How the interviewer opens it
“Design a distributed in-memory cache, like a Memcached or Redis cluster, that many internal services can use for low-latency key-value reads and writes.”
The prompt is vague on purpose. The first few minutes are yours: narrow it to the core features and agree on the numbers before you draw anything.
What to clarify first
- commands and semantics
- traffic and read:write mix
- data size
- deployment
- latency
- consistency versus availability
- durability and failure recovery
- TTL
- memory pressure
- miss handling and scope boundary
The answers stay with the interviewer. In a SysRound round, Alex answers them as an interviewer would.
Scope of a strong answer
Core requirements
- GET, SET, and DELETE individual keys, with TTL support
- Distribute keys and requests across cache nodes
- Enforce memory limits and evict entries when full
- Handle node failures and membership changes without disrupting the whole cluster
Usually out of scope
- Durable storage or disk persistence
- Multi-key transactions, scripting, pub/sub, and rich Redis data types
- Cross-region operation
- Building the backing databases or application-specific loading logic
What interviewers probe
- Ownership and routing during membership changes
- Changing a hash ring is easy; changing it while clients have different maps and writes are still arriving can lose updates or create competing owners.
- Bounded memory, eviction, and TTL correctness
- The cache must stay inside a hard RAM budget while values vary widely in size, entries expire, and replication or migration needs temporary space.
- Replica failover without pretending the cache is durable
- Replication reduces miss storms, but asynchronous failover can lose acknowledged writes or resurrect an older value. The contract must match that behavior.
- Hot keys and miss amplification
- Hashing balances many keys, not requests for one key. A popular entry can overload its owner, and its disappearance can move that overload to a backing service.
Common mistakes
- Designing an application that uses Redis instead of designing the cache cluster itself.
- Choosing hash(key) modulo node count without addressing membership changes, or assuming all clients receive topology updates simultaneously.
- Claiming strong consistency from asynchronous replication, especially for DELETE and replica promotion.
- Counting only value bytes and overlooking eviction, expiry checks, allocator overhead, and bounded replication buffers.
- Assuming more shards solve a single hot key.
- Treating cache failures as free database fallbacks, with no miss-storm protection or retry budget.
The bar by level
- Mid-level (L4/E4)
- Produces a working GET/SET/DELETE design with TTLs, a local hash table, bounded memory and eviction, key partitioning, and a plausible client-routing path. Understands that cache loss is allowed and describes how one failed node affects only part of the keyspace.
- Senior (L5/E5)
- Quantifies memory, request rate, and network needs; handles hot keys and miss storms; gives a concrete ownership-change and replica-failover flow. Explains asynchronous replication loss, expiry during migration, stale routing, and bounded recovery work without turning the cache into a durable database.
- Staff (L6+/E6+)
- Defines the failure and consistency contract precisely, including invalidation limits and control-plane quorum loss. Explains how ownership is fenced during partitions, how rebalancing and replica repair stay within resource budgets, and how client behavior protects backing services during broad failures. Distinguishes required guarantees from optional complexity and adapts the design when stronger semantics or tenant isolation are introduced.