Redis

As an expert AI infrastructure and LLM engineer, I’ll guide you from zero Redis knowledge to production-ready LLM applications. Redis supercharges LLMs by providing sub-millisecond caching, vector similarity search, session memory, and real-time streaming—solving the core bottlenecks of cost, latency, and scalability in AI apps.[1][2] This comprehensive tutorial covers why Redis excels for LLMs, practical Python implementations with redis-py and Redis OM, integration patterns for RAG/CAG/LMCache, best practices, pitfalls, and production deployment strategies. ...

Introduction Redis Access Control Lists (ACLs) let you define who can do what across commands, keys, and channels. Introduced in Redis 6 and expanded since, ACLs are now the standard way to secure multi-tenant applications, microservices, and administrative workflows without resorting to a single, global password. In this guide, you’ll learn how Redis ACLs work, how to design least-privilege access for different use cases, how to manage ACLs safely in production (files, replication, rotation), and how to audit and test your permissions before you deploy. ...

Table of contents Introduction High-level overview: goals and building blocks Key distribution: hash slots and key hashing Cluster topology and the cluster bus Replication, failover and election protocol Client interaction: redirects and MOVED/ASK Rebalancing and resharding Failure detection and split-brain avoidance Performance and consistency trade-offs Practical tips for operating Redis Cluster Conclusion Resources Introduction Redis Cluster is Redis’s native distributed mode that provides horizontal scaling and high availability by partitioning the keyspace across multiple nodes and using master–replica groups for fault tolerance[1]. This article explains the cluster’s internal design and runtime behavior so you can understand how keys are routed, how nodes coordinate, how failover works, and what trade-offs Redis Cluster makes compared to single-node Redis[1][2]. ...

Introduction “Elastic cache” can mean two things depending on context: the architectural idea of a cache that scales elastically with demand, and Amazon’s managed in-memory service, Amazon ElastiCache. In practice, both converge on the same goals—low latency, high throughput, and the ability to scale up or down as workloads change. In this guide, we’ll cover the fundamentals of elastic caching, common patterns, and operational considerations. We’ll then dive into Amazon ElastiCache (for Redis and Memcached), including architecture choices, security, observability, cost optimization, and sample code/infra to get you started. Whether you’re building high-traffic web apps, real-time analytics, or microservices, this article aims to be a practical, complete resource. ...

Introduction Redis has been the de facto standard for in-memory data structures for over a decade, powering low-latency caching, ephemeral data, and real-time features. In recent years, Dragonfly emerged as a modern, Redis-compatible in-memory store that promises higher throughput, lower tail latencies, and significantly better memory efficiency on today’s multi-core machines. If you’re evaluating Dragonfly vs Redis for new projects or considering switching an existing workload, this article offers a comprehensive, practical comparison based on architecture, features, performance, durability, operational models, licensing, and migration paths. It’s written for engineers and architects who want to make an informed, low-risk choice. ...