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Clarify the requirements and constraints first, then design a data structure that supports efficient key-field-value operations with timestamps. Implement the operations with careful attention to atomicity and timestamp handling, and discuss trade-offs and potential extensions.
Pro tip: Mention that compare-and-set/delete should be atomic to avoid race conditions, and consider using a version or timestamp to resolve conflicts. Also, discuss how this could be extended to a distributed setting, showing awareness of real-world systems.
Ask questions to understand the expected operations, data types, concurrency requirements, and timestamp semantics (e.g., logical vs. physical clocks).
Propose an in-memory data structure, such as a nested hash map (key -> field -> (value, timestamp)), and justify its efficiency for the required operations.
Write pseudocode or describe the logic for set, get, compare-and-set, and compare-and-delete, ensuring correct timestamp updates and atomicity.
Discuss how to make operations thread-safe, e.g., using locks or atomic operations, and explain the trade-offs.
Outline test cases (e.g., concurrent updates, stale timestamps) and mention possible extensions like persistence or distribution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty straightforward extension once level 1 was done.
Clarify the data model and visibility semantics, then design scan and prefix-filtered scan as range queries over a versioned key-value store. Explain how to efficiently retrieve all visible field-value pairs for a given key at a timestamp, handling multiple versions and deletions, and discuss trade-offs between storage layout and query performance.
Pro tip: Emphasize that visibility at a timestamp requires resolving the latest version of each field with a version timestamp ≤ the query timestamp, and that tombstones must be respected. Mention that prefix scans can leverage sorted storage (e.g., LSM-trees or sorted string tables) to avoid full scans.
Ask about the underlying storage (e.g., LSM-tree, B-tree), versioning scheme (timestamps, sequence numbers), and deletion semantics (tombstones). Confirm that 'visible' means the latest version of each field with version ≤ query timestamp, excluding deleted fields.
For a given key and timestamp, retrieve all field-value pairs by scanning the version chain for that key, filtering versions with timestamp ≤ query timestamp, and keeping the latest per field. Exclude fields whose latest visible version is a tombstone.
Extend the scan to only include fields whose names start with a given prefix. Leverage sorted storage to seek to the prefix range and iterate only relevant fields, applying the same visibility logic.
Discuss indexing strategies (e.g., per-key sorted field lists, prefix bloom filters) and caching to speed up scans. Compare approaches like merging on read vs. maintaining materialized views, and note impacts on write amplification and latency.
Address concurrent writes, timestamp consistency, and how to ensure snapshot isolation. Consider cases like empty results, non-existent keys, and fields with multiple versions within the same timestamp.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started feeling the pressure.
Start by clarifying the requirements: per-write TTL means each write has its own expiration, and the half-open window [write_timestamp, write_timestamp + ttl) defines visibility. Then propose a data model that stores the expiration timestamp alongside the value, and discuss how reads and writes handle expired entries, including cleanup strategies and trade-offs between eager and lazy expiration.
Pro tip: Mention that you would use monotonic time (e.g., time.monotonic()) for TTL calculations to avoid issues with clock adjustments, and consider the impact on replication and consistency in distributed systems.
Confirm that TTL is per-write, the window is half-open [start, end), and that expired entries are invisible. Ask about expected TTL ranges, read/write patterns, and consistency needs.
Store each value with its expiration timestamp (write_time + ttl). For key-value stores, this could be a tuple (value, expire_at). Consider if multiple versions per key are needed (e.g., for time-travel queries).
On read, check if current time is within [write_time, expire_at). If expired, treat as missing. On write, compute expire_at and store. Discuss atomicity and concurrency.
Choose between lazy deletion (on read) and active cleanup (background sweeper). Discuss trade-offs: lazy saves CPU but may leak memory; active keeps memory bounded but adds overhead.
If distributed, ensure TTL is based on a consistent clock or logical time. Consider replication of expiration and how to handle clock skew. Discuss sharding and impact on cleanup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: what granularity of time-travel, consistency guarantees, and scale are needed. Then propose a versioned data model (e.g., append-only log or bitemporal table) that captures every write, delete, TTL expiry, and CAS as an immutable event with a timestamp. Finally, describe how to query the state as of a past timestamp by replaying or indexing events, and discuss trade-offs between storage, latency, and complexity.
Pro tip: Emphasize that TTL expirations and CAS updates are just special types of writes that must be recorded as events with timestamps; treating them uniformly simplifies the design and avoids missing edge cases.
Ask about time-travel granularity (exact timestamp vs. version), consistency needs (linearizable vs. eventual), data volume, and query latency SLAs. This scopes the solution and shows you think before coding.
Propose an append-only event log or bitemporal table where each row includes the field value, operation type (write/delete/TTL/CAS), and a timestamp (or version). Ensure all mutations are captured immutably.
Specify how to resolve the value at a past timestamp: find the latest event for the field with timestamp <= target, and interpret deletes/TTL as null or absent. Handle CAS by storing the expected value and only applying if it matches at that time.
Describe indexing strategies (e.g., timestamp-indexed event store, periodic snapshots, or delta encoding) to avoid full scans. Discuss trade-offs between storage overhead and query performance.
Cover handling of clock skew, concurrent writes, TTL expiration timing, and CAS conflicts. Discuss garbage collection of old versions and how to balance retention with storage cost.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.