The article explains how a MySQL instance on a CentOS 7.1 VM repeatedly suffered connection failures due to out‑of‑memory (OOM) events triggered by a mis‑configured 40 GB hugepage allocation that remained reserved for MySQL even after the large‑page setting was disabled.
This article investigates native memory out‑of‑memory crashes on certain Android devices caused by excessive CameraMetadata allocations, explains how delayed finalization and GC timing exacerbate the issue, and proposes proactive memory release and GC triggering techniques that dramatically reduce crash rates.
This article details a comprehensive investigation of MySQL out‑of‑memory (OOM) alerts on a Tencent Cloud CDB instance during a high‑traffic marketing event, covering background, observed symptoms, step‑by‑step debugging, analysis of server and engine memory usage, relevant MySQL bugs, fetch_cache behavior, and the final configuration recommendations to prevent OOM.
This article explains the definition of thread pools, why using Executors to create them is discouraged, details the ThreadPoolExecutor constructor and its parameters, compares different Executors factory methods, demonstrates OOM testing, and provides practical guidelines for defining safe thread‑pool parameters in Java.
The article recounts a production OOM incident triggered by a misconfigured HttpClient evictExpiredConnections setting that caused uncontrolled thread growth, explains the underlying keep‑alive TCP behavior and NoHttpResponseException, and presents the corrective measures of using a singleton HttpClient and proper connection‑pool monitoring.
This article explains the definition of thread pools, why using Executors to create them is discouraged, details the ThreadPoolExecutor constructor and its parameters, compares different Executors factory methods, demonstrates OOM risks with tests, and provides practical guidelines for configuring safe thread pools.
During a million‑connection WebSocket stress test, four 32‑core, 128 GB Nginx servers repeatedly ran out of memory, prompting an investigation that revealed oversized proxy buffers as the root cause and showed that disabling buffering and tuning buffer sizes stabilizes memory usage.
A misconfigured Dubbo consumer created both Dubbo and REST invokers, causing thousands of failed REST invoker attempts that filled a synchronized List of ResteasyClient objects, exhausting the old generation heap and triggering OOM, which was fixed by replacing the List with a WeakHashMap‑based client map.
This article explains Spark's memory architecture, identifies common out‑of‑memory scenarios such as map‑stage and shuffle‑stage overloads, and provides practical solutions and performance‑tuning tips—including repartitioning, off‑heap storage, code optimizations, and key configuration parameters—to prevent and mitigate OOM errors in large‑scale data processing.
The article explains common Android Out‑Of‑Memory scenarios, traditional analysis methods, and introduces the Shooter offline memory analysis system that parses hprof snapshots, builds dominator trees, computes shallow and retained sizes, and visualizes reference chains to help developers locate memory leaks and OOM causes.
This article explains the definition of thread pools, the pitfalls of using Executors to create them, details the ThreadPoolExecutor constructor and parameters, illustrates execution logic, demonstrates OOM scenarios with code examples, and provides guidelines for safely configuring thread pools in Java.
This article explains the definition of Java thread pools, why creating them via Executors is discouraged, details the ThreadPoolExecutor constructor and parameters, compares different Executors factory methods, demonstrates OOM risks with tests, and offers guidelines for configuring thread pool size and rejection policies.
This article explains the definition of thread pools, the pitfalls of using Executors factory methods, details the ThreadPoolExecutor constructor parameters, compares different Executors implementations, demonstrates OOM tests, and offers guidelines for configuring thread pool parameters and rejection policies.
This article explains how iOS manages low‑memory conditions through the Jetsam mechanism, analyzes the kernel code that decides which processes to kill, and provides practical image‑handling and autorelease‑pool techniques to lower the probability of Out‑Of‑Memory crashes in iOS apps.
The Probe component continuously monitors Android app memory, automatically captures trimmed HPROF snapshots when usage spikes, and analyzes them either on‑device with the HAHA library or on a server, enabling rapid OOM root‑cause localization, thread/FD tracking, and has lowered the rider app’s crash rate from 2‰ to roughly 0.02‰.
This article walks through a real‑world Node.js out‑of‑memory crash, explains how to detect the OOM killer via system logs, uses Heap Profiler and process.memoryUsage() to pinpoint memory growth, and presents a practical fix by throttling file‑write operations and improving monitoring.
This article explains the fastjson vulnerability where malformed \x escape sequences cause endless EOF reads leading to out‑of‑memory crashes, outlines the affected versions, provides detailed exploitation mechanics, and lists recommended upgrade paths to mitigate the risk.
This article explores Linux kernel memory management, detailing process address space allocation, the behavior of the OOM killer, various memory mapping types (shared, private, anonymous), cache usage, manual and automatic memory reclamation, and related kernel parameters, providing practical insights and examples.
This article explains the complete HBase write flow, the problems caused by rapid writes such as memstore overflow and RegionTooBusyException, and provides configuration and operational strategies to avoid RegionServer out‑of‑memory crashes.
This article explores Linux kernel memory management in depth, covering process address space layout, allocation mechanisms, OOM killer behavior, the whereabouts of allocated memory, and both manual and automatic memory reclamation techniques.
The article analyzes an OOM issue in the Beike Android app caused by large image memory usage, compares Picasso and Glide memory footprints, describes the refactoring effort required to switch libraries, explains Glide's two‑level caching mechanism, and highlights the performance and stability benefits for pre‑Android 8.0 devices.
This article explains Linux kernel memory management from a global perspective, covering process address space layout, memory allocation mechanisms, OOM handling, the location of allocated memory, cache behavior, tmpfs usage, and the kernel's automatic memory reclamation strategies.
This article explores Linux kernel memory management, detailing process address space layout, allocation mechanisms, OOM handling, various mmap mappings, cache behavior, tmpfs usage, and the kernel's automatic memory reclamation strategies.
This article explains Linux kernel memory management, covering process address space allocation, OOM selection criteria, memory mapping types, cache behavior, tmpfs, and both manual and automatic memory reclamation mechanisms.
This article explains Linux kernel memory management, covering process address space layout, memory allocation, OOM handling, where allocated memory resides, and both manual and automatic memory reclamation techniques, with practical examples and visual illustrations.
When synchronizing an Ethereum geth node, the process may terminate unexpectedly due to memory exhaustion, and this guide explains how to identify the out‑of‑memory killer cause, examine system resources, and apply practical fixes such as upgrading RAM or adding swap space.
This article examines common Java singleton implementations—including eager, thread‑safe lazy, double‑checked locking, and static inner‑class approaches—explaining their requirements, potential memory‑leak pitfalls such as ThreadLocal‑induced OOM, and how improper usage in multithreaded environments can lead to unexpected instance creation and performance issues.
This article explains why Greenplum can encounter out‑of‑memory (OOM) errors, lists the typical causes, and provides detailed recommendations for memory‑related OS and database parameters, statement_mem settings, and the proper use of Greenplum resource queues to achieve stable and efficient cluster operation.
This article explains Linux kernel memory management, covering process address space layout, memory allocation methods, OOM killer behavior, where different types of memory reside, and both manual and automatic memory reclamation techniques, illustrated with diagrams and command examples.
This article examines how enabling MySQL 5.6 performance_schema with extremely high max_connections causes excessive virtual memory usage, swap pressure, and eventual out‑of‑memory errors, presents test data, references the related bug, and offers practical recommendations and discussion questions.
