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Rust for Coding Rounds: Writing Clean Code That Interviewers Notice

From JOHNWICK

When you’re in a coding interview, every line you type reveals your mindset — are you hacking something together or building thoughtfully? While Python and C++ often dominate coding rounds, Rust is quietly emerging as the language that interviewers notice. Its clean syntax, safety guarantees, and functional flavor make it a great tool for expressing logic clearly. In this post, you’ll learn how to use Rust’s clarity and design to your advantage — even if you’re new to the language.


🧩 Problem: Find the First Non-Repeating Character Question:
“Given a string, find the first character that doesn’t repeat.” It’s a simple problem that reveals how cleanly you can think — and Rust is perfect for showing that.


🧠 What Interviewers Are Really Looking For Interviewers don’t just check if your code works. They subconsciously evaluate:

  • ✅ Clarity — Is it readable at a glance?
  • ⚡ Efficiency — Does it scale well?
  • 🧹 Clean Structure — Can it handle edge cases gracefully?
  • 🦀 Language fluency — Are you using Rust’s strengths, not fighting them?


💡 Step 1: Start Simple — The Naive Way Here’s a first attempt — clean and beginner-friendly: fn first_non_repeating_char(s: &str) -> Option<char> { 

   for c in s.chars() {
       let count = s.chars().filter(|&x| x == c).count();
       if count == 1 {
           return Some(c);
       }
   }
   None

} fn main() { 

   let s = "swiss";
   match first_non_repeating_char(s) {
       Some(ch) => println!("First non-repeating char: {}", ch),
       None => println!("No unique character found."),
   }

} 🧩 What’s happening here:

  • s.chars() → turns the string into an iterator of characters
  • .filter() → keeps only matching characters
  • .count() → counts how many times each appears

🕓 Time Complexity: O(n²)
💭 Still readable, but not efficient.


📊 Flow Diagram — Naive Approach flowchart 

   A[String Input: "swiss"] --> B[Loop through each char]
   B --> C[Count occurrences using filter()]
   C --> D{Count == 1?}
   D -->|Yes| E[Return char]
   D -->|No| F[Continue loop]
   E --> G[Print result]


⚙️ Step 2: Optimize Using HashMap (O(n) Approach) Now let’s optimize while keeping the code clean. use std::collections::HashMap; fn first_non_repeating_char(s: &str) -> Option<char> { 

   let mut freq = HashMap::new();
   // Step 1: Count frequency
   for c in s.chars() {
       *freq.entry(c).or_insert(0) += 1;
   }
   // Step 2: Find the first non-repeating
   for c in s.chars() {
       if freq[&c] == 1 {
           return Some(c);
       }
   }
   None

} fn main() { 

   let s = "swiss";
   println!("{:?}", first_non_repeating_char(s));

} ✅ Efficient (O(n))
✅ Readable
✅ Idiomatic Rust


🧩 Explanation

  • HashMap stores frequency of each character.
  • entry(c).or_insert(0) is Rust’s elegant way to initialize counters.
  • We then loop again to find the first character with frequency 1.


📈 Visualization Input: "swiss" Step 1: s → 3 w → 1 i → 1 Step 2: 's' -> 3 (skip) 'w' -> 1 ✅


🧭 Flow Diagram — Optimized Approach flowchart 

   A[Input String] --> B[Initialize empty HashMap]
   B --> C[Loop: Count frequency]
   C --> D[Loop again through chars]
   D --> E{freq[c] == 1?}
   E -->|Yes| F[Return char]
   E -->|No| G[Continue loop]


✨ Step 3: Make It Elegant With Iterators Rust’s iterators can make even dense logic feel poetic. use std::collections::HashMap; fn first_unique(s: &str) -> Option<char> { 

   let freq = s.chars().fold(HashMap::new(), |mut acc, c| {
       *acc.entry(c).or_insert(0) += 1;
       acc
   });
   s.chars().find(|&c| freq[&c] == 1)

} fn main() { 

   let input = "success";
   println!("🦀 First unique char: {:?}", first_unique(input));

} 💬 What’s new here:

  • .fold() → elegant accumulator for counting.
  • .find() → locates the first match, cleanly.
  • Fewer lines, more intent.


🔍 Why Interviewers Love This Rust iterators show that you:

  • Understand functional patterns
  • Know how to express logic without clutter
  • Write maintainable code


⚙️ Bonus Example: Rust’s Ownership Model in Action Imagine a follow-up question: “Can you return a string with all duplicates removed?” Here’s how you’d do it the Rust way 👇 fn remove_duplicates(s: &str) -> String { 

   let mut seen = Vec::new();
   let mut result = String::new();

for c in s.chars() { 

       if !seen.contains(&c) {
           seen.push(c);
           result.push(c);
       }
   }
   result

} fn main() { 

   println!("{}", remove_duplicates("programming"));

} 🧠 Rust Concepts at Play:

  • Borrowing: We take &str by reference, not by ownership.
  • Ownership: Return an owned String safely.
  • Memory safety: No dangling pointers, no garbage collector needed.


🧩 Ownership Flow Diagram Flow Graph 

   A[&str borrowed from input] --> B[Loop over chars]
   B --> C[Push unseen chars to Vec and String]
   C --> D[Return owned String]
   D --> E[Main owns result safely]


🧭 Recap: Traits of Interview-Ready Rust Code 🧠 Quality In Rust It Looks Like Clarity find(), fold(), readable loops Safety Borrowing & ownership principles Efficiency HashMap, iterators (O(n)) Idiomatic Pattern matching, functional style Expressive Code reads like natural logic


💬 Final Thoughts Rust lets you write expressive, safe, and elegant solutions — the kind of code that makes interviewers stop and say, “Now that’s clean.” Once you internalize Rust’s ownership model and functional style, even simple problems become opportunities to show craftsmanship. 💡 “Code is like humor. When you have to explain it, it’s bad.” — Cory House

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