Xiaohongshu’s middleware team migrated thousands of Java services from JDK 8 to RedJDK 11/17, achieving over 10% performance gains, 50% GC pause reduction, and eliminating OOM crashes through systematic JDK upgrades, GC tuning, native‑memory improvements, and standardized deployment pipelines.
After migrating a project to the MDP framework based on Spring Boot, the system repeatedly reported high swap usage; the author investigated JVM settings, used tools like jcmd, pmap, gperftools, strace, and GDB, identified native memory leaks caused by Spring Boot’s Reflections scanning and Inflater usage, and resolved the issue by configuring scan paths and fixing Inflater handling.
This article documents a step‑by‑step investigation of a Java 11 application that suffered OS‑level OOM despite a 4 GB heap, revealing that multiple Netty PooledByteBufAllocator instances loaded by different class loaders consumed far more native memory than expected.
This article details a step‑by‑step investigation of a Spring Boot application that consumed far more physical memory than its 4 GB heap limit, revealing a native‑memory leak caused by MCC's package‑scanning using Reflections and the Spring Boot ZipInflaterInputStream, and explains how configuration changes and newer Spring Boot versions resolve the issue.
After migrating a project to the MDP framework based on Spring Boot, the author observed swap overuse and physical memory far exceeding the 4 GB heap; using JVM native memory tracking, gperftools, strace, pmap, and a custom allocator, they traced a native‑memory leak to Spring Boot’s ZipInflaterInputStream during JAR scanning and resolved it by limiting scan paths.
After migrating a project to the MDP framework based on Spring Boot, the system repeatedly reported high swap usage despite a 4 GB heap configuration, leading to an investigation that uncovered native memory consumption caused by unchecked JAR scanning and allocator behavior, which was resolved by limiting scan paths and updating Spring Boot's inflater implementation.
This article provides a comprehensive introduction to Unity memory concepts—including physical, virtual, native, and managed memory—and offers practical optimization techniques for native and managed resources, graphics rendering, and tooling to improve game performance on both desktop and mobile platforms.
The article details a step‑by‑step investigation of a SpringBoot application that repeatedly triggered high swap usage, describing how JVM parameters, native memory tracking, gperftools, strace, and custom memory allocators were used to pinpoint and resolve off‑heap memory leaks caused by the Inflater implementation and glibc memory pools.
The article details a thorough investigation of native‑memory crashes in the K歌 Android app, describing how memory usage was profiled, comparing three analysis tools, building a custom loli_profiler‑based hook to track malloc/free and bitmap allocations, exposing leaks, and outlining fixes and future optimization plans.
This article explains Java 8's memory architecture, detailing the differences between JVM-managed memory and native memory, and describing each runtime data area—including the program counter, JVM stack, native method stack, heap, method area, and direct memory—along with common questions about variable storage and native methods.
The article explains Android virtual memory concepts, address space limits for 32‑ and 64‑bit apps, layout of QQ Music/Karaoke process memory, tools for inspecting /proc/pid/smaps, and shows how memory growth (e.g., loading libYTCommon.so) can cause 32‑bit apps to hit the 4 GB limit and crash.
After migrating a project to the MDP framework based on Spring Boot, the system repeatedly reported excessive swap usage; the investigation revealed that native memory allocated by the Spring Boot classloader’s Reflections scanning and InflaterInputStream caused 700 MB–800 MB of off‑heap memory to remain unreleased, which was eventually resolved by limiting the scan path and updating Spring Boot.
To eliminate the K歌 Android app’s frequent OOM crashes, a four‑stage framework was implemented—development self‑checks, automated testing baselines, gray‑release real‑time monitoring, and production continuous sampling—collecting virtual memory, Java heap, file‑descriptor, thread, and native‑heap metrics, feeding a backend platform that visualizes leaks, guides fixes, and has already resolved over 120 leaks, cutting crashes by more than 25 %.
The article details a step‑by‑step investigation of abnormal swap memory consumption in a Spring Boot project, revealing that native memory allocated by the Inflater during JAR scanning was not released promptly, leading to apparent memory leaks that were ultimately resolved by configuring package scanning and updating Spring Boot.
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.
The article details a step‑by‑step forensic analysis of why a Spring Boot application migrated to the MDP framework consumed far more physical memory than its configured 4 GB heap, uncovering off‑heap allocations caused by native code, package‑scanning, and glibc memory‑pool behavior, and explains how limiting scan paths or upgrading Spring Boot resolves the issue.
After migrating a project to the MDP framework based on Spring Boot, the author observed excessive swap usage and physical memory consumption of 7 GB despite a 4 GB heap limit, and through a series of JVM, system, and native‑code diagnostics identified Spring Boot’s ZipInflaterInputStream native‑memory leak caused by unchecked package scanning.
The article details a step‑by‑step investigation of why a Spring Boot service migrated to the MDP framework consumed far more physical memory than its 4 GB heap, revealing native‑code allocations, memory‑pool behavior of glibc and tcmalloc, and how limiting MCC scan paths or upgrading Spring Boot resolves the off‑heap leak.
The article details a step‑by‑step investigation of a Spring Boot application that unexpectedly used 7 GB of physical memory despite a 4 GB heap limit, revealing how native memory allocations, the Inflater class, and glibc memory pools caused off‑heap leaks and how proper configuration and library updates resolved the issue.
After moving a project to Spring Boot, the system reported unusually high swap usage despite a 4 GB heap, prompting a detailed investigation using JVM native‑memory tracking, pmap, gperftools, strace, and GDB, which ultimately identified Spring Boot’s JAR scanning and glibc memory arenas as the root causes and led to a configuration fix and an upgrade to resolve the off‑heap memory leak.
This article details a step‑by‑step investigation of unusually high native memory consumption in a Spring Boot service, covering JVM configuration, system‑level diagnostics with jcmd, pmap, gperftools, strace, GDB, and jstack, and explains how the MCC component’s default package scanning caused the leak and how configuring scan paths or upgrading Spring Boot resolved the issue.
A Spring Boot project migrated to the MDP framework exhibited excessive native memory usage, leading to swap errors; the article details step‑by‑step investigation using JVM tools, system utilities, and custom allocators to pinpoint and resolve the hidden native memory leak caused by unchecked JAR scanning and glibc memory pools.
This article walks through a real‑world investigation of excessive native memory usage in a Spring Boot application, detailing JVM settings, Linux‑level tracing, custom memory allocators, and the root cause in Spring Boot’s ZipInflaterInputStream, ultimately providing a fix and best‑practice recommendations.
After moving to Spring Boot, the application consumed up to 7 GB of native memory because Meituan’s MCC package scanner invoked Spring’s ZipInflaterInputStream, which allocated large off‑heap buffers during JAR decompression that were only freed by the JVM finalizer and retained by glibc’s 64 MB arenas; restricting the scan scope or upgrading to Spring Boot 2.0.5 eliminated the excess usage.
