## Test scenario

I have taken the Two sum problem from Leetcode.

The problem statement:

Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.

You may assume that each input would have exactly one solution, and you may not use the same element twice.

You can return the answer in any order.

Example 1:

Input: nums = [2,7,11,15], target = 9

Output: [0,1]

Explanation: Because nums[0] + nums[1] == 9, we return [0, 1].Example 2:

Input: nums = [3,2,4], target = 6

Output: [1,2]Example 3:

Input: nums = [3,3], target = 6

Output: [0,1]Constraints:

`2 <= nums.length <= 104 -109 <= nums[i] <= 109 -109 <= target <= 109 Only one valid answer exists.`

## Implementation

I have used a hash map to solve this problem across all three languages.

### Python

```
class Solution:
def twoSum(self, nums: List[int], target: int) -> List[int]:
hash_table = {}
for i, num in enumerate(nums):
target_num = target - num
if num in hash_table:
return i, hash_table[num]
else:
hash_table[target_num] = i
return None
```

#### Python stats

- Run time:
**40ms** - Memory usage:
**14.5 MB**

### Golang

```
func twoSum(nums []int, target int) []int {
hashMap := make(map[int] int)
for i := 0; i < len(nums); i++{
if _, found := hashMap[nums[i]]; found {
ans := []int{i, hashMap[nums[i]]}
return ans
} else {
hashMap[target- nums[i]]= i
}
}
return nil
}
```

#### Golang stats

- Run time:
**4ms** - Memory usage:
**4.3 MB**

### Rust

```
use std::collections::HashMap;
impl Solution {
pub fn two_sum(nums: Vec<i32>, target: i32) -> Vec<i32> {
let mut hash_table: HashMap<i32, i32> = HashMap::new();
for i in 0..nums.len() {
// println!("Processing number: {}", nums[i]);
match hash_table.get(&nums[i]){
Some(&x) => return vec![x, i as i32],
None => hash_table.insert(target - nums[i], i as i32),
};
};
return vec![-1, -1]
}
}
```

#### Rust stats

- Run time:
**2ms** - Memory usage:
**2.2 MB**

## Conclusion

- As per the results, Rust took the least memory and was the fastest of all three.

For more such insights follow me on Twitter.

Original post: Hashnode

## Top comments (3)

Rust's default hashing algorithm is slow; using an algorithm like fx hash or aHash is likely to give you better results.

I tested the slower one π± Thank you. Will try those out.

We'll be soon publishing an article about comparing Rust and Go in 2024, stay tuned here - packagemain.tech/