Hash Table Concept and Hash Functions in DSA Rust - Time & Space Complexity

Time Complexity: Hash Table Concept and Hash Functions

O(k + m)

Understanding Time Complexity

We want to understand how fast a hash table can find or store data.

The question is: how does the time to find or add items grow as we add more items?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

struct HashTable {
    buckets: Vec>,
}

impl HashTable {
    fn hash(&self, key: &str) -> usize {
        key.bytes().fold(0, |acc, b| acc.wrapping_add(b as usize)) % self.buckets.len()
    }

    fn insert(&mut self, key: String, value: i32) {
        let index = self.hash(&key);
        self.buckets[index].push((key, value));
    }
}
    

This code creates a simple hash table that stores key-value pairs in buckets based on a hash function.

Identify Repeating Operations

• Primary operation: Calculating the hash by adding byte values of the key.
• How many times: Once per insert or lookup, iterating over each byte of the key.
• Secondary operation: Searching inside the bucket list if needed (not shown here).
• How many times: Depends on bucket size, which grows with collisions.

How Execution Grows With Input

As more items are added, the hash function still processes each key's bytes once.

However, if many keys hash to the same bucket, searching that bucket takes longer.

Input Size (n)	Approx. Operations
10	Hash calculation over key length + small bucket search
100	Hash calculation over key length + moderate bucket search if collisions occur
1000	Hash calculation over key length + longer bucket search if collisions increase

Pattern observation: Hash calculation cost grows with key length, but bucket search cost grows with number of collisions.

Final Time Complexity

Time Complexity: O(k + m)

This means time grows with key length k plus the number of items m in the bucket due to collisions.

Common Mistake

[X] Wrong: "Hash table operations always take constant time no matter what."

[OK] Correct: If many keys collide, searching inside a bucket can take longer, so time depends on collisions and key length.

Interview Connect

Understanding how hash functions and collisions affect speed helps you explain and improve data lookup in real systems.

Self-Check

What if we changed the hash function to a more complex one that takes longer to compute? How would the time complexity change?

Practice

1. What is the main purpose of a hash function in a hash table?

easy

A. To sort the keys in ascending order

B. To convert a key into an index for storing values

C. To encrypt the data stored in the table

D. To count the number of elements in the table

Solution

1. Step 1: Understand the role of a hash function

   A hash function takes a key and converts it into a number that can be used as an index in the hash table.

2. Step 2: Identify the correct purpose

   This index helps to quickly find where the value related to the key is stored.

3. Final Answer:

   To convert a key into an index for storing values -> Option B

4. Quick Check:

   Hash function = key to index [OK]

Hint: Hash function maps keys to indexes fast [OK]

Common Mistakes:

• Thinking hash functions sort keys
• Confusing hashing with encryption
• Believing hash functions count elements

2. Which of the following is the correct way to declare a hash map in Rust?

easy

A. let mut map = HashMap::new();

B. let map = new HashMap();

C. HashMap map = new HashMap();

D. var map = HashMap();

Solution

1. Step 1: Recall Rust syntax for hash maps

   In Rust, hash maps are created using HashMap::new() and usually declared mutable with let mut.

2. Step 2: Check each option

   let mut map = HashMap::new(); uses correct Rust syntax. Options A, B, and C use syntax from other languages or incorrect Rust syntax.

3. Final Answer:

   let mut map = HashMap::new(); -> Option A

4. Quick Check:

   Rust hash map creation = let mut map = HashMap::new() [OK]

Hint: Rust uses HashMap::new() with let mut [OK]

Common Mistakes:

• Using syntax from other languages like Java or JavaScript
• Forgetting to make the map mutable
• Missing the HashMap:: prefix

3. What will be the output of the following Rust code?

use std::collections::HashMap;

fn main() {
    let mut map = HashMap::new();
    map.insert("apple", 3);
    map.insert("banana", 2);
    map.insert("apple", 5);
    println!("{:?}", map.get("apple"));
}

medium

A. Error: duplicate key

B. Some(3)

C. None

D. Some(5)

Solution

1. Step 1: Understand insert behavior in HashMap

   In Rust's HashMap, inserting a value with an existing key replaces the old value.

2. Step 2: Trace the inserts and get

   First insert: "apple" -> 3, second insert: "banana" -> 2, third insert: "apple" -> 5 replaces 3. So map.get("apple") returns Some(5).

3. Final Answer:

   Some(5) -> Option D

4. Quick Check:

   Last insert replaces value = Some(5) [OK]

Hint: Last insert with same key overwrites value [OK]

Common Mistakes:

• Assuming duplicate keys cause error
• Thinking old value remains after insert
• Expecting None for existing key

4. Identify the error in this Rust code snippet using a hash map:

use std::collections::HashMap;

fn main() {
    let map = HashMap::new();
    map.insert("key", 10);
    println!("{:?}", map.get("key"));
}

medium

A. HashMap is not imported

B. get method does not exist

C. map is immutable but insert is called

D. Missing semicolon after insert

Solution

1. Step 1: Check mutability of map

   The map is declared with let (immutable) but insert tries to modify it.

2. Step 2: Identify the error cause

   In Rust, to modify a HashMap, it must be declared mutable with let mut.

3. Final Answer:

   map is immutable but insert is called -> Option C

4. Quick Check:

   Insert needs mutable map [OK]

Hint: Use let mut for hash maps you modify [OK]

Common Mistakes:

• Forgetting to import HashMap
• Assuming get method is missing
• Thinking semicolon is missing

5. You want to create a hash function for strings that sums the ASCII values of characters and then takes modulo with table size. Which of these Rust functions correctly implements this?

hard

A. fn hash(key: &str, size: usize) -> usize { key.bytes().map(|b| b as usize).sum::<usize>() % size }

B. fn hash(key: &str, size: usize) -> usize { key.len() % size }

C. fn hash(key: &str, size: usize) -> usize { key.chars().count() % size }

D. fn hash(key: &str, size: usize) -> usize { key.as_bytes().len() % size }

Solution

1. Step 1: Understand the required hash function

   The function must sum ASCII values of all characters and then take modulo with size.

2. Step 2: Analyze each option

   fn hash(key: &str, size: usize) -> usize { key.bytes().map(|b| b as usize).sum::<usize>() % size } sums bytes (ASCII values) and mods by size correctly. Options A, B, and D only use length or count, not sum of ASCII values.

3. Final Answer:

   fn hash(key: &str, size: usize) -> usize { key.bytes().map(|b| b as usize).sum::<usize>() % size } -> Option A

4. Quick Check:

   Sum ASCII bytes then mod size = fn hash(key: &str, size: usize) -> usize { key.bytes().map(|b| b as usize).sum::<usize>() % size } [OK]

Hint: Sum bytes, then modulo table size for hash [OK]

Common Mistakes:

• Using length instead of sum of ASCII values
• Using chars count instead of bytes
• Forgetting to modulo by table size