Why Subarray Sum Equals K Using Hash Map in DSA Rust?

The Big Idea

What if you could find matching sums in a list without adding every group again and again?

The Scenario

Imagine you have a long list of daily expenses and want to find if any continuous days add up exactly to your budget.

Doing this by checking every possible group of days one by one is tiring and slow.

The Problem

Manually adding sums for every possible group means repeating work many times.

This takes a lot of time and can easily cause mistakes when the list is long.

The Solution

Using a hash map, we keep track of sums we have seen before.

This helps us quickly find if a group of days adds up to the budget without checking all groups again.

Before vs After

✗ Before

for i in 0..nums.len() {
    let mut sum = 0;
    for j in i..nums.len() {
        sum += nums[j];
        if sum == k {
            return true;
        }
    }
}
return false;

✓ After

use std::collections::HashMap;

let mut sum_map = HashMap::new();
sum_map.insert(0, 1);
let mut current_sum = 0;
for &num in &nums {
    current_sum += num;
    if sum_map.contains_key(&(current_sum - k)) {
        return true;
    }
    *sum_map.entry(current_sum).or_insert(0) += 1;
}
return false;

What It Enables

This method lets you find continuous groups that add up to a target quickly, even in very long lists.

Real Life Example

Checking if a series of sales over days matches a target revenue without adding every possible day group again and again.

Key Takeaways

Manual checking repeats sums and is slow.

Hash map remembers sums to find matches fast.

Great for finding continuous sums equal to a target efficiently.

Practice

1. What is the main purpose of using a hash map in the Subarray Sum Equals K problem?

easy

A. To store cumulative sums and their frequencies for quick lookup

B. To sort the array elements before processing

C. To store the original array elements as keys

D. To reverse the array for easier traversal

Solution

1. Step 1: Understand the problem requirement

   The problem requires finding subarrays whose sums equal a target value k. Using a hash map helps track cumulative sums efficiently.

2. Step 2: Role of hash map in the solution

   The hash map stores cumulative sums as keys and their counts as values. This allows quick checking if a previous sum exists that forms a subarray summing to k.

3. Final Answer:

   To store cumulative sums and their frequencies for quick lookup -> Option A

4. Quick Check:

   Hash map stores sums = A [OK]

Hint: Hash map tracks sums to find subarrays fast [OK]

Common Mistakes:

• Thinking hash map stores individual elements only
• Using hash map to sort elements
• Ignoring cumulative sums concept

2. Which of the following Rust code snippets correctly initializes a hash map to store cumulative sums and their counts?

easy

A. let map = std::collections::HashMap;

B. let mut map = std::collections::HashMap::new();

C. let mut map = HashMap();

D. let map = HashMap::new();

Solution

1. Step 1: Identify correct HashMap initialization syntax in Rust

   Rust requires importing and initializing a mutable HashMap with std::collections::HashMap::new() and mut if we want to modify it.

2. Step 2: Check each option

   let mut map = std::collections::HashMap::new(); uses correct syntax with mut and full path. let map = HashMap::new(); misses mut. let mut map = HashMap(); uses wrong constructor syntax. let map = std::collections::HashMap; is incomplete.

3. Final Answer:

   let mut map = std::collections::HashMap::new(); -> Option B

4. Quick Check:

   Correct Rust HashMap init = D [OK]

Hint: Use mut and full path to create modifiable HashMap [OK]

Common Mistakes:

• Forgetting mut for mutable map
• Using wrong constructor syntax
• Omitting std::collections prefix

3. What will be the output of the following Rust code snippet that counts subarrays summing to k = 3?

use std::collections::HashMap;

fn subarray_sum(nums: Vec<i32>, k: i32) -> i32 {
    let mut count = 0;
    let mut sum = 0;
    let mut map = HashMap::new();
    map.insert(0, 1);
    for num in nums {
        sum += num;
        if let Some(&c) = map.get(&(sum - k)) {
            count += c;
        }
        *map.entry(sum).or_insert(0) += 1;
    }
    count
}

fn main() {
    let nums = vec![1, 2, 1, 2, 1];
    let k = 3;
    println!("{}", subarray_sum(nums, k));
}

medium

A. 5

B. 3

C. 2

D. 4

Solution

1. Step 1: Trace cumulative sums and map updates

   nums = [1,2,1,2,1], k=3 - sum=1, map={0:1,1:1}, count=0 - sum=3, map has (3-3)=0 count=1, count=1, map={0:1,1:1,3:1} - sum=4, map has (4-3)=1 count=1, count=2, map={0:1,1:1,3:1,4:1} - sum=6, map has (6-3)=3 count=1, count=3, map={0:1,1:1,3:1,4:1,6:1} - sum=7, map has (7-3)=4 count=1, count=4, map={0:1,1:1,3:1,4:1,6:1,7:1}

2. Step 2: Calculate final count of subarrays

   Total count of subarrays summing to 3 is 4.

3. Final Answer:

   4 -> Option D

4. Quick Check:

   Count of subarrays = 4 [OK]

Hint: Track cumulative sums and check map for (sum-k) [OK]

Common Mistakes:

• Not initializing map with (0,1)
• Incorrectly updating count
• Confusing sum and index

4. Identify the bug in this Rust function for counting subarrays summing to k and select the fix:

fn subarray_sum(nums: Vec<i32>, k: i32) -> i32 {
    let mut count = 0;
    let mut sum = 0;
    let mut map = std::collections::HashMap::new();
    for num in nums {
        sum += num;
        if let Some(&c) = map.get(&(sum - k)) {
            count += c;
        }
        map.insert(sum, 1);
    }
    count
}

medium

A. Change map.insert(sum, 1) to map.entry(sum).or_insert(0) += 1

B. Initialize map with (0,1) before loop

C. Remove the if let block checking map.get

D. Initialize count to 1 instead of 0

Solution

1. Step 1: Identify problem with map insertion

   Using map.insert(sum, 1) overwrites previous counts, losing frequency data needed for correct counting.

2. Step 2: Correct way to update map counts

   Use map.entry(sum).or_insert(0) += 1 to increment count if sum exists or insert 0 then increment, preserving frequencies.

3. Final Answer:

   Change map.insert(sum, 1) to map.entry(sum).or_insert(0) += 1 -> Option A

4. Quick Check:

   Use entry API to update counts [OK]

Hint: Use entry().or_insert(0) += 1 to update counts safely [OK]

Common Mistakes:

• Overwriting counts with insert instead of incrementing
• Not initializing map with (0,1)
• Ignoring frequency updates

5. Given an array nums = [3, 4, 7, 2, -3, 1, 4, 2] and k = 7, how many subarrays sum to k? Use the hash map approach to find the answer.

hard

A. 5

B. 3

C. 4

D. 6

Solution

1. Step 1: Calculate cumulative sums and track counts

   nums = [3,4,7,2,-3,1,4,2], k=7 - sum=3, map={0:1,3:1}, count=0 - sum=7, map has (7-7)=0 count=1, count=1, map={0:1,3:1,7:1} - sum=14, map has (14-7)=7 count=1, count=2, map={0:1,3:1,7:1,14:1} - sum=16, map has (16-7)=9 no, count=2, map={0:1,3:1,7:1,14:1,16:1} - sum=13, map has (13-7)=6 no, count=2, map={0:1,3:1,7:1,14:1,16:1,13:1} - sum=14, map has (14-7)=7 count=1, count=3, map={0:1,3:1,7:1,14:2,16:1,13:1} - sum=18, map has (18-7)=11 no, count=3, map={0:1,3:1,7:1,14:2,16:1,13:1,18:1} - sum=20, map has (20-7)=13 count=1, count=4, map={0:1,3:1,7:1,14:2,16:1,13:1,18:1,20:1}

2. Step 2: Count subarrays carefully including duplicates

   The trace shows four increments corresponding to the four subarrays summing to 7: [3,4], [7], [7,2,-3,1], [1,4,2]. Total count is 4.

3. Final Answer:

   4 -> Option C

4. Quick Check:

   Count subarrays with sum k = 4 [OK]

Hint: Count frequencies of sums and check (sum-k) in map [OK]

Common Mistakes:

• Not counting repeated cumulative sums correctly
• Missing negative numbers effect
• Forgetting to initialize map with (0,1)