Start by clarifying requirements and constraints, then design a data model using nested hashmaps: an outer map from record IDs to inner maps of field-value pairs. Walk through the implementation of add and delete operations for both records and fields, analyzing time and space complexity, and discuss potential optimizations and edge cases.
Pro tip: Demonstrate awareness of concurrency and memory management by mentioning thread-safety options (e.g., ConcurrentHashMap) and strategies for handling large datasets, such as eviction policies or sharding. This shows you think beyond basic functionality and consider production-level concerns.
Ask questions to understand the expected operations, data types, concurrency needs, and performance constraints. Confirm whether records are identified by unique IDs and fields are key-value pairs.
Propose using a HashMap where keys are record IDs and values are HashMaps representing fields. This allows O(1) average-time access for add, delete, and lookup operations.
Detail the logic for addRecord, deleteRecord, addField, and deleteField. For add, check for existing keys and update accordingly; for delete, remove entries and handle missing keys gracefully.
Discuss time and space complexity: O(1) average for operations, O(n) worst-case for hash collisions. Mention that space is proportional to the number of records and fields.
Cover thread-safety, memory management, and potential improvements like using a trie for field names or adding indexing. Address edge cases such as deleting non-existent records or fields.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Storing the caller ID alongside the lock state was the obvious move but I second-guessed myself on whether to use a separate map or embed it in the record struct.
Start by clarifying the requirements: what operations are needed (lock, unlock, modify), how caller IDs are provided, and concurrency expectations. Then design a data structure that associates each record with a lock owner and ensures atomicity of lock acquisition and modification. Discuss trade-offs between different locking granularities and failure handling.
Pro tip: Mention that locks should be released automatically if the owner crashes or times out, and discuss how to handle lock expiration to avoid deadlocks. This shows you think about real-world reliability, not just the happy path.
Ask about the expected concurrency level, whether locks are exclusive or shared, and how caller IDs are authenticated. Confirm if locks should persist across sessions or have timeouts.
Propose extending each record with a lock owner field (e.g., caller ID) and a lock status. Consider using a separate lock manager or embedding lock info in the record.
Specify atomic operations: lock(recordId, callerId) fails if already locked by another; unlock(recordId, callerId) only succeeds if caller is owner. Ensure modification checks lock ownership.
Discuss using mutexes, compare-and-swap, or database transactions to make lock acquisition and modification atomic. Consider optimistic vs pessimistic locking.
Plan for lock timeouts, owner crashes, and deadlock prevention. Discuss how to release locks safely and notify waiters.
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 design a lock manager with TTL-based expiration and a fair queue for pending writers. Discuss trade-offs such as TTL duration, queue ordering, and failure handling, and outline how to ensure correctness and avoid deadlocks.
Pro tip: Emphasize the importance of idempotent lock acquisition and release, and consider using a monotonic clock for TTL to avoid issues with system clock changes. Also, mention that the queue should be fair (FIFO) to prevent starvation.
Ask questions to understand the expected scale, concurrency level, and consistency requirements. Clarify whether the lock is distributed or single-node, and what happens when a lock expires while a writer is still working.
Describe how locks are acquired and released, and how TTL is implemented (e.g., using a timestamp and a background sweeper or lazy expiration). Discuss how to handle lock renewal and what happens on expiration.
Explain how to maintain a FIFO queue of writers waiting for a lock. Detail how a writer is enqueued when the lock is unavailable, and how the next writer is notified when the lock is released or expires.
Discuss synchronization mechanisms (e.g., mutexes, condition variables) to protect shared state. Cover failure cases: what if a writer crashes while holding the lock? How does TTL help? What if the queue grows unbounded?
Compare different TTL strategies (fixed vs. sliding), queue implementations (in-memory vs. distributed), and fairness vs. throughput. Suggest optimizations like lock striping or backoff for high contention.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran out of time before getting deep into this one.
Start by clarifying the requirements: what types of bulk operations, how conditional updates are specified, and the expected scale and consistency guarantees. Then propose a data model and API design that supports efficient batch processing and atomic conditional updates, discussing trade-offs between consistency, latency, and throughput.
Pro tip: Emphasize idempotency and partial failure handling—Meta operates at massive scale, so showing you can design for retries and exactly-once semantics will set you apart.
Ask about the scale (number of records per bulk operation), latency requirements, consistency needs (strong vs. eventual), and how conditional predicates are expressed (e.g., SQL-like, JSON).
Propose a schema that supports efficient lookups and updates, and define an API that accepts a list of operations with optional conditions. Consider using a batch endpoint with a predicate language.
Discuss how to achieve atomicity for conditional updates, such as using transactions, optimistic concurrency control (versioning), or compare-and-swap. Address isolation levels and potential conflicts.
Explain how to process bulk operations efficiently: batching, parallelization, indexing, and avoiding hotspots. Consider sharding or partitioning strategies.
Describe how to handle partial failures, retries, and ensure idempotency (e.g., using request IDs, deduplication). Discuss monitoring and rollback strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.