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Soumendra Kumar Sahoo
Soumendra Kumar Sahoo

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Python vs Golang vs Rust

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
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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
}
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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]
    }
}
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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.

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Original post: Hashnode

Discussion (2)

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chayimfriedman2 profile image
Chayim Friedman

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

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soumendrak profile image
Soumendra Kumar Sahoo Author

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