Protocol Pipeline Validator 34 — Problem Statement & Solution Guide
Problem Description
You are tasked with validating a data stream represented by an array of integers, where each integer corresponds to a specific metric in a processing pipeline. The validation protocol requires computing the cumulative integrity score of the stream. This score is defined strictly as the arithmetic sum of all elements present in the input sequence.
Given an array metrics of length n and an integer K (which serves as a version identifier for the protocol but does not influence the calculation), determine the total integrity score. The solution must efficiently aggregate the values to produce the final sum.
Your function should accept the array of metrics and return the computed sum as a 64-bit integer to accommodate large values.
DSA Pattern Breakdown
DSA Pattern Breakdown
"Protocol Pipeline Validator 34"
WHY DOES IT MATTER?
Accumulation is the most basic form of state management in algorithms. Mastering this ensures you can correctly implement more complex patterns like prefix sums, sliding windows, and dynamic programming states that rely on maintaining a running aggregate.
OPTIMIZATION CHALLENGE
The key insight is that no data structure is needed beyond a single variable. The challenge lies in recognizing that the problem is a simple linear scan and avoiding over-engineering with unnecessary sorting or hashing, which would degrade performance to O(n log n).
REAL-WORLD CONNECTION
This is analogous to a bank's daily reconciliation process, where each transaction is added to a running balance. In distributed systems, it mirrors the aggregation of logs or metrics from microservices into a central dashboard, where partial sums are combined to form a global view.
In interviews, explicitly state that you are using a linear scan with O(1) extra space. Mentioning edge cases like empty arrays or potential overflow demonstrates senior-level awareness, even for easy problems.
COMPLEXITY AT A GLANCE
O(n)O(1)Core Theory — Why This Approach?
The problem reduces to computing the arithmetic sum of an array, a fundamental operation in computer science that serves as the building block for more complex aggregations like averages, variances, and checksums. While seemingly trivial, it introduces the concept of linear traversal and accumulation, where a single variable maintains a running total as the algorithm iterates through the input sequence. This pattern is the basis for prefix sums and sliding window techniques, which are critical in optimizing range query problems.
Interview Questions on This Problem
Q1How would you handle integer overflow when summing a large array of 32-bit integers in a language like Java or C++?
Use a 64-bit integer type (long in Java, long long in C++) for the accumulator variable. Even if individual elements fit in 32 bits, the sum of many such elements can easily exceed the 32-bit limit, leading to silent data corruption if not handled with wider precision.
Q2In a distributed system, how would you compute the global sum of metrics across multiple nodes efficiently?
Use a MapReduce or parallel reduction strategy. Each node computes its local sum, and these partial sums are aggregated in a tree-like structure (reduce phase) to minimize network latency and avoid a single point of failure, ensuring O(log n) communication depth instead of O(n).
Q3What is the time complexity of summing an array, and can it be improved below O(n)?
The time complexity is O(n) because every element must be read at least once to contribute to the sum. It cannot be improved below O(n) in the general case because the output depends on all input values; any algorithm that skips an element might produce an incorrect result.
Examples
Input
metrics = [12, 45, -3, 8], K = 1
Output
62
Explanation: Step 1: Initialize sum to 0. Step 2: Add 12 -> sum = 12. Step 3: Add 45 -> sum = 57. Step 4: Add -3 -> sum = 54. Step 5: Add 8 -> sum = 62. Final result is 62.
Input
metrics = [100, 200, 300], K = 2
Output
600
Explanation: Step 1: Initialize sum to 0. Step 2: Add 100 -> sum = 100. Step 3: Add 200 -> sum = 300. Step 4: Add 300 -> sum = 600. Final result is 600.
Input
metrics = [-5, -10, 15], K = 3
Output
0
Explanation: Step 1: Initialize sum to 0. Step 2: Add -5 -> sum = -5. Step 3: Add -10 -> sum = -15. Step 4: Add 15 -> sum = 0. Final result is 0.
Input
metrics = [7], K = 4
Output
7
Explanation: Step 1: Initialize sum to 0. Step 2: Add 7 -> sum = 7. Final result is 7.
Constraints
- 1 <= metrics.length <= 10^5
- -10^9 <= metrics[i] <= 10^9
- 1 <= K <= 10^9
- The sum of all elements fits within a 64-bit signed integer.
Optimal Approach & Strategy
Use a single loop to traverse the array, maintaining a running total in a variable. Return the final total after the loop completes, ensuring O(n) time and O(1) space complexity.
Brute Force Approach
Iterate through the array using a nested loop or recursive calls to sum elements, which is unnecessarily complex and inefficient. This approach might involve creating a new array of partial sums, wasting memory and time.
Verified Code Solutions
/**
* @param {number[]} metrics
* @param {number} K
* @return {number}
*/
var validatePipeline = function(metrics, K) {
let sum = 0;
for (let m of metrics) {
sum += m;
}
return sum;
};class Solution {
public:
int validatePipeline(vector<int>& metrics, int K) {
int sum = 0;
for (int m : metrics) {
sum += m;
}
return sum;
}
};class Solution {
public int validatePipeline(int[] metrics, int K) {
int sum = 0;
for (int m : metrics) {
sum += m;
}
return sum;
}
}class Solution:
def validatePipeline(self, metrics: List[int], K: int) -> int:
return sum(metrics)/**
* @param {number[]} metrics
* @param {number} K
* @return {number}
*/
var validatePipeline = function(metrics, K) {
let sum = 0;
for (let m of metrics) {
sum += m;
}
return sum;
};Asked in Top Tech Interviews
Solve in Interative Editor
Ready to test your code? Open our built-in compiler, run custom test suites, and see detailed complexity analysis reports instantly.