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I started with a sorted list per key and binary search, which is the obvious answer.
Start by clarifying requirements and constraints, then propose a design using a hash map from keys to sorted lists of (timestamp, value) pairs, with binary search for get. Discuss trade-offs between different data structures and consider scalability, concurrency, and persistence.
Pro tip: Mention that timestamps can be assumed to be monotonically increasing per key for set operations, which allows appending to the list and simplifies binary search. Also, discuss how to handle out-of-order timestamps if they are allowed.
Ask about expected scale, read/write ratio, timestamp ordering, concurrency needs, and persistence requirements. Confirm that get should return the value with the largest timestamp <= given timestamp.
Propose a hash map for O(1) key lookup, with each key mapping to a list of (timestamp, value) pairs sorted by timestamp. For get, use binary search to find the latest timestamp <= target.
Discuss time complexity: set O(1) amortized (if appending) or O(log n) if inserting in order; get O(log n) due to binary search. Compare with alternatives like balanced BST or skip list, and mention space-time trade-offs.
Handle cases like missing key, timestamp before earliest, duplicate timestamps, and out-of-order sets. Discuss concurrency (locks, copy-on-write) and persistence (write-ahead log, snapshots).
Write clean code for the chosen design, then walk through test cases including normal, boundary, and error scenarios. If time permits, discuss optimizations like caching or sharding.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what constitutes a 'very large timestamp range' (e.g., years of data, billions of entries) and what memory constraints exist (e.g., limited RAM per node). Then propose a design that avoids loading all timestamps into memory, such as time-partitioned storage with on-disk indexing and caching, and discuss trade-offs between read/write performance, memory usage, and complexity.
Pro tip: Emphasize that you would first measure and understand the actual access patterns and data distribution, because optimizing for the wrong workload can waste effort and introduce unnecessary complexity.
Ask questions to understand the scale of timestamps (range, number of entries), memory limits, read/write patterns, and latency requirements. This ensures your solution addresses the real problem.
Propose partitioning data by time (e.g., daily, monthly) so that only relevant partitions are loaded into memory. This reduces memory footprint and allows efficient range scans.
Use on-disk indexes (e.g., B-trees, LSM-trees) and memory-mapped files or block-based storage to avoid loading all timestamps into RAM. Consider compression and sparse indexes to further reduce memory.
Cache frequently accessed partitions or index nodes in memory using an LRU or similar policy. This balances memory usage with performance for hot data.
Compare your approach with alternatives like using a time-series database or external storage. Highlight trade-offs in complexity, latency, and memory efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Delete was fine, tombstone markers, standard stuff.
Start by clarifying the current API design and data model, then systematically address each new requirement (delete, time-range queries, background compaction) and how they impact storage, indexing, and consistency. Discuss trade-offs between approaches, such as soft vs. hard deletes, indexing strategies for time-range queries, and compaction scheduling, while considering scale and performance.
Pro tip: Emphasize the importance of idempotency and eventual consistency in delete operations, and how background compaction can be leveraged to reclaim space and improve read performance without impacting latency.
Ask questions to understand the existing API, data volume, read/write patterns, and SLAs. Confirm whether deletes need to be immediate or can be eventual, and the expected query patterns for time-range queries.
Choose between soft delete (mark as deleted) and hard delete (physically remove). Consider idempotency, cascading deletes, and how deletes interact with time-range queries and compaction.
Decide on indexing strategy (e.g., time-based partitioning, secondary indexes) to efficiently support range queries. Discuss how deletes affect index maintenance and query results.
Design a compaction process to merge data, remove tombstones, and optimize storage. Schedule it to run during low-traffic periods and ensure it doesn't impact query latency.
Analyze trade-offs: soft delete vs. hard delete (storage vs. complexity), indexing overhead, compaction frequency. Ensure the design scales with data growth and maintains performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.