I started by asking clarifying questions about scale and use cases, which felt right, but then I kind of froze on the schema side.
Start by clarifying the scope and requirements of the translation service, such as the languages supported, expected query volume, latency requirements, and whether it's for text or speech. Then, propose a high-level data model that separates static linguistic data (e.g., dictionaries, language models) from dynamic data (e.g., user queries, translations), and discuss storage choices (SQL vs NoSQL, caching, etc.) with trade-offs. Finally, dive into how you would handle scale, consistency, and performance.
Pro tip: Emphasize the importance of caching frequent translations and using a distributed, sharded database to handle Google-scale traffic, and mention that you would consider using a combination of relational and NoSQL databases for different data types.
Ask questions to understand the scale, latency, consistency, and features needed (e.g., number of languages, real-time translation, offline support).
Outline the main entities: languages, phrases, translations, user queries, and possibly language models. Define relationships and access patterns.
Choose appropriate databases for each entity: e.g., a relational DB for language pairs and dictionaries, a NoSQL store for user logs, and a distributed cache for frequent translations.
Discuss sharding, replication, indexing, and caching strategies to handle high read throughput and low latency. Mention trade-offs between consistency and availability.
Summarize key trade-offs (e.g., SQL vs NoSQL, normalized vs denormalized) and justify your choices based on the requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.