Overview

Build fast, safe, and search powered systems on Redis 8, from caching and sessions to vector search and hybrid AI retrieval. Many teams get Redis working in development, then hit problems under real traffic: cache stampedes, unsafe session invalidation, messy keyspaces, slow tail latency, and search results that do not match what users expect. This book shows you how to design Redis 8 as an application platform, with clear schemas, predictable performance, production hardening, and a practical query layer that supports full text, aggregations, vectors, and hybrid retrieval for GenAI workflows. set up Redis 8 projects with clean keyspace design, multi tenant isolation, and baseline smoke tests apply production security with authentication, ACL design, least privilege, and operational safety practices harden transport and networks with TLS, timeouts, limits, and safe defaults choose persistence strategy with RDB and AOF, then validate recovery behavior understand Redis threading and configure IO threads with measurement driven tuning benchmark correctly using throughput plus p95 and p99 latency across realistic load shapes implement cache aside write through and write behind patterns with failure mode thinking design TTL strategy with jitter, eviction policy selection, and memory protection controls prevent stampedes with request coalescing locks and single flight patterns define consistency boundaries using invalidation versioned keys and negative caching build session stores with rotation logout semantics multi device bindings and risk based revocation enforce rate limits with counters sliding windows and penalties model application state with Redis JSON using paths updates and concurrency expectations design index friendly JSON layouts for stable querying and safer schema evolution create and debug query engine indexes on hashes and JSON, then fix missing or stale search results ship full text search with predictable relevance controls, typo tolerance, facets, and filters build analytics with FT AGGREGATE pipelines, grouping reducers, and performance discipline store embeddings and tune vector search with recall and latency controls plus churn handling implement hybrid retrieval for RAG with fusion strategies and grounding filters use time series for metrics with retention, labels, multi series queries, and compaction rules put it together into hybrid AI application architecture with semantic caching and safe operations Working command and configuration examples are included throughout so you can reproduce benchmarks, validate behavior, and build real features without guessing. Grab your copy today.

Full Product Details

9798248654420

Table of Contents

Reviews

Author Information

Tab Content 6

Countries Available