← Snowflake Interview Insights
The insert part was fine, basically just a hashmap from filename to content.
Start by clarifying the scale and requirements (read/write ratio, document sizes, concurrency), then design the data model and storage layer before tackling the predicate parsing logic. Treat the predicate evaluation as a mini expression-parser problem, implementing a recursive descent parser or shunting-yard algorithm to correctly handle operator precedence and boolean short-circuit evaluation.
Pro tip: Snowflake is a data warehousing company that deeply values query optimization and predicate pushdown — explicitly mention how your design could index keywords at insert time (inverted index) to avoid full document scans during CheckContains, demonstrating awareness of read-heavy workload optimization.
Ask about expected document volume, average document size, read vs. write ratio, and whether predicates need to support NOT or parentheses grouping. Confirm whether CheckContains must be strongly consistent or can tolerate eventual consistency.
Define the InsertDoc API to store documents in a key-value store (filename → content) and simultaneously build an inverted index mapping each keyword to the set of filenames containing it. Discuss trade-offs between in-memory (HashMap) and persistent storage (e.g., a database or distributed store).
Implement a tokenizer that splits the predicate string into keywords and operators, then use a recursive descent parser or the shunting-yard algorithm to build an AST that respects && (higher precedence) over || (lower precedence). Walk through a concrete example like 'snow && (flake || cloud)' to demonstrate correctness.
Traverse the AST and evaluate each leaf node by looking up the keyword in the inverted index to get the set of matching filenames, then apply set intersection for && and set union for || operations. Return true if the target filename appears in the final result set.
Address concurrency with read-write locks or copy-on-write data structures, and discuss short-circuit evaluation to skip unnecessary index lookups. Mention horizontal scaling strategies such as sharding the inverted index by keyword and caching frequently queried predicates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My first instinct was to just iterate all documents and call CheckContains on each.
Start by clarifying the requirements: what types of predicates are supported, expected scale, and consistency guarantees. Then propose a design that parses the predicate into an expression tree, evaluates it against document metadata (e.g., inverted index or keyword index), and returns matching filenames. Discuss trade-offs between precomputation and on-the-fly evaluation, and outline how to extend the existing service without disrupting current APIs.
Pro tip: Demonstrate awareness of Snowflake's scale by discussing how to push predicate evaluation down to the storage layer or leverage indexing to avoid full scans, and mention the importance of returning results incrementally or with pagination for large result sets.
Ask about predicate complexity (e.g., boolean operators, nesting), expected data volume, latency requirements, and consistency needs. Confirm whether the API should support pagination or streaming.
Propose parsing the boolean keyword expression into an abstract syntax tree (AST) or using a standard query parser. Define a clear grammar and handle operator precedence.
Evaluate the predicate against an index (e.g., inverted index mapping keywords to filenames) or scan documents. Discuss trade-offs: index maintenance cost vs. query speed, and whether to precompute or evaluate on-the-fly.
Extend the service API with GetAllFiles(predicate), ensuring backward compatibility. Consider adding caching, rate limiting, and monitoring. Discuss how to handle failures and partial results.
Propose optimizations like parallel evaluation, pushdown to storage, or using a distributed index. Discuss how to handle large result sets with pagination or streaming.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the service's requirements (e.g., document size, read/write ratio, consistency needs) and then propose a sharding strategy that distributes data evenly and a replication policy that ensures fault tolerance and low-latency reads. Emphasize trade-offs between consistency, availability, and partition tolerance, and how your choices align with Snowflake's cloud-native, scalable architecture.
Pro tip: Tie your answer to Snowflake's separation of storage and compute and its multi-cluster shared data architecture, showing you understand how to leverage cloud-native principles for scalability and elasticity.
Ask about document size, access patterns (read/write ratio), consistency requirements, and latency SLAs to ground your design in realistic constraints.
Propose a sharding key (e.g., document ID hash, tenant ID, or range-based) that ensures even distribution and avoids hotspots; discuss rebalancing and metadata management.
Specify replication factor, placement (e.g., across availability zones), consistency model (e.g., quorum-based, eventual), and failover mechanisms to balance durability and performance.
Explain how your design handles node failures, network partitions, and scaling events; discuss consistency vs. availability trade-offs (e.g., CAP theorem) and mitigation strategies.
Conclude by reiterating how your approach enables elastic scalability and high availability, and relate it to Snowflake's architecture (e.g., micro-partitions, multi-cluster warehouses).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.