Start by clarifying the requirements: in-memory, key-field-value store, operations SET, GET, DELETE. Then propose a nested hash map (dictionary) where the outer map maps keys to inner maps, and inner maps map fields to values. Discuss time complexity, edge cases, and potential extensions like transactions or TTL.
Pro tip: Mention that you would use a hash map for O(1) average time complexity, but also consider thread-safety if the database is accessed concurrently. This shows awareness of real-world constraints beyond the basic algorithm.
Ask questions to confirm the exact behavior: Are keys and fields strings? What should GET return if key or field doesn't exist? Should DELETE remove the entire key or just a field? Are there any constraints on memory or concurrency?
Propose a nested hash map: a top-level map from keys to inner maps, and each inner map from fields to values. This allows O(1) average time for SET, GET, and DELETE.
Specify the semantics: SET(key, field, value) inserts or updates; GET(key, field) returns value or null; DELETE(key, field) removes the field, and if the inner map becomes empty, optionally remove the key to save memory.
State that all operations are O(1) average time, O(n) worst-case due to hash collisions. Discuss edge cases: missing key/field, deleting non-existent entries, and potential memory leaks from empty inner maps.
Mention possible extensions: thread-safety using locks or concurrent data structures, TTL for automatic expiration, transactions, or persistence. Compare with alternative designs like a single flat map with composite keys.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and constraints, then propose a data structure that stores per-field expiry timestamps alongside values. Discuss how to handle lazy vs. active expiration, and analyze trade-offs in terms of time/space complexity and system design.
Pro tip: Mention that lazy expiration is often preferred for read-heavy systems to avoid background overhead, but active expiration can be combined for memory efficiency. Also, highlight the importance of using a monotonic clock to avoid issues with system time changes.
Ask about expected read/write patterns, memory constraints, and whether expiration should be lazy or active. Confirm that SET_AT sets a field with an absolute expiry timestamp, and SET_AT_WITH_TTL sets a relative TTL.
Propose storing each field's value along with its expiry timestamp in a hash map or similar structure. For per-field expiry, consider a nested map: field -> (value, expiry).
On read, check if the field's expiry timestamp is in the past; if so, return nothing and optionally delete the field. Discuss lazy deletion vs. background sweeping for memory reclamation.
Compare lazy vs. active expiration: lazy avoids background overhead but may retain expired data; active frees memory but adds complexity. Discuss time/space complexity of operations.
If this is part of a larger system, discuss how to handle persistence, replication, and clock synchronization across nodes. Mention potential use of a min-heap or time wheel for efficient active expiration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the data model and requirements (e.g., key-value store, sorted order, prefix semantics) before designing the solution. Propose an ordered data structure like a balanced BST or trie to support efficient range scans and prefix filtering, then analyze time and space complexity. Discuss trade-offs and potential optimizations for large-scale systems.
Pro tip: Mention that SCAN should be inclusive of the start key and that SCAN_BY_PREFIX can be implemented as a range scan from prefix to prefix + '\xff' (or equivalent), showing attention to edge cases. Also, consider concurrency and consistency if the store is distributed.
Ask about the data model (e.g., key-value store), expected operations, and constraints like ordering, prefix semantics, and performance goals.
Select an ordered structure such as a balanced BST (e.g., red-black tree) or trie that supports efficient range queries and prefix matching.
Implement SCAN by performing an in-order traversal starting from the given key (inclusive) to collect all matching fields in lexicographic order.
Implement SCAN_BY_PREFIX by converting it to a range scan from the prefix to the prefix with the highest possible suffix (e.g., prefix + '\xff'), then filter results.
Discuss time complexity (O(log n + k) for balanced BST, O(k) for trie) and space complexity, and mention alternatives like sorted arrays or skip lists.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then design a snapshot mechanism that captures the full database state at a given timestamp, likely using a copy-on-write or versioned approach. For restore, focus on how to adjust TTLs relative to the restore time, ensuring expired keys are handled correctly and consistency is maintained.
Pro tip: Emphasize the trade-offs between storage overhead and restore speed, and propose a lazy expiration strategy for TTLs to avoid scanning the entire dataset during restore.
Ask about expected data size, frequency of backups, acceptable downtime during restore, and consistency requirements. Confirm that TTLs should be recalculated based on restore time, not original set time.
Propose a method to capture the database state at a timestamp, such as periodic full snapshots with incremental changes, or a versioned key-value store. Consider using a write-ahead log or copy-on-write for efficiency.
Explain how to store TTLs as absolute expiration times or as remaining TTL at snapshot time. On restore, compute new expiration times relative to restore time, and discard keys that would have already expired.
Describe the restore process: load the snapshot, apply any necessary transformations (like TTL adjustment), and atomically swap the new state into place. Discuss how to handle concurrent writes during restore.
Discuss trade-offs between storage cost, restore time, and consistency. Cover edge cases like keys expiring during backup, clock skew, and partial failures during restore.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.