This article explains how Eureka's service registry works, detailing the client’s initial full‑registry fetch during startup, the server’s two‑level caching strategy, and how both sides store and retrieve registration information for efficient service discovery.
This article examines frequent Redis pitfalls—including unexpected key expiration, blocking DEL commands, RANDOMKEY performance issues, SETBIT memory spikes, MONITOR OOM risks, persistence challenges, and master‑slave replication quirks—providing detailed explanations and practical mitigation strategies for each scenario.
This article explains how a Eureka client initially retrieves the full service registry from the server, the conditions that trigger a full fetch, and how the server caches the registry using a two‑level read‑only and read‑write cache architecture.
This article explains how Linux manages process memory, covering allocation via mmap and brk, OOM killer selection, the role of page cache in different mapping types, manual and automatic memory reclamation, and related kernel parameters.
This comprehensive guide covers Redis fundamentals, data structures, performance tricks, cache pitfalls, expiration policies, persistence options, high‑availability architectures, distributed locking techniques, and practical use‑cases, providing clear explanations and code examples for each topic.
This article provides a comprehensive overview of cache memory, explaining why caches are needed, their placement strategies, operation principles, replacement policies, write handling methods, and coherence protocols such as MESI, offering essential knowledge for computer architecture and system design.
This article provides a comprehensive overview of Redis, covering its definition, basic and special data structures, performance optimizations, expiration and eviction policies, common application scenarios, persistence mechanisms, high‑availability architectures, distributed lock implementations, transaction handling, and related algorithms such as Redlock and Bloom filters.
This article provides a comprehensive list of typical Redis interview questions covering its features, performance, data structures, supported data types, common use cases, eviction policies, persistence methods, clustering, high‑availability mechanisms, transaction handling, and comparisons with local caches like Guava and Caffeine.
This article explains iQIYI TV's backend distributed real-time local cache solution, comparing local and centralized caches, detailing its advantages, drawbacks, and a message-driven update mechanism that improves high‑concurrency read performance and reduces cache‑related risks.
This article explains the purpose of CPU caches, their classification, placement and replacement strategies, write policies, and coherence protocols, providing a comprehensive overview of cache concepts essential for modern computer architecture.
iQIYI TV’s backend implements a distributed real‑time local cache that combines hotspot local storage with a unified messaging bus, management console, and business‑filtering to deliver instant updates, improve hit rates, reduce read‑bottleneck risk, and alleviate load on centralized caches under extreme concurrency.
This article explains Redis's virtual memory (VM) feature, how it swaps cold data to disk while keeping keys in memory, details configuration parameters, describes the two threading models for data swap-in/out, and highlights why Redis's custom VM implementation boosts performance.
This article explains the DNS hijacking threat, shares a real incident affecting NetEase Cloud IM, and details a comprehensive high‑availability architecture—including HttpDNS, laddered HTTP requests, caching strategies, and SNI handling—that protects the SDK from DNS attacks and ensures reliable service.
This article explains the Least Frequently Used (LFU) cache eviction strategy, contrasts it with LRU, and provides a complete Java implementation—including node definition, insertion, sorting, removal, and retrieval logic—along with a test scenario and discussion of LFU's drawbacks.
The article explains the concepts of cache penetration, cache breakdown, and cache avalanche in Redis‑based systems, analyzes the performance problems they cause under high concurrency, and presents practical mitigation techniques such as Bloom filters, caching empty objects, distributed locks, high‑availability clusters, rate limiting, and data pre‑warming.
Vivo Mall transformed its monolithic coupon component into an independent, sharded microservice through a rolling, dual‑write migration, employing Redis distributed locks, batch inserts, multi‑level caching, and read‑write splitting to eliminate bottlenecks, achieve non‑downtime deployment, and deliver scalable, high‑performance coupon handling for future growth.
This article introduces the various Redis eviction strategies—including volatile-ttl, volatile-random, volatile-lru, volatile-lfu, allkeys-random, allkeys-lru, and allkeys-lfu—explains their behavior, shows where they are configured in redis.conf and the initServer source code, and notes the default noeviction policy when memory exceeds maxmemory.
This article walks you through using an Android phone to locate Xianyu’s video cache folder, rename the hidden file to MP4, and play or save the downloaded cat video, providing clear screenshots for each step.
The article introduces Youzan's Transparent Multilevel Cache (TMC), detailing its three‑layer architecture, hotspot detection and local caching mechanisms, integration approaches for Java applications, stability and consistency features, and performance results from real‑world e‑commerce campaigns.
The article explains the concepts of cache penetration, cache breakdown, and cache avalanche in Redis‑based systems, describes the performance risks they pose to persistent databases, and presents practical mitigation techniques such as Bloom filters, empty‑object caching, hot‑key permanence, distributed locks, high‑availability clusters, rate limiting, and data pre‑warming.
The Transparent Multilevel Cache (TMC) solution adds application‑level hotspot detection, local caching, and hit‑rate statistics to a standard distributed cache stack, enabling automatic hotspot discovery, reducing load on backend cache clusters, and improving system stability and latency during traffic spikes.
This article outlines the main server hardware architectures—SMP, UMA, and NUMA—explains shared-storage models, details multi-core cache structures from private L1 to shared L3, compares access latencies, and discusses inter-core communication mechanisms and cache coherency protocols.
At Gopher China 2021, Go expert Yang Le Duo presented a detailed exploration of a proactive in‑memory cache architecture, describing how his team tackled real‑time data, complex queries, hot‑cold data swapping, and language constraints to improve performance and developer productivity in large‑scale streaming applications.
This article introduces Spring Cache as a framework‑agnostic caching abstraction that eliminates manual cache code, explains its core concepts, annotations, configuration options, and demonstrates practical usage with Redis and custom key, condition, and eviction strategies in Spring Boot applications.
This article introduces Caffeine Cache, explains its W‑TinyLFU eviction algorithm, compares it with Guava, and provides detailed usage examples—including manual, synchronous, and asynchronous loading, eviction policies, Spring Boot integration, annotations, and configuration snippets—for building high‑performance backend caches.
This guide demonstrates how to add a custom AOP‑based caching layer to a SpringBoot application, using Redis to store frequently queried MySQL table data, defining @AopCacheEnable and @AopCacheEvict annotations, implementing an aspect, handling key generation, expiration, and cache eviction on CRUD operations.
The article introduces the pain points of using Redis at Tencent IEG, explains the three Tendis product editions, and provides an in‑depth description of the hybrid storage version’s architecture, components, version control, cold‑hot data interaction, scaling mechanisms, and the stateless Redis‑sync layer.
This article introduces the pain points of using Redis as a cache, presents Tencent's Tendis solution with its three product variants, and provides an in‑depth explanation of the hybrid storage version’s overall architecture, component functions, version control, cold‑hot data interaction, eviction policies, and scaling mechanisms.
The article introduces Transparent Multilevel Cache (TMC), a comprehensive caching solution that adds hotspot detection and local caching to existing distributed cache systems, explains its three‑layer architecture, transparent Java integration, real‑time hotspot discovery process, and demonstrates performance gains in high‑traffic e‑commerce scenarios.
The article introduces Youzan's Transparent Multilevel Cache (TMC), explains why hotspot cache access harms e‑commerce applications, describes its three‑layer architecture, details the Java client integration with Hermes‑SDK for automatic hotspot detection and local caching, and presents real‑world performance gains during large‑scale promotional events.
This article explains cache‑database consistency challenges, outlines three classic caching patterns, compares delete‑versus‑update strategies, discusses operation ordering, and presents three practical solutions—including delayed double delete, retry mechanisms, and binlog‑based asynchronous eviction—to help maintain data integrity in distributed systems.
This article explains the concepts of strong, weak, and eventual consistency, introduces three classic cache patterns (Cache‑Aside, Read‑Through/Write‑Through, Write‑Behind), and discusses practical strategies such as delayed double‑delete, retry mechanisms, and binlog‑based asynchronous eviction to keep Redis and MySQL data in sync.
This article explains how UCloud's US3 object storage, combined with a custom Hadoop adapter, separates compute and storage, optimizes file system operations, and leverages caching and specialized APIs to dramatically reduce storage costs and improve read/write performance for large‑scale Hadoop workloads.
The article explains Youzan's Transparent Multilevel Cache (TMC), a solution that automatically detects cache hotspots, adds an application‑level local cache, and provides hit statistics to reduce load on distributed caches, improve consistency, and boost performance for high‑traffic e‑commerce scenarios.
The article explains Youzan’s Transparent Multilevel Cache (TMC), detailing its architecture, hotspot detection, local caching, consistency mechanisms, and real‑world performance gains during flash‑sale events, showing how it reduces cache pressure and improves latency for Java‑based services.
This case study details how integrating JuiceFS with Presto reduced HDFS cluster load by about 26%, achieved over 90% cache hit rate for ad‑hoc queries, and lowered average query latency by roughly 13%, while simplifying operations and improving system stability.
The article explains the purpose and structure of the memory hierarchy—from ultra‑fast registers and caches inside the CPU, through volatile main memory, to slower non‑volatile disks—showing how programs are loaded, executed, and how virtual memory and locality principles extend usable memory beyond physical limits.
This article explains the architecture and core mechanisms of Eureka, a service registry used in Spring Cloud micro‑service environments, covering server components such as Lease and caching, client registration and discovery processes, self‑preservation mode, consistency trade‑offs, and comparisons with other discovery solutions.
This guide outlines Redis's fundamental limitations, common performance issues such as big keys, slow queries, and connection‑pool exhaustion, and provides practical recommendations on architecture, configuration, and operational diagnostics to help developers avoid hitting Redis's boundaries.
This article explains how bitmap and Bloom filter data structures reduce memory usage for large integer sets, demonstrates a Java bitmap implementation, and explores high‑concurrency cache design, consistency, penetration, and avalanche problems with practical mitigation techniques.
This article explains Spark's cache and checkpoint mechanisms, detailing when to use each, how they are implemented internally, how cached and checkpointed RDDs are stored and retrieved, and the differences between caching, persisting, and checkpointing for reliable big‑data processing.
The article shares eight authentic production incident cases—from frequent JVM Full GC and memory leaks to cache avalanches, DNS hijacking, and database deadlocks—detailing their root causes, diagnostic steps, code snippets, and practical remediation strategies for engineers facing similar challenges.
This article examines cache consistency challenges in Redis-backed systems and compares several update strategies—including TTL, delayed double‑delete, cache‑aside, and message‑queue approaches—detailing their workflows, code examples, advantages, and drawbacks to guide backend developers toward reliable cache invalidation.
This guide explains how the Linux free command reports memory usage, clarifies the roles of total, used, free, buffers, and cache, describes the meaning of the -/+ buffers/cache line, and provides practical steps to clear swap and drop caches.
This article explains how to create a simple single‑node in‑memory cache that supports per‑entry expiration by defining a generic cache interface and implementing it with Caffeine, using ConcurrentHashMap to store multiple caches keyed by expiration time.
This guide explains how to interpret the Linux free command, clarifies the roles of total, used, free, shared, buffers, and cached memory, and provides practical commands to clear swap and drop caches for better system performance.
The article reviews common cache‑side data‑sync patterns, highlights their inconsistency and data‑loss risks under high load, and presents JD's solution that combines Cache‑Aside, Change Data Capture, message queues, delayed consumption, versioning, and persistence to ensure eventual consistency between cache and relational databases.
This article explains the LRU cache eviction algorithm, its core principle, real‑world applications in Redis and MySQL, compares naive array and linked‑list solutions, and provides a complete O(1) doubly‑linked‑list‑plus‑hash‑map implementation in Python for interview preparation.
This guide walks through customizing Nginx 404 error pages, enabling and reading the stub_status module, increasing worker processes and connections, adjusting kernel limits, expanding header buffers, and configuring browser caching for static assets to dramatically improve server performance and reliability.
This article explains how RedisSyncer enables fast, reliable cross‑version and cross‑cluster Redis data migration in multi‑cloud environments, addressing challenges such as version mismatches, node inconsistencies, and rapid rollback while improving access speed and reducing database load.
This article explains why cache‑database inconsistencies occur in large systems, details the cache‑aside, read‑through, write‑through and write‑behind strategies, highlights three common pitfalls with concrete examples, and offers practical recommendations such as proper update ordering and cache expiration to ensure data freshness.
This article provides a comprehensive analysis of the Caffeine Java caching library, covering its performance‑focused W‑TinyLFU eviction algorithm, detailed source‑code walkthroughs of frequency sketching, read/write buffers, timer‑wheel expiration, and practical usage examples compared with Guava Cache.
This article provides a comprehensive analysis of ThinkPHP’s cache system, detailing the execution flow for setting and retrieving cache, exploring the underlying source code, illustrating static method behavior, initialization, file handling, compression techniques, and practical examples with code snippets.
This article explains Caffeine, a high‑performance Java local cache, comparing it with Guava Cache, detailing its W‑TinyLFU eviction policy, asynchronous read/write buffers, timer‑wheel expiration, and provides extensive source code analysis to illustrate its design and optimization techniques.
Caffeine is a high‑performance Java 8 local‑cache library that replaces Guava by using the W‑TinyLFU algorithm with three‑queue LRU structures and lock‑free read/write buffers, offering extensive configuration, dynamic runtime adjustments, and safe back‑source loading with distributed locks to prevent cache‑stampede.
This article provides a detailed, practical guide to optimizing HBase in production, covering table pre‑splitting, RowKey design, JVM memory and GC settings, MSLAB and BucketCache configuration, read‑side client and server tuning, write‑side strategies, and additional tips such as compression and scan caching.
The article explains the concepts of cache penetration, cache breakdown, and cache avalanche in Redis, illustrates each problem with real‑world analogies, and presents practical mitigation techniques such as null caching, Bloom filters, mutex locks, asynchronous refresh, varied TTLs, and clustering to ensure robust backend performance.
This article explains the concepts of cache penetration, cache breakdown, and cache avalanche in Redis, presents common mitigation techniques such as request validation, empty‑value caching, Bloom filters, mutex locks, and high‑availability strategies, and includes Java code examples for practical implementation.
This guide explains how to boost MySQL performance through soft optimizations like query analysis, index usage, and table restructuring, as well as hard optimizations involving hardware upgrades, configuration tuning, sharding, read‑write splitting, and cache clustering.
This article explains CPU architecture, cache hierarchy, the concept of cache lines, how false sharing degrades performance, mitigation techniques like cache‑line alignment, and the Linux scheduler's task prioritisation, virtual runtime, and run‑queue mechanisms for fair and real‑time execution.
This article explains Caffeine, a high‑performance Java local cache, covering its design improvements over Guava Cache, the W‑TinyLFU eviction algorithm, asynchronous read/write buffers, timer‑wheel expiration, and detailed source code analysis with examples.
This article explains Caffeine, a high‑performance Java local cache that supersedes Guava Cache, by detailing its design principles such as the W‑TinyLFU eviction algorithm, FrequencySketch implementation, adaptive window sizing, asynchronous read/write buffers, and timer‑wheel expiration, accompanied by extensive code examples.
This article provides a thorough overview of Redis, covering its eight core data types, essential commands, practical usage examples, and advanced features such as geospatial indexes, hyperloglog, and bitmap operations, making it a valuable reference for developers and interview preparation.
This article reviews five widely used cache read/write strategies—including simple write‑DB‑then‑cache, Cache‑Aside, Write/Read‑Through, Write‑Back, and LRU eviction—explaining their workflows, advantages, disadvantages, and suitability for different production scenarios.
This article outlines the essential design points for building a local Java cache—including data structures, size limits, eviction policies, expiration handling, thread safety, blocking mechanisms, simple APIs, and optional persistence—while providing concrete code snippets and implementation guidance.
This article explains why Redis creates 16 default databases, how to configure their number, the proper way to view these databases as namespaces, and why multiple applications should use separate Redis instances, especially noting that Redis clusters support only a single db0.
Redis, an in‑memory C‑language database, offers ultra‑fast read/write speeds, rich data structures, persistence, clustering, and multi‑threaded I/O, making it ideal for distributed caching, locks, queues, and more; this guide explains its fundamentals, comparisons with Memcached, data types, eviction policies, and operational best practices.
This article explains Redis's cache expiration policies—including timed, periodic, and lazy eviction—details the internal structure and parsing of RDB files, and describes the AOF persistence mechanism with its write‑ahead logging, synchronization options, and rewrite process, providing a comprehensive view of Redis data durability.
This article explains how real‑world business requirements drive storage performance configuration, details I/O aggregation and RAID write penalties, compares OLTP, OLAP, VDI and SPC‑1 workloads, and discusses the impact of SSD, SAS, FC links, cache, and RAID levels on throughput and latency.
This article explains the role of CPU cache in modern processors, covering its types, how it works within the memory hierarchy, the differences among L1, L2, and L3 levels, and the impact of cache hits, misses, and latency on overall system performance.
Cache is a temporary storage layer that speeds data access by keeping frequently used items close to the processor, spanning hardware (CPU registers, multi‑level caches) to software (database, Redis, Memcached), and requires careful design to avoid penetration, breakdown, and consistency issues through techniques such as empty‑result caching, Bloom filters, pre‑warming, jittered expirations, logical refreshes, and multi‑level strategies.
This article introduces the core features of Google Guava—including Joiner, Splitter, CharMatcher, primitive type helpers, Multiset, immutable collections, Multimap, BiMap, Table, functional utilities like Functions and Predicates, Optional, Preconditions, and a powerful local Cache—showcasing how they simplify Java development and improve code readability, safety, and performance.
This article explains the three major Redis cache problems—cache penetration, cache breakdown, and cache avalanche—describes why they occur, and provides practical mitigation techniques such as cache‑null objects, Bloom filters, locking, high‑availability, rate limiting, data pre‑warming, and staggered expiration.
Laravel-Quick is an open‑source Laravel package that provides translated validation messages, standardized HTTP status responses, exception handling, Redis‑based cache helpers, and artisan generators, with installation and usage instructions for backend developers.
This guide demonstrates how to set up a Redis multi-database singleton wrapper in a ThinkPHP project, covering prerequisite installation, configuration of connection parameters, creation of a custom Redis class with static instance management, and example usage of Redis commands across different databases.
This article explains the cache breakdown problem, introduces Laravel 7's built‑in RedisLock class, details its constructor parameters, and provides a complete code example demonstrating how to acquire and release a Redis lock to safely populate and update cached data.
This article explains why and how to clear Linux RAM cache and swap space using built‑in kernel interfaces, provides three safe echo commands, shows how to automate the process with a cron‑scheduled shell script, and warns against unsafe production usage.
This article explains how Redis introduced Lua scripting to combine multiple commands atomically, covers Lua basics, data types, control structures, script execution with EVAL/EVALSHA, script management commands, and practical tips for writing efficient, safe Redis Lua scripts.
This article examines the YYCache iOS library, covering its multi‑level architecture, LRU implementation, thread‑safe APIs, eviction policies, lock choices, disk‑storage strategies, Swift integration, performance benchmarks, and practical takeaways for building high‑performance mobile caches.
This article explains the three major Redis cache problems—cache penetration, cache breakdown, and cache avalanche—describes their causes, illustrates solutions such as empty-object caching, Bloom filters, locking, and provides practical code examples and mitigation strategies.
This article explains the concepts of cache penetration, cache breakdown, and cache avalanche in Redis, and provides practical mitigation strategies including null caching, distributed locks, random expiration times, and the use of Bloom filters to reduce database load.
This article explains the three major cache issues—penetration, breakdown, and avalanche—describes why they occur in high‑traffic systems, and presents four practical mitigation strategies including Bloom filters, short‑lived empty caches, distributed mutex locks with Redis or Memcache, and resource isolation with Hystrix.
This article explains why a multi‑level cache combining JVM‑level Caffeine and distributed Redis is essential for modern Spring Boot applications, outlines design challenges, provides step‑by‑step integration instructions, and demonstrates performance gains with benchmark results and core implementation code.
This article walks through building a two‑level caching system in Java, starting with a generic cache interface, implementing a local Guava cache, then adding a Redis distributed cache, and finally combining them with versioned keys to achieve timely expiration and seamless cache switching.
This article compares five common software architecture patterns—layered, event‑driven, microkernel‑plugin, microservices, and cache—detailing their structures, advantages, drawbacks, and ideal use cases, helping architects choose the most suitable design for new or existing applications.
The article explains three common Redis cache problems—avalanche, penetration, and stampede—detailing their causes, impacts on system stability, and practical mitigation techniques such as random expiration, Bloom filters, mutex locks, and rate limiting, accompanied by code examples.
This article explains the concept of caching, why database caching with Redis is needed for MySQL, the consistency challenges it introduces, and compares four practical solutions—TTL, write‑through, message‑queue, and binlog replication—offering guidance on selecting the appropriate approach for different latency and reliability requirements.
This article compiles common Redis interview questions, compares Redis with Memcached, explains eviction policies, and provides a concise introduction to distributed systems, high‑availability architectures, master‑slave replication, synchronization methods, and Redis‑based distributed lock solutions.
This article explains how to accelerate service response by integrating Redis as a key‑value cache with Spring Cache, covering the three‑step implementation, required Maven dependencies, XML configuration, property settings, a custom Cache implementation class, and common Spring Cache annotations.
This article explains the cache‑aside pattern, why deleting cache entries is often preferable to updating them, outlines basic and complex cache‑database inconsistency scenarios, and presents a queue‑driven approach with practical considerations for maintaining data consistency in high‑concurrency backend systems.
This article explains the distinction between Linux Buffers and Cache, how they appear in the free command, their representation in /proc/meminfo, and demonstrates practical experiments using vmstat and dd to observe their behavior during read and write operations, concluding with clear usage guidelines.
This article examines common requirements for like, comment, and collection features in apps, compares MySQL partition‑and‑cache designs with Redis storage and caching solutions, provides schema examples, query patterns, and discusses trade‑offs, scalability, and data consistency considerations.
This guide explains Linux's memory architecture, how virtual memory (swap) works, and provides step‑by‑step commands to adjust swappiness, release page caches, and safely free swap space, helping you optimize system performance and avoid memory‑related issues.
This article analyzes the startup latency of React Native pages in the 58 real‑estate app, identifies bottlenecks such as bundle size, hot‑update and Java‑to‑JS bridge delays, and presents a series of optimization techniques—including bundle splitting, silent updates, on‑demand loading, and stepwise rendering—to achieve faster, more stable page launches.
This article describes how Ctrip's ticket order post‑service team identified scaling bottlenecks, selected Redis, designed a two‑level cache architecture, implemented active and passive caching strategies, tackled consistency and failure issues, and applied various optimizations to achieve over 90% cache hit rate and dramatically reduce database load.
This article provides a comprehensive overview of Redis, covering its definition, advantages over memcached, supported data types, memory consumption, eviction policies, clustering options, persistence mechanisms, distributed lock implementations, cache penetration and avalanche solutions, and best-use scenarios compared to other caching systems.
This article explains how to implement a host cache within the JVM and a MySQL‑based distributed lock in a Java backend service, including detailed code examples for the service layer, a static cache class, lock acquisition, and release logic.
This article explains Caffeine's high‑performance local cache features, presents JMH benchmark results that show it beating Guava, ConcurrentMap and ehcache, details its expiration, refresh and eviction mechanisms, compares memory usage and eviction algorithms, and provides migration code for Guava users.
The article explains Alluxio's tiered metadata management architecture, describing how the system separates hot and cold metadata into cached and persisted layers, and details the custom asynchronous eviction thread and cache implementation that replace Guava cache for efficient large‑scale metadata handling.
This article explains how Content Delivery Networks (CDNs) dramatically reduce page load times by caching static resources near users, outlines the step‑by‑step request flow, discusses cache strategies, and highlights both the advantages and limitations of CDN adoption.
