The base store wasn't bad, I've done enough of these.
Start by clarifying requirements: operations (SET, GET, GET_AT_TIMESTAMP), timestamp semantics, and concurrency needs. Then propose a data model using a nested hash map (key -> field -> list of (timestamp, value) entries) and discuss how to efficiently implement GET_AT_TIMESTAMP via binary search. Finally, analyze time/space complexity and potential optimizations like versioned data structures or time-indexed storage.
Pro tip: Mention that timestamps can be monotonically increasing per key-field pair, allowing append-only logs and binary search; also discuss how to handle out-of-order writes or clock skew if timestamps are client-provided.
Ask about expected operations (SET, GET, GET_AT_TIMESTAMP, DELETE), timestamp source (client vs server), and concurrency/consistency requirements. Confirm whether timestamps are unique per key-field and if out-of-order writes are possible.
Propose a nested map: outer map keyed by key, inner map keyed by field, each field maps to a list of (timestamp, value) pairs sorted by timestamp. Alternatively, use a single map with composite key (key, field) to a version list.
For SET, append (timestamp, value) to the list for the (key, field). For GET, return the latest value. For GET_AT_TIMESTAMP, binary search the list for the largest timestamp <= given timestamp and return its value (or null if none).
Discuss time complexity: O(1) for SET (amortized), O(log n) for GET_AT_TIMESTAMP, O(1) for GET if latest cached. Space O(total versions). Suggest optimizations: pruning old versions if retention policy, using balanced BST or skip list for dynamic inserts, or time-bucketed storage.
Handle missing key/field, timestamp before first version, and concurrent writes. Discuss locking (per key-field) or lock-free approaches with atomic appends, and how to ensure consistency during binary search.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you've got the history layer working, but you have to be careful about what 'current' means.
Clarify the data structure and concurrency context, then design the operations to atomically check the expected value before mutating. Discuss implementation using locks or lock-free primitives like CAS, and analyze trade-offs in performance, correctness, and contention.
Pro tip: Emphasize atomicity and linearizability: these operations must appear instantaneous to other threads. Mention that COMPARE_AND_DELETE is not natively supported by hardware, so it requires a loop or lock, and discuss ABA problem mitigation.
Ask about the underlying data structure (e.g., hash map, list), concurrency model (threads, async), and expected performance. Confirm semantics: operations succeed only if current value equals expected, and what happens on failure (e.g., return false, throw exception).
For COMPARE_AND_SET, use a lock or CAS loop to atomically compare and update. For COMPARE_AND_DELETE, since no direct hardware primitive exists, use a lock or a CAS loop that marks the entry as deleted before removal.
Discuss ABA problem, memory reclamation (e.g., hazard pointers, RCU), and contention. Explain how to ensure linearizability and avoid race conditions.
Compare lock-based vs lock-free implementations: simplicity, scalability, and risk of deadlock/livelock. Discuss performance under high contention and potential optimizations like backoff.
Outline testing strategies: unit tests for correctness, stress tests with multiple threads, and model checking for linearizability. Mention tools like ThreadSanitizer or Jepsen.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the whole thing gets gnarly.
Start by clarifying the requirements and constraints, then propose a design that stores per-record TTL as an absolute expiry timestamp, and define consistent semantics for reads and CAS operations. Walk through how TTL interacts with timestamped reads and CAS, addressing edge cases like expired records and concurrent operations.
Pro tip: Emphasize that using absolute timestamps avoids clock skew issues and simplifies expiry checks; also highlight the importance of atomicity in CAS operations to prevent race conditions with TTL.
Ask questions to understand the expected scale, consistency requirements, and whether TTL is set at write time or can be updated. Confirm if timestamped reads and CAS are already supported and how they should interact with TTL.
Propose storing each record with an absolute expiry timestamp (e.g., Unix epoch milliseconds) instead of a relative TTL. This ensures consistent expiry semantics regardless of when reads occur.
Specify that timestamped reads should return the record only if the read timestamp is before the expiry timestamp. If the read timestamp is after expiry, the record is considered non-existent, even if not yet physically deleted.
For CAS operations, ensure that the operation fails if the record has expired at the time of the operation. The CAS should atomically check expiry and the expected value before applying the update.
Discuss handling of expired records (lazy deletion vs. background cleanup), clock synchronization, and atomicity guarantees. Mention potential performance implications and trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.