Talked through it orally in about five minutes which felt fine.
Clarify the problem definition (e.g., 4-directional vs 8-directional connectivity, matrix dimensions, and whether the matrix can be modified). Then, explain a graph traversal approach (DFS or BFS) to explore each island, marking visited cells to avoid revisiting. Finally, discuss time and space complexity and potential optimizations like Union-Find or handling large matrices.
Pro tip: Mention that you can solve it in-place by modifying the matrix (e.g., changing '1' to '0') to save space, but confirm with the interviewer if mutation is allowed. Also, be prepared to discuss iterative vs recursive DFS to avoid stack overflow for large matrices.
Ask about connectivity (4 or 8 directions), matrix size, and whether the matrix can be modified. Confirm input format (e.g., '1' for land, '0' for water).
Decide between DFS, BFS, or Union-Find. DFS/BFS are simpler and efficient for most cases; Union-Find is good for dynamic connectivity but overkill here.
Iterate through each cell. When encountering unvisited land, increment island count and traverse all connected land cells, marking them visited (e.g., set to '0' or use a visited matrix).
Time complexity is O(rows * cols) since each cell is visited once. Space complexity is O(rows * cols) for visited matrix or O(min(rows, cols)) for BFS queue, or O(rows * cols) for DFS recursion stack in worst case.
Test with edge cases (empty matrix, all land, all water). Discuss optimizations like using BFS with a queue to avoid recursion depth issues, or Union-Find for streaming data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward once I realized what they were actually asking.
Start by explaining the core benefits of chunking: improved upload reliability, parallelism, and resumability. Then discuss trade-offs like increased complexity and potential overhead, and tie it back to real-world scenarios such as large media uploads or backups. Conclude with how this applies to Blinkit's scale and use cases.
Pro tip: Mention that chunking enables efficient use of multipart upload APIs (e.g., S3 Multipart Upload) and allows you to pause/resume uploads, which is crucial for mobile or unreliable networks—a common scenario for Blinkit's delivery partners.
Explain why uploading a large file as a single request is problematic: timeouts, memory usage, and inability to resume on failure.
Cover improved reliability (retry only failed chunks), parallelism (faster uploads), and resumability (pause/resume).
Acknowledge increased complexity, need for chunk management (ordering, metadata), and potential overhead from smaller chunks.
Mention how object storage services support multipart uploads, and how chunking aligns with their APIs.
Give examples like uploading large images/videos from delivery partners or backups, and how chunking improves user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: data volume, write rate, acceptable purge latency, and whether deletes must be transactional. Then propose a batched, throttled deletion strategy using the timestamp index, and discuss how to avoid performance pitfalls like long transactions and table bloat.
Pro tip: Emphasize that purging should be decoupled from the write path and run as a background job with small batches to avoid lock contention and replication lag. Also mention that in append-only systems, you might consider partitioning by time to make purges a metadata operation instead of row-by-row deletes.
Ask about data volume, write throughput, acceptable purge latency, and whether the purge must be atomic. Understand if the table is used for analytics or OLTP, as this affects the approach.
Decide between batched DELETE with a timestamp filter, partition-based dropping, or a soft-delete + background hard-delete. Consider using an index on timestamp to make deletes efficient.
Outline a scheduled job that runs periodically (e.g., hourly), deletes in small batches (e.g., 1000 rows) with a LIMIT, and sleeps between batches to avoid overloading the database. Include error handling and idempotency.
Discuss how to avoid long transactions, table bloat, and replication lag. Mention monitoring, alerting, and the ability to pause/resume the purge. Consider using partitioning to make purges instant.
Compare row-by-row deletes vs. partition dropping vs. TTL features in databases. Discuss the impact on indexes, vacuuming, and storage reclamation. Mention any need for archiving before deletion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
For each project, use the STAR method to concisely describe the situation, task, action, and result, emphasizing the ambiguity you faced and how you adapted. Focus on your specific contributions and the impact, and be ready to dive deeper into technical details and decision-making processes.
Pro tip: Quantify results and highlight trade-offs you considered; this shows you think like a senior engineer who balances multiple factors under uncertainty.
Briefly describe the project, your role, and the team's goal, highlighting any ambiguous requirements or constraints.
Explain the specific problem you tackled, why it was ambiguous, and what made it difficult (e.g., unclear specs, changing priorities).
Detail the steps you took to resolve ambiguity, such as gathering data, prototyping, or collaborating with stakeholders, and the technical decisions you made.
Share the measurable results (e.g., performance improvements, user impact) and what you learned from the experience.
Relate the experience to Blinkit's fast-paced environment, showing how you can handle similar challenges there.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.