Diagonal Traverse II
A medium-tier problem at 58% community acceptance, tagged with Array, Sorting, Heap (Priority Queue). Reported in interviews at BP and 1 others.
Diagonal Traverse II is a medium-difficulty array problem that looks deceptively simple on the surface. You're asked to traverse a jagged 2D array along its diagonals, which sounds straightforward until you realize the input isn't a standard rectangle. BP and Liftoff both ask this problem. The real trick is understanding how diagonals map across rows of different lengths, and the pattern that determines grouping. Nearly 58% of candidates solve it, but the remaining 42% usually struggle with the diagonal ordering logic or inefficient implementations. If this hits your live assessment and you blank on the pattern, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Diagonal Traverse II"
Diagonal Traverse II is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe trap most candidates fall into is trying to simulate a traditional diagonal traversal on a standard grid. With a jagged array, you can't rely on simple row-column arithmetic. The key insight is that elements belong to the same diagonal based on the sum of their indices: element at position (row, col) shares a diagonal with all other elements where row + col equals the same value. Once you group by this sum, you need to handle the order correctly. Sorting approaches work, but a heap-based solution often outperforms naive grouping. Many candidates get the grouping right but mess up the traversal direction or order within each diagonal. StealthCoder handles both the indexing logic and order validation, letting you skip the trial-and-error cycle on the live OA.
Pattern tags
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Diagonal Traverse II recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Diagonal Traverse II interview FAQ
What's the trick to grouping diagonals in a jagged array?+
Elements share a diagonal when their row and column indices sum to the same value. Group by row + col, then sort groups by their sum. The challenge is ensuring correct order within each group, not just across groups. This pattern avoids the complexity of simulating movement across variable-length rows.
Is Diagonal Traverse II still asked at companies like BP and Liftoff?+
Yes. Both reportedly ask it, and it appears in medium-tier system design and coding rounds. It's less common than array basics but frequent enough that ignoring it before an OA is risky, especially if those companies are on your list.
Why do sorting and heap approaches both work here?+
Sorting groups by index sum and then ordering elements within each group is straightforward and clean. A heap (priority queue) lets you process diagonals in order without explicit pre-sorting, which can be slightly faster. Both solve the problem; heap is more elegant if you're comfortable with PQ mechanics.
What's the most common mistake on this problem?+
Getting the diagonal grouping logic right but reversing or incorrectly ordering elements within each diagonal. Candidates also sometimes assume standard grid rules apply, leading to index-out-of-bounds errors. Test your order on a small example before submitting.
How does Diagonal Traverse II relate to Heap and Sorting topics?+
You can solve it purely with sorting (grouping by row + col sum), but heap is often the intended optimization. Understanding both approaches shows you can pick the right tool. Sorting is simpler to code; heap is faster if you need to process results in real time without full pre-computation.
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