Best of Redis — July 2025

1. 1
   Article

   freeCodeCamp·45w

   How In-Memory Caching Works in Redis

   Redis is an in-memory data store that dramatically improves application performance by caching frequently accessed data in RAM. The guide covers Redis installation, core data types (strings, lists, hashes, sets, sorted sets), and Python integration. Key features include automatic key expiration, persistence options, and scalability through replication and clustering. Common use cases include API response caching, session management, and real-time data storage, making Redis essential for building fast, scalable web applications.

   126
2. 2
   Article

   Anton Zhiyanov·45w

   Redka: Redis re-implemented with SQL

   Redka is a Redis-compatible server and Go module that reimplements Redis functionality using SQL databases (SQLite or PostgreSQL) as the backend. It supports five core Redis data types (strings, lists, sets, sorted sets, hashes) and offers both standalone server and embedded library usage. While not matching Redis performance, Redka handles tens of thousands of operations per second and provides benefits like SQL introspection, embedded caching for Go apps, and simplified testing environments.

   90

   4
3. 3
   Article

   Platformatic·44w

   Platformatic MCP Overview

   Platformatic introduces @platformatic/mcp, a production-ready Fastify adapter for the Model Context Protocol that enables horizontal scaling, type safety, and enterprise-grade reliability. The package supports multiple transports (HTTP/SSE and stdio), Redis-backed session management, automatic reconnection with message replay, and complete TypeScript definitions. It addresses the gap between development-focused MCP implementations and production requirements by providing high availability, session persistence, and cross-instance message broadcasting for scalable AI applications.

   21
4. 4
   Article

   Towards Dev·43w

   MySQL + CDC (without Kafka) to update configs in real-time

   A technical implementation guide for setting up real-time configuration updates using MySQL Change Data Capture (CDC) with Debezium in embedded mode, streaming to AWS Kinesis instead of Kafka, and updating Redis configurations. The solution uses AWS services like DynamoDB for offset storage, Secrets Manager for credentials, and ECS for deployment, with comprehensive monitoring through Prometheus, Grafana, and ELK stack.

   19
5. 5
   Article

   Baeldung·46w

   Implementing the Core Services of Spring Authorization Server with Redis

   Spring Authorization Server's default in-memory storage becomes problematic for production applications due to lack of persistence and horizontal scaling support. This guide demonstrates implementing Redis-based persistence for core authorization services including registered clients, tokens, authorization states, and consents. The implementation involves creating entity models, Spring Data repositories, custom service implementations, and Redis configuration. Key components include OAuth2RegisteredClient entities, authorization grant entities for different OAuth2 flows, and corresponding repositories and services that handle persistence operations. The tutorial uses an embedded Redis server and provides a complete working example with authentication flow demonstration.

   17
6. 6
   Article

   Towards Dev·43w

   Implementing Distributed Locks Correctly

   Distributed locks coordinate access to shared resources across multiple processes by ensuring mutual exclusion. The guide covers common implementation pitfalls like single points of failure, missing TTLs, and lack of fencing tokens. It examines various solutions including RDBMS advisory locks, Redis simple locks, Redlock algorithm, ZooKeeper ephemeral sequential nodes, and etcd's Raft-based approach. Each method has different trade-offs in consistency, performance, and complexity. Fencing tokens are crucial for preventing stale lock holders from corrupting state after timeouts. The choice of locking mechanism should match the required safety guarantees and system constraints.

   15