I started with namespacing because it felt like the safest entry point, something like service/environment/feature_flag as a hierarchical key.
Start by clarifying requirements and scale, then propose a high-level architecture with a globally distributed, eventually consistent store (e.g., multi-region replication). Dive into the key schema design (namespacing, versioning, composability) and value schema (serialization, validation, evolution), explaining trade-offs and how they support thousands of microservices.
Pro tip: Emphasize that configuration is read-heavy and latency-sensitive, so caching and push-based updates are critical; also highlight the need for a robust schema registry to manage evolution across teams.
Ask about consistency needs, read/write patterns, latency SLAs, and number of microservices. Establish that the system must be highly available and partition-tolerant.
Propose a multi-region, eventually consistent key-value store with replication and caching layers. Discuss how to handle global distribution and failover.
Define a hierarchical key structure with namespacing (e.g., /service/env/config), versioning (e.g., v1, v2), and composability (e.g., inheritance or overlays). Explain how this enables isolation and reuse.
Choose a serialization format (e.g., JSON, Protobuf, Avro) with validation (e.g., JSON Schema, Protobuf descriptors). Discuss schema evolution strategies (backward/forward compatibility) and a schema registry.
Discuss trade-offs between consistency and latency, push vs. pull updates, and how to handle schema evolution without breaking consumers. Mention monitoring, access control, and auditability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I spent the most time and also made the most mistakes.
Start by clarifying the config service's requirements (read/write ratio, consistency, scale) and then propose a partitioning strategy that balances load and minimizes cross-shard operations. Compare LSM-tree and B-tree storage engines in terms of write amplification, read performance, and space efficiency, and justify your choice based on the workload. Finally, detail the read/write paths, including caching and replication, and address hot keys with techniques like key salting or dynamic partitioning.
Pro tip: Demonstrate awareness of real-world trade-offs by mentioning that hot keys often require a combination of client-side caching, request coalescing, and adaptive sharding, and that the choice of storage engine should align with the dominant access pattern (e.g., LSM for write-heavy, B-tree for read-heavy).
Ask about the expected read/write ratio, data size, latency requirements, consistency needs, and access patterns to inform design decisions.
Propose a sharding key (e.g., config ID or namespace) and strategy (range, hash, or consistent hashing) to distribute load evenly and avoid hotspots.
Contrast LSM-tree (write-optimized, higher read amplification) and B-tree (read-optimized, write amplification) and select based on workload characteristics.
Choose appropriate indexes (e.g., primary key, secondary indexes) to support efficient queries, considering trade-offs in write overhead and storage.
Describe how reads and writes flow through caches, storage, and replication, and detail mitigation strategies for hot keys such as key salting, caching, and load balancing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said linearizability for config reads because a microservice getting a stale feature flag could cause real production issues.
Start by clarifying the service's requirements (e.g., consistency, availability, latency) and then propose a consistency model that balances these needs. Explain how you would implement leader election, replication quorums, partition handling, and failover, referencing established algorithms like Raft or Paxos and discussing trade-offs.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that the choice often depends on the specific use case, and cite examples from systems like Spanner, DynamoDB, or Cassandra to show practical knowledge.
Ask questions to understand the service's consistency, availability, and latency requirements, as well as the expected scale and failure modes.
Select a consistency model (e.g., strong, eventual, causal) that aligns with the requirements, and justify your choice with trade-offs.
Describe how data will be replicated across nodes and how quorums (e.g., majority) will be used to ensure consistency and fault tolerance.
Explain the leader election algorithm (e.g., Raft, Paxos) and how failover occurs when the leader fails, ensuring minimal downtime.
Discuss how the system behaves during network partitions, referencing CAP theorem and strategies like quorum-based decisions or conflict resolution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale (e.g., read/write ratio, consistency needs, latency targets) to frame the design. Then, systematically address each feature—transactions, watch/subscribe, TTLs, and backup/restore—by proposing appropriate technologies and trade-offs, ensuring they integrate cohesively. Conclude by discussing how these features interact and potential optimizations for Meta's scale.
Pro tip: Emphasize idempotency and versioning for watch notifications and transactions to handle retries and out-of-order updates, which is critical in distributed systems. Also, mention using a write-ahead log or change data capture for backup/restore to ensure consistency without impacting live traffic.
Ask questions to understand expected throughput, latency, consistency requirements, and client capabilities. This ensures your design aligns with Meta's scale and the specific use case.
Propose a transactional model (e.g., ACID via a distributed database or optimistic concurrency control) and discuss isolation levels, conflict resolution, and how to handle failures.
Outline a pub/sub mechanism for live config updates, using technologies like WebSockets, long polling, or a message queue. Discuss how to ensure reliable delivery, ordering, and scalability.
Explain how to implement TTLs, such as using a time-to-live index, lazy deletion, or a background sweeper. Consider trade-offs between precision and performance.
Describe a backup approach (e.g., periodic snapshots plus incremental logs) and a restore process that minimizes downtime. Highlight consistency guarantees and testing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's requirements and scale, then walk through each area (capacity planning, SLAs, multi-region replication, end-to-end testing) in a logical order, emphasizing trade-offs and how they interconnect. Use concrete examples and metrics to demonstrate practical experience and a holistic view.
Pro tip: Tie everything back to user experience and business impact—e.g., how SLA targets drive capacity buffers and replication strategy—showing you think beyond technical components to product outcomes.
Ask questions to understand expected traffic, growth, latency targets, consistency needs, and budget constraints. This sets the foundation for all subsequent decisions.
Estimate resource needs using historical data and load testing, then plan for headroom and elasticity. Discuss techniques like forecasting, autoscaling, and bottleneck analysis.
Translate business requirements into measurable SLIs (e.g., latency, availability) and set SLOs with error budgets. Explain how SLAs are derived and monitored.
Choose a replication strategy (active-active, active-passive) based on consistency, latency, and failover needs. Address data synchronization, conflict resolution, and traffic routing.
Outline a testing plan covering load, failover, chaos, and canary tests. Emphasize automation, observability, and validating SLAs under realistic conditions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.