Design a Distributed Cache System
Build a high-performance, fault-tolerant distributed cache handling millions of operations per second with consistent hashing, automatic failover, and configurable consistency models.
Interviewer:
What's the expected scale in terms of operations per second and concurrent users?
Candidate:
1M+ operations/second across 100+ nodes, 10M+ concurrent connections
Analysis & Implications:
This drives our partitioning strategy - consistent hashing with virtual nodes to handle scale and node additions/removals. Connection pooling and load balancing at client layer.
Interviewer:
What types of data will be cached and what are the typical key-value sizes?
Candidate:
Mixed workload: 80% small objects (<1KB), 15% medium (1-100KB), 5% large (<1MB). JSON, HTML fragments, session data, computed results
Analysis & Implications:
Different eviction strategies per data type. Small objects in main memory tier, large objects with compression. Size-based memory management and allocation.
Interviewer:
What are the consistency requirements and acceptable staleness?
Candidate:
Eventual consistency acceptable, max 100ms staleness for most data, some critical data needs read-your-writes consistency
Analysis & Implications:
Async replication for performance, with sync replication option for critical keys. Client-side read preferences and consistency levels.
Interviewer:
What's the read/write ratio and access patterns?
Candidate:
80/20 read-heavy workload, 10% hot keys receive 90% traffic, temporal locality patterns
Analysis & Implications:
Read replicas for hot keys, predictive caching, multi-tier architecture (L1/L2 caches), hot key detection and mitigation.
Interviewer:
Are there specific availability and latency requirements?
Candidate:
99.9% availability, <1ms P95 read latency, <2ms P95 write latency, max 30sec recovery time
Analysis & Implications:
Master-slave with auto-failover, connection multiplexing, local read replicas, circuit breakers, graceful degradation patterns.
Interviewer:
Do we need persistence and what are the durability requirements?
Candidate:
Optional persistence for session data and computed results, acceptable to lose pure cache data
Analysis & Implications:
Hybrid approach: configurable persistence per key namespace, append-only log for durability, snapshot-based backups.
Interview Practice Questions
Practice these open-ended questions to prepare for system design interviews. Think through each scenario and discuss trade-offs.
Global Content Delivery Cache: Design a distributed cache system for a CDN serving 100TB+ of content globally with 10M+ requests per second. Address edge server deployment, cache hierarchy, content propagation, and regional failover strategies.
Session Store for Microservices: Build a distributed session cache supporting 500M+ active users across microservices with sub-millisecond access times. Handle session replication, cross-service sharing, security, and compliance requirements.
Real-Time Analytics Cache: Design a cache system for real-time analytics supporting complex aggregations on streaming data. Handle time-series data, sliding windows, pre-computation strategies, and memory optimization for billions of events daily.
Multi-Tenant Application Cache: Build a distributed cache for SaaS platforms with strict tenant isolation, configurable performance tiers, usage metering, and compliance features. Address resource allocation, security boundaries, and cost optimization.
Gaming Leaderboard Cache: Design a distributed cache for real-time gaming leaderboards supporting millions of concurrent players with instant score updates, global and regional rankings, and tournament features. Handle write-heavy workloads and complex queries.
Machine Learning Model Cache: Build a cache system for ML model serving with support for different model formats, A/B testing, canary deployments, and performance optimization. Handle large model files, versioning, and real-time inference requirements.