← Snowflake Interview Insights
I started with a Kafka-style ingest layer and the interviewer seemed satisfied with that direction pretty quickly.
Start by clarifying requirements: audit logs are append-only, immutable, and must be durable with high availability. Then design a decoupled write path using an ingestion API (e.g., gRPC/REST) that validates and buffers events in a durable message queue (e.g., Kafka) before asynchronously persisting to a scalable storage layer (e.g., object storage or a distributed database). Emphasize trade-offs around latency, durability guarantees, and operational complexity.
Pro tip: Highlight that producers should not block on storage; instead, they get an acknowledgment once the event is durably enqueued, and the ingestion service handles retries and backpressure. This decouples availability and allows independent scaling.
Ask about expected throughput, latency tolerance, durability guarantees (e.g., no data loss), and retention. Confirm that audit logs are write-once, read-many, and must be tamper-evident.
Define a simple, versioned API (e.g., gRPC or REST) for producers to submit events. Include client libraries that handle batching, retries, and authentication to reduce producer burden.
Use a distributed log like Kafka or Pulsar to durably persist events immediately upon receipt. This decouples producers from storage and provides backpressure and replay capabilities.
Consume from the buffer and write to a scalable, durable storage layer (e.g., S3, HDFS, or a distributed database). Ensure idempotent writes and exactly-once semantics if needed.
Discuss replication, acknowledgments, and monitoring for lag and errors. Explain how to handle poison pills, schema evolution, and disaster recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Keyset pagination was something I'd read about but never had to defend under pressure.
Start by clarifying the requirements: scale, data volume, query patterns, and latency SLA. Then propose a read-optimized storage design (e.g., columnar, partitioned by account and time) with caching and efficient pagination, and discuss trade-offs like consistency vs. latency.
Pro tip: Mention that you would use a covering index or materialized view to avoid scanning the base table, and that you would implement pagination using keyset (seek) rather than offset to maintain stable performance at deep pages.
Ask about data volume, query patterns (e.g., time range size, frequency), latency SLA (p99 target), and consistency requirements to scope the design.
Propose a columnar, partitioned storage (e.g., by account_id and time) with clustering or sort keys to enable efficient pruning and range scans.
Use covering indexes or materialized views for common filters, and push down predicates to avoid full scans. Consider pre-aggregation if needed.
Use keyset pagination (based on a monotonic key like timestamp + id) instead of offset to avoid performance degradation at deep pages.
Introduce caching (e.g., Redis) for frequent queries, and discuss trade-offs between latency, consistency, and cost (e.g., cache invalidation, eventual consistency).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the constraints: no downtime, time-ordered queries must remain correct, and the hot partition is tenant-specific. Then propose a multi-phase migration: introduce a new partitioning scheme (e.g., composite key with time bucket), dual-write to both old and new partitions, backfill historical data, switch reads gradually, and finally decommission the old partition. Emphasize that the solution must preserve ordering within the tenant by using a monotonic sequence or timestamp-based ordering across partitions.
Pro tip: Mention that you would use a shadow read to validate correctness before cutting over, and that you'd monitor for any ordering violations or increased latency during the migration. This shows you think about safety and observability, not just the happy path.
Confirm that downtime is unacceptable, time-ordered queries must return correct results, and the hot partition is caused by a single tenant's high volume. Ask about acceptable latency and consistency guarantees.
Propose a composite partition key that includes the tenant ID and a time bucket (e.g., hour or day) to distribute the tenant's events across multiple partitions. Ensure that ordering within the tenant is preserved by including a monotonic sequence number or timestamp in the sort key.
Enable dual-writes to both the old and new partitions for new events. Backfill historical data from the old partition to the new partitions in a background job, ensuring no data loss and minimal impact on production traffic.
Gradually shift read traffic to the new partitions, starting with a small percentage. Use shadow reads to compare results between old and new partitions for correctness, especially for time-ordered queries. Monitor latency and error rates.
Once all reads are served from the new partitions and validation confirms correctness, stop dual-writes and remove the old partition. Update any metadata or routing logic to reflect the new scheme.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the compliance requirements and threat model, then propose a layered approach combining cryptographic techniques like hash chaining and digital signatures with append-only storage and external anchoring. Discuss trade-offs between security, performance, and operational complexity, and how to verify integrity at scale.
Pro tip: Mention that tamper-evidence is not just about cryptography but also about operational controls (e.g., strict access policies, immutable storage) and that you would design for verifiability by auditors without giving them write access.
Ask about specific compliance standards (e.g., HIPAA, GDPR, SOX), who the adversaries are (insiders, outsiders), and what 'provably unmodified' means in practice (e.g., cryptographic proof).
Propose using hash chains (each entry includes hash of previous) and digital signatures to ensure any modification is detectable. Consider Merkle trees for efficient proofs.
Store logs in append-only systems (e.g., WORM storage, blockchain, or cloud immutable storage) and enforce strict IAM policies to prevent deletion or alteration.
Provide tools for auditors to independently verify integrity (e.g., recompute hashes, verify signatures) and consider periodic anchoring to external trusted sources (e.g., public blockchain, notary).
Discuss performance overhead of cryptography, storage costs, and how to handle key management and rotation. Consider batch signing or Merkle trees to reduce overhead.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said Elasticsearch or a similar search index as a secondary store fed from the same event stream.
Start by clarifying the requirements: data volume, query patterns, latency expectations, and retention. Then evaluate whether the existing storage (likely a relational DB or data warehouse) can support full-text search and aggregations efficiently, or if a specialized search engine (e.g., Elasticsearch) is needed. Propose a hybrid architecture that balances cost, complexity, and performance, and discuss trade-offs.
Pro tip: Emphasize the importance of separating the write path (audit ingestion) from the read path (search/aggregation) to avoid impacting the primary audit logging system. Also, mention the need for a data pipeline to keep the search index updated in near real-time.
Ask about data volume, query latency, retention period, and expected query patterns (e.g., ad-hoc vs. predefined). This determines the scale and necessary technology.
Assess if the existing database (e.g., Snowflake) can handle full-text search and aggregations. Consider using Snowflake's built-in search optimization service or external functions, but note limitations for free-text search.
If current storage is insufficient, propose a dedicated search engine like Elasticsearch or OpenSearch for full-text search and aggregations. Discuss the trade-offs: added complexity, cost, and operational overhead.
Outline how to ingest audit data into the search system, ensuring near real-time indexing and handling failures. Use change data capture (CDC) or batch ingestion depending on latency needs.
Compare options: extending existing storage vs. separate system. Discuss cost, scalability, maintenance, and consistency. Mention possible use of columnar storage with inverted indexes or Snowflake's search optimization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: retention on the message log buys you the replay window.
Start by clarifying the scenario: what is the ingest architecture (e.g., Kafka, Kinesis, Pub/Sub), what is the downstream consumer, and what are the SLAs for latency and durability? Then walk through the catch-up strategy: how you decouple ingestion from consumption, buffer events durably, and scale consumption to drain the backlog without overwhelming storage. Finally, discuss trade-offs between storage cost, latency, and complexity, and how you monitor and alert on lag.
Pro tip: Emphasize that you design for backpressure and idempotency from the start, and that you use tiered storage or retention policies to avoid unbounded growth. Mention that you'd validate the catch-up path with chaos testing or game days.
Ask about the ingest volume, event size, retention requirements, consumer SLAs, and whether exactly-once or at-least-once semantics are needed. This shows you don't assume and helps tailor the design.
Explain how events are ingested (e.g., via a distributed log like Kafka) and persisted durably before consumption. Highlight decoupling of producers and consumers, and how the log acts as a buffer.
Detail how the consumer resumes from its last committed offset and processes the backlog. Discuss scaling consumers horizontally, increasing parallelism, and using batch processing to drain faster.
Describe how you prevent storage from being overwhelmed: tiered storage (hot/cold), retention policies, compaction, and possibly offloading to object storage. Mention monitoring storage growth and setting alerts.
Talk about trade-offs: latency vs. throughput, cost vs. durability, and complexity of exactly-once semantics. Explain how you handle failures during catch-up (e.g., idempotent writes, dead-letter queues) and ensure no data loss.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked this pretty early and I think I gave a decent list: write-to-read lag tolerance, whether events are truly immutable, which filter dimensions need dedicated indexes, and tenant size distribution.
Start by acknowledging that system design is inherently ambiguous and that clarifying questions are essential to scope the problem. Then, walk through a structured set of questions covering functional requirements, non-functional requirements, scale, and constraints, explaining how each answer would pivot your design. Emphasize that the goal is to demonstrate adaptability and trade-off analysis, not to find the 'right' answer.
Pro tip: Tie your clarifying questions to Snowflake's core strengths—data warehousing, separation of storage and compute, and multi-cloud—to show you understand the company's domain and can tailor your design accordingly.
Ask what the system should do, who the users are, and what the core use cases are. This defines the scope and prevents over-engineering.
Ask about scale (data volume, QPS, users), latency, consistency, availability, and durability. These drive architectural decisions like partitioning, replication, and caching.
Ask about budget, timeline, existing tech stack, compliance, and data retention. These limit your choices and may force trade-offs.
For each key answer, describe how you would adapt the design—e.g., if low latency is critical, you might add caching; if strong consistency is needed, you might choose a different storage engine.
Restate the most critical requirements and outline a high-level design that addresses them, noting any remaining ambiguities and how you would resolve them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.