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Memory Optimization

236 articles · Page 2 of 3
21CTO
21CTO
Sep 21, 2023 · Operations

How Desktop QQ Tamed Memory Bloat: Deep Dive into Electron Optimization

This article details the systematic memory‑usage analysis and multi‑stage optimization performed on the desktop QQ Electron client, covering background challenges, target metrics, tooling, code and resource slimming, rendering layer tuning, Lottie handling, online monitoring, and the resulting stable sub‑300 MB memory footprint.

Desktop applicationElectronMemory Optimization
0 likes · 24 min read
How Desktop QQ Tamed Memory Bloat: Deep Dive into Electron Optimization
Tencent Cloud Developer
Tencent Cloud Developer
Sep 21, 2023 · Frontend Development

Memory Optimization of Desktop QQ: A Stage Summary

To curb Desktop QQ’s excessive Electron memory consumption, the team applied comprehensive profiling, code and resource slimming, thumbnail image generation, visible‑only DOM rendering, layer merging, Lottie animation tuning, API caching, and leak removal, achieving average usage around 228 MB and keeping all six processes below 300 MB with ongoing monitoring.

Desktop applicationElectronMemory Optimization
0 likes · 24 min read
Memory Optimization of Desktop QQ: A Stage Summary
Sohu Tech Products
Sohu Tech Products
Sep 20, 2023 · Mobile Development

Memory Optimization and GPUImage‑Based Gaussian Blur for iOS Live‑Stream Images

The iOS live‑stream app was crashing from out‑of‑memory due to high‑resolution images and CPU‑based Gaussian blur, so the solution reduced download size, skipped downloads during fast scrolling, limited cached blurred images, and switched to GPUImage‑accelerated blur with a custom composite shader, cutting memory use and processing time.

GPUImageGaussian BlurMemory Optimization
0 likes · 12 min read
Memory Optimization and GPUImage‑Based Gaussian Blur for iOS Live‑Stream Images
Architecture Digest
Architecture Digest
Sep 12, 2023 · Fundamentals

Understanding Java Integer Caching and the == Operator

This article explains how Java caches Integer objects in the range -128 to 127, why the == operator behaves differently for small and large integers, and demonstrates how to inspect and manipulate the cache using reflection.

Integer cachingJavaMemory Optimization
0 likes · 4 min read
Understanding Java Integer Caching and the == Operator
High Availability Architecture
High Availability Architecture
Aug 30, 2023 · Backend Development

Diagnosing and Optimizing JVM Memory Issues in a Core Service

This article details the identification, analysis, and resolution of JVM memory problems in a core music metadata service, covering GC tuning, large‑object handling, fault‑tolerance strategies, custom Dubbo codec monitoring, and non‑intrusive memory object tracking to improve performance and stability.

DubboGCJVM
0 likes · 14 min read
Diagnosing and Optimizing JVM Memory Issues in a Core Service
政采云技术
政采云技术
Aug 15, 2023 · Databases

Understanding and Handling Redis Bigkey Issues

This article explains what Redis bigkeys are, their impact on performance and memory, how they are generated, methods to detect and analyze them—including built‑in commands, SCAN, memory usage, and RDB file inspection—and practical steps to mitigate their effects.

BigKeyMemory OptimizationPerformance
0 likes · 12 min read
Understanding and Handling Redis Bigkey Issues
ITPUB
ITPUB
Aug 9, 2023 · Operations

Why Is My Elasticsearch Cluster Using 15 GB Heap? A Deep Dive into Memory Bottlenecks

The article examines a 7‑node Elasticsearch cluster with 500 million documents, uncovering excessive heap usage, high OS memory pressure, numerous deleted documents, large translog, low query‑cache hit rate, and an over‑sharded design, then offers concrete tuning and redesign recommendations to restore performance.

Cluster OperationsElasticsearchMemory Optimization
0 likes · 16 min read
Why Is My Elasticsearch Cluster Using 15 GB Heap? A Deep Dive into Memory Bottlenecks
dbaplus Community
dbaplus Community
Aug 1, 2023 · Operations

Why Is My Elasticsearch Cluster Consuming 15 GB Heap? A Deep Dive into Memory, Sharding, and Performance Bottlenecks

A 7‑node Elasticsearch cluster handling 500 million documents shows excessive heap usage, many deleted documents, high translog size, saturated OS memory, and inefficient sharding, prompting a detailed analysis of stats, root‑cause identification, and concrete recommendations for mapping, shard design, and JVM tuning.

Cluster MonitoringIndexingJVM
0 likes · 16 min read
Why Is My Elasticsearch Cluster Consuming 15 GB Heap? A Deep Dive into Memory, Sharding, and Performance Bottlenecks
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Jul 17, 2023 · Artificial Intelligence

How MindSpore’s Auto Parallel Tech Simplifies Large-Model Training

During a livestream titled “Solving the ‘Development Difficulty’ of Large Models with MindSpore Auto Parallel”, Huawei’s MindSpore experts explained how the framework’s distributed training techniques—including data, model, and pipeline parallelism as well as memory‑saving strategies—enable efficient pre‑training of trillion‑parameter models across diverse AI domains.

Data ParallelDistributed TrainingMemory Optimization
0 likes · 6 min read
How MindSpore’s Auto Parallel Tech Simplifies Large-Model Training
Programmer DD
Programmer DD
Jul 15, 2023 · Operations

Why Is My Elasticsearch Cluster Using 99% Memory? Sharding, Translog & JVM Insights

This article analyzes a 7‑node Elasticsearch cluster with 500 million documents, revealing excessive shard count, high heap and OS memory usage, large translog, low query‑cache hit rate, and heavy I/O, and offers concrete recommendations on mapping, sharding, JVM tuning, and resource management to restore performance.

Cluster MonitoringElasticsearchJVM
0 likes · 17 min read
Why Is My Elasticsearch Cluster Using 99% Memory? Sharding, Translog & JVM Insights
Python Programming Learning Circle
Python Programming Learning Circle
Jun 27, 2023 · Fundamentals

Useful Python Tricks: String Cleaning, Iterator Slicing, Keyword‑Only Arguments, Context Managers, __slots__, Resource Limits, __all__, and Total Ordering

This article presents a collection of lesser‑known Python tricks—including string normalization, iterator slicing, keyword‑only functions, custom context managers, memory‑saving __slots__, CPU/memory limits, import control with __all__, and simplified ordering with total_ordering—to help developers write cleaner and more efficient code.

IteratorMemory OptimizationPerformance
0 likes · 10 min read
Useful Python Tricks: String Cleaning, Iterator Slicing, Keyword‑Only Arguments, Context Managers, __slots__, Resource Limits, __all__, and Total Ordering
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Jun 16, 2023 · Fundamentals

The Semantics of nil in Swift

The article explains that Swift’s nil differs from Objective‑C’s null pointer by representing ‘no value’ for any type through optionals, details how various optionals (Bool?, String?, class?, enum?) are encoded in memory, shows the extra byte cost for Int?, and advises minimizing optional Int fields in structs, using 0 when appropriate.

EnumMemory OptimizationStruct
0 likes · 6 min read
The Semantics of nil in Swift
Tencent Cloud Developer
Tencent Cloud Developer
May 31, 2023 · Cloud Computing

TencentOS "Wujing": Server Memory Multi-Level Offloading Solution for Cloud Data Centers

TencentOS “Wujing” provides a server‑memory multi‑level offloading framework that uses kernel‑side reclamation, heat‑aware page classification, SWAP balancing, and CXL promotion to shift cold pages to cheaper storage, cutting data‑center memory use by up to 50 % while preserving performance.

Cloud InfrastructureDAMONLinux kernel
0 likes · 14 min read
TencentOS "Wujing": Server Memory Multi-Level Offloading Solution for Cloud Data Centers
DataFunSummit
DataFunSummit
Apr 11, 2023 · Artificial Intelligence

OneFlow Coop: Joint Optimization of Dynamic‑Graph Recomputation and Memory Allocation

This article introduces OneFlow Coop, a memory‑optimization technique that jointly optimizes dynamic‑graph recomputation strategies and GPU memory allocation by analyzing existing DTR limitations, proposing recomputable in‑place, op‑guided tensor allocation, and layout‑aware eviction modules, and demonstrating superior experimental results.

Dynamic GraphGPU memoryMemory Optimization
0 likes · 18 min read
OneFlow Coop: Joint Optimization of Dynamic‑Graph Recomputation and Memory Allocation
Python Programming Learning Circle
Python Programming Learning Circle
Apr 7, 2023 · Fundamentals

Lesser‑Known Python Tricks and Techniques

This article introduces a collection of lesser‑known Python tricks—including string cleaning with translate, iterator slicing via itertools, skipping header lines, keyword‑only functions, custom context managers, memory‑saving __slots__, resource limits, import control with __all__, and total_ordering—to help developers write cleaner, more efficient code.

Memory OptimizationPerformancecode snippets
0 likes · 10 min read
Lesser‑Known Python Tricks and Techniques
21CTO
21CTO
Mar 31, 2023 · Backend Development

Boost Go Performance: 6 Proven Techniques for Faster, Leaner Apps

This article presents six practical Go performance optimizations—including GOMAXPROCS tuning for Kubernetes, struct field ordering, garbage‑collection limits, zero‑copy unsafe conversions, jsoniter usage, and sync.Pool pooling—that together can dramatically lower CPU, memory, and latency in production services.

Garbage CollectionGoKubernetes
0 likes · 9 min read
Boost Go Performance: 6 Proven Techniques for Faster, Leaner Apps
Tencent Cloud Developer
Tencent Cloud Developer
Mar 22, 2023 · Artificial Intelligence

How AngelPTM Cuts Large Model Training Costs with ZeRO-Cache Optimizations

This article analyzes Tencent's AngelPTM framework, detailing its ZeRO-Cache strategy, unified storage management, multi‑stream async execution, SSD tiered storage, and performance benchmarks that show up to 95% larger model capacity and over 44% speedup compared to community solutions.

AI infrastructureGPU accelerationMemory Optimization
0 likes · 12 min read
How AngelPTM Cuts Large Model Training Costs with ZeRO-Cache Optimizations
Tencent Advertising Technology
Tencent Advertising Technology
Mar 10, 2023 · Artificial Intelligence

Optimizing Large-Scale Model Training with Tencent's AngelPTM and ZeRO-Cache

This article presents Tencent's latest advancements in large‑scale model training, detailing the AngelPTM framework and its ZeRO‑Cache optimization techniques that reduce memory and storage costs, improve hardware utilization, and achieve high‑performance training for trillion‑parameter AI models across various applications.

AI modelsAngelPTMLarge‑Scale Training
0 likes · 14 min read
Optimizing Large-Scale Model Training with Tencent's AngelPTM and ZeRO-Cache
Ctrip Technology
Ctrip Technology
Mar 9, 2023 · Backend Development

Optimizing Hotel Query Service Memory Usage: GC Tuning, Native Memory Management, and Migration to jemalloc

This article details the systematic reduction of memory consumption for Ctrip's hotel query service by halving container memory, evaluating and tuning modern garbage collectors, diagnosing off‑heap leaks, and ultimately replacing the default ptmalloc allocator with jemalloc to achieve stable performance and lower resource costs.

Backend PerformanceContainerizationGarbage Collection
0 likes · 22 min read
Optimizing Hotel Query Service Memory Usage: GC Tuning, Native Memory Management, and Migration to jemalloc
NiuNiu MaTe
NiuNiu MaTe
Mar 9, 2023 · Databases

Understanding Redis Ziplist and Listpack: Memory‑Efficient List Implementations

This article explains Redis's compressed list structures—ZIPLIST and its successor LISTPACK—detailing their layouts, encoding schemes, query and update complexities, and how LISTPACK resolves ZIPLIST's chain‑update performance issues to provide a more memory‑efficient list implementation.

Data StructuresListpackMemory Optimization
0 likes · 10 min read
Understanding Redis Ziplist and Listpack: Memory‑Efficient List Implementations
Tencent Database Technology
Tencent Database Technology
Jan 31, 2023 · Databases

Common MySQL OOM Scenarios and TDSQL‑C Memory Optimization Strategies

This article examines typical MySQL out‑of‑memory (OOM) situations in production, explains how to diagnose memory usage with Performance Schema and other tools, and presents a series of TDSQL‑C‑specific optimization techniques—including server‑parameter tuning, process‑list monitoring, cold‑page detection, buffer‑pool limits, and dynamic resizing—to mitigate OOM risks.

Memory OptimizationMySQLOOM
0 likes · 12 min read
Common MySQL OOM Scenarios and TDSQL‑C Memory Optimization Strategies
ByteDance SYS Tech
ByteDance SYS Tech
Jan 13, 2023 · Fundamentals

How HVO Cuts HugeTLB Memory Overhead by Up to 99% in Linux Kernels

This article, based on ByteDance STE’s 2022 Linux Kernel Developer Conference talk, explains the HVO (HugeTLB vmemmap Optimization) feature that dramatically reduces struct page memory for huge pages, details its latest enhancements, cross‑architecture support, usage steps, performance trade‑offs, and future plans.

HVOHugeTLBLinux kernel
0 likes · 13 min read
How HVO Cuts HugeTLB Memory Overhead by Up to 99% in Linux Kernels
Meituan Technology Team
Meituan Technology Team
Nov 10, 2022 · Big Data

Optimizing Spark mapPartitions: Memory Management and Best Practices

The article details how Meituan’s Turing machine‑learning platform cut offline resource use by 80% and task time by 63% through memory‑level techniques such as column pruning, adaptive caching, and a deep dive into Spark’s mapPartitions operator, including source‑code analysis, GC behavior, and a low‑memory batch‑iterator best practice.

Memory OptimizationSparkbig data
0 likes · 19 min read
Optimizing Spark mapPartitions: Memory Management and Best Practices
Java Architect Essentials
Java Architect Essentials
Oct 31, 2022 · Big Data

How to Process 10 GB of Age Data on a 4 GB Machine Using Java

This article walks through generating a 10 GB file of age values, reading it line‑by‑line on a 4 GB RAM, 2‑core machine, measuring single‑thread performance, then redesigning the pipeline with a producer‑consumer model, blocking queues and multithreaded string splitting to dramatically boost CPU utilization and cut processing time while managing memory consumption.

File ProcessingJavaMemory Optimization
0 likes · 12 min read
How to Process 10 GB of Age Data on a 4 GB Machine Using Java
Bilibili Tech
Bilibili Tech
Oct 4, 2022 · Operations

How to Optimize AMD Milan Server Performance: BIOS, Memory, and Power Tuning

This article provides a detailed, data‑driven guide to evaluating and tuning AMD Milan‑based servers, covering SPEC CPU benchmarking, BIOS options such as SMT and Boost, memory channel and NUMA configurations, interleaving, IOMMU, and power‑state settings to achieve up to 30% performance gains.

AMD MilanBIOS tuningMemory Optimization
0 likes · 27 min read
How to Optimize AMD Milan Server Performance: BIOS, Memory, and Power Tuning
NetEase LeiHuo Testing Center
NetEase LeiHuo Testing Center
Sep 23, 2022 · Game Development

Memory Optimization Techniques for Mobile Games

This article explains mobile game memory optimization, covering RAM vs ROM, resource memory reduction for textures, models, animations, streaming load, unused resource unloading, and detecting memory leaks, while providing practical QA testing methods to ensure performance without compromising visual quality.

Memory OptimizationMobile GamesQA Testing
0 likes · 20 min read
Memory Optimization Techniques for Mobile Games
ITPUB
ITPUB
Sep 18, 2022 · Databases

Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM

This article explains how Redis stores key‑value pairs, details the internal data structures such as dict, dictEntry and redisObject, and provides practical memory‑saving tricks—including key shortening, encoding choices, object sharing, bitmap usage, hash consolidation, fragmentation handling, and 32‑bit deployment—to dramatically reduce RAM consumption while maintaining performance.

Data StructuresMemory OptimizationRedis
0 likes · 19 min read
Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM
Java Captain
Java Captain
Sep 10, 2022 · Backend Development

Designing a Scalable Backend for One Billion Users with Minimal Resources

The article outlines how a modest cluster of 20 virtual machines with 16 GB RAM each can handle 1 billion user records and 20 million hourly queries by using simple in‑memory arrays, basic indexing, modest bandwidth, and straightforward failover mechanisms, without requiring advanced technologies.

Memory Optimizationdistributed systems
0 likes · 7 min read
Designing a Scalable Backend for One Billion Users with Minimal Resources
Shepherd Advanced Notes
Shepherd Advanced Notes
Aug 18, 2022 · Databases

Is Redis Really Single‑Threaded? Exploring Redis 6.0 Multithreading and Bottlenecks

The article explains why Redis has traditionally been single‑threaded, details the new IO‑thread multithreading model introduced in Redis 6.0, walks through its four processing stages, lists essential INFO monitoring commands, examines memory overhead of strings versus hashes, compares List and Sorted Set ordering, and identifies five key performance‑blocking points.

Blocking operationsINFO commandIO threads
0 likes · 17 min read
Is Redis Really Single‑Threaded? Exploring Redis 6.0 Multithreading and Bottlenecks
ITPUB
ITPUB
Jul 20, 2022 · Databases

Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM

This article explains how Redis stores key‑value pairs, dives into the underlying dict, dictEntry and redisObject structures, and presents seven practical memory‑saving tricks—including key shortening, encoding tweaks, object sharing, bitmap usage, hash consolidation, fragmentation cleanup, and 32‑bit deployment—to dramatically reduce RAM consumption while preserving performance.

BitmapData StructuresMemory Optimization
0 likes · 19 min read
Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM
ITPUB
ITPUB
Jul 12, 2022 · Databases

Inside Redis: Ziplist, Skiplist, Quicklist & Stream – Memory & Performance Secrets

This article explains Redis's low‑level data structures—including ziplist, skiplist, intset, quicklist, zipmap and stream—detailing their memory layouts, implementation code, use‑case selection criteria, performance trade‑offs, and configurable parameters that balance speed and space.

Data StructuresMemory OptimizationPerformance
0 likes · 26 min read
Inside Redis: Ziplist, Skiplist, Quicklist & Stream – Memory & Performance Secrets
HelloTech
HelloTech
Jul 11, 2022 · Databases

Redis Internal Data Structures: Ziplist, Skiplist, Intset, Quicklist, Zipmap, and Stream

The article explains Redis’s internal low‑level structures—ziplist, skiplist, intset, quicklist, zipmap, and stream—detailing their memory‑efficient layouts, operations, use cases, and how they combine (e.g., quicklist merging linked lists with ziplists) to deliver high performance in the in‑memory database.

Data StructuresMemory OptimizationQuicklist
0 likes · 27 min read
Redis Internal Data Structures: Ziplist, Skiplist, Intset, Quicklist, Zipmap, and Stream
Su San Talks Tech
Su San Talks Tech
Jul 6, 2022 · Databases

Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM

This article explains Redis's internal storage mechanisms and presents seven practical optimization techniques—including key‑value compression, small‑collection encoding, object sharing, bitmap usage, hash structuring, memory‑fragment defragmentation, and 32‑bit deployment—to dramatically reduce memory consumption while increasing data capacity.

Data StructuresMemory OptimizationRedis
0 likes · 21 min read
Master Redis Memory: 7 Proven Techniques to Store More Data with Less RAM
Selected Java Interview Questions
Selected Java Interview Questions
Jul 2, 2022 · Backend Development

Understanding Java String Constant Pool and the Behavior of String.intern() Across JDK Versions

This article explains the Java constant pool concept, details how the String constant pool works in different JDK versions, demonstrates the effects of String.intern() with code examples, and discusses practical applications and pitfalls of using intern for memory optimization and synchronization.

Constant PoolJVMMemory Optimization
0 likes · 16 min read
Understanding Java String Constant Pool and the Behavior of String.intern() Across JDK Versions
Python Programming Learning Circle
Python Programming Learning Circle
Jun 27, 2022 · Big Data

Six Common Beginner Mistakes When Using Pandas and How to Avoid Them

This article outlines six typical errors beginners make with Pandas—slow CSV reads, lack of vectorization, improper dtypes, ignoring styling, inefficient CSV saving, and not consulting documentation—and provides faster alternatives, memory‑saving techniques, and best‑practice tips for handling large datasets.

Memory OptimizationPerformancebig data
0 likes · 10 min read
Six Common Beginner Mistakes When Using Pandas and How to Avoid Them
ITPUB
ITPUB
Jun 3, 2022 · Backend Development

Unlocking Redis Speed: A Deep Dive into Its Core Data Structures

This article explains why Redis is ultra‑fast by exploring its in‑memory design and the nine fundamental data structures—SDS, linked lists, ziplist, hash tables, skiplist, intset, quicklist, and listpack—that power keys, values, and internal objects, complete with code examples and version‑specific changes.

Data StructuresMemory OptimizationRedis
0 likes · 45 min read
Unlocking Redis Speed: A Deep Dive into Its Core Data Structures
Ctrip Technology
Ctrip Technology
May 26, 2022 · Backend Development

Memory Structure Selection and Optimization for Hotel Query Service

This article examines how Ctrip's hotel query service selects and optimizes in‑memory cache structures—covering Java object layout, HashMap overhead, alternative collections, and various encoding techniques—to achieve high‑performance reads and updates while drastically reducing memory consumption.

HashMapJavaMemory Optimization
0 likes · 23 min read
Memory Structure Selection and Optimization for Hotel Query Service
Programmer DD
Programmer DD
Apr 19, 2022 · Backend Development

Why Java 8 Switched String Storage to byte[] and How It Saves Memory

The article explains how Java 8 changed the internal representation of String from a char[] to a byte[] to reduce memory consumption, the role of Latin‑1 encoding, the impact on garbage collection, and why UTF‑16 remains the practical choice for Java strings.

JavaMemory OptimizationString
0 likes · 8 min read
Why Java 8 Switched String Storage to byte[] and How It Saves Memory
vivo Internet Technology
vivo Internet Technology
Apr 13, 2022 · Databases

Redis Integer Set Optimization for Game Recommendation Deduplication: RoaringBitMap vs intset vs Bloom Filter

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.

Bloom FilterData Structure OptimizationMemory Optimization
0 likes · 9 min read
Redis Integer Set Optimization for Game Recommendation Deduplication: RoaringBitMap vs intset vs Bloom Filter
IT Services Circle
IT Services Circle
Apr 12, 2022 · Big Data

Finding Missing Unsigned Integers in a 4‑Billion‑Element File Using Interval Counting and Bitmap Technique

The article explains how to locate all missing 32‑bit unsigned integers in a 4 billion‑entry file by first partitioning the range into intervals, counting entries per interval with a tiny int[64] array, and then applying a bitmap method only to under‑filled intervals, achieving a memory footprint of just a few hundred bytes.

BitmapMemory Optimizationalgorithm
0 likes · 5 min read
Finding Missing Unsigned Integers in a 4‑Billion‑Element File Using Interval Counting and Bitmap Technique
Java Captain
Java Captain
Mar 1, 2022 · Fundamentals

Understanding Java Integer Caching and the == Operator

This article explains why comparing Integer objects with == yields false for large values but true for small values due to Java's internal IntegerCache, demonstrates the caching mechanism with code examples, and shows how reflection can manipulate the cache, highlighting memory optimization considerations.

Integer cachingJavaMemory Optimization
0 likes · 4 min read
Understanding Java Integer Caching and the == Operator
macrozheng
macrozheng
Jan 28, 2022 · Backend Development

What’s Coming in Java 2022? Inside the Valhalla Project’s Value Classes

The article explains the upcoming Java Valhalla project, detailing how value objects, primitive classes, and specialized generics will reshape the JVM’s type system, reduce memory overhead, and introduce new value‑class semantics for more efficient Java programming.

JVMJavaMemory Optimization
0 likes · 8 min read
What’s Coming in Java 2022? Inside the Valhalla Project’s Value Classes
Kuaishou Tech
Kuaishou Tech
Jan 11, 2022 · Mobile Development

Texture Compression Technique for Mobile Effects: Removing Hollow Areas to Reduce Memory Usage

This article presents a texture compression method that eliminates hollow (alpha‑zero) regions by reorganizing textures into macro‑blocks, storing index maps for reconstruction, and demonstrates significant memory savings and unchanged visual quality on mobile devices, even after video encoding.

Memory OptimizationTexture CompressionVideo Effects
0 likes · 8 min read
Texture Compression Technique for Mobile Effects: Removing Hollow Areas to Reduce Memory Usage
Liangxu Linux
Liangxu Linux
Dec 15, 2021 · Fundamentals

Cracking the 4‑Billion QQ Deduplication Challenge with 1 GB Memory

This article walks through four approaches—sorting, hashmap, file splitting, and a bitmap technique—to deduplicate 4 billion QQ numbers within a 1 GB memory limit, explains why the first three fail, and shows how a bitmap solves the problem efficiently.

BitmapMemory Optimizationalgorithm
0 likes · 8 min read
Cracking the 4‑Billion QQ Deduplication Challenge with 1 GB Memory
Python Crawling & Data Mining
Python Crawling & Data Mining
Dec 13, 2021 · Big Data

How to De‑duplicate 4 Billion QQ Numbers with Only 1 GB RAM

This article explains several algorithmic strategies—including sorting, hash maps, file splitting, and bitmap techniques—to remove duplicates from a file containing 4 billion QQ numbers while staying within a 1 GB memory limit, and it provides extension exercises for sorting, median, top‑K, and duplicate detection.

BitmapMemory Optimizationalgorithm
0 likes · 8 min read
How to De‑duplicate 4 Billion QQ Numbers with Only 1 GB RAM
Sohu Tech Products
Sohu Tech Products
Oct 6, 2021 · Mobile Development

Android Memory Optimization: Causes, Tools, Image Strategies, and Online OOM Monitoring with KOOM

This article explains why Android memory optimization is needed, introduces background concepts such as garbage collection and memory leaks, reviews common tools like Memory Profiler, MAT and LeakCanary, details image‑related memory reduction techniques, and presents the open‑source KOOM framework for online OOM detection and analysis.

AndroidImage OptimizationKOOM
0 likes · 23 min read
Android Memory Optimization: Causes, Tools, Image Strategies, and Online OOM Monitoring with KOOM
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Sep 27, 2021 · Databases

Redis Usage Best Practices and Guidelines

This article presents comprehensive Redis usage standards—including key naming conventions, avoiding big keys, efficient serialization, safe command usage, data persistence strategies, and operational recommendations—to ensure high performance, memory efficiency, and reliable service under heavy traffic.

Cache ManagementCommand GuidelinesKey Naming
0 likes · 11 min read
Redis Usage Best Practices and Guidelines
Java Interview Crash Guide
Java Interview Crash Guide
Sep 8, 2021 · Backend Development

How to Scale Redis for Billions of Keys: Memory‑Efficient Strategies

This article explores the challenges of storing billions of mapping records for a DMP in Redis and presents practical solutions such as eviction policies, key bucketing, hash‑map optimization, fragmentation reduction, and specialized memory allocators to achieve millisecond‑level real‑time queries.

HashMapJavaKey Bucketing
0 likes · 11 min read
How to Scale Redis for Billions of Keys: Memory‑Efficient Strategies
Tencent Music Tech Team
Tencent Music Tech Team
Aug 20, 2021 · Mobile Development

How to Slash Flutter App Memory Usage: Practical Image Optimization Strategies

This article details concrete techniques for reducing Flutter app memory consumption, focusing on image cache sizing, resource cropping, local caching, asset compression, cache thresholds, and page‑stack management, while providing step‑by‑step experiments, code snippets, and visual illustrations to guide developers.

FlutterMemory OptimizationMobile Development
0 likes · 9 min read
How to Slash Flutter App Memory Usage: Practical Image Optimization Strategies
MaGe Linux Operations
MaGe Linux Operations
Aug 15, 2021 · Fundamentals

Cut Pandas DataFrame Memory Usage by 90% with Simple Type Conversions

This tutorial shows how to dramatically reduce pandas DataFrame memory consumption—by up to 90%—by inspecting internal storage, downcasting numeric columns, converting object columns to categoricals, and specifying optimal dtypes while reading CSV data, all demonstrated on a large MLB game logs dataset.

DataFrameMemory Optimizationcategorical
0 likes · 18 min read
Cut Pandas DataFrame Memory Usage by 90% with Simple Type Conversions
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 12, 2021 · Backend Development

How to Compress Large Java int/long Arrays for Massive Memory Savings

This article explains how to reduce memory usage of massive Java int/long arrays by applying real‑time compression, eliminating redundancy, using indexed buckets, offset storage, and a series of low‑level optimizations that boost TPS from dozens to over a thousand while preserving random‑access capabilities.

Memory OptimizationPerformancearray
0 likes · 14 min read
How to Compress Large Java int/long Arrays for Massive Memory Savings
Amap Tech
Amap Tech
Jun 4, 2021 · Artificial Intelligence

Deploying Multiple CNN Models on Low‑End Devices with MNN: Memory Tricks and Error Debugging

This article explains how a high‑traffic map service captures road features using client‑side computer‑vision models, details the deployment of many CNNs with the lightweight MNN engine on memory‑constrained devices, and shares practical memory‑saving techniques, inference scheduling, and error‑analysis methods.

AndroidEdge AIMNN
0 likes · 12 min read
Deploying Multiple CNN Models on Low‑End Devices with MNN: Memory Tricks and Error Debugging
Tencent IMWeb Frontend Team
Tencent IMWeb Frontend Team
May 27, 2021 · Frontend Development

How to Generate Excel‑Based Posters in the Browser Without Crashing

This article walks through building a front‑end‑only tool that reads student data from Excel, creates customized poster images using SheetJS, html2canvas, and JSZip, and solves out‑of‑memory crashes by analyzing JavaScript heap usage, refactoring recursive code, and optimizing DOM cloning with ignoreElements.

ExcelJSZipMemory Optimization
0 likes · 8 min read
How to Generate Excel‑Based Posters in the Browser Without Crashing
ITFLY8 Architecture Home
ITFLY8 Architecture Home
May 26, 2021 · Databases

How to Store Billions of IDs in Redis Without Running Out of Memory

This article examines the challenges of storing massive DMP ID mappings in Redis—including memory fragmentation, expansion, and latency constraints—and presents eviction, bucket‑hashing, and fragmentation‑reduction techniques to achieve efficient, real‑time, large‑scale key‑value storage.

Key-value hashingMemory OptimizationRedis
0 likes · 11 min read
How to Store Billions of IDs in Redis Without Running Out of Memory
Didi Tech
Didi Tech
May 17, 2021 · Backend Development

Implementation of JSValue in JavaScript Engines: Tagged, Boxing, and Pointer Techniques

The article surveys how major JavaScript engines represent the dynamic JSValue type—using QuickJS’s tagged unions, JavaScriptCore’s NaN‑boxing, SpiderMonkey’s nun‑/pun‑boxing, V8’s tagged pointers with compression, and iOS’s Objective‑C pointer tags—explaining each scheme’s memory layout, performance trade‑offs, and design rationale.

JSValueJavaScript engineMemory Optimization
0 likes · 11 min read
Implementation of JSValue in JavaScript Engines: Tagged, Boxing, and Pointer Techniques
Efficient Ops
Efficient Ops
May 11, 2021 · Big Data

How to Store Billions of Keys in Redis: Cut Memory, Reduce Fragmentation, and Scale Real‑Time DMP

This article examines the challenges of storing massive DMP data in Redis, analyzes memory fragmentation, key‑size issues, and latency constraints, and presents practical strategies such as TTL eviction, bucket‑hashing, custom key compression, and fragmentation‑reduction techniques to enable scalable, real‑time querying.

BucketIdDMPMemory Optimization
0 likes · 11 min read
How to Store Billions of Keys in Redis: Cut Memory, Reduce Fragmentation, and Scale Real‑Time DMP
Architect
Architect
Apr 30, 2021 · Backend Development

Designing a Scalable Real‑Time Data Warehouse with Redis: Challenges and Solutions

The article analyzes the massive storage and performance challenges of a real‑time DMP cache built on Redis, outlines data characteristics and technical obstacles, and proposes eviction policies, bucket‑based hashing, and fragmentation‑reduction techniques with Java code examples to achieve billion‑scale in‑memory key‑value storage.

JavaMemory OptimizationReal-time Data Warehouse
0 likes · 10 min read
Designing a Scalable Real‑Time Data Warehouse with Redis: Challenges and Solutions
Programmer DD
Programmer DD
Apr 25, 2021 · Fundamentals

How Bitmaps and Bloom Filters Supercharge Massive Data Lookups

This article explains the bitmap concept, demonstrates how to store and query billions of integers using bit-level operations, introduces BitSet for dynamic bit vectors, and describes Bloom filters as probabilistic structures for fast, memory‑efficient membership testing in large‑scale data scenarios.

BitmapBloom FilterMemory Optimization
0 likes · 12 min read
How Bitmaps and Bloom Filters Supercharge Massive Data Lookups
ITPUB
ITPUB
Mar 23, 2021 · Databases

13 Redis Best Practices to Save Memory, Boost Performance, Ensure Reliability

This guide presents thirteen practical Redis best‑practice recommendations covering memory optimization, performance tuning, high reliability, routine operations, resource planning, monitoring, and security, offering concrete steps such as key length control, maxmemory configuration, lazy‑free, command selection, connection pooling, replication settings, and safe deployment to help developers and DBA operators run Redis efficiently and safely.

Memory OptimizationPerformancereliability
0 likes · 24 min read
13 Redis Best Practices to Save Memory, Boost Performance, Ensure Reliability
Tencent Cloud Developer
Tencent Cloud Developer
Mar 23, 2021 · Mobile Development

TRouter: A Hybrid Stack Routing Framework for Flutter with Memory Optimization and Visible Native Layer Modifications

TRouter is a hybrid stack routing framework for Flutter that reuses a single engine through native Activity/ViewController containers, offloading page transitions and URL parsing to the native layer, clearing iOS view bitmaps and applying bytecode‑hooked Android fixes to dramatically reduce memory usage while keeping native modifications visible, low‑risk, and compatible with multiple Flutter SDK versions.

FlutterHybrid RoutingMemory Optimization
0 likes · 10 min read
TRouter: A Hybrid Stack Routing Framework for Flutter with Memory Optimization and Visible Native Layer Modifications
Practical DevOps Architecture
Practical DevOps Architecture
Feb 4, 2021 · Databases

Redis Interview Questions: Caching Pitfalls, Persistence Options, Memory Optimization, and Eviction Policies

This article presents common Redis interview questions covering cache penetration, breakdown, and avalanche, explains RDB and AOF persistence mechanisms with configuration commands, and details memory‑size tuning, LRU/LFU/RANDOM eviction policies, plus a practical flushAll scenario.

CachingMemory OptimizationPersistence
0 likes · 9 min read
Redis Interview Questions: Caching Pitfalls, Persistence Options, Memory Optimization, and Eviction Policies
Top Architect
Top Architect
Jan 2, 2021 · Fundamentals

Understanding Bitmap, BitSet, and Bloom Filter: Memory‑Efficient Data Structures for Large‑Scale Data

This article explains the bitmap concept, demonstrates how to store and query billions of integers using bitwise operations, introduces BitSet and Bloom Filter implementations, and provides practical Java code snippets for adding, removing, and checking elements while highlighting their memory‑saving advantages.

BitmapBitsetBloom Filter
0 likes · 12 min read
Understanding Bitmap, BitSet, and Bloom Filter: Memory‑Efficient Data Structures for Large‑Scale Data
Taobao Frontend Technology
Taobao Frontend Technology
Dec 30, 2020 · Frontend Development

Why Assigning Beyond Array Length Saves Memory in V8

An in‑depth analysis shows why assigning a value at index 99999 in a large JavaScript array triggers V8 to switch from fast to slow (dictionary) mode, dramatically reducing memory usage by expanding capacity and converting holey arrays into a more efficient storage structure.

JavaScriptMemory OptimizationNode.js
0 likes · 5 min read
Why Assigning Beyond Array Length Saves Memory in V8
php Courses
php Courses
Nov 4, 2020 · Backend Development

Using Unbuffered Queries in PHP to Reduce Memory Usage

The article explains how PHP's default buffered queries can exhaust memory when processing large result sets and demonstrates using unbuffered query APIs with mysqli, PDO, and the old mysql extension to fetch rows incrementally, reducing memory consumption.

Memory Optimizationunbuffered query
0 likes · 4 min read
Using Unbuffered Queries in PHP to Reduce Memory Usage
Python Programming Learning Circle
Python Programming Learning Circle
Oct 29, 2020 · Fundamentals

Optimizing Pandas Memory Usage for Baseball Game Data

This article demonstrates how to reduce pandas DataFrame memory consumption by selecting appropriate column data types, downcasting numeric types, converting object columns to categorical, and specifying optimal dtypes during CSV import, using a 130‑year baseball dataset as a practical example.

DataFrameDowncastingMemory Optimization
0 likes · 12 min read
Optimizing Pandas Memory Usage for Baseball Game Data
Xianyu Technology
Xianyu Technology
Oct 27, 2020 · Mobile Development

Optimizing Flutter Image and Texture Caching for Memory Efficiency

By extending Flutter’s ImageCache to also store external texture data and using reference‑counted texture identifiers, the Xianyu app eliminates duplicate bitmap‑to‑texture conversions, dramatically lowers physical memory consumption and GC spikes on image‑heavy pages, though it requires invasive engine‑code modifications and ongoing maintenance.

FlutterImageCacheMemory Optimization
0 likes · 7 min read
Optimizing Flutter Image and Texture Caching for Memory Efficiency
Beike Product & Technology
Beike Product & Technology
Oct 24, 2020 · Mobile Development

Understanding Tagged Pointers in iOS: Memory Savings and Performance Boosts

This article explains the Tagged Pointer mechanism introduced in iOS 64‑bit environments, detailing how it reduces memory usage, speeds up object creation and method dispatch, and describing the underlying tag‑plus‑data representation, class registration, and pointer generation with code examples.

Memory OptimizationObjective-C++Tagged Pointer
0 likes · 22 min read
Understanding Tagged Pointers in iOS: Memory Savings and Performance Boosts
Architecture Digest
Architecture Digest
Oct 21, 2020 · Backend Development

Design and Optimization of Massive Push Services Using Netty

This article analyzes the challenges of large‑scale mobile and IoT push services, presents a real‑world Netty case study, and provides detailed design guidelines—including file‑descriptor tuning, heartbeat handling, buffer management, memory pooling, and JVM/TCP tuning—to build stable, high‑performance backend push systems.

JavaMemory OptimizationNIO
0 likes · 23 min read
Design and Optimization of Massive Push Services Using Netty
MaGe Linux Operations
MaGe Linux Operations
Oct 4, 2020 · Fundamentals

How Python’s __slots__ Can Slash Memory Usage by Up to 7×

This article explains why Python objects consume more memory than expected, demonstrates how to measure the true size of objects with a recursive get_size function, and shows that adding __slots__ to a class can dramatically reduce memory consumption while slightly improving performance.

Memory OptimizationPythonslots
0 likes · 9 min read
How Python’s __slots__ Can Slash Memory Usage by Up to 7×
Tencent Cloud Developer
Tencent Cloud Developer
Sep 14, 2020 · Mobile Development

Memory Optimization Techniques for Image-Intensive iOS Applications

To prevent crashes, forced‑out‑of‑memory terminations, and battery drain in image‑heavy iOS apps, developers should avoid retaining unused UIImages, use efficient scaling with UIGraphicsImageRenderer, employ autorelease pools, stream thumbnails via ImageIO, and downsample to view size, cutting peak memory usage from ~600 MB to ~221 MB.

Memory OptimizationSwiftiOS
0 likes · 10 min read
Memory Optimization Techniques for Image-Intensive iOS Applications
MaGe Linux Operations
MaGe Linux Operations
Aug 30, 2020 · Fundamentals

Unlock Hidden Python Tricks: From String Cleanup to Memory Limits

Explore a collection of lesser‑known Python tricks—including string sanitization, iterator slicing, context‑manager shortcuts, memory‑saving slots, resource limits, controlled imports, and simplified ordering—each illustrated with concise code examples to help you write cleaner, more efficient programs.

IteratorMemory OptimizationResource Limits
0 likes · 10 min read
Unlock Hidden Python Tricks: From String Cleanup to Memory Limits
Alibaba Terminal Technology
Alibaba Terminal Technology
Jun 12, 2020 · Frontend Development

How to Optimize Front-End Memory Usage for Massive Map Annotations

This article explores front‑end memory challenges when rendering large numbers of map annotations, explains heap vs. stack storage, hash‑based object handling, TypedArray/DataView usage, shared memory with Web Workers, and provides performance test results and practical optimization recommendations.

Memory OptimizationWeb Workersfrontend
0 likes · 17 min read
How to Optimize Front-End Memory Usage for Massive Map Annotations
FunTester
FunTester
Jun 3, 2020 · Backend Development

Uncovering 65% Memory Waste in Spring Petclinic: Causes and Fixes

Through a 30‑minute Apache JMeter load test on the Spring Boot Petclinic sample, we captured heap dumps and used HeapHero to reveal that 65% of memory is wasted due to string duplication, inefficient collections, and poor initialization, offering concrete code‑level recommendations to improve memory efficiency.

Heap AnalysisJavaMemory Optimization
0 likes · 9 min read
Uncovering 65% Memory Waste in Spring Petclinic: Causes and Fixes
DataFunTalk
DataFunTalk
Apr 22, 2020 · Game Development

How Classic 8‑Bit Games Like Contra Fit 128 KB Through Efficient Asset Reuse

This article explains how 1980s games like Contra and Super Mario Bros. managed to deliver multiple levels and rich gameplay within just 64–128 KB by reusing tiles, compressing assets, leveraging simple code and specialized audio chips, illustrating the ingenuity of early game developers.

8-bitMemory OptimizationRetro Gaming
0 likes · 8 min read
How Classic 8‑Bit Games Like Contra Fit 128 KB Through Efficient Asset Reuse