Tag

Cache Optimization

0 views collected around this technical thread.

JD Tech Talk
JD Tech Talk
Jan 22, 2025 · Operations

Preface

This article addresses a high-concurrency inventory problem in a second-hand market's second-hand market system, focusing on solving the system's inventory deduction issue during flash sales using cache and asynchronous processing to achieve horizontal scalability.

Cache OptimizationTransaction Handlinghigh concurrency
0 likes · 10 min read
Preface
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 21, 2024 · Big Data

Alluxio Integration and Optimization for Multi‑AZ Big Data Analytics at iQIYI

iQIYI integrates Alluxio with its QBFS multi‑AZ unified scheduling system, automatically caching hot tables, applying table‑level policies, page‑level storage and AZ‑aware worker selection, which together cut cross‑zone traffic, halve query latency, achieve up to 20× I/O speedup and a three‑fold overall performance boost.

AlluxioBig DataCache Optimization
0 likes · 23 min read
Alluxio Integration and Optimization for Multi‑AZ Big Data Analytics at iQIYI
JD Retail Technology
JD Retail Technology
Mar 7, 2024 · Backend Development

Handling Large Keys in Redis: Causes, Impacts, and Optimization Strategies

This article explains what constitutes a large Redis key, the performance problems it can cause such as CPU spikes, client timeouts and memory imbalance, and presents practical solutions including key cleanup, splitting, compression, pipeline usage, and alternative storage options.

BackendCache OptimizationJava
0 likes · 14 min read
Handling Large Keys in Redis: Causes, Impacts, and Optimization Strategies
Architect's Guide
Architect's Guide
Nov 24, 2023 · Databases

Implementing Conditional Query and Pagination with Redis

This article explains how to use Redis' Sorted Set and Hash structures to achieve efficient pagination, multi‑condition fuzzy queries, and their combination, while also discussing performance optimizations such as expiration handling and data synchronization strategies.

Cache OptimizationFuzzy QueryRedis
0 likes · 10 min read
Implementing Conditional Query and Pagination with Redis
Selected Java Interview Questions
Selected Java Interview Questions
Oct 28, 2023 · Backend Development

Analyzing and Resolving an R2M Cache Usage Alert Before the 618 Promotion

This article walks through a real‑world R2M (Redis‑like) cache alert, detailing the email notification, large‑key analysis, code inspection, root‑cause identification, and both immediate and long‑term solutions that reduced cache usage by over 97% and prevented future incidents.

Cache OptimizationPerformance TuningRedis
0 likes · 12 min read
Analyzing and Resolving an R2M Cache Usage Alert Before the 618 Promotion
vivo Internet Technology
vivo Internet Technology
Oct 25, 2023 · Backend Development

Optimizing Dubbo Routing and Load Balancing at Scale: Vivo's Practice

Vivo tackled high CPU overhead in large‑scale Dubbo deployments by disabling unused routers, caching routing results with BitMap intersections and epoch validation, optimizing weight calculations, and adding a grouping router, which together delivered over 100 % TPS gains for 20 k+ providers and cut CPU usage by roughly 27 %.

BitMapCache OptimizationDubbo
0 likes · 18 min read
Optimizing Dubbo Routing and Load Balancing at Scale: Vivo's Practice
Laravel Tech Community
Laravel Tech Community
Jun 6, 2023 · Fundamentals

Understanding Bloom Filters, Counting Bloom Filters, and Cuckoo Filters

This article explains how Bloom filters, their counting variant, and Cuckoo filters work to reduce unnecessary database I/O by using bitmap or fingerprint techniques, discusses their false‑positive and deletion limitations, and presents practical optimizations for high‑performance hash‑based filtering.

Bloom FilterCache OptimizationCounting Bloom Filter
0 likes · 11 min read
Understanding Bloom Filters, Counting Bloom Filters, and Cuckoo Filters
Bilibili Tech
Bilibili Tech
May 19, 2023 · Backend Development

Local Cache Optimization for Outbox Redis in a High‑Traffic Feed Stream Service

To protect the outbox Redis cluster from extreme read amplification during hot events, the service adds a resident local cache for hot creators’ latest posts, using a threshold‑based list, change‑broadcast updates, and checksum verification, which achieved over 55% cache hits and cut peak Redis load by roughly 44% and CPU usage by 37%.

Cache OptimizationPerformance ScalingRedis
0 likes · 10 min read
Local Cache Optimization for Outbox Redis in a High‑Traffic Feed Stream Service
Bilibili Tech
Bilibili Tech
Jan 6, 2023 · Backend Development

Hotspot Detection and Local Cache Framework for High‑Traffic Applications

The presented hotspot detection and local‑cache framework leverages the HeavyKeeper streaming top‑k algorithm with decay‑based burst detection, integrates zero‑code SDK support and a whitelist‑enabled LRU cache, enabling a few megabytes of memory to achieve up to 85% hit rates and dramatically reduce Redis load in high‑traffic applications.

Cache Optimizationdistributed cachingheavykeeper
0 likes · 21 min read
Hotspot Detection and Local Cache Framework for High‑Traffic Applications
Top Architect
Top Architect
Jan 20, 2022 · Fundamentals

Understanding Bloom Filter, Counting Bloom Filter, and Cuckoo Filter: Principles, Issues, and Optimizations

This article explains the concepts, advantages, and limitations of Bloom filters, Counting Bloom filters, and Cuckoo filters, illustrating how they reduce unnecessary I/O in backend systems and offering practical improvements such as multi‑hash functions and bucket designs to enhance space and time efficiency.

BackendBloom FilterCache Optimization
0 likes · 13 min read
Understanding Bloom Filter, Counting Bloom Filter, and Cuckoo Filter: Principles, Issues, and Optimizations
Top Architect
Top Architect
Dec 30, 2021 · Fundamentals

Understanding Bloom Filter, Counting Bloom Filter, and Cuckoo Filter: Principles, Issues, and Optimizations

This article explains the motivation behind using probabilistic filters to reduce I/O, describes how Bloom filters, Counting Bloom filters, and Cuckoo filters work, analyzes their false‑positive and deletion problems, and presents practical optimizations such as multiple hash functions and multi‑slot buckets.

Bloom FilterCache OptimizationCounting Bloom Filter
0 likes · 12 min read
Understanding Bloom Filter, Counting Bloom Filter, and Cuckoo Filter: Principles, Issues, and Optimizations
Top Architect
Top Architect
Oct 29, 2021 · Fundamentals

Understanding Bloom Filters, Counting Bloom Filters, and Cuckoo Filters

The article explains how Bloom filters, Counting Bloom filters, and Cuckoo filters work, their hash‑based bitmap mechanisms, advantages and limitations such as false positives and deletion issues, and presents practical improvements and hash functions for efficient cache and database query optimization.

AlgorithmsBloom FilterCache Optimization
0 likes · 12 min read
Understanding Bloom Filters, Counting Bloom Filters, and Cuckoo Filters
IT Architects Alliance
IT Architects Alliance
Oct 28, 2021 · Fundamentals

Understanding Bloom Filters, Counting Bloom Filters, and Cuckoo Filters

The article explains the principles, advantages, and limitations of Bloom filters, introduces Counting Bloom filters as an enhanced version, and then details Cuckoo filters and Cuckoo hashing, including their algorithms, performance trade‑offs, and practical improvements for reducing unnecessary I/O operations.

Bloom FilterCache OptimizationCounting Bloom Filter
0 likes · 11 min read
Understanding Bloom Filters, Counting Bloom Filters, and Cuckoo Filters
Dada Group Technology
Dada Group Technology
Oct 15, 2021 · Backend Development

Redis Cache Optimization and Architecture Evolution in JD Daojia Coupon System

This article details the JD Daojia coupon system's high‑traffic architecture, describing its multi‑layer design, Redis cache challenges such as large‑key and hot‑key issues, and practical optimization techniques including key redesign, expiration strategies, and active‑expire algorithms to improve performance and scalability.

Cache OptimizationRediscoupon system
0 likes · 17 min read
Redis Cache Optimization and Architecture Evolution in JD Daojia Coupon System
Tencent Cloud Developer
Tencent Cloud Developer
Jul 21, 2021 · Big Data

Bloom Filter: Introduction, Theory, Construction, Query, and Applications

The article explains Bloom filters—a probabilistic, space‑efficient data structure using multiple hash functions on a bit array to answer set‑membership queries with controllable false‑positive rates, detailing their construction, query process, optimal parameters, and common uses such as URL deduplication, cache protection, and spam filtering.

Big DataBloom FilterCache Optimization
0 likes · 8 min read
Bloom Filter: Introduction, Theory, Construction, Query, and Applications
Tencent Cloud Developer
Tencent Cloud Developer
Jan 10, 2020 · Cloud Computing

Tencent Classroom Cloud VOD HLS Playback Architecture and Optimization

The article outlines Tencent Classroom’s cloud VOD solution, detailing HLS streaming fundamentals, a Mongoose‑based local HTTP proxy with LFU caching and pre‑loading, performance optimizations for latency, buffering, security, and playback reliability, and common transcoding pitfalls with practical fixes, highlighting cloud migration benefits.

Cache OptimizationCloud VideoHLS
0 likes · 13 min read
Tencent Classroom Cloud VOD HLS Playback Architecture and Optimization
Tencent Database Technology
Tencent Database Technology
Nov 7, 2019 · Databases

MonetDB: History, Storage Model, Execution Model, Architecture, and Key Technologies

This article provides a comprehensive overview of MonetDB, covering its origins at CWI, column‑oriented storage with BATs, memory‑mapped and vectorized execution, three‑layer system architecture, cache‑aware optimizations such as vector operations and radix‑partitioned hash joins, as well as its limitations and reference sources.

Cache OptimizationColumnar DatabaseMonetDB
0 likes · 10 min read
MonetDB: History, Storage Model, Execution Model, Architecture, and Key Technologies
iQIYI Technical Product Team
iQIYI Technical Product Team
Jan 12, 2018 · Backend Development

Couchbase Caching Optimization and Case Studies in iQIYI's Bubble Social Backend

The article details iQIYI’s Bubble social service cache architecture, comparing Couchbase and Redis, explaining vBucket design and management UI, and presenting three real‑world optimizations—like‑system key redesign, voting‑system aggregation, and SDK upgrade—along with migration, synchronization, and operational best‑practice recommendations.

Cache OptimizationCouchbaseDistributed Systems
0 likes · 16 min read
Couchbase Caching Optimization and Case Studies in iQIYI's Bubble Social Backend
Baidu Tech Salon
Baidu Tech Salon
May 28, 2014 · Game Development

C++ Performance Optimization Techniques for Ray Tracing

The article outlines 27 C++ performance optimization techniques for ray tracing, emphasizing profiling hot paths, minimizing branches and memory accesses, using inline and reference passing, aligning data, loop unrolling, avoiding unnecessary temporaries, and simplifying math to exploit cache locality and modern CPU parallelism.

Amdahl's lawC++ optimizationCache Optimization
0 likes · 12 min read
C++ Performance Optimization Techniques for Ray Tracing