Pinduoduo·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Pinduoduo coding interview, algorithm-heavy, basically just the triplet sum problem with a follow-up on optimization. Pretty standard for a backend/SWE role but the follow-up on bounded values tripped me up a bit.

Questions Asked (1)

Given an array and a target value T, count all index triplets (i, j, k) where i < j < k and the three elements sum to T. Start with a brute-force solution, then optimize it.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the O(N^3) triple loop, which they were fine with as a starting point.

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AI HintsAI Generated

Suggested Approach

Start by clearly stating the brute-force O(n^3) approach with three nested loops, then optimize to O(n^2) by sorting the array and using a two-pointer technique for each fixed first element. Discuss trade-offs: sorting changes indices but since we only count triplets, it's acceptable; also mention handling duplicates and edge cases.

Pro tip: After presenting the O(n^2) solution, briefly mention that for very large arrays or multiple queries, a hash map based approach or precomputation might be considered, but clarify that the two-pointer method is optimal for a single query. This shows you think about scalability and practical constraints.

1. Clarify and Confirm

Restate the problem to ensure understanding: count triplets (i,j,k) with i<j<k and sum T. Ask about constraints: array size, possible duplicates, negative numbers, and whether indices matter for output (only count).

2. Brute-Force Solution

Describe the naive O(n^3) approach: triple nested loops checking all i<j<k and summing to T. Mention time complexity and that it's inefficient for large n.

3. Optimized Two-Pointer Approach

Sort the array (O(n log n)). For each i from 0 to n-3, set left = i+1, right = n-1, and use two-pointer to find pairs summing to T - arr[i]. Count valid triplets, handling duplicates by skipping equal elements to avoid double-counting.

4. Complexity and Trade-offs

State time complexity O(n^2) and space O(1) (ignoring sorting). Discuss that sorting changes original indices but since we only count, it's fine. Mention alternative hash map approach for unsorted but O(n^2) time and O(n) space.

5. Edge Cases and Testing

Cover edge cases: array size <3, no triplets, all elements same, negative numbers, large values causing overflow. Suggest testing with small examples and verifying counts.

Key Points to Mention

Brute-force O(n^3) vs optimized O(n^2) time complexity
Two-pointer technique after sorting for pair sum
Handling duplicates to avoid overcounting
Trade-off: sorting modifies indices but counting is unaffected
Edge cases: n<3, no solution, integer overflow
Alternative hash map approach for unsorted arrays

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.