I went straight to Cassandra LWT with IF NOT EXISTS, which felt right, but I fumbled when they pushed on failure modes.
Start by clarifying requirements: exactly-once creation with user-provided names, concurrent requests, and failure handling. Then propose a Cassandra schema using the artifact name as the partition key to enforce uniqueness, and implement idempotency via lightweight transactions (LWT) with IF NOT EXISTS. Finally, discuss retry logic with exponential backoff, handling LWT failures, and ensuring idempotent responses.
Pro tip: Emphasize that LWT in Cassandra uses Paxos and can be slow; consider using a separate uniqueness service or external lock if performance is critical, but for exactly-once semantics, LWT is the simplest correct approach. Also, mention that retries should be idempotent and that clients should use idempotency keys to avoid duplicate side effects.
Confirm that artifact names are unique per user or globally, and that creation must be exactly-once. Discuss consistency requirements and acceptable latency.
Use artifact name as the partition key (and possibly user ID as a clustering column) to ensure uniqueness. This allows efficient lookups and enforces uniqueness at the storage layer.
Use Cassandra's lightweight transactions (LWT) with IF NOT EXISTS to atomically create the artifact. Return the existing artifact if it already exists to make the operation idempotent.
Implement retry logic with exponential backoff for LWT timeouts or conflicts. On conflict, fetch the existing artifact and return it. Handle partial failures by ensuring the operation is idempotent.
Mention that LWT can be slow and may not scale well; consider alternatives like a dedicated uniqueness service or using an external lock (e.g., Redis) if performance is critical. Also, discuss the impact of eventual consistency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by clarifying the system's requirements and constraints (e.g., consistency, scale, latency). Then propose a soft-delete model with tombstones and a name-reuse policy, explaining how to handle races and duplicates using versioning or timestamps. Finally, discuss compaction and TTL strategies to balance storage and correctness.
Pro tip: Emphasize that deletes are not just data removal but a state transition; use monotonic versioning (e.g., Lamport timestamps) to resolve conflicts deterministically. Also, mention that grace periods and TTLs must be tuned based on business needs and system load.
Ask about consistency needs, scale, latency, and whether name reuse is allowed. This shapes the design choices for deletes and re-adds.
Decide between hard deletes, soft deletes, or tombstones. Soft deletes with tombstones are common to avoid races and enable recovery.
Specify if names can be reused immediately or after a grace period. Use versioning or timestamps to detect and resolve races between delete and re-add.
Set TTLs for tombstones to eventually purge them. Design compaction to remove tombstones safely without resurrecting deleted data.
Cover trade-offs like storage overhead vs. correctness, and edge cases like concurrent delete and re-add, or clock skew.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the read workload characteristics and consistency requirements, then propose a layered architecture that balances latency and throughput through caching, replication, and tunable consistency. Walk through trade-offs for each component (e.g., cache invalidation, pagination strategies, read repair) and describe how to handle failures gracefully with fallbacks. Emphasize monitoring and iterative optimization based on metrics.
Pro tip: Quantify trade-offs with concrete numbers (e.g., '99th percentile latency increases by X ms when consistency level is raised from eventual to strong') and mention how you'd validate with load testing and chaos engineering. This shows you think in terms of measurable impact, not just theory.
Ask about expected read QPS, data size, latency SLOs, consistency needs (strong vs eventual), and failure tolerance. This ensures your design targets the right priorities.
Propose a multi-layer cache (client, CDN, application, database) and discuss replication factor and consistency levels (e.g., QUORUM vs ONE). Explain how caching reduces latency and offloads the database.
Detail how to handle read repair, hinted handoff, and anti-entropy for eventual consistency. For pagination, discuss cursor-based vs offset-based and how to maintain consistency across pages.
Describe techniques like sharding with composite keys, adding random suffixes, or using a cache to absorb hot keys. Mention load balancing and auto-scaling to handle throughput spikes.
Outline fallback strategies: degrade to stale cache reads, reduce consistency level, or serve partial results. Discuss circuit breakers, timeouts, and graceful degradation to maintain availability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.