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Start by clarifying requirements: expected operations, data types, concurrency needs, and memory constraints. Then outline a design using a hash map for O(1) average-case operations, and discuss potential extensions like thread safety or persistence. Walk through the implementation of set, get, and delete, and analyze time and space complexity.
Pro tip: Mention that while a simple hash map suffices for basic operations, real-world systems often require handling concurrency, eviction policies, and persistence—showing you think beyond the immediate problem. Also, proactively discuss trade-offs between different data structures (e.g., hash map vs. balanced tree) to demonstrate depth.
Ask about expected operations, data types, concurrency, persistence, and memory constraints to scope the problem appropriately.
Select a hash map for O(1) average-case set, get, and delete, and justify why it's suitable over alternatives like balanced trees.
Write pseudocode or code for set, get, and delete, handling edge cases like missing keys and updating existing keys.
State time and space complexity for each operation and overall, noting average vs. worst-case scenarios.
Propose enhancements like thread safety, eviction policies (LRU), persistence, or distributed scaling to show system design awareness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I stored the expiry timestamp alongside the value and checked it on read.
Start by clarifying requirements: TTL granularity, expiration semantics (lazy vs. active), and consistency needs. Then present a design that stores expiration timestamps alongside values and handles expiration on read, with optional background cleanup. Discuss trade-offs between approaches and consider concurrency and memory management.
Pro tip: Mention that lazy expiration alone can cause memory bloat, so a hybrid approach with periodic active expiration is often used in production systems like Redis. Also, discuss how to handle TTL updates and the impact on existing keys.
Ask about TTL precision, whether expired keys should be removed immediately or lazily, and if TTL can be updated. Also consider persistence and concurrency requirements.
Store each key's value along with an expiration timestamp (e.g., absolute time in milliseconds). Consider using a separate data structure for efficient expiration, like a min-heap or time wheel.
On get, check if the key exists and if its expiration timestamp is in the past. If expired, return nothing and optionally delete the key (lazy expiration).
Use a background thread or scheduler to periodically scan and remove expired keys, preventing memory buildup. Discuss trade-offs between scanning frequency and overhead.
Ensure thread-safe operations for get, set, and expiration. Handle cases like TTL update, deletion, and clock skew. Discuss trade-offs between different expiration strategies.
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 is the expected query pattern (point-in-time vs. range), read/write ratio, and latency/consistency needs. Then propose a versioned data model (e.g., append-only log or version chain per key) that captures all mutations including deletes and TTLs, and discuss how to index it for efficient timestamp lookups. Finally, analyze the time/space trade-offs of different structures (e.g., B-tree, LSM-tree, in-memory version lists) and justify your choice based on the workload.
Pro tip: Emphasize that TTLs are just deletes scheduled at a future time, so they should be modeled as tombstones with an expiration timestamp; this simplifies the design and avoids special cases. Also, mention that you can use a binary search over version chains to achieve O(log n) lookups, which is a practical optimization.
Ask about query patterns (point-in-time vs. range), read/write ratio, latency and consistency requirements, and retention policy. This determines the appropriate data structure and trade-offs.
Propose a versioned model: each key has a list of versions (timestamp, value, type) where type indicates set/delete/TTL. TTLs are represented as tombstones with an expiration timestamp. This captures all history.
Select a structure to store versions: e.g., append-only log with an index, LSM-tree with timestamp as part of the key, or in-memory version chains. Discuss how to efficiently find the latest version <= query timestamp (e.g., binary search).
Compare time/space trade-offs: version chains use more space but allow O(log n) reads; LSM-trees optimize writes but may have read amplification; in-memory is fast but limited by RAM. Consider compaction and garbage collection for old versions.
Discuss handling of TTL expiration (tombstone with expiry), deletes (tombstones), and clock skew. Also consider how to support range queries and whether to keep all history or expire old versions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.