I started with two hashmaps, one mapping document IDs to their tag sets and another mapping tags to document sets.
Start by clarifying requirements and scale, then define clear class interfaces and choose data structures that optimize for the most frequent operations. Discuss trade-offs between different indexing strategies and how to handle multi-tag intersection queries efficiently.
Pro tip: At Amazon, always tie your design decisions back to customer impact and operational excellence—mention how your data structures affect latency, throughput, and cost at scale, and consider eventual consistency vs strong consistency for tag updates.
Ask about expected number of documents, tags, operations per second, read/write ratio, and consistency requirements. This will guide your data structure and indexing choices.
Outline classes like Document, Tag, DocumentManager, and TagManager with methods for CRUD and association operations. Specify method signatures and return types.
Choose structures like hash maps for document/tag storage and inverted indexes (tag -> set of documents) for efficient lookups. Consider using sets for fast add/remove and intersection.
Explain how to compute intersection of multiple tags efficiently, e.g., by iterating over the smallest set and checking membership in others, or using sorted lists and merge algorithms.
Address trade-offs between memory and speed, handling large-scale data (sharding, caching), and consistency models. Mention potential bottlenecks and mitigation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started to sweat a little.
Start by clarifying the data model and access patterns (read-heavy vs write-heavy, document-tag relationships). Then propose a layered locking strategy: fine-grained locks for individual documents/tags, coarse-grained locks for global structures, with a clear lock ordering to prevent deadlocks. Finally, discuss consistency guarantees (e.g., linearizability vs eventual consistency) and how you'd test concurrency with stress tests and model checking.
Pro tip: Emphasize that thread-safety is not just about locks—consider lock-free data structures, immutable snapshots, and optimistic concurrency control with versioning. Also, mention that you'd measure contention and adapt granularity based on real workload, showing a pragmatic trade-off mindset.
Ask about read/write ratio, consistency needs, and the relationships between documents and tags (e.g., many-to-many). Define what operations must be atomic (e.g., adding a tag to a document).
Propose fine-grained locks per document and per tag, plus a global lock for the tag index. Discuss trade-offs: finer locks increase concurrency but add overhead and deadlock risk.
Establish a strict lock acquisition order (e.g., always lock document before tag) and use timeouts or deadlock detection. Consider lock-free alternatives like copy-on-write for read-heavy paths.
State whether you provide linearizability, serializability, or eventual consistency. Explain how you'd implement it (e.g., two-phase locking, MVCC, or versioned snapshots) and the impact on performance.
Describe stress tests with many threads, using tools like ThreadSanitizer, Jepsen-style linearizability checks, and model checking (e.g., TLA+). Include deterministic simulation and fault injection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.