The TTL piece is what tripped me up first.
Start by clarifying requirements: what consistency guarantees are needed, how large the store can be, and whether backup can block writes. Then propose a design that snapshots data and TTLs atomically, using a serialization format that stores absolute expiration timestamps or remaining TTLs, and discuss trade-offs between blocking vs. non-blocking approaches.
Pro tip: Mention that TTLs should be stored as absolute expiration times (or remaining TTLs adjusted for backup duration) to avoid extending or shortening TTLs incorrectly on restore. Also, highlight the importance of atomicity: use a copy-on-write or fork-like mechanism to avoid locking the entire store during backup.
Ask about store size, read/write throughput, consistency requirements, and whether backup can pause writes. This determines if you need a blocking or non-blocking snapshot.
Define a serializable format that captures each key's value and its TTL as an absolute expiration timestamp (or remaining TTL with a reference time). Include metadata like snapshot timestamp and version.
Choose a mechanism to capture a consistent point-in-time view: e.g., lock the store briefly to copy references (copy-on-write), or use a persistent data structure. Ensure TTLs are adjusted to the snapshot time.
On restore, clear the store and load the snapshot. For each key, compute remaining TTL from the absolute expiration time relative to the current time; discard expired keys. Ensure atomicity so readers see either old or new state.
Cover trade-offs: blocking vs. non-blocking, memory overhead, snapshot size, and performance. Address edge cases: 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.
Start by defining each snapshot strategy clearly, then compare them along the axes of memory usage and restore latency. Use a concrete example (e.g., a database or versioned key-value store) to illustrate the tradeoffs, and conclude with guidance on when to choose each approach.
Pro tip: Emphasize that the best choice depends on the workload's read/write ratio and latency requirements—showing you can map technical tradeoffs to business needs will set you apart.
Briefly explain what deep copy, copy-on-write (COW), and append-only mutation log mean in the context of snapshots.
Compare how each strategy consumes memory: deep copy duplicates all data, COW shares until modification, and append-only log stores only changes.
Discuss how quickly a snapshot can be restored: deep copy is immediate, COW may require reconstructing from shared pages, and append-only log may need replaying mutations.
Mention factors like write amplification, complexity, concurrency control, and durability that influence the choice.
Provide scenarios where each strategy excels, e.g., deep copy for small datasets, COW for read-heavy workloads, append-only log for high write throughput.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's requirements and the snapshot's purpose, then describe the concurrency control mechanism (e.g., MVCC, copy-on-write, or locking) that handles writes during snapshot creation. Finally, explicitly state the consistency guarantees (e.g., snapshot isolation, linearizability) and discuss trade-offs like latency, throughput, and staleness.
Pro tip: Tie the answer to Coinbase's need for strong consistency in financial transactions, and mention how you'd validate the snapshot's correctness under concurrent writes with stress tests or formal verification.
Ask or state the snapshot's use case (e.g., backups, analytics, read replicas) and the required consistency level (strong vs. eventual). This frames the design choices.
Explain how writes are handled during snapshot creation: e.g., MVCC with versioning, copy-on-write, or two-phase locking. Mention how you avoid blocking writes or ensure isolation.
State exactly what the snapshot represents (e.g., a point-in-time consistent view) and the guarantees (e.g., snapshot isolation, serializable). Discuss anomalies prevented (e.g., dirty reads, lost updates).
Cover trade-offs: latency vs. consistency, storage overhead, and impact on write throughput. Explain how you handle failures (e.g., retries, aborting snapshots) and ensure durability.
Describe how you'd test the snapshot under concurrent writes (e.g., Jepsen-style tests) and monitor for consistency violations or performance regressions in production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.