System Design: Scale a Key-Value Store with Consistent Hashing
Context
Design a horizontally scalable key-value store from an initial single-server implementation. The system must maintain high availability and low latency. It should support point lookups and updates by key, with no multi-key transactions. Tunable consistency is an acceptable design parameter.
Requirements
Your design must address the following aspects in a diagram-free written explanation:
- Key Partitioning: Implement consistent hashing using virtual nodes (vnodes). Explain the hash ring, token assignment, and how clients or gateways route requests to the correct nodes.
- Node Lifecycle Management: Describe how new nodes join and existing nodes leave the cluster, focusing on mechanisms that trigger minimal data movement and ensure efficient rebalancing.
- Replication and Quorum: Define the replication factor (N), read quorum (R), and write quorum (W). Justify your choices by discussing the inherent trade-offs.
- Read/Write Paths: Detail the behavior of the coordinator node for read and write operations, including conflict/version handling and read repair strategies.
- Failure Detection and Recovery: Explain the mechanisms for membership management, failure detection, re-replication, hinted handoff, anti-entropy, and data rebuild processes.
- Load Balancing and Skew: Address strategies for handling hot keys, data skew, and ensuring effective load balancing across the cluster.
- Operational Safety: Describe how to ensure data migration safety during ownership changes, implement monitoring, and perform rolling upgrades.
- Alternative Approaches: Compare your chosen consistent hashing approach with other partitioning strategies (e.g., directory service, range partitioning) and justify the trade-offs.
Your design should clearly state assumptions, identify potential pitfalls, and specify relevant metrics and Service Level Objectives (SLOs).
AI interview prep
Core dilemma
Balancing the inherent trade-offs between consistency, availability, and partition tolerance (CAP theorem) in a distributed key-value store, specifically when dealing with dynamic membership, data replication, and failure recovery under high load and strict performance requirements.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
System Design: Scale a Key-Value Store with Consistent Hashing
Context
Design a horizontally scalable key-value store from an initial single-server implementation. The system must maintain high availability and low latency. It should support point lookups and updates by key, with no multi-key transactions. Tunable consistency is an acceptable design parameter.
Requirements
Your design must address the following aspects in a diagram-free written explanation:
- Key Partitioning: Implement consistent hashing using virtual nodes (vnodes). Explain the hash ring, token assignment, and how clients or gateways route requests to the correct nodes.
- Node Lifecycle Management: Describe how new nodes join and existing nodes leave the cluster, focusing on mechanisms that trigger minimal data movement and ensure efficient rebalancing.
- Replication and Quorum: Define the replication factor (N), read quorum (R), and write quorum (W). Justify your choices by discussing the inherent trade-offs.
- Read/Write Paths: Detail the behavior of the coordinator node for read and write operations, including conflict/version handling and read repair strategies.
- Failure Detection and Recovery: Explain the mechanisms for membership management, failure detection, re-replication, hinted handoff, anti-entropy, and data rebuild processes.
- Load Balancing and Skew: Address strategies for handling hot keys, data skew, and ensuring effective load balancing across the cluster.
- Operational Safety: Describe how to ensure data migration safety during ownership changes, implement monitoring, and perform rolling upgrades.
- Alternative Approaches: Compare your chosen consistent hashing approach with other partitioning strategies (e.g., directory service, range partitioning) and justify the trade-offs.
Your design should clearly state assumptions, identify potential pitfalls, and specify relevant metrics and Service Level Objectives (SLOs).