Redis_Data_Structures.md

Skill: Redis Data Structures

Purpose

To utilize Redis beyond simple key-value caching by leveraging its advanced data structures for high-performance use cases like leaderboards, queues, and real-time analytics.

When to Use

• Lists/Streams: For message queues or activity feeds.
• Sets: For unique collections (e.g., tags, online users).
• Sorted Sets: For leaderboards or priority queues.
• Hashes: For storing object-like data (e.g., user profiles) efficiently.
• HyperLogLog: For probabilistic counting (e.g., unique visitors) with minimal memory.

Procedure

1. Connection

Using ioredis (Node.js):

const Redis = require('ioredis');
const redis = new Redis(process.env.REDIS_URL);

2. Common Patterns

A. Leaderboards (Sorted Sets)

Use ZADD to add scores and ZREVRANGE to get top users.

// Add user score
await redis.zadd('leaderboard', 100, 'user:1');
await redis.zadd('leaderboard', 150, 'user:2');

// Get top 10
const topUsers = await redis.zrevrange('leaderboard', 0, 9, 'WITHSCORES');

B. Caching Objects (Hashes)

Use HSET and HGETALL.

// Store user profile
await redis.hset('user:100', {
  name: 'John',
  email: 'john@example.com',
  login_count: 5
});

// Retrieve
const user = await redis.hgetall('user:100');

C. Rate Limiting (Strings with Expiry)

Simple atomic counter pattern.

const key = `rate_limit:${ip}`;
const current = await redis.incr(key);
if (current === 1) {
  await redis.expire(key, 60); // 1 minute window
}
if (current > 100) throw new Error("Rate limit exceeded");

Constraints

• Memory: Redis stores everything in RAM. Use maxmemory policy (e.g., allkeys-lru) to evict old data if cache fills up.
• Persistence: Configure RDB or AOF if data durability is required (not just caching).

Expected Output

Efficient data storage and retrieval patterns tailored to specific application needs, reducing load on the primary database.