Each test is 5 questions with varying difficulty.
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Designing a distributed cache is a cornerstone of modern system design interviews, testing a candidate's ability to balance latency, consistency, and availability at scale. In 2026, as AI-driven applications demand sub-millisecond data retrieval for context injection and feature stores, the requirements for distributed caching have evolved beyond simple key-value lookups. Interviewers look for deep understanding of how to handle massive concurrent read/write loads, mitigate hot-key issues, and ensure data integrity across geographically distributed nodes. Junior candidates are expected to understand basic eviction policies and single-node caching, while senior engineers must demonstrate expertise in consistent hashing for cluster membership, cache coherence strategies, and handling network partitions. This topic is essential for Backend, Data, and SRE roles, as it directly impacts the performance of every high-traffic service.
Distributed caching is the primary lever for reducing database load and latency in high-scale systems. A well-designed cache can handle millions of operations per second, effectively shielding the primary database from read-heavy workloads. In 2026, the rise of real-time AI inference pipelines makes distributed caching even more critical, as low-latency access to pre-computed embeddings and user context is a competitive necessity. This interview topic is high-signal because it forces a candidate to move beyond abstract theory into practical trade-offs. A strong answer demonstrates an understanding of how to handle cache stampedes, the nuances of write-through vs. write-back strategies, and the operational complexity of maintaining cache coherence in a distributed environment. Weak answers often ignore the reality of network partitions or fail to account for the memory overhead of metadata in large-scale clusters. Candidates who can articulate the impact of cache hit ratios on infrastructure costs and user experience stand out significantly.
The architecture involves a client-side library or load balancer that routes requests based on a consistent hashing ring. Each cache node maintains a local memory store, while a control plane manages node membership and health checks.
Requests are hashed to determine the target node. If a hit occurs, data is returned. If a miss occurs, the node fetches from the database, populates the cache, and returns the result.
[Client Request]
↓
[Load Balancer]
↓
[Consistent Hash Ring]
↓ ↓ ↓
[Node A] [Node B] [Node C]
↓ ↓ ↓
[Primary Database]
↑
[Invalidation Bus]Assign multiple logical points on the hash ring to a single physical node to prevent hotspots.
Trade-offs: Increases memory usage for the ring structure but improves load distribution.
Application checks cache first; if miss, reads from DB and updates cache.
Trade-offs: Simple to implement but requires handling cache invalidation on writes.
Nodes recompute cache values before expiration based on a probability function.
Trade-offs: Reduces latency spikes but slightly lowers the effective cache hit rate.
| Reliability | Use replication groups and automated failover; implement circuit breakers for database fallbacks. |
| Scalability | Horizontal scaling via consistent hashing; add nodes to the ring to distribute load. |
| Performance | Target sub-millisecond latency; optimize serialization formats like Protobuf. |
| Cost | Optimize memory footprint; use tiered storage (RAM + SSD) for large datasets. |
| Security | Implement TLS for data in transit and authentication for node communication. |
| Monitoring | Track hit/miss ratio, eviction rate, memory utilization, and network throughput. |
A distributed cache is optimized for low-latency read access and typically stores data in RAM, often with a shorter TTL. A distributed database is designed for durability, complex querying, and persistent storage on disk. Caches are often used to front databases to improve performance.
It involves ensuring that all copies of data remain consistent across a distributed system. Due to network latency, partial failures, and race conditions, maintaining strict consistency is expensive and often conflicts with the availability requirements of high-performance systems.
Modulo hashing (key % N) causes almost all keys to be remapped when the number of nodes (N) changes, leading to massive cache misses. Consistent hashing maps keys to a ring, ensuring that only a small fraction of keys need to move when nodes are added or removed.
A cache stampede occurs when a popular key expires, causing many concurrent requests to miss the cache and hit the database simultaneously. It is prevented using techniques like mutex locks, probabilistic early expiration, or background refreshing.
Write-through is used when data consistency is critical, as the database is updated synchronously. Write-back is used when write performance is the priority, as it acknowledges the write immediately and flushes to the database asynchronously, accepting higher risk of data loss on failure.
Hot keys occur when a single key receives a disproportionate amount of traffic. They can be mitigated by replicating the key across all cache nodes, using local caching on the application server, or splitting the key into smaller sub-keys.
A Time-To-Live (TTL) defines the duration an item remains in the cache before it is considered expired. It is essential for preventing stale data and managing memory by ensuring that unused or old data is eventually removed.
Achieving perfect consistency in a distributed cache requires strong synchronization, which significantly increases latency and reduces availability. Most distributed caches opt for eventual consistency to maintain the performance benefits that justify their use.
Memory fragmentation occurs when objects of varying sizes are allocated and deallocated, leaving gaps in memory that cannot be used. This can lead to OOM errors even when total memory seems sufficient, requiring techniques like slab allocation to manage.
Nodes typically use a heartbeat mechanism, where they periodically send signals to a cluster manager or to each other. If a node fails to send a heartbeat within a threshold, it is marked as unhealthy and removed from the hash ring.
AI Prep covers AI Agents, Generative AI, ML Fundamentals, NLP & LLMs and a lot more, with adaptive tests and daily challenges. Fully offline on Android. Free to try, one-time unlock for lifetime access.