Start by clarifying requirements and scale, then propose a storage layout that balances row-oriented and column-oriented designs. Discuss how to support point lookups, column projections, and filtered scans efficiently, and evaluate trade-offs between different indexing and partitioning strategies.
Pro tip: Anchor your design around the read patterns Snapchat likely cares about—low-latency point reads and selective column projections—and explicitly discuss how you'd evolve the schema as access patterns change. Mentioning real-world systems like Bigtable, Cassandra, or Parquet shows practical awareness.
Ask about data volume, read/write ratio, latency SLAs, consistency needs, and query patterns (e.g., how often full scans occur). This shapes whether you optimize for point reads or analytical scans.
Decide between row-oriented (good for point reads and full-row retrieval) and column-oriented (good for column projections and scans). Consider a hybrid or column-family approach to balance both.
Use a primary index on the key for point lookups, and secondary indexes on frequently filtered columns to speed up predicate scans. Partition data by key range or hash to distribute load.
For column reads, fetch only the needed column families or segments. For filtered scans, use predicate pushdown and index scans where possible; otherwise, fall back to full scans with early projection.
Compare row vs. column storage, index maintenance overhead, and consistency models. Suggest optimizations like caching, bloom filters, or materialized views for common queries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I sketched out set(key, col, val), get(key, col), and scan(col_predicate, projection) pretty quickly.
Start by clarifying the system's requirements (e.g., data model, consistency, scale) and then propose a clean, typed API with clear semantics. Focus on the signatures for set, get, and scan, explaining how predicates and projection are expressed and pushed down for efficiency.
Pro tip: Emphasize that the API should be designed for the common case while allowing advanced features like predicates and projection to be optional, and mention how these features enable pushdown optimizations to reduce network and storage overhead.
Ask about the data model (e.g., key-value, wide-column), consistency needs, and expected scale. This ensures your API design aligns with the system's goals.
Specify the signatures for set, get, and scan. For set, include key, value, and optional metadata like TTL. For get, include key and optional projection. For scan, include range, predicates, and projection.
Explain how predicates (e.g., column filters) and projection (selecting specific columns) are represented in the API, such as using a filter expression object or a list of column names.
Describe how the API enables pushdown of predicates and projection to storage nodes to minimize data transfer. Mention indexing or columnar storage if relevant.
Cover how errors are returned and how the API can evolve (e.g., versioning, optional parameters) without breaking clients.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a sorted index per column, something like a B-tree or skip list.
Start by clarifying the requirements: data type, query patterns, read/write ratio, and whether the data is in memory or on disk. Then discuss appropriate secondary index structures like B-trees, LSM-trees, or skip lists, explaining their trade-offs for range queries. Conclude with a recommendation based on the specific use case, mentioning real-world systems like PostgreSQL or RocksDB.
Pro tip: Mention that range query performance depends heavily on data locality and that you'd consider composite indexes or covering indexes to avoid expensive random I/O. Also, show awareness of write amplification in LSM-trees versus read-optimized B-trees.
Ask about data size, query frequency, read/write ratio, latency requirements, and whether the data fits in memory. This determines the choice of index.
Explain B-trees (balanced, good for range scans, used in databases), LSM-trees (write-optimized, used in NoSQL), and skip lists (in-memory, used in Redis). Mention their range query efficiency.
Analyze read vs write performance, memory overhead, and maintenance cost. For example, B-trees offer fast reads but slower writes; LSM-trees have fast writes but reads may be slower due to compaction.
Mention composite indexes, covering indexes, partitioning, and caching to improve range query performance. Also discuss using bloom filters to skip irrelevant data.
Based on the clarified requirements, recommend a specific structure (e.g., B+ tree for read-heavy, LSM-tree for write-heavy) and explain why it fits the scenario.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked about how secondary indices hurt write throughput and column-store layouts hurt point reads.
Start by defining read and write amplification and explaining their inverse relationship in storage systems. Then, describe how your design balances them based on Snapchat's access patterns (e.g., high read throughput for stories, high write throughput for messages). Finally, discuss specific techniques like caching, LSM trees, and replication to manage query latency under different loads.
Pro tip: Quantify tradeoffs with concrete numbers (e.g., 'LSM trees reduce write amplification by 10x at the cost of 2x read amplification') and tie them to Snapchat's scale (e.g., millions of concurrent users). This shows you think in terms of real-world impact, not just theory.
Explain that read amplification is the number of disk reads per logical read, while write amplification is the number of disk writes per logical write. Highlight that optimizing for one often degrades the other.
Identify Snapchat's key workloads: high-volume writes (messages, snaps) and high-volume reads (stories, feeds). Discuss how each pattern stresses the system differently.
Explain how your design handles the tradeoff, e.g., using LSM trees for write-heavy workloads (low write amplification, higher read amplification) and B-trees or caching for read-heavy workloads (low read amplification, higher write amplification).
Detail how latency is managed: for read-heavy patterns, use caching (e.g., Redis) and read replicas; for write-heavy patterns, use write-ahead logs and asynchronous replication. Mention tail latency and how to mitigate it.
Conclude by summarizing the tradeoffs made and why they are appropriate for Snapchat's requirements, emphasizing scalability and user experience.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and access patterns, then propose a persistence layer (e.g., a distributed database) that supports sharding by key. Explain how you would choose a shard key to balance load and minimize cross-shard queries, and describe consistency mechanisms like quorum reads/writes or eventual consistency with conflict resolution.
Pro tip: Emphasize that sharding by key is a trade-off: it improves scalability but complicates cross-shard operations. Show you understand Snapchat's scale by mentioning real-world constraints like low latency and high availability.
Ask about data volume, read/write ratio, latency requirements, and consistency needs to tailor your design.
Select a distributed database (e.g., Cassandra, DynamoDB) that supports sharding and replication, and explain why it fits the requirements.
Pick a shard key (e.g., user ID) that evenly distributes data and avoids hotspots; discuss techniques like consistent hashing.
Describe consistency models (strong vs. eventual) and mechanisms (quorum, vector clocks) to manage cross-shard operations.
Discuss rebalancing, hot shards, cross-shard transactions, and how to handle failures (e.g., replication, retries).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.