Common Redis Use Cases and Implementation Patterns

Redis is a versatile in‑memory data store that can be applied to many scenarios; the following sections describe typical use cases and the corresponding Redis data structures and commands.

1. Caching – Use string keys to store hot data such as reports or frequently accessed objects, improving read performance.

2. Distributed Data Sharing – Because Redis runs as an independent service, it can be shared across multiple applications, e.g., for distributed session storage.

org.springframework.session
spring-session-data-redis

3. Distributed Lock – Implemented with the SETNX command; a lock is acquired only when the key does not exist.

public static boolean getLock(String key) {
    Long flag = jedis.setnx(key, "1");
    if (flag == 1) {
        jedis.expire(key, 10);
    }
    return flag == 1;
}

public static void releaseLock(String key) {
    jedis.del(key);
}

4. Global ID – Use integer keys with the atomic INCRBY command to generate sequential IDs, e.g., incrby userid 1000 .

5. Counter – Simple integer increment via INCR , suitable for page views, likes, etc.; values can be written to Redis first and later synchronized to a database.

6. Rate Limiting – Store request counts per IP (or other identifier) using INCR ; reject requests when the count exceeds a threshold.

7. Bit Statistics (Bitmaps) – Use BITCOUNT and related bitmap commands to store binary flags efficiently, e.g., for online‑user statistics.

set k1 a
setbit k1 6 1
setbit k1 7 0
get k1

Bitwise operations such as BITOP AND , OR , XOR , and NOT enable set‑based analytics, for example calculating users online for seven consecutive days.

BITOP AND "7_days_both_online_users" "day_1_online_users" "day_2_online_users" ... "day_7_online_users"

8. Shopping Cart – Store cart data in a HASH where the key is the user ID, the field is the product ID, and the value is the quantity; operations include HINCRBY , HDEL , HGETALL , etc.

9. User Timeline – Use a LIST (double‑ended queue) to maintain an ordered timeline of events.

10. Message Queue – Redis lists provide blocking pop commands BLPOP and BRPOP for reliable queue consumption.

11. Lottery – Random selection can be performed with the set command SPOP .

spop myset

12. Likes, Check‑ins, and Clock‑ins – Model each post’s likes as a set like:postId ; add, remove, check membership, and count members with SADD , SREM , SISMEMBER , and SCARD .

13. Product Tags – Use a set tags:productId to store all tags associated with a product.

14. Product Filtering – Combine multiple sets with SINTER , SUNION , and SDIFF to filter products by brand, OS, screen size, etc.

15. Follow / Recommendation Model – Represent follow relationships with sets user:follow and user:fans ; intersections and differences reveal mutual follows or potential connections.

16. Leaderboard – Use sorted sets; increment a news item’s score with ZINCRBY and retrieve the top N items with ZREVRANGE including scores.

zincrby hotNews:20190926 1 n6001
zrevrange hotNews:20190926 0 15 withscores