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Start by clarifying the requirements: what operations are needed, how many tiers, and what the expected access patterns are. Then design a nested map structure where each tier is a map, and operations traverse the tiers in order. Implement the operations with careful handling of edge cases like missing keys and deletion across tiers.
Pro tip: Discuss the trade-offs between different tiering strategies (e.g., LRU vs. LFU eviction) and how you would handle concurrency if multiple threads access the store. This shows you think beyond basic functionality.
Ask questions to understand the scope: number of tiers, expected operations (set, get, delete), and any constraints like memory limits or concurrency. Confirm whether the store should be thread-safe.
Propose a nested map structure: an outer map for tiers, each tier being a map from keys to values. Consider using a list of maps or a map of maps. Explain how keys are stored and retrieved across tiers.
Detail the algorithms for set, get, and delete. For set, decide which tier to insert into (e.g., always top tier). For get, search tiers from top to bottom. For delete, remove from all tiers or just the top occurrence.
Discuss scenarios like key not found, deleting a non-existent key, and tier overflow. Explain how to maintain consistency and avoid stale data.
Analyze time and space complexity for each operation. Discuss trade-offs between tiering strategies (e.g., LRU, LFU) and potential optimizations like indexing or caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the data model and constraints first, then propose an algorithm that iterates over the outer key's inner entries and checks each against the next tier's lookup with the predicate. Discuss trade-offs between eager and lazy evaluation, and how to handle missing lookups or predicate failures.
Pro tip: Mention that you would push the predicate down to the next tier if possible to avoid fetching unnecessary data, and highlight the importance of defining behavior for missing lookups (e.g., skip or count as false).
Ask questions to understand the structure of tiers, how inner entries are stored, and what the predicate operates on. Confirm whether the count should include only matches or also handle missing lookups.
Outline a step-by-step approach: retrieve the outer key's inner entries, for each entry perform a lookup in tier N+1, apply the predicate, and increment a counter if it matches.
Discuss time and space complexity, and compare approaches like batch lookups vs. individual lookups, or pushing the predicate to the next tier to reduce data transfer.
Address scenarios like missing inner entries, missing lookups in tier N+1, predicate exceptions, and concurrency issues if the store is mutable.
Suggest caching, parallelization, or indexing strategies to improve performance, and discuss how the operation could be extended to multiple tiers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran out of time before fully implementing this.
Start by clarifying the requirements: TTL per entry, lazy expiration on read, and cleanup strategy. Then design a data structure that stores value with expiration timestamp, and implement read path to check and evict expired entries. Discuss trade-offs between lazy and active expiration, and consider concurrency and memory implications.
Pro tip: Mention that lazy expiration alone can lead to memory bloat if keys are never read; propose a hybrid approach with occasional active cleanup or a max size eviction policy to demonstrate production awareness.
Ask about expected read/write patterns, memory limits, concurrency needs, and whether TTL is per-key or global. Confirm that lazy cleanup on read is sufficient or if background cleanup is needed.
Store each entry as a struct containing value, expiration timestamp (or TTL), and possibly creation time. Use a map for O(1) access. Consider using a min-heap or time-ordered structure for efficient active cleanup if needed.
On get, check if entry exists and if current time > expiration. If expired, delete the entry and return not found. Ensure atomicity if concurrent access is possible.
On set, accept an optional TTL parameter. If TTL provided, compute expiration time and store it; if not, store without expiration or use default. Consider updating TTL on existing keys.
Explain that lazy cleanup only removes expired entries when accessed, which can waste memory. Propose optional active cleanup (e.g., periodic scan or background thread) and discuss trade-offs in complexity, CPU, and memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.