values (like searching or parsing).

`if let` and `while let`: Cleaner Alternatives

When you only care about one match case (usually Some(_)), use if let:

if let Some(val) = maybe {
    println!("Found {}", val);
}

For loops that extract values from an iterator or state:

while let Some(x) = stack.pop() {
    println!("{}", x);
}

These are very common in DFS, BFS, or stack-based problems.

Matching Tuples

In many LeetCode problems, you’ll be storing state as (index, value) or (row, col) pairs. Pattern matching makes accessing them trivial.

let pair = (2, 5);
match pair {
    (x, 5) => println!("Second is 5, first is {}", x),
    (0, _) => println!("First is zero"),
    _ => println!("Other pair"),
}

You can destructure tuples right inside the match — no .0 and .1 needed.

Destructuring in Loops and Let-Bindings

Destructure inline with let or for:

let (a, b) = (1, 2);
for (i, val) in vec.iter().enumerate() {
    println!("Index {}, value {}", i, val);
}

This keeps your logic clean and close to the data.

Pattern Matching with Structs and Enums

Not super common in LeetCode, but worth knowing if you're building helpers or solving problems involving your own types.

enum Direction {
    Up,
    Down,
    Left,
    Right,
}

let dir = Direction::Left;
match dir {
    Direction::Left => println!("Going left"),
    Direction::Right => println!("Right"),
    _ => println!("Vertical movement"),
}

Using Guards in Match Arms

Add extra logic to a pattern with if guards:

match x {
    n if n % 2 == 0 => println!("even"),
    _ => println!("odd"),
}

Or more idiomatically:

match x {
    n if n > 100 => println!("large"),
    n => println!("normal: {}", n),
}

Useful for catching edge cases cleanly in one line.

🔥 Pro Tips

Use _ to ignore values you don’t care about.
Combine with ref, mut, and destructuring for precise control.
Never unwrap() blindly — match or if let is always safer.
Combine pattern matching with recursion and functional methods for expressive solutions.

Iterators & Functional Tools: Solving Problems Without the For Loop

Rust’s iterator system is one of the most elegant and expressive parts of the language. It lets you write declarative, readable, and often faster code — ideal for the kinds of data transformation-heavy problems you’ll see on LeetCode. And while you can solve everything with traditional for loops, using iterators like map, filter, collect, and fold will make your code more idiomatic and often cleaner under time pressure. In this section, we’ll walk through the functional core of Rust’s standard library — everything you need to know to manipulate collections like a pro.

🔄 Transforming Collections: `map()`, `filter()`, `collect()`

These are your go-to methods for converting, cleaning, and reshaping vectors — a constant task in LeetCode.

Mapping

let nums = vec![1, 2, 3];
let squares: Vec<i32> = nums.iter().map(|x| x * x).collect();

.map() applies a function to each element.
You’ll often see .iter().map(|&x| ...) for primitive values — |&x| dereferences from &i32 to i32.

Filtering

let evens: Vec<_> = nums.iter().copied().filter(|x| x % 2 == 0).collect();

.filter() keeps only the values that match a condition.
Use .copied() if you want to avoid working with &i32.

Collecting

.collect() turns an iterator back into a collection (like Vec, HashSet, HashMap, etc.).
Use collect::<Vec<_>>() or let type inference do the work.

These tools are ideal for:

Removing duplicates
Building frequency maps
Cleaning input data
Transforming characters into ASCII values

➕ Reducing Elements: `sum()`, `product()`, `fold()`

Aggregating values is a common task: summing arrays, multiplying digits, building cumulative results.

Summing and Multiplying

let nums = vec![1, 2, 3];
let total: i32 = nums.iter().sum();         // 6
let product: i32 = nums.iter().product();   // 6

Use .iter() when reducing without consuming.
Can also call .into_iter() to consume if you don’t need the original.

Folding

let sentence = vec!["Rust", "is", "fun"];
let joined = sentence.iter().fold(String::new(), |acc, &word| acc + word + " ");

fold is like reduce — it takes an initial value and accumulates from there.
Useful for calculating things like prefix sums, custom reductions, or string concatenation.

🔍 Finding Elements: `find()`, `position()`

Efficient tools for searching without writing loops.

Finding a Match

let nums = vec![1, 2, 3];
if let Some(x) = nums.iter().find(|&&x| x > 1) {
    println!("Found {}", x);
}

Finding Index

let idx = nums.iter().position(|&x| x == 2); // Some(1)

find() gives you the first matching element.
position() gives you the index instead.
Use these when solving problems like:
- First unique character
- Search insert position (as a fast check before binary search)
- Finding the pivot or rotation point

📐 Sliding Window with `.windows(n)`

For overlapping subarrays of size n, .windows() is a clean and efficient approach.

let v = vec![1, 2, 3, 4, 5];
for window in v.windows(3) {
    println!("{:?} → sum = {}", window, window.iter().sum::<i32>());
}

.windows(n) returns a &[T] slice — non-owning, no allocation.
Perfect for problems like:
- Maximum sum of subarray of size k
- Subarray with k distinct elements
- Anagram sliding window search

⚠️ Remember: .windows(n) only works if n <= vec.len(), otherwise it returns no elements.

🧱 Partitioning with `.chunks(n)`

Use .chunks(n) when you want non-overlapping groups of size n.

let data = vec![1, 2, 3, 4, 5];
for chunk in data.chunks(2) {
    println!("{:?}", chunk);
}

Output:

[1, 2]
[3, 4]
[5]

Common in problems involving:
- Grouped decoding/encoding (e.g. breaking into pairs or triplets)
- Transforming flat arrays into matrices or rows
- Parsing grid-based inputs

⚠️ Common Pitfalls

Forgetting .copied() or |&x| when dealing with references in iter().
Forgetting .collect() at the end of a transformation chain.
Trying to sort() an iterator — sort on Vec, not Iterator.
Using chunks() when you need windows() (overlapping vs non-overlapping!).

Bit-Fu in Rust: When You Absolutely, Definitely Need Bit Manipulation

Bit manipulation isn’t the most common technique in LeetCode — but when it shows up, you can’t fake your way through it. Problems like finding the single number in an array, checking if a number is a power of two, or manipulating subsets all rely on smart use of bits. Rust gives you a powerful, expressive, and safe toolkit for bitwise operations — but it’s low-level enough that you need to know what you're doing.

This section covers the Rust bit manipulation features you’ll need to breeze through those rare but critical problems.

Basic Bitwise Operators in Rust

Rust gives you the standard set of bitwise operators — all operate on integer types (i32, u32, u64, etc.):

Operator	Symbol	Description
AND	`&`	1 if both bits are 1
OR	\|	1 if any bit is 1
XOR	`^`	1 if bits are different
NOT	`!`	Flip all bits
SHL	`<<`	Left shift (multiply by 2ⁿ)
SHR	`>>`	Right shift (divide by 2ⁿ)

let a = 0b1010; // 10
let b = 0b1100; // 12

let and = a & b; // 0b1000 = 8
let or  = a | b; // 0b1110 = 14
let xor = a ^ b; // 0b0110 = 6
let not = !a;    // Bitwise NOT (inverts every bit)

These work just like in C or Java — no surprises here.

Checking If a Number Is a Power of Two

This classic trick shows up in problems like Power of Two, Counting Bits, or anything involving subset masks.

fn is_power_of_two(n: i32) -> bool {
    n > 0 && (n & (n - 1)) == 0
}

The trick relies on the fact that powers of two have exactly one bit set.
(n & (n - 1)) clears the lowest set bit — so if the result is zero, only one bit was set.

Counting Set Bits (Hamming Weight)

fn count_ones(mut n: u32) -> u32 {
    let mut count = 0;
    while n > 0 {
        count += n & 1;
        n >>= 1;
    }
    count
}

Or use the built-in:

let n: u32 = 13;
let ones = n.count_ones(); // 3

Use .count_ones() for fast and clear solutions.
You also have .leading_zeros(), .trailing_zeros(), .count_zeros(), and .swap_bytes() for other tasks.

Finding the Lowest Set Bit

let x = 0b10100;
let lowest_bit = x & (!x + 1); // Isolates the rightmost 1-bit

This technique is used in problems like Sum of Two Integers, Bitmask DP, and Fenwick Trees (Binary Indexed Tree).

Bitmasking for Subsets

Many subset or combinatorics problems can be solved with bitmasks:

let nums = vec![1, 2, 3];
let n = nums.len();

for mask in 0..(1 << n) {
    let mut subset = vec![];
    for i in 0..n {
        if (mask >> i) & 1 == 1 {
            subset.push(nums[i]);
        }
    }
    println!("{:?}", subset);
}

Use 1 << i to set the i-th bit.
Loop through 0..(1 << n) to iterate all subsets of a size-n array.
This is perfect for:
- Subsets
- Maximum AND/OR/XOR of Subsets
- Bitmask Dynamic Programming

Toggle a Bit

let mut flags = 0b1010;
flags ^= 1 << 1; // Toggles the second bit

Bit toggling is used in some puzzle-style problems to track visited states or flips.

Rust Traits and Methods That Help

.count_ones() → Count number of 1s
.count_zeros() → Count number of 0s
.leading_zeros() → Leading 0s from left (MSB side)
.trailing_zeros() → Trailing 0s from right (LSB side)
.rotate_left(n) / .rotate_right(n) → Useful in circular problems

BinaryHeap in Rust: Practical Priority Queues Without the Panic

Rust’s BinaryHeap is the standard way to implement a priority queue — a data structure where elements are always retrieved in priority order. It’s part of std::collections, and it gives you a fast, flexible tool for working with dynamic, sorted data. The default behavior is a max-heap, but Rust provides a clever way to use it as a min-heap, too. Here's everything you need to know about using it effectively in Rust.

📦 Bring It In

use std::collections::BinaryHeap;

Also add this if you plan to use it as a min-heap:

use std::cmp::Reverse;

🧱 Creating a BinaryHeap

Empty Heap

let mut heap = BinaryHeap::new();

From a Vector

let nums = vec![4, 1, 7, 3];
let heap: BinaryHeap<_> = nums.into_iter().collect();

Collecting from an iterator builds the heap in O(n) time.

➕ Adding Elements: `.push()`

heap.push(10);
heap.push(3);

Inserts the element in O(log n).
Maintains heap property automatically.

➖ Removing Elements: `.pop()`

while let Some(top) = heap.pop() {
    println!("{}", top);
}

Pops the greatest element (max-heap) or smallest (min-heap with Reverse).
O(log n) per removal.

👁️ Peeking at the Top Element: `.peek()`

if let Some(&top) = heap.peek() {
    println!("Top of heap: {}", top);
}

Non-destructive. Returns a reference to the current highest-priority element.
Use it when you want to inspect the heap without modifying it.

🔻 Using `Reverse` for Min-Heap

Rust’s BinaryHeap is a max-heap by default. To simulate a min-heap, wrap your elements in Reverse.

use std::cmp::Reverse;

let mut heap = BinaryHeap::new();
heap.push(Reverse(5));
heap.push(Reverse(2));
heap.push(Reverse(8));

if let Some(Reverse(val)) = heap.pop() {
    println!("Smallest: {}", val);
}

You wrap with Reverse(value) when inserting.
You unwrap with Reverse(x) when popping or peeking.
Works for any type that implements Ord.

⚙️ Other Useful Methods

Method	Description
`push(val)`	Insert a value into the heap (O(log n))
`pop()`	Remove and return the top value (O(log n))
`peek()`	Borrow the top value without popping (O(1))
`len()`	Get number of elements
`is_empty()`	Check if the heap is empty
`clear()`	Remove all elements
`into_sorted_vec()`	Converts to a sorted `Vec<T>`

let sorted: Vec<_> = heap.into_sorted_vec(); // Descending order

Converts the heap into a sorted vector in O(n log n).
Helpful when you want to flatten the heap after use.

⚠️ Common Pitfalls

It’s a max-heap by default — don’t forget Reverse() if you need min-heap behavior.
Custom types must implement Ord, PartialOrd, Eq, and PartialEq to be stored in the heap.
You need mut to push or pop — don't forget to make your heap mutable.

Queues and Deques in Rust: Fast, Flexible, and Built Right In

Many LeetCode problems — from BFS traversals to sliding window maximums — rely on queue or double-ended queue (deque) behavior. Rust’s standard library gives us a powerful and efficient tool for both: VecDeque. It’s the right structure when you need O(1) insertion and removal from both ends, which you can’t get from Vec alone without performance penalties.

This section covers the core patterns and methods for using VecDeque as a queue or deque in LeetCode-style problems.

📦 Importing VecDeque

use std::collections::VecDeque;

VecDeque lives in std::collections, just like HashMap and BinaryHeap.

🚶‍♂️ Using `VecDeque` as a Queue (FIFO)

Queues follow First-In, First-Out behavior — used in BFS, simulations, or level-order traversal.

let mut queue: VecDeque<i32> = VecDeque::new();

queue.push_back(1);           // enqueue
queue.push_back(2);
let front = queue.pop_front(); // dequeue (Some(1))

push_back() adds to the back
pop_front() removes from the front
Both operations are O(1)

This is the go-to structure for breadth-first search, task scheduling, and level-order tree traversals.

🔁 Using `VecDeque` as a Deque (Double-Ended Queue)

A deque supports pushing and popping at both ends.

let mut dq = VecDeque::new();

dq.push_front(10);
dq.push_back(20);
dq.push_front(5);       // [5, 10, 20]

let front = dq.pop_front(); // 5
let back = dq.pop_back();   // 20

push_front() and pop_back() allow stack-like behavior from either side.
Very useful for two-pointer techniques or tracking max/min in windows.

🌪️ Deque-Based Sliding Window Maximum

Problems like “Sliding Window Maximum” require maintaining a monotonic deque to track potential candidates for the max.

Pattern:

let mut dq: VecDeque<usize> = VecDeque::new();
let nums = vec![1, 3, -1, -3, 5, 3, 6, 7];
let k = 3;
let mut result = vec![];

for i in 0..nums.len() {
    // Remove indices outside the window
    if let Some(&front) = dq.front() {
        if front + k <= i {
            dq.pop_front();
        }
    }

    // Maintain descending order in deque
    while let Some(&back) = dq.back() {
        if nums[back] < nums[i] {
            dq.pop_back();
        } else {
            break;
        }
    }

    dq.push_back(i);

    // Push result once window is full
    if i + 1 >= k {
        result.push(nums[*dq.front().unwrap()]);
    }
}

VecDeque allows us to efficiently remove both old values (from the front) and dominated candidates (from the back).
Total time complexity is O(n) due to amortized deque operations.

🧠 Key Methods on `VecDeque`

Method	Description
`push_back(val)`	Append to the end
`push_front(val)`	Insert at the front
`pop_back()`	Remove and return last element
`pop_front()`	Remove and return first element
`front()`	Borrow reference to first element
`back()`	Borrow reference to last element
`is_empty()`	Check if the deque is empty
`len()`	Get number of elements
`clear()`	Remove all elements

⚠️ Common Pitfalls

VecDeque is only useful when you need fast push/pop at both ends — don’t use it like a Vec unless you need that.
Avoid calling .pop_front() without checking is_empty() or using if let Some(...) to handle None.
Don’t forget mut on your VecDeque — all operations that modify it require it.

Graphs in Rust: Navigating Nodes the Idiomatic Way

Graphs come up in many classic LeetCode problems — from pathfinding and connectivity to topological sorts and cycle detection. While Rust doesn't have a built-in graph type, it's easy and efficient to build your own using Vec<Vec<T>> for adjacency lists.

This section focuses on practical, interview-ready ways to represent and traverse graphs in Rust using only standard library tools.

🧱 Graph Representation Using `Vec<Vec<T>>`

The most common and flexible graph structure in Rust (and in LeetCode) is an adjacency list:

let n = 5;
let mut graph: Vec<Vec<usize>> = vec![vec![]; n];
graph[0].push(1);
graph[1].push(2);
graph[2].push(3);
// and so on...

Each graph[u] holds all neighbors of node u.
Works for both directed and undirected graphs:
- For undirected, insert both u → v and v → u.
Use Vec<Vec<(usize, i32)>> if you need edge weights.

🔍 Depth-First Search (DFS)

Rust’s ownership model makes recursive DFS very clean and safe.

fn dfs(u: usize, visited: &mut Vec<bool>, graph: &Vec<Vec<usize>>) {
    if visited[u] {
        return;
    }
    visited[u] = true;
    for &v in &graph[u] {
        dfs(v, visited, graph);
    }
}

Use a visited vector to track state.
DFS is ideal for:
- Cycle detection
- Connected components
- Topological sort (with post-order)

🔁 Breadth-First Search (BFS) Using `VecDeque`

BFS requires a queue — and VecDeque is the perfect fit.

use std::collections::VecDeque;

fn bfs(start: usize, graph: &Vec<Vec<usize>>, visited: &mut Vec<bool>) {
    let mut queue = VecDeque::new();
    queue.push_back(start);
    visited[start] = true;

    while let Some(u) = queue.pop_front() {
        for &v in &graph[u] {
            if !visited[v] {
                visited[v] = true;
                queue.push_back(v);
            }
        }
    }
}

BFS is best for:
- Shortest path in unweighted graphs
- Level-order traversal
- Bipartite graph checking

📏 Dijkstra’s Algorithm with `BinaryHeap`

For weighted graphs, use a min-heap (via BinaryHeap + Reverse) to track the next closest node.

use std::collections::BinaryHeap;
use std::cmp::Reverse;

fn dijkstra(start: usize, graph: &Vec<Vec<(usize, i32)>>, n: usize) -> Vec<i32> {
    let mut dist = vec![i32::MAX; n];
    dist[start] = 0;

    let mut heap = BinaryHeap::new();
    heap.push(Reverse((0, start))); // (distance, node)

    while let Some(Reverse((d, u))) = heap.pop() {
        if d > dist[u] {
            continue;
        }
        for &(v, weight) in &graph[u] {
            let new_dist = d + weight;
            if new_dist < dist[v] {
                dist[v] = new_dist;
                heap.push(Reverse((new_dist, v)));
            }
        }
    }

    dist
}

Use Vec<Vec<(usize, i32)>> for storing (neighbor, weight) edges.
Dijkstra gives shortest paths from one source in weighted graphs with non-negative edges.

🔗 Union-Find (Disjoint Set Union, DSU)

This structure is used in problems involving connected components, cycle detection, and Kruskal’s MST algorithm.

struct DSU {
    parent: Vec<usize>,
}

impl DSU {
    fn new(n: usize) -> Self {
        DSU { parent: (0..n).collect() }
    }

    fn find(&mut self, x: usize) -> usize {
        if self.parent[x] != x {
            self.parent[x] = self.find(self.parent[x]); // path compression
        }
        self.parent[x]
    }

    fn union(&mut self, x: usize, y: usize) -> bool {
        let (xr, yr) = (self.find(x), self.find(y));
        if xr == yr {
            false
        } else {
            self.parent[xr] = yr;
            true
        }
    }
}

Very efficient: O(α(n)) per operation with path compression.
Core structure for:
- Graph connectivity
- Detecting cycles in undirected graphs
- MST (Minimum Spanning Tree)

Linked Lists in Rust: Owning the Pointer Game Safely

Linked lists are everywhere on LeetCode — and while they’re straightforward in many languages, Rust makes you think carefully about ownership, mutability, and recursion. That’s a good thing: it keeps you honest. But it also means you need to be prepared with the right patterns to work with them efficiently in interviews.

This section walks through how to define, insert, reverse, and detect cycles in singly linked lists — using idiomatic, interview-friendly Rust.

🔧 Defining a `ListNode` in Rust

LeetCode’s Rust setup usually provides this for you, or expects you to use it:

#[derive(PartialEq, Eq, Clone, Debug)]
pub struct ListNode {
    pub val: i32,
    pub next: Option<Box<ListNode>>,
}

Box<ListNode> means the node owns its next node, on the heap.
Option<Box<...>> means the end of the list is None.

You can define a helper constructor:

impl ListNode {
    #[inline]
    fn new(val: i32) -> Self {
        ListNode { val, next: None }
    }
}

This struct supports fully owned, mutable linked list construction and traversal.

➕ Inserting into a Linked List

Manually building a list from a vector:

fn from_vec(values: Vec<i32>) -> Option<Box<ListNode>> {
    let mut head: Option<Box<ListNode>> = None;
    for &val in values.iter().rev() {
        let mut node = Box::new(ListNode::new(val));
        node.next = head;
        head = Some(node);
    }
    head
}

Build the list in reverse to avoid nested traversal.
In-place insertion at the head is efficient and ownership-safe.

🔁 Reversing a Linked List (Iteratively)

One of the most common list tasks on LeetCode:

fn reverse_list(mut head: Option<Box<ListNode>>) -> Option<Box<ListNode>> {
    let mut prev = None;

    while let Some(mut node) = head.take() {
        head = node.next.take();
        node.next = prev;
        prev = Some(node);
    }

    prev
}

take() moves out of the Option, replacing it with None.
This avoids borrowing issues and gives you full ownership to rearrange the list.
No recursion, no smart pointer gymnastics.

🌀 Finding a Cycle (Floyd’s Tortoise and Hare)

Use two pointers — fast and slow — to detect a cycle:

fn has_cycle(head: Option<Box<ListNode>>) -> bool {
    let mut slow = head.as_ref();
    let mut fast = head.as_ref();

    while let (Some(s), Some(f)) = (slow, fast.and_then(|n| n.next.as_ref())) {
        slow = s.next.as_ref();
        fast = f.next.as_ref();
        if std::ptr::eq(slow?, fast?) {
            return true;
        }
    }

    false
}

You can’t use normal equality here — use std::ptr::eq() to compare node addresses.
as_ref() lets you traverse the list without taking ownership.
and_then() safely follows .next inside an Option.

⚠️ Rust Pitfalls to Avoid

You can’t mutate through a shared reference (&) — use .take() or own the node via mut Some(...).
You can’t compare boxed nodes directly — use pointer equality (ptr::eq()).
You can’t freely clone or copy nodes — they’re heap-allocated and non-Copy.

Trees & Binary Trees in Rust: Strong Roots, Safe Branches

Trees — especially binary trees — show up constantly in LeetCode: traversals, depth calculations, symmetry checks, search trees, and lowest common ancestors. Rust doesn’t have built-in support for trees, but you can express them elegantly using Option<Box<TreeNode>>, just like linked lists.

This section walks through how to define and work with binary trees in Rust safely and idiomatically, covering all the major patterns you’ll need to tackle LeetCode tree problems.

🌲 Defining a `TreeNode` in Rust

Most LeetCode problems involving binary trees will provide or expect this structure:

#[derive(Debug, PartialEq, Eq)]
pub struct TreeNode {
    pub val: i32,
    pub left: Option<Box<TreeNode>>,
    pub right: Option<Box<TreeNode>>,
}

And optionally:

impl TreeNode {
    pub fn new(val: i32) -> Self {
        TreeNode {
            val,
            left: None,
            right: None,
        }
    }
}

Box gives heap allocation and ownership.
Option<Box<...>> is used for nullable child pointers (empty leaf nodes).

🧭 Depth-First Search: Preorder, Inorder, Postorder

DFS is best expressed recursively in Rust.

Preorder Traversal (Root → Left → Right)

fn preorder(node: &Option<Box<TreeNode>>, output: &mut Vec<i32>) {
    if let Some(n) = node {
        output.push(n.val);
        preorder(&n.left, output);
        preorder(&n.right, output);
    }
}

Inorder Traversal (Left → Root → Right)

fn inorder(node: &Option<Box<TreeNode>>, output: &mut Vec<i32>) {
    if let Some(n) = node {
        inorder(&n.left, output);
        output.push(n.val);
        inorder(&n.right, output);
    }
}

Postorder Traversal (Left → Right → Root)

fn postorder(node: &Option<Box<TreeNode>>, output: &mut Vec<i32>) {
    if let Some(n) = node {
        postorder(&n.left, output);
        postorder(&n.right, output);
        output.push(n.val);
    }
}

Each traversal is clean, safe, and stack-safe unless the tree is very deep.

🌊 Breadth-First Search (Level-Order)

Use VecDeque to implement level-order traversal:

use std::collections::VecDeque;

fn level_order(root: &Option<Box<TreeNode>>) -> Vec<i32> {
    let mut output = vec![];
    let mut queue = VecDeque::new();

    if let Some(n) = root {
        queue.push_back(n);
    }

    while let Some(node) = queue.pop_front() {
        output.push(node.val);
        if let Some(left) = &node.left {
            queue.push_back(left);
        }
        if let Some(right) = &node.right {
            queue.push_back(right);
        }
    }

    output
}

This traversal is perfect for level-based problems, such as computing depth, checking completeness, or zigzag level traversal.

🔗 Finding the Lowest Common Ancestor (LCA)

Classic recursive pattern:

fn lowest_common_ancestor(
    root: &Option<Box<TreeNode>>,
    p: i32,
    q: i32,
) -> Option<i32> {
    fn helper(node: &Option<Box<TreeNode>>, p: i32, q: i32) -> Option<i32> {
        if let Some(n) = node {
            if n.val == p || n.val == q {
                return Some(n.val);
            }

            let left = helper(&n.left, p, q);
            let right = helper(&n.right, p, q);

            if left.is_some() && right.is_some() {
                return Some(n.val);
            }

            left.or(right)
        } else {
            None
        }
    }

    helper(root, p, q)
}

Works for binary trees (not necessarily BSTs).
Traverse both sides; if both sides return non-None, current node is LCA.

📏 Balanced vs. Unbalanced Trees

A binary tree is balanced if, for every node, the height difference between the left and right subtrees is ≤ 1.

fn is_balanced(node: &Option<Box<TreeNode>>) -> bool {
    fn height(n: &Option<Box<TreeNode>>) -> Option<i32> {
        if let Some(node) = n {
            let left = height(&node.left)?;
            let right = height(&node.right)?;
            if (left - right).abs() > 1 {
                return None;
            }
            Some(1 + left.max(right))
        } else {
            Some(0)
        }
    }

    height(node).is_some()
}

Use Option<i32> to short-circuit if unbalanced.
Recursive DFS solution is clean and avoids repeated height calculations.

🌳 Binary Search Tree (BST) Operations

BSTs have a special property: left < root < right. You can use this to optimize search and insert logic.

Search a BST

fn search_bst(node: &Option<Box<TreeNode>>, target: i32) -> bool {
    match node {
        Some(n) if n.val == target => true,
        Some(n) if target < n.val => search_bst(&n.left, target),
        Some(n) => search_bst(&n.right, target),
        None => false,
    }
}

Insert into BST

fn insert_bst(node: &mut Option<Box<TreeNode>>, val: i32) {
    match node {
        Some(n) => {
            if val < n.val {
                insert_bst(&mut n.left, val);
            } else {
                insert_bst(&mut n.right, val);
            }
        }
        None => {
            *node = Some(Box::new(TreeNode::new(val)));
        }
    }
}

You’ll use mutable references to update the tree in-place.

You Made It. Now Go Break Things (Safely)

If you made it this far — congratulations. You’ve absorbed a battle-tested toolbox of Rust patterns specifically designed for LeetCode-style coding interviews. No fluff, no “intro to Rust” filler — just the things you actually need when time is tight, the question is tough, and the interviewer is watching. At this point, you should feel confident working with Vec, HashMap, BinaryHeap, recursion, and even linked lists without panicking. You’ve seen how to structure your code safely, mutate data intentionally, and write logic that is both expressive and correct. Most people won’t dare use Rust in an interview — and that’s exactly why you’re going to stand out. If you’re fast and fluent with the tools in this guide, Rust becomes a strength, not a liability. Keep your solutions clean, readable, and idiomatic — no unsafe shortcuts, no ownership drama. Take a deep breath, trust your prep, and go in knowing that you're playing the coding game on hard mode — and winning anyway. Good luck out there, Rustacean.