Explain Redis design, persistence, and scaling

Q: Explain Redis design, persistence, and scaling

This question evaluates understanding of Redis internals and operational trade-offs across core data structures, persistence options, memory eviction, high availability and sharding, transactions and scripting, distributed locking, and large-scale cache reliability.

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Question

Redis: Data Structures, Persistence, Eviction, High Availability, and Operational Pitfalls

Context: You are designing a latency-critical backend that relies on Redis both as a cache and as a data store. Explain the following topics to demonstrate practical, production-oriented understanding.

Core data structures
- Strings, Lists, Sets, Sorted Sets (ZSET), Hashes, Streams
- Typical time and space characteristics
Persistence mechanisms
- Compare RDB and AOF: durability, performance, recovery trade-offs
Memory pressure
- Eviction policies and TTL behavior under memory pressure
High availability and scaling
- Replication, Sentinel failover, and Cluster sharding
Programmatic semantics
- Transactions, Lua scripting
Distributed locks
- Pitfalls (e.g., clock skew, process pauses, network partitions) and fencing tokens
Cache reliability at scale
- Strategies to prevent cache stampede and handle hotspot keys

Explain Redis design, persistence, and scaling

Redis: Data Structures, Persistence, Eviction, High Availability, and Operational Pitfalls

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Explain Redis design, persistence, and scaling

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Redis: Data Structures, Persistence, Eviction, High Availability, and Operational Pitfalls

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