Databases 55 min read

Practical Redis Use Cases and Code Examples

This guide walks backend developers through twenty real‑world Redis use cases—from basic caching, lotteries, and like/collect features to ranking, PV/UV counting, Bloom filters, sign‑in, geo‑search, rate limiting, ID generation, distributed locks, messaging, session sharing, and more—providing Spring Boot code samples, limitations, and best‑practice advice.

Java Tech Enthusiast
Java Tech Enthusiast
Java Tech Enthusiast
Practical Redis Use Cases and Code Examples

This article provides a comprehensive guide to using Redis beyond simple caching, covering a wide range of real-world scenarios with detailed explanations and code samples.

It starts with basic caching and then explores 20 practical use cases, including:

Lottery implementation using SADD and SRANDMEMBER.

Like/collect functionality with SADD, SCARD, SISMEMBER, and SREM.

Ranking with sorted sets using ZADD, ZREVRANGE, and ZRANGE.

Page view (PV) counting via INCR and unique visitor (UV) counting using HyperLogLog commands PFADD, PFCOUNT, PFMERGE.

Bloom filter operations ( bf.add, bf.exists) for probabilistic existence checks.

Sign‑in system using BitMap commands SETBIT, GETBIT, BITCOUNT, and BITFIELD.

Nearby location search with Redis GEO commands ( GEOADD, GEOPOS, GEODIST, GEORADIUS, GEORADIUSBYMEMBER).

Simple rate limiting using INCR with expiration.

Global unique ID generation via atomic INCR.

Basic distributed lock using SETNX with expiration.

Friend recommendation using set difference SDIFF.

Publish/subscribe pattern with PUBLISH and SUBSCRIBE.

Message queue implementation using list commands RPUSH, BLPOP, BRPOP.

Session sharing across services with Spring Session and Redis.

Product filtering using set intersections SINTER.

Shopping cart management with hash commands HSET, HMSET, HINCRBY, HLEN.

Order auto‑cancellation by listening to key expiration events.

Logistics timeline using list commands RPUSH / LPUSH and LRANGE.

Each use case includes Spring Boot code snippets wrapped in ... tags, demonstrating how to interact with Redis programmatically. The article also discusses limitations of certain approaches (e.g., distributed lock reliability, key‑expiration event latency) and suggests best‑practice considerations.

Overall, the guide serves as a practical reference for backend developers looking to leverage Redis for caching, data structures, rate limiting, distributed coordination, and more.

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Distributed SystemsBackend DevelopmentrediscachingMessage QueueData Structuresrate limiting
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