This article explains the design and implementation of a two‑level caching architecture using local Caffeine cache as L1 and remote Redis as L2 in Spring Boot, covering manual cache handling, annotation‑based management with Spring Cache, and a custom annotation with AOP to minimize code intrusion.
This article explains how Redis achieves high‑performance single‑threaded networking by leveraging Linux epoll for I/O multiplexing, detailing the server initialization, event‑loop architecture, connection handling, command processing, and write‑back mechanisms with code examples, providing a deep dive for backend developers.
This article provides a comprehensive technical guide on Redis performance, covering single‑threaded characteristics, master‑slave replication, Sentinel high‑availability mechanisms, Redis Cluster architecture, configuration steps, code examples, and alternative middleware solutions for scaling and fault tolerance.
This article details the end‑to‑end development of a supplier management system using SpringBoot, Thymeleaf, Mybatis‑Plus, MySQL and Redis, covering project planning, code implementation, file upload handling, caching optimization, and deployment considerations.
This article explains Redis 6's client‑side caching feature, describing its purpose, the two tracking modes (default and broadcast), a forward mode for RESP 2 clients, their advantages over traditional remote caching, and provides step‑by‑step command‑line examples with code snippets.
This article shares a curated selection of interview questions and answers covering concurrency programming, MySQL, and Redis, extracted from the author's extensive interview notes, with links to the full content on a personal website and instructions for obtaining the complete material.
RedisInsight is a lightweight, cross‑platform visual tool for Redis that offers data browsing, filtering, real‑time command analysis, support for multiple modules like RedisJSON and RediSearch, and includes a Workbench CLI, with installation steps using Node.js and Yarn.
This article provides a comprehensive introduction to Redis, covering its core features, supported data types, common caching problems and solutions, performance characteristics, eviction policies, persistence mechanisms, master‑slave replication, and Sentinel high‑availability architecture.
Redis 7.0 has been officially launched, bringing nearly 50 new commands, major enhancements like Redis Functions, ACLv2, command introspection, and Sharded Pub/Sub, plus extensive performance and stability improvements across memory, compute, networking, and storage subsystems.
The article explains how to prevent duplicate user requests on the server by using unique request IDs, hashing request parameters, excluding volatile fields, and implementing an atomic Redis SETNX‑with‑expiration pattern with Java code examples and a reusable deduplication helper class.
This article explains the concept of Bloom filters, their probabilistic behavior and limitations, demonstrates how to integrate them into Redis via the RedisBloom module, and provides complete Java/Redisson examples for creating, populating, and querying Bloom filters to prevent cache penetration and duplicate processing.
This article explains the structures of B+ trees and skip lists, compares their insertion and search behaviors, and shows why MySQL prefers B+ trees for disk‑based indexing while Redis adopts skip lists for in‑memory operations.
This article explains how to design Redis cache for relational list data such as user timelines or news feeds, covering fixed-length caching, consistency, resource utilization, using ZSET structures, handling additions, deletions, queries, and strategies like preloading, retry mechanisms, and asynchronous rebuilding.
The article explains Redis’s in‑memory architecture, detailing memory components, object encodings, buffer limits, fragmentation, and forked‑process overhead, and offers practical optimization tips—such as using compact encodings, controlling client buffers, disabling THP, and enabling active defragmentation—illustrated by real‑world case studies.
This article examines performance bottlenecks in a high‑traffic e‑commerce product service and proposes data‑centric optimizations—including read‑only focus, field‑level selection via bit‑masking, and Redis hash storage—to reduce payload size, lower GC pressure, and improve latency while maintaining scalability.
The article describes how a large‑scale membership system achieves high performance and fault tolerance by deploying a dual‑center Elasticsearch cluster, isolating traffic with multiple ES clusters, adding a Redis cache with distributed locks, migrating the primary relational store from SQL Server to a partitioned MySQL cluster, and implementing fine‑grained flow‑control and degradation strategies.
This article introduces Redis, explains how to install it on various platforms, demonstrates integrating Redis with Spring Boot using Spring Cache annotations, configures connection pools for Lettuce and Jedis, and provides custom Redis service implementations for flexible cache operations.
This article explains how Redis handles memory pressure using configurable maxmemory limits and a variety of eviction policies—including noeviction, volatile‑lru, volatile‑lfu, allkeys‑lru, and allkeys‑random—while offering guidance on selecting appropriate policies and sizing cache capacity for optimal performance.
This article explains the challenges of mutual exclusion and idempotency in distributed systems, reviews Java concurrency primitives, compares common distributed lock implementations such as Zookeeper, Redis, and Tair, and introduces Cerberus and GTIS as robust solutions for high‑availability and repeatable operations.
The article explains how to prevent duplicate user requests on the server side by using unique request IDs with Redis, constructing business‑parameter keys, computing MD5 digests of sorted JSON payloads, and provides a complete Java utility class with code examples and testing logs.
This article explains the mutual‑exclusion and idempotency challenges in distributed environments, analyzes multi‑thread and multi‑process solutions, introduces the principles and implementations of distributed locks (including ReentrantLock, synchronized, Zookeeper, Redis, Tair, and the Cerberus framework), and presents GTIS as a reliable idempotency guard.
This article explains Redis's original single‑threaded event‑driven design, the problems it causes, how the Lazy Free mechanism and asynchronous deletion were introduced in Redis 4.0, and how Redis 6.0 adds multi‑threaded I/O while discussing its limitations and comparing it with Tair's approach.
This article explains why cache penetration can cripple ultra‑large systems, how storing full data in Redis eliminates the risk, and presents two reliable cache‑update strategies—message‑queue‑driven updates and real‑time MySQL Binlog subscription via Canal—complete with configuration steps and Java code examples.
This article provides a comprehensive overview of Redis, covering its core characteristics, supported data types, common caching challenges such as consistency, avalanche, penetration and breakdown, as well as its high performance mechanisms, eviction policies, persistence options, master‑slave replication, and Sentinel high‑availability architecture.
This article provides a comprehensive introduction to Redis, covering its core characteristics, supported data types, caching usage patterns, typical issues such as consistency, avalanche, penetration and breakdown, performance reasons, eviction policies, persistence options, master‑slave replication, and Sentinel high‑availability mechanisms.
This article explains the structures of B+ trees and skip lists, compares their read and write performance, and details why MySQL chooses B+ trees for indexing while Redis and RocksDB prefer skip lists, highlighting trade‑offs in disk I/O, fan‑out, and balancing overhead.
This article demonstrates how to build a simple hot‑search functionality with user search history, hot‑keyword ranking, and profanity filtering in a Spring Boot application using Java, Redis ZSet operations, and a DFA‑based sensitive‑word filter, providing complete controller and service code examples.
This article explains how to use Redis key‑expiration events in a Spring Boot application to automatically close unpaid orders by configuring Redis, adding the necessary dependencies, and implementing a listener that reacts to expired keys.
This article provides a comprehensive introduction to Redis, covering its core characteristics, supported data structures, common caching use‑cases and associated challenges such as consistency, cache avalanche, penetration and breakdown, as well as performance reasons, eviction policies, persistence options, master‑slave replication, and Sentinel high‑availability mechanisms.
This article surveys various delayed‑queue solutions—including Kafka pause/resume, RocketMQ delay levels, Redis/Redisson zset timers, Youzan's custom design, Netty's HashedWheelTimer, and a Kafka‑based time‑wheel—analyzing their architectures, pros and cons, and offering practical implementation guidance.
This article provides a comprehensive introduction to Redis, covering its core features, supported data structures, common caching patterns, typical cache‑related problems and mitigation techniques, performance characteristics, eviction policies, persistence options, replication mechanisms, and Sentinel high‑availability architecture.
This article explains Redis's two expiration strategies—periodic active deletion and lazy deletion—detailing how the EXPIRE command works, the new NX/XX/GT/LT options introduced in version 7.0, the impact of unsynchronized clocks, and the underlying implementation code.
This article compares native JDK ScheduledExecutorService, Spring Task, Redis‑based approaches, and popular distributed scheduling frameworks such as Quartz, Elastic‑Job, LTS, and XXL‑Job, providing code examples, usage scenarios, advantages, and best‑practice recommendations for implementing reliable timed jobs in microservice environments.
This article walks through adapting a custom Redis client to Spring Cache, explains the cache abstraction, demonstrates annotation‑driven caching, shows how to integrate Caffeine and Redisson, and builds a simple two‑level cache to illustrate advanced scenarios for Spring Cache users.
A recent hardware failure left a physical server's disk read‑only, causing Redis to hang; this article explains the AOF mechanism, its potential pitfalls, log strategies, and practical steps to prevent and mitigate such issues in production environments.
This article explains how to use Redis data structures—Set, Hash, Bitmap, and HyperLogLog—along with Redisson in Java to efficiently count unique page visitors (UV) in high‑traffic mobile internet scenarios, comparing memory usage, precision, and scalability.
The article surveys common delayed‑message solutions in distributed MQ systems—including external storage, RocksDB, Redis, thread‑based scanning, and the designs of RocketMQ, Pulsar, and QMQ—detailing their architectures, advantages, and drawbacks for backend developers.
For deduplicating game recommendations in Redis, RoaringBitMap outperforms intset and Bloom filters by storing 300 auto‑incrementing game IDs in roughly 0.5 KB—over twice the compression of intset and far smaller than the 29 KB Bloom filter—thereby cutting memory use, latency, and hardware costs.
This article demonstrates how to implement Redis distributed locks using Jedis in Java, covering lock creation with SETNX, lock release via Lua scripts, and a high‑concurrency flash‑sale simulation with 100,000 virtual users, illustrating key concepts such as lock expiration, atomic operations, and performance testing.
This article explains how to prevent duplicate user requests on the server side by leveraging unique request IDs, hashing request parameters (excluding time fields) to generate MD5 signatures, and implementing an atomic Redis SETNX‑with‑expiration solution in Java, complete with utility code and test logs.
This article explains how to prevent harmful duplicate write requests by using unique request IDs or parameter‑based signatures, leveraging Redis for atomic SETNX with expiration, computing MD5 hashes of sorted JSON payloads, and providing a reusable Java helper class for robust backend deduplication.
This article presents a detailed evaluation of Intel® Optane™ Persistent Memory, including benchmark comparisons with DRAM and NVMe using Redis and RocksDB workloads, analysis of latency and throughput, and step‑by‑step configuration instructions for Memory Mode, App Direct, and KMEM DAX.
This article explains how to handle flash‑sale (seckill) scenarios in PHP by reducing database pressure with caching, preventing stock oversell using row locks, transactions, file locks, and Redis queues, and provides complete code examples for each technique.
This article introduces CIM, a developer‑oriented distributed instant messaging platform, outlines its current features and roadmap, explains its SpringBoot‑Netty‑Redis‑Zookeeper architecture, and provides detailed installation, configuration, and usage instructions with code examples.
This article explains why data loss can occur in Redis master‑slave clusters due to split‑brain scenarios, outlines step‑by‑step troubleshooting methods—including checking replication lag, analyzing client logs, and monitoring resource usage—and recommends configuration settings such as min‑slaves‑to‑write and min‑slaves‑max‑lag to prevent the issue.
Redis 7.0-rc3 introduces new cluster commands, configuration ordering changes, ACL restrictions, function load adjustments, performance optimizations, enhanced CLI behavior, expanded INFO fields, module API extensions, and a comprehensive set of bug fixes, providing developers with important updates and potential migration considerations.
This article examines why Redis HSCAN can cause high CPU usage on ziplist‑encoded hash objects, compares its performance with HGETALL through benchmark tests, and provides practical recommendations for avoiding costly full‑scan operations in production environments.
Rb (Redis Blaster) is a Python library built on python‑redis that provides a custom routing system for non‑replicated sharding, allowing automatic key‑based routing, parallel execution, fan‑out and mapping across multiple Redis nodes without manual request distribution.
This guide explains caching fundamentals, purposes, and optimal use cases, details hit rates and eviction policies such as FIFO, LRU, LFU, TTL/TTI, compares client‑side (HTTP, browser) and server‑side caches (CDN, Redis, Memcached), explores multi‑level architectures, common pitfalls like avalanche, penetration and breakdown, and best‑practice strategies including pre‑warming, update patterns, and consistency management.
Server‑side caching improves performance by trading space for time, using local caches like HashMap, Guava, Ehcache, and Caffeine, distributed caches such as Redis, and multi‑level architectures that combine in‑process, distributed, and database layers, while employing consistency patterns, monitoring, and hot‑key detection.
This article provides a step‑by‑step guide to installing RedisInsight, configuring its environment variables, launching the service on Linux, deploying it with Kubernetes, and using its graphical interface to monitor and manage Redis clusters, keys, and memory.
This article compares three popular open‑source message brokers—RabbitMQ, Kafka, and Redis—examining their scalability, persistence, consumer models, and ideal use cases to help developers select the most suitable asynchronous communication solution for their microservice architecture.
This article explains the challenges of generating globally unique identifiers in distributed systems and presents various solutions—including UUID, database auto‑increment, Redis atomic counters, and the Snowflake algorithm—detailing their requirements, implementations in Java and SpringBoot, and their pros and cons.
This article details the design and implementation of a highly available, high‑performance membership system serving over a billion users, covering Elasticsearch dual‑center clusters, traffic isolation, Redis caching, MySQL migration, and fine‑grained flow‑control and degradation strategies.
The article explains how to prevent duplicate requests on the server side by using unique request identifiers stored in Redis, hashing request parameters (excluding volatile fields) with MD5, and provides a reusable Java helper class with example code and test logs.
This article explains why storing JSON strings in Redis is inefficient, compares JSON with binary‑safe formats like MessagePack, evaluates compression algorithms such as ZSTD and LZ4, and provides Python examples showing how to reduce latency and memory usage when using Redis strings.
This guide explains what RedisInsight is, outlines its key features, and provides step‑by‑step instructions for installing it on a physical server or via Kubernetes, configuring environment variables, launching the service, and using the GUI to monitor and manage Redis instances.
This comprehensive guide explores Redis fundamentals, including NoSQL concepts, its five primary data structures, internal storage mechanisms, persistence options, replication, sentinel failover, and strategies to prevent cache penetration, breakdown, and avalanche, providing developers with deep insights for building robust, high‑performance applications.
This article details the design and implementation of a highly available, high‑performance membership system serving billions of users, covering Elasticsearch dual‑center clusters, traffic‑isolated architectures, deep ES optimizations, Redis caching with distributed locks, dual‑center MySQL partitioning, migration strategies, abnormal account handling, and future fine‑grained flow‑control and degradation policies.
This article explains how a large‑scale membership system serving over a billion users achieved high performance and availability by deploying dual‑center Elasticsearch clusters, traffic‑isolated ES clusters, deep ES optimizations, a Redis caching layer with dual‑center replication, and a seamless migration from SQL Server to sharded MySQL, while also outlining future fine‑grained flow‑control and degradation strategies.
The article details Redis’s internal architecture, explaining how strings use SDS structures, sorted sets rely on skip‑lists, integers are stored in compact intsets, hash tables employ incremental rehashing, ziplist and listpack provide memory‑efficient encodings, the RAX radix tree underpins key lookup and streams, and the threading model has evolved from a single‑threaded event loop to multithreaded I/O for improved concurrency.
This article compares several approaches to implementing delayed tasks in Java, including Quartz polling, JDK DelayQueue, Netty's HashedWheelTimer, Redis sorted sets and keyspace notifications, and RabbitMQ delayed queues, analyzing their principles, code examples, advantages, and drawbacks for order timeout handling.
This article explains server‑side strategies for preventing duplicate user requests—covering unique request IDs, parameter‑based deduplication with MD5 hashes, handling time‑dependent fields, and a complete Java utility class that leverages Redis SETNX with expiration to ensure idempotent operations.
RedisInsight is a powerful GUI for Redis that offers cluster support, SSL/TLS connections, and memory analysis; this guide walks you through downloading, installing on Linux, configuring environment variables, running the service, deploying via Kubernetes, and basic usage for monitoring and managing Redis instances.
Learn how to install, configure, and operate RedisInsight—an intuitive Redis GUI—on Linux and Kubernetes, covering package download, environment setup, service deployment, basic usage, memory analysis, and key management, with step‑by‑step commands and visual screenshots.
The article presents a comprehensive design of the Cleaner anti‑crawler system, detailing its background, current challenges, related research, system architecture—including data processing, ban center, and ban store modules built on Flink, MQ, and Redis—implementation details, effectiveness evaluation, and concluding insights on achieving real‑time accuracy in protecting platform data.
This article details the design and implementation of a high‑availability membership system, covering Elasticsearch dual‑center master‑slave clusters, traffic‑isolated three‑cluster ES architecture, Redis multi‑center caching, MySQL dual‑center partitioning, data migration strategies, and refined flow‑control and degradation mechanisms to ensure stable, low‑latency service under massive concurrent load.
This article examines the characteristics of Redis and Memcached, identifies typical design mistakes such as consistency, cache avalanche, hot‑key, penetration and breakdown issues, and presents practical mitigation strategies—including consistent hashing, binlog‑driven cache invalidation, distributed locking, and multi‑node replication—to help backend engineers build reliable high‑concurrency cache layers.
This article presents a comprehensive study of local vector retrieval optimization, covering memory‑based filtering techniques, Redis‑backed vector storage designs, and various sorting algorithms—including radix and heap‑based approaches—to achieve lower latency and higher throughput for large‑scale ad recommendation systems.
This article explains Java NIO’s non‑blocking I/O model, compares it with traditional BIO, details zero‑copy techniques, and walks through core components such as Channel, Buffer, and Selector with concrete code examples and diagrams, then shows how Redis leverages I/O multiplexing for high performance.
This article provides a detailed overview of Redis, covering its core concepts, high‑performance features, common use cases, installation steps, memory‑management policies, supported data structures, persistence mechanisms, transaction model, and best practices for cache consistency and failure handling.
This article explains how to enhance Spring Cache by creating custom cache annotations, replacing the costly KEYS command with SCAN in a rewritten DefaultRedisCacheWriter, and dynamically registering RedisCacheManager beans to support per‑module TTL and transaction‑aware caching, all illustrated with full Java code examples.
The article details a comprehensive high‑availability solution for a large‑scale membership system, covering Elasticsearch dual‑center master‑slave clusters, traffic‑isolated three‑cluster designs, deep ES optimizations, Redis caching with consistency safeguards, MySQL partitioned migration, and fine‑grained flow‑control and degradation mechanisms.
This article provides a comprehensive guide to cache usage, covering its performance benefits and costs, various update strategies, granularity control, and detailed optimizations for issues such as penetration, avalanche, hot key reconstruction, and distributed batch operations.
This article details the design and implementation of a high‑performance, highly available membership system, covering Elasticsearch dual‑center master‑slave clusters, traffic‑isolated three‑cluster ES architecture, Redis cache strategies, MySQL dual‑center partitioning, seamless migration, abnormal member handling, and fine‑grained flow‑control and degradation policies.
This article presents sixteen practical Redis use cases—including caching, distributed sessions, locks, global IDs, counters, rate limiting, bitmaps, shopping carts, timelines, queues, lotteries, likes, tags, filtering, follow models, and ranking—illustrating how to leverage Redis data structures for high‑performance, scalable backend applications.
Shopee’s Real‑time Checking System provides configurable, non‑intrusive data consistency verification for micro‑services by capturing change events via CDC, streaming them through Kafka, applying flexible rules and expressions, and instantly alerting mismatches, delivering second‑level detection while scaling to tens of thousands of checks per second.
This article provides a comprehensive overview of Redis, covering its definition, data structures, performance characteristics, persistence mechanisms, expiration strategies, memory eviction policies, hot key handling, cache pitfalls, deployment modes, Sentinel and Cluster features, and replication details, all framed as interview questions.
This article provides a thorough step‑by‑step guide to identify why Redis latency spikes, covering baseline performance testing, slowlog analysis, big‑key handling, expiration patterns, memory limits, fork overhead, huge pages, AOF settings, CPU binding, swap usage, memory fragmentation, network bandwidth, and practical optimization techniques.
Learn how to resolve WebSocket session sharing issues in load‑balanced Java applications by leveraging Redis’s publish/subscribe mechanism and Spring Session, with step‑by‑step configuration, code examples, and deployment instructions for multiple distributed environments.
This post shares a comprehensive Java interview handbook containing over 1,000 questions covering MyBatis, Zookeeper, Dubbo, Redis, MySQL, concurrency, Spring, Kafka, micro‑services and more, hosted on GitHub with 30K+ stars, and explains how to obtain the latest PDF via a WeChat QR code.
This article explains the inventory oversell problem caused by Java's JVM‑local locks in multi‑machine deployments, introduces distributed locks as a solution, and compares practical implementations using Redis (including RedLock and Redisson) and Zookeeper (with Curator), providing code snippets, deployment considerations, and a pros‑cons analysis to guide technology selection.
This article explains Redis's persistence mechanisms, detailing the snapshot-based RDB approach and the append‑only file (AOF) method, their configuration commands, advantages, disadvantages, and how to choose between them for reliable data durability.
Redis stores data in memory, risking loss on shutdown, so it offers two persistence methods—RDB snapshots and AOF append‑only logs—each with distinct commands, configuration options, advantages, and trade‑offs, helping developers choose the right strategy for data durability and performance.
This article explains Redis's in‑memory database architecture, detailing data locality, various eviction policies such as LRU, LFU, random and TTL, their configuration via maxmemory settings, and provides code examples of the eviction process and memory‑freeing functions.
This article explains how Redis ensures data durability through two persistence methods—Append‑Only File (AOF) and Redis Database (RDB) snapshots—covering their principles, configuration options, risks, rewrite processes, recovery procedures, handling of expired keys, and the hybrid approach introduced in Redis 4.0.
This article explains how Redis stores Set and Sorted Set objects using dual internal representations—intset and hashtable for sets, skiplist and ziplist for sorted sets—detailing their structures, encoding selection criteria, upgrade processes, command usage, and the reasons behind combining dictionaries with skiplists for optimal performance.
This article explains how Redis initializes, creates a TCP listener, registers file events, runs a single‑threaded reactor loop, accepts client connections, processes commands via a command table, and sends replies, illustrating the core backend architecture of the in‑memory database.
Learn to protect your server from malicious crawlers by creating a dynamic IP blacklist using Nginx, Lua, and Redis, covering architecture choices, installation steps, configuration snippets, and how to share and update the blacklist across multiple machines.
This article reviews common distributed ID generation methods—including UUID, database auto‑increment, segment mode, Redis, Snowflake, Baidu UidGenerator, Meituan Leaf, and Zookeeper—detailing their principles, advantages, drawbacks, and suitable scenarios to help developers select the most appropriate solution for their microservice architecture.
This article explains Redis's two persistence mechanisms—RDB snapshots and AOF append‑only logs—detailing how they work, how to enable them via commands or configuration, their advantages and drawbacks, and guidance on choosing the right method for your workload.
This article explains how a large‑scale membership platform achieved over 20,000 TPS with high performance and zero downtime by deploying dual‑center Elasticsearch clusters, traffic‑isolated three‑cluster ES architecture, Redis caching with distributed locks, and a seamless MySQL migration and HA strategy.
This article explains why naive Redis lock usage can cause non‑atomic operations, forgotten releases, lock‑stealing, re‑entrancy, read‑write contention, segmentation, timeout, master‑slave failures, and offers practical Java/Redisson/Lua solutions to make distributed locks reliable.
This article shares a junior engineer's detailed journey of securing a backend engineering offer at Tencent, covering three interview rounds, key technical topics like Spring, HashMap, Elasticsearch, Redis, and personal reflections on strengths, weaknesses, and preparation strategies.
RedisOM, the official Redis ORM for Java, lets Spring Boot developers replace RedisTemplate with object‑oriented data handling and built‑in full‑text search, covering JDK 11 setup, Maven dependencies, configuration, entity annotations, repository interfaces, and REST controller examples for managing product data.
This article explains Redis Sentinel’s role in achieving high availability, details its core functions, underlying Raft‑based algorithm, configuration parameters, practical setup steps, fault‑tolerance mechanisms, quorum and majority calculations, and demonstrates failover and recovery scenarios with real command‑line examples.
This article explains how Redis achieves high performance by using specialized in‑memory data structures such as SDS, quicklist, listpack, hash tables, skip‑lists and integer sets, detailing their design, evolution across versions, memory‑efficiency tricks, and the trade‑offs that make Redis a lightning‑fast key‑value store.
This article explains the concept of skip lists as an ordered random data structure, illustrates how Redis uses skip lists for sorted sets, and provides a complete Java implementation with detailed code examples and analysis of their performance characteristics.
The article explains how Redis implements a high‑performance single‑threaded architecture by using a Reactor‑style file‑event handler (FEH) built on I/O multiplexing, detailing socket events, the I/O multiplexing program, dispatcher, handler mapping, and the overall client‑server communication flow.
This article explains why Redis achieves high throughput—up to 100,000 QPS—by leveraging in‑memory storage, simple data structures, a single‑threaded event loop with I/O multiplexing, and the optional multithreaded I/O model introduced in Redis 6.0, including configuration details and safety considerations.
