Not too bad once I stopped overthinking the setup and just mapped out the relationships.
Clarify the problem constraints and graph representation first, then identify the appropriate algorithm (e.g., topological sort for dependencies, BFS/DFS for traversal, or union-find for connectivity). Walk through a small example to validate your approach, then discuss complexity and edge cases before coding.
Pro tip: Explicitly state assumptions about input size, graph directionality, and whether cycles are possible—interviewers at IBM value candidates who surface ambiguities early and design robust solutions rather than jumping straight to code.
Ask about graph size, directed vs. undirected edges, possibility of cycles, and expected output format. Confirm whether the problem is about ordering, connectivity, shortest path, or cycle detection.
Map the problem to a known pattern: topological sort (Kahn's or DFS) for dependencies, BFS for shortest path in unweighted graphs, DFS for cycle detection, or union-find for dynamic connectivity. Justify your choice based on constraints.
Trace your algorithm on a simple graph (including edge cases like a cycle or disconnected components) to verify correctness and catch off-by-one errors before coding.
Write modular code with clear variable names, then state time and space complexity (e.g., O(V+E) for traversal). Mention trade-offs if multiple approaches exist.
Cover empty graph, single node, self-loops, disconnected components, and large inputs. Briefly mention how the solution scales or could be optimized.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the failing tests and the spec to understand expected behavior, then systematically isolate each failure (filtering, pagination, caching) using logs and test output. For each, trace the code path, reproduce the issue in isolation, and verify fixes against the spec and edge cases, especially cache invalidation scenarios.
Pro tip: Demonstrate a methodical debugging process: always reproduce the failure first, then form a hypothesis and test it with minimal changes. Mention writing additional tests for edge cases like cache stampede or stale data after invalidation to show proactive quality focus.
Review the technical spec to confirm expected API behavior for filtering, pagination, and caching. Run the failing tests to capture exact error messages and identify which assertions fail.
For each failing test, isolate the scenario by mocking dependencies or using focused test runs. Reproduce the failure locally to confirm it's deterministic and gather logs/stack traces.
Follow the request flow from API endpoint through filtering/pagination logic to Redis cache interactions. Form hypotheses about root causes, such as incorrect query parameters, off-by-one pagination, or stale cache keys.
Implement targeted fixes and rerun tests. Add or adjust tests for edge cases like cache invalidation on updates, TTL expiry, and concurrent requests to ensure robustness.
Confirm all tests pass and behavior matches the spec. Consider adding integration tests or monitoring to catch similar issues in the future.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.