Start by clarifying requirements (e.g., timestamp handling, concurrency, persistence) and then propose a design using a hash map for O(1) average-time put/get. Discuss how to handle multiple writes with timestamps, such as storing the latest value per key or maintaining versioned entries, and consider edge cases like out-of-order timestamps.
Pro tip: Mention that in a real system like Coinbase, you'd likely need to handle concurrent access and persistence, so briefly touch on thread-safety (e.g., using locks or concurrent data structures) and durability options (e.g., write-ahead log) to show production awareness.
Ask about expected operations, timestamp semantics (e.g., should get return the value with the highest timestamp or the latest write?), concurrency needs, and memory constraints.
Propose using a hash map (dictionary) to store key-value pairs, where each value includes the string value and timestamp. For handling multiple writes, either overwrite if the new timestamp is greater, or store a list of versions per key.
Implement put(key, value, timestamp) to update the store, ensuring the most recent timestamp wins. Implement get(key) to return the value associated with the highest timestamp for that key.
Consider out-of-order timestamps, duplicate timestamps, missing keys, and concurrency. Decide on behavior (e.g., ignore older timestamps, return null for missing keys).
Discuss time and space complexity (O(1) average for put/get with hash map, O(n) worst-case for versioned lists). Mention potential improvements like using a balanced BST for ordered timestamps if range queries are needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easy to get wrong if you're not careful about the sort.
Start by clarifying requirements (e.g., expected scale, read/write patterns, consistency needs) and then propose a data structure that supports efficient prefix scans, such as a trie or a sorted index (e.g., LSM-tree with SSTables). Discuss how to implement the scan operation, including lexicographic ordering, and address trade-offs like memory usage, latency, and concurrency.
Pro tip: Mention that many production databases (e.g., LevelDB, RocksDB) use a sorted string table (SSTable) with a block index to support efficient prefix scans, and highlight the importance of considering range scans in the context of the underlying storage engine.
Ask about the expected data size, read/write ratio, latency requirements, and whether the scan needs to be consistent or can be eventually consistent. This shapes the design.
Propose a trie, a sorted array with binary search, or an LSM-tree with SSTables. Explain how each supports prefix scans and lexicographic ordering.
Describe how to traverse the data structure to find all keys with the given prefix, ensuring they are returned in sorted order. For a trie, do a DFS from the prefix node; for SSTables, seek to the prefix and iterate.
Discuss how the design handles large datasets, concurrency, and persistence. Mention indexing, caching, and partitioning strategies if needed.
Compare alternatives (e.g., trie vs. sorted index) in terms of memory, speed, and complexity. Explain why your chosen approach is suitable for the given context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: TTL is optional per put, expiration is lazy or active, and get/scan must filter expired entries. Then propose a design that stores an expiration timestamp alongside each value, and discuss trade-offs between lazy deletion (on read) and active deletion (background sweeper). Finally, address edge cases like clock skew, TTL updates, and scan efficiency.
Pro tip: Mention that lazy expiration alone can cause memory bloat, so a hybrid approach with periodic cleanup is often best—this shows you think about production concerns beyond just correctness.
Ask whether TTL is per-key or per-put, if updates reset TTL, and whether expired entries should be removed immediately or lazily. Also confirm if scan needs to be efficient for large datasets.
Store each entry as a value plus an optional expiration timestamp (e.g., Unix epoch seconds). If TTL is not provided, expiration is null (never expires).
On get and scan, check if the entry's expiration timestamp is in the past; if so, treat it as absent and optionally delete it. For active cleanup, use a background thread that periodically scans and removes expired entries.
For scan, iterate over entries and skip expired ones. If the data store supports range queries, consider indexing by expiration time to quickly find and purge expired entries.
Compare lazy vs. active expiration: lazy saves CPU but may return stale data if not checked; active uses more resources but keeps memory clean. Address clock skew, TTL updates, and concurrency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The TTL-on-restore behavior is the nastiest part of this whole problem.
Start by clarifying requirements and constraints, then outline a high-level design for backup and restore, focusing on data modeling and TTL handling. Dive into technical details like snapshot isolation, ID generation, and TTL adjustment, and discuss trade-offs (e.g., storage overhead, consistency). Conclude with potential optimizations and edge cases.
Pro tip: Emphasize the importance of atomicity and consistency during backup and restore, and propose using a copy-on-write or versioning mechanism to avoid blocking writes. Also, discuss how to handle concurrent modifications during restore to ensure a consistent snapshot.
Ask questions to understand the scale, consistency requirements, and expected backup frequency. Clarify whether backups should be incremental or full, and how restore should handle concurrent writes.
Propose a method to capture all non-expired entries at a given timestamp, such as iterating over the keyspace or using a snapshot isolation technique. Generate a unique backup ID and store metadata (timestamp, ID) for later retrieval.
Outline how to restore the database to the snapshot, ensuring that TTLs are adjusted to preserve remaining lifetime relative to restore time. Discuss how to handle entries that expired between backup and restore.
Explain the calculation: for each entry, compute remaining TTL at backup time (original TTL minus elapsed time since entry creation). During restore, set new TTL as remaining TTL plus time elapsed since backup, or simply remaining TTL if restore time is considered as new 'now'.
Compare approaches (e.g., full vs. incremental backup, in-memory vs. persistent storage). Discuss trade-offs in terms of performance, storage, and consistency. Suggest optimizations like lazy deletion or background cleanup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.