Best of Redis — January 2025

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   Video

   JavaScript Mastery·1y

   Build and Deploy a Fullstack App with Admin Dashboard | Next.js, PostgreSQL, Redis, Auth.js

   This post guides you through building and deploying a production-grade University Library management system using Next.js, PostgreSQL, Redis, and Auth.js. The project consists of two interconnected applications within a monorepo architecture, teaching industry-standard practices such as rate limiting, DDoS protection, caching, database queries, multimedia uploads, and automated workflows. Technologies like Tailwind CSS, Drizzle ORM, and ImageKit are used to create a polished and scalable application, catering to both user-facing and admin interfaces.

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2. 2
   Article

   Foojay.io·1y

   Rate limiting with Redis: An essential guide

   Rate limiting is essential for managing traffic, preventing abuse, and ensuring fair access to resources. Redis is a preferred tool for implementing rate limiting due to its speed, reliability, and features. The choice of rate-limiting pattern, such as leaky bucket, token bucket, fixed window counter, sliding window log, or sliding window counter, depends on traffic patterns, precision needs, and resource constraints. Understanding these patterns and their trade-offs helps in building efficient, fair, and user-friendly systems.

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   Article

   Community Picks·1y

   SSE (server sent events) is fun / Go

   Creating a live counter for a popular interior design quiz using Server-Sent Events (SSE) to keep track of user presence, quiz results, and broadcasting messages with efficient use of Go, Redis, and a thoughtful UI design. The approach emphasizes simplicity, scalability, and practical solutions for persistent connections and event handling.

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4. 4
   Article

   Foojay.io·1y

   Token Bucket Rate Limiter (Redis & Java)

   The token bucket rate limiter is an efficient mechanism to manage request rates by using tokens added to a bucket at fixed intervals. Redis is used for tracking tokens, while Jedis provides a simple API for Redis commands from Java. The implementation includes adding dependencies, creating a TokenBucketRateLimiter class, validating requests, refilling tokens, and updating Redis. Thorough testing ensures correct behavior, including independent handling of clients, token refills, and capacity limits.

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