Started with a hash map, which is the obvious answer, and they just nodded and waited.
Start by clarifying requirements (e.g., expected operations, concurrency, persistence) and then propose a hash table as the core data structure. Explain how to handle collisions, resizing, and thread safety, and analyze time and space complexity for each operation.
Pro tip: Mention that while average-case complexity is O(1), worst-case can degrade to O(n) with poor hash functions or collisions, and discuss strategies like open addressing or chaining to mitigate. Also, consider bringing up real-world systems like Redis for inspiration.
Ask about expected scale, concurrency needs, persistence, and any additional operations like TTL. This shows you think about the broader context.
Propose a hash table (e.g., using separate chaining or open addressing) as the primary structure. Justify why it's suitable for O(1) average-case operations.
Explain how get, set, and delete work: hashing the key, handling collisions, updating values, and removing entries. Mention resizing when load factor exceeds threshold.
State average and worst-case time complexity for each operation, and space complexity. Discuss factors affecting performance (hash function quality, load factor).
If needed, discuss thread safety (e.g., locks, concurrent hash maps) and edge cases like null keys, resizing during operations, and memory management.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by outlining the data model changes needed to store expiration times, then describe the two main expiration strategies (lazy and eager) and their tradeoffs. Finally, propose a hybrid approach that balances memory efficiency and latency, and discuss how to handle edge cases like clock skew and persistence.
Pro tip: Mention that the choice depends on workload characteristics—read-heavy vs write-heavy, memory constraints, and latency SLAs—and that a hybrid approach with periodic sampling (like Redis) is often optimal. Also, highlight the importance of making expiration atomic with reads/writes to avoid race conditions.
Explain how to store expiration timestamps alongside values, either in the value metadata or a separate index. Discuss API changes like SET with TTL and GET that respects expiration.
Describe lazy expiration: on access, check if the key is expired and delete it if so. Highlight pros (no background overhead, simple) and cons (expired keys linger, wasting memory; latency spikes on access).
Describe eager expiration: a background process periodically scans and removes expired keys. Highlight pros (frees memory promptly) and cons (CPU overhead, potential contention, complexity in distributed settings).
Compare the two strategies in terms of memory, CPU, latency, and complexity. Propose a hybrid: lazy on access plus periodic sampling (e.g., random sampling of keys with TTL) to bound memory usage without full scans.
Discuss handling clock skew, persistence (expiration times must survive restarts), replication (expiration events propagated), and concurrency (atomic checks).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: what isolation levels are needed, expected concurrency, and whether nested transactions are true savepoints or independent. Then design a transaction manager that tracks transaction state, uses a write-ahead log for durability, and implements rollback via undo logs. For nested transactions, use savepoints to allow partial rollback, and discuss isolation via locking or MVCC, highlighting trade-offs.
Pro tip: Emphasize that nested transactions are typically implemented as savepoints, not true independent transactions, and that rollback of a savepoint only undoes changes after that point. Also, mention that isolation level choice directly impacts performance and consistency, so it's a trade-off to discuss with stakeholders.
Ask about expected isolation levels, concurrency, durability needs, and whether nested transactions should be true independent transactions or savepoints. This ensures you design the right solution.
Define how BEGIN, COMMIT, and ROLLBACK change transaction state. Use a transaction manager to track active transactions, assign IDs, and maintain a write-ahead log (WAL) for durability and undo logs for rollback.
Choose an isolation approach: locking (e.g., two-phase locking) or MVCC. Discuss how each handles read phenomena (dirty reads, non-repeatable reads, phantoms) and the trade-offs in performance and complexity.
Implement nested transactions as savepoints: each BEGIN inside a transaction creates a savepoint. ROLLBACK to a savepoint undoes changes after that point; COMMIT of a savepoint merges changes into the parent. Only the outermost COMMIT makes changes durable.
Ensure rollback correctly undoes changes using undo logs, and handle failures (e.g., crashes) by recovering from WAL. Discuss how rollback interacts with isolation (e.g., releasing locks) and nested rollbacks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Copy-on-write came up and I walked through how you'd version the underlying structure.
Start by clarifying the scope: what data, how often, and recovery objectives. Then outline a high-level design (e.g., periodic full snapshots + incremental logs) and systematically compare approaches (full vs. incremental, in-place vs. copy-on-write) on space and time tradeoffs, tying back to Coinbase's needs for consistency, durability, and low-latency recovery.
Pro tip: Emphasize that snapshots must be crash-consistent and that restore time is often more critical than snapshot time; mention how you'd validate restores regularly to avoid silent corruption.
Ask about data size, change rate, RPO/RTO, consistency needs, and budget constraints to scope the problem.
Propose a snapshot mechanism (e.g., periodic full + incremental) and a restore process, mentioning storage and metadata management.
Contrast full vs. incremental, in-place vs. copy-on-write, and block-level vs. file-level, highlighting space and time tradeoffs.
Quantify space overhead (storage cost, duplication) and time overhead (snapshot duration, restore latency, impact on production).
Choose an approach based on requirements, and describe how to test restores and monitor performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Suggested a secondary index using a sorted structure for prefix scans.
Start by clarifying the primary data model and access patterns, then propose adding secondary indexes (e.g., inverted index or B-tree) to support lookups by field value or key prefix. Discuss the trade-offs in terms of storage overhead, write amplification, and read performance, and mention how you would keep indexes consistent.
Pro tip: Emphasize that secondary indexes are a classic space-time trade-off: you pay extra storage and write latency to gain fast reads. Also, mention that for key prefix scans, a sorted index (like a B-tree or SSTable) is more efficient than a hash index.
Ask about the expected query patterns, data volume, read/write ratio, and latency requirements to determine which secondary indexes are needed.
For field value lookups, consider inverted indexes or hash maps; for key prefix scans, use sorted structures like B-trees, skip lists, or SSTables with prefix compression.
Discuss storage overhead (duplicate data), write amplification (index updates on writes), and read performance improvements. Mention consistency challenges and maintenance.
Explain how to keep indexes in sync with the primary data, e.g., via transactions, write-ahead logs, or asynchronous updates, and how to handle failures.
Mention partitioning, sharding, or using a dedicated search engine (e.g., Elasticsearch) if the secondary lookups become complex or high-volume.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.