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Tencent Cloud Developer
Tencent Cloud Developer
Apr 28, 2025 · Backend Development

Performance Optimization Techniques for High‑Throughput Backend Systems

The article outlines seven practical performance‑optimization techniques for high‑throughput back‑ends—including replacing protobuf with native C++ classes, adopting cache‑friendly data structures, using jemalloc/tcmalloc, implementing lock‑free double buffers, simplifying structs for specific scenarios, and leveraging profiling tools—while stressing balanced, incremental improvements.

C++Performance OptimizationProtobuf
0 likes · 16 min read
Performance Optimization Techniques for High‑Throughput Backend Systems
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Apr 27, 2025 · Databases

Why MySQL Memory Stays High and How to Optimize It

This article explains MySQL's memory architecture, why memory usage often stays high after spikes, and provides practical steps—including connection checks, slow query analysis, workload scaling, and switching to jemalloc—to diagnose and reduce memory consumption on 360's internal cloud platform.

Connection ManagementMemory OptimizationMySQL
0 likes · 7 min read
Why MySQL Memory Stays High and How to Optimize It
Deepin Linux
Deepin Linux
Feb 14, 2025 · Fundamentals

Understanding Jemalloc: Principles, Comparisons, and Optimization Practices

This article provides a comprehensive overview of Jemalloc, covering its architecture, memory allocation fundamentals, performance comparison with ptmalloc and tcmalloc, practical optimization cases across web, database, and big‑data workloads, and detailed configuration guidelines to improve memory efficiency and multithreaded performance.

Performance Optimizationfragmentationjemalloc
0 likes · 31 min read
Understanding Jemalloc: Principles, Comparisons, and Optimization Practices
vivo Internet Technology
vivo Internet Technology
Dec 11, 2024 · Databases

RocksDB Memory Usage Analysis and Optimization: Troubleshooting Excessive Memory Consumption in Production

The article examines a production RocksDB memory‑usage problem where two instances consumed 59 GB on a 32‑CPU, 64‑GB server, identifies glibc ptmalloc’s unreclaimed free memory as the main cause, and shows that switching to jemalloc cuts usage by roughly 25 % while improving I/O and CPU efficiency.

Linux Memory ManagementMemory OptimizationRocksDB
0 likes · 11 min read
RocksDB Memory Usage Analysis and Optimization: Troubleshooting Excessive Memory Consumption in Production
vivo Internet Technology
vivo Internet Technology
Nov 6, 2024 · Fundamentals

Analysis of glibc Memory Management and Solutions to an Online Memory Incident

The article examines a real‑world memory alarm in a Vivo service, explains how glibc’s ptmalloc allocator manages heap memory via brk, sbrk, and mmap, shows why freed chunks stay in bins, and recommends limiting heap growth or switching to jemalloc for faster reclamation.

C runtimeMemory Managementbackend development
0 likes · 20 min read
Analysis of glibc Memory Management and Solutions to an Online Memory Incident
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 8, 2024 · Backend Development

Understanding Netty's Pooled Memory Allocation Mechanism Based on jemalloc4

This article explains Netty's memory pool architecture after switching from jemalloc3 to jemalloc4, detailing the new memory size classes, core components such as PoolArena, PoolChunkList, PoolChunk, and PoolSubpage, and provides Java code snippets to illustrate their implementations.

JavaNettybackend
0 likes · 10 min read
Understanding Netty's Pooled Memory Allocation Mechanism Based on jemalloc4
FunTester
FunTester
Apr 12, 2024 · Backend Development

Performance Optimization Techniques for Backend Systems: Replacing Protobuf with C++ Classes, Cache‑Friendly Structures, jemalloc, and Lock‑Free Data Structures

The article presents practical backend performance optimization methods—including substituting Protobuf with native C++ classes, employing cache‑friendly data structures, integrating jemalloc/tcmalloc, using lock‑free double‑buffer designs, and tailoring data formats—to achieve up to three‑fold speed improvements and significant latency reductions.

C++Performance OptimizationProtobuf
0 likes · 15 min read
Performance Optimization Techniques for Backend Systems: Replacing Protobuf with C++ Classes, Cache‑Friendly Structures, jemalloc, and Lock‑Free Data Structures
High Availability Architecture
High Availability Architecture
Mar 6, 2024 · Backend Development

Performance Optimization Techniques: Replacing Protobuf with C++ Classes, Cache‑Friendly Structures, jemalloc, and Lock‑Free Designs

This article presents practical performance‑optimization strategies for high‑throughput C++ services, including replacing Protobuf with hand‑written classes, adopting cache‑friendly data structures, using jemalloc/tcmalloc instead of the default allocator, employing lock‑free double‑buffer designs, tailoring data formats for specific workloads, and leveraging profiling tools to measure gains.

C++Protobufcache-friendly
0 likes · 17 min read
Performance Optimization Techniques: Replacing Protobuf with C++ Classes, Cache‑Friendly Structures, jemalloc, and Lock‑Free Designs
Tencent Cloud Developer
Tencent Cloud Developer
Feb 29, 2024 · Backend Development

Performance Optimization Strategies for High‑Throughput Backend Services

The article outlines practical, continuous performance‑optimization tactics for high‑throughput back‑end services—replacing Protobuf with lightweight C++ classes, using cache‑friendly data structures, adopting jemalloc/tcmalloc, employing lock‑free double buffers, tailoring data formats, and leveraging profiling tools—to achieve multi‑fold speedups while balancing maintainability.

C++Memory ManagementPerformance Optimization
0 likes · 18 min read
Performance Optimization Strategies for High‑Throughput Backend Services
政采云技术
政采云技术
Sep 7, 2023 · Backend Development

Understanding Netty's Memory Management and Allocation Strategies

This article explains how Netty implements memory management by borrowing concepts from Jemalloc and Tcmalloc, detailing the hierarchy of arenas, chunks, pages and sub‑pages, the allocation algorithms for both large and small buffers, and the role of thread‑local caches in reducing fragmentation and improving performance.

JavaMemory ManagementNetty
0 likes · 24 min read
Understanding Netty's Memory Management and Allocation Strategies
iQIYI Technical Product Team
iQIYI Technical Product Team
Aug 11, 2023 · Artificial Intelligence

Debugging Random OOM Issues in PyTorch Distributed Training on A100 Clusters

The iQIYI backend team traced random OOM crashes in PyTorch Distributed Data Parallel on an A100 cluster to a malformed DDP message injected by a security scan, which forced a near‑terabyte allocation; using jemalloc for diagnostics, they mitigated the issue by adjusting scan policies and collaborating with PyTorch to harden the protocol.

AI infrastructureMemory DebuggingOOM
0 likes · 9 min read
Debugging Random OOM Issues in PyTorch Distributed Training on A100 Clusters
ByteDance SYS Tech
ByteDance SYS Tech
May 26, 2023 · Fundamentals

Unlock Faster C++ Performance: Practical Jemalloc Optimization Techniques

This article explains the fundamentals of Linux memory allocation, introduces Jemalloc’s core algorithms and data structures, and provides concrete optimization steps—including arena tuning, tcache configuration, and slab size adjustments—to achieve measurable CPU savings in high‑concurrency C++ services.

C++LinuxPerformance Optimization
0 likes · 19 min read
Unlock Faster C++ Performance: Practical Jemalloc Optimization Techniques
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.

ContainerizationGarbage CollectionJVM
0 likes · 22 min read
Optimizing Hotel Query Service Memory Usage: GC Tuning, Native Memory Management, and Migration to jemalloc
Qunar Tech Salon
Qunar Tech Salon
Jan 31, 2023 · Operations

Root Cause Analysis and Mitigation of JVM GC‑Induced OOM and Memory Fragmentation in a Containerized Hotel Pricing Service

This article details how long JVM garbage‑collection pauses and glibc ptmalloc memory‑fragmentation caused container OOM kills in a hotel‑pricing system, and explains the step‑by‑step diagnosis, JVM tuning, Kubernetes health‑check adjustments, and the replacement of ptmalloc with jemalloc to eliminate the issue.

GCJVMKubernetes
0 likes · 9 min read
Root Cause Analysis and Mitigation of JVM GC‑Induced OOM and Memory Fragmentation in a Containerized Hotel Pricing Service
ByteDance Web Infra
ByteDance Web Infra
Aug 19, 2022 · Fundamentals

In‑Depth Analysis of dlmalloc, jemalloc, Scudo, and PartitionAlloc for Virtual‑Machine Memory Management

This article examines the design goals, key implementation details, strengths and weaknesses of four widely used memory allocators—dlmalloc, jemalloc, Scudo, and PartitionAlloc—highlighting how they address fragmentation, performance, and security in virtual‑machine runtimes and offering guidance for building efficient, safe allocators.

dlmallocjemallocmalloc
0 likes · 27 min read
In‑Depth Analysis of dlmalloc, jemalloc, Scudo, and PartitionAlloc for Virtual‑Machine Memory Management
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 27, 2020 · Artificial Intelligence

Optimizing TensorFlow Serving Model Hot‑Update to Eliminate Latency Spikes in CTR Recommendation Systems

By adding model warm‑up files, separating load/unload threads, switching to the Jemalloc allocator, and isolating TensorFlow’s parameter memory from RPC request buffers, iQIYI’s engineers reduced TensorFlow Serving hot‑update latency spikes in high‑throughput CTR recommendation services from over 120 ms to about 2 ms, eliminating jitter.

AI inferenceModel Hot UpdateTensorFlow Serving
0 likes · 11 min read
Optimizing TensorFlow Serving Model Hot‑Update to Eliminate Latency Spikes in CTR Recommendation Systems
58 Tech
58 Tech
Oct 21, 2020 · Backend Development

Understanding Java Memory Pools: Netty’s Implementation and Underlying Theory

This article revisits memory allocation and reclamation concepts by examining Java's Netty memory pool implementation, its theoretical basis in jemalloc, and practical design choices such as arena allocation, thread‑local caches, pool chunks, sub‑pages, and multi‑threaded performance considerations.

Garbage CollectionJavaNetty
0 likes · 21 min read
Understanding Java Memory Pools: Netty’s Implementation and Underlying Theory
Architecture Digest
Architecture Digest
Mar 27, 2020 · Databases

Understanding Redis Memory Model: Objects, Allocation, and Internal Encoding

This article explains Redis's memory model by describing how to query memory usage, the roles of used_memory, used_memory_rss, mem_fragmentation_ratio, and mem_allocator, and then dives into the internal structures such as redisObject, SDS, jemalloc, and the encoding strategies for strings, lists, hashes, sets, and sorted sets.

Data StructuresMemory ManagementObject Encoding
0 likes · 28 min read
Understanding Redis Memory Model: Objects, Allocation, and Internal Encoding
Ctrip Technology
Ctrip Technology
Oct 17, 2018 · Databases

Root Cause Analysis and Resolution of Intermittent Redis Connection Failures

This article presents a detailed investigation of occasional Redis connection errors in a large‑scale production environment, analyzing network packets, TCP backlog behavior, Redis internal client‑cron logic, jemalloc memory reclamation, and ultimately resolving the issue by adjusting query‑buffer handling and upgrading Redis to a newer version.

Connection TimeoutPerformance TuningRedis
0 likes · 19 min read
Root Cause Analysis and Resolution of Intermittent Redis Connection Failures