The article argues that local caches add significant consistency and management complexity, so they should be avoided unless a genuine performance bottleneck exists, illustrating the point with real‑world promotion spikes, GC concerns, and careful off‑heap testing.
Milvus 3.0 introduces a lightweight Snapshot feature that creates a point‑in‑time, read‑only view of a collection by freezing metadata and segment manifests instead of copying data, and explains how restore, GC protection, and pin mechanisms work together to enable fast, safe rollbacks.
This guide walks through a systematic troubleshooting checklist for online service incidents, covering CPU, disk, memory, GC, and network problems, and demonstrates how to use Linux tools such as ps, top, jstack, jmap, vmstat, iostat, netstat, ss, and tcpdump to pinpoint root causes.
During a major sales promotion, a sudden P99 latency jump can tempt teams to tweak JVM settings, but this article shows how to build a three‑layer evidence chain—GC logs, JFR, business and container metrics—to confirm whether the JVM is truly at fault before making any parameter changes.
The article walks through a real e‑commerce order service where afternoon spikes caused response times to jump to 2‑3 seconds, Full GC to run every few minutes, and Metaspace to hit 99.9%, then demonstrates step‑by‑step analysis using monitoring, GC logs, jstat, jmap, and heap dumps, fixes a custom class‑loader leak, adjusts JVM flags, and achieves sub‑200 ms latency with no Full GC.
This article walks through Java GC tuning by defining low‑latency and high‑throughput goals, comparing major collectors, presenting G1 and ZGC configuration examples, and demonstrating a real‑world payment system case where pause times were reduced from 150‑200 ms to under 50 ms.
This article explains how to differentiate memory leaks from out‑of‑memory errors, identifies classic GC‑based leak signals, introduces a toolchain (jstat, jmap, MAT, Arthas, JProfiler), walks through a step‑by‑step investigation workflow, lists common leak patterns, presents a real‑world ThreadLocal leak case, and offers preventive measures such as monitoring, regular heap dumps, code review, and stress testing.
This article launches a Java performance tuning series, explaining why GC logs are the starting point, reviewing the JVM memory model, showing how to enable and read GC logs, dissecting minor and full GC entries, comparing common GC algorithms, and introducing visualization tools to help pinpoint memory issues.
Python 3.15 brings lazy imports, a built‑in frozendict type, unpacking in comprehensions, JIT improvements, the Tachyon sampling profiler, default UTF‑8 encoding, GC rollback and .start files, delivering up to 12‑13% faster execution on ARM Macs and offering a step‑by‑step migration guide for data engineers.
This comprehensive guide covers the most frequent Go interview topics for 2026, including project engineering, slice and map internals, interface mechanics, the GMP scheduler, channel implementation, context usage, memory‑leak pitfalls, pprof profiling, garbage‑collection details, escape analysis, memory alignment, defer traps, sync.Pool, and reflection, all illustrated with concrete code examples and step‑by‑step explanations.
This article examines the often‑overlooked performance “tax” in Go microservices, detailing how misuse of goroutines, channels, interfaces, object allocation, and fan‑out patterns inflates CPU, memory, and tail‑latency costs, and provides concrete engineering strategies—such as request‑level concurrency limits, bulkheads, and efficient logging—to achieve production‑grade scalability.
An online service experienced frequent Full GC due to a ThreadLocal memory leak in a thread‑pool scenario; the article walks through GC log analysis, heap dump inspection, root‑cause discovery in ThreadFactory logic, and presents a robust fix using Spring’s TaskDecorator to ensure proper cleanup.
A production outage caused by OutOfMemoryError revealed that millions of duplicate String objects—originating from HTTP headers, JSON keys, and other common fields—were silently inflating heap usage, and the article shows how to diagnose, optimize, and prevent such hidden String-related memory problems in Java applications.
Through a controlled experiment with four Spring Boot service groups on Kubernetes, this article shows that relying on Java’s default GC and heap settings can drastically reduce throughput and increase tail latency, especially under higher load, and demonstrates how explicit GC algorithm and Xms/Xmx tuning restores performance.
This article examines why Java is often labeled slow, dispels three major misconceptions about interpretation, garbage‑collection pauses, and startup time, presents concrete benchmark data against other languages, and explains how modern JVM features and framework choices actually shape performance.
A NetEase interview probes deep JVM garbage‑collection knowledge, exposing misconceptions about default GC, demanding precise G1/ZGC tuning, and challenging candidates with performance‑critical scenarios that reveal hidden latency and memory‑usage pitfalls.
The article reveals that Kuaishou’s 2023 campus hires for backend and frontend roles typically earn 40‑50 wan yuan annually, discusses salary trends compared with previous years, and then provides a comprehensive Java backend interview guide covering JVM memory, garbage collectors, CMS GC process, concurrency primitives, and SQL join differences.
A real‑world production incident where GC pauses, thread‑pool exhaustion, and slow SQL combined to drop QPS from 3000 to 1400 and inflate response times from 200 ms to over 2 s, with detailed analysis, diagnostic criteria, and step‑by‑step optimizations that restored performance.
This article presents a step‑by‑step experiment that uses JMeter to generate 20,000 HTTP calls against a SpringBoot 2.5.4 service, captures detailed GC logs, calculates the heap memory allocated per request, analyses the impact of request payload and logging, and offers practical tuning recommendations.
This comprehensive guide walks you through diagnosing Full GC pauses on a Java 2020 production outage, explains JVM memory structures, compares JDK 21 garbage collectors, and provides step‑by‑step installation, configuration, and tuning instructions for Tomcat, monitoring tools, and real‑world case studies to achieve stable, high‑throughput services.
The article dissects a JD interview question about concatenating one hundred million strings, comparing String, StringBuilder, and StringBuffer, and explains how immutability leads to object explosion, how StringBuilder’s default capacity causes costly expansions, and why StringBuffer’s synchronized methods become a performance bottleneck in high‑concurrency scenarios.
A reproducible benchmark of 62 languages shows Java running on modern JVMs is only ~17% slower than C, more than 13 times faster than Python, while Spring’s runtime overhead can inflate pure‑Java code by 30×, highlighting common misconceptions about Java performance and how to avoid them.
This guide explains how to identify and resolve sudden latency increases in running services by collecting key metrics, focusing on GC behavior, thread and lock analysis, CPU and system resources, and using diagnostic tools such as jstat, jstack, and distributed tracing.
This guide explains how to systematically analyze and optimize Java garbage‑collection pauses—cutting typical 200 ms stalls down to around 20 ms—by enabling detailed logs, selecting the right collector, tuning heap and generation settings, minimizing allocation, handling large objects, and balancing GC threads with CPU resources.
This article examines common pitfalls of asynchronous Go logging—such as channel blocking, GC pressure, and poor API design—and presents concrete solutions using non‑blocking writes, sync.Pool object reuse, and context‑aware variadic APIs, all demonstrated with real code from the easyms.golang project.
After migrating over 40 Spring Boot microservices to Docker and Kubernetes, the authors encountered slow startups, OOM kills, unexpected latency, and pod restarts, and they detail a step‑by‑step analysis and concrete Dockerfile, JVM, CPU, GC, and Kubernetes configurations that turned the services into fast, stable, and observable production workloads.
This guide explains how to determine the heap memory consumption of individual HTTP and RPC calls in a SpringBoot application by setting up a JMeter load test, enabling detailed GC logging, and analyzing the results to optimize JVM parameters and improve performance.
This article explains how to measure the exact heap memory consumption of individual SpringBoot HTTP and RPC requests by calculating required memory from request size, configuring JVM GC parameters, creating a JMeter test plan, collecting GC logs, and analyzing results to identify optimization opportunities.
When a production pod suddenly hit 90% CPU and dozens of young and full GCs within two hours, the author walks through a step‑by‑step investigation using top, thread‑level monitoring, jstack, and stack analysis to pinpoint a Java‑level memory issue and resolve it.
The article walks through a real‑world OOM incident in a high‑traffic hotel information service, detailing the root‑cause analysis of memory exhaustion, JVM heap configuration, GC behavior, heap‑dump inspection, and the concrete optimizations applied to prevent similar failures.
This article explains how Go's experimental goroutineleak experiment integrates leak detection into the garbage collector, describes the underlying sudog mechanism, and provides step‑by‑step instructions to enable the feature, run a demo, and analyze leaks using pprof.
This article explains Go's memory escape mechanism, how the compiler decides to move variables from stack to heap, shows practical commands and code examples, lists common escape scenarios, and provides concrete optimization techniques to reduce unnecessary heap allocations and improve performance.
The article explains what a GC spike (Garbage Collection Spike) is, analyzes its typical causes such as large short‑lived objects, memory leaks, and heap configuration, presents a real‑world high‑concurrency case study, and details step‑by‑step JVM tuning and architectural strategies that reduced latency spikes and raised service availability from 95% to over 99.99%.
After a recent deployment, a Java service began experiencing over five Full GC events per minute, traced to Log4j2’s thread‑local buffer misconfiguration and a -XX:PretenureSizeThreshold setting that forced large StringBuilder objects directly into the old generation, leading to memory pressure and frequent Full GCs.
An in‑depth investigation of a Java service’s memory alarm reveals that a surge of temporary NioChannel objects, caused by high QPS and insufficient socket reuse, prematurely promotes objects to the old generation, leading to uncollected memory growth, and the article details the diagnosis, GC tuning, and mitigation steps.
This article analyzes frequent timeout alerts caused by the getUiToken API, investigates JVM garbage‑collection pauses linked to excessive PhantomReference objects from MySQL connections, and presents configuration, code, and scheduling solutions that dramatically reduce GC latency and improve service stability.
The article investigates frequent timeout alerts on the getUiToken API, tracing the issue to excessive PhantomReference objects causing long GC pauses, explains how MySQL’s AbandonedConnectionCleanupThread and default Druid pool settings generate these references, and presents configuration tweaks, JVM flags, and cleanup tasks that reduced GC pause times from 1.25 s to 0.1 s.
This article walks through a real‑world performance investigation, covering CMS GC misconfigurations, biased‑lock revocation delays, Netty worker thread queuing, and log‑framework overhead, while providing concrete JVM flags, code snippets, and diagnostic steps to pinpoint and resolve latency spikes.
This article walks through a real‑world production‑environment debugging case, showing how tracing with SkyWalking, deep GC log analysis, mis‑configured c3p0 pools and Finalizer behavior can combine to cause intermittent time‑outs and high latency in a distributed Java service.
This article shows how a senior architect measured the exact heap memory allocated by individual HTTP and RPC requests in a SpringBoot 2.5.4 application using JMeter load testing and detailed GC logs, revealing that a minimal HTTP call consumes about 34 KB while an RPC call can use up to 1 MB, and discusses the performance implications of payload size and logging.
This article walks through a real‑world production case, showing how to systematically detect, analyze, and resolve severe JVM garbage‑collection pauses that caused service timeouts, by examining CPU, memory, GC logs, adjusting collector settings, and aligning JVM threads with Kubernetes resource limits.
This tutorial explains the purpose and usage of JVM argument prefixes—including system properties, standard, non‑standard, and advanced options—along with execution modes like interpreted, compiled, and mixed, providing code examples and performance comparisons to help Java developers optimize and debug their applications.
This article explains how Go's concurrent mark‑sweep garbage collector can become a bottleneck in high‑performance scenarios and provides practical techniques—such as using sync.Pool, avoiding unnecessary pointers, pre‑allocating memory, and applying zero‑copy and unsafe tricks—to dramatically lower GC overhead and improve overall program speed.
This article details how replacing Java's native double parsing with the Ryu and FastFloat algorithms, alongside JVM GC tuning, dramatically reduces CPU usage and latency in backend services, offering practical code examples, benchmark results, and actionable optimization strategies.
This article explores the puzzling "prevent gc" comment in RocketMQ source code, explains how Thread.sleep(0) interacts with JVM safepoints, distinguishes counted and uncounted loops, and demonstrates practical experiments that reveal why altering loop variables can change GC behavior.
This article details a step‑by‑step experiment using SpringBoot 2.5.4 and JMeter to quantify how much heap memory a single HTTP or RPC request consumes, analyzes GC logs, presents code snippets for the test endpoints, and discusses the impact of logging and payload size on memory usage.
This article explains why the JVM throws java.lang.OutOfMemoryError: GC overhead limit exceeded, outlines common causes such as traffic spikes, memory leaks, insufficient heap and inefficient GC, and provides practical solutions and debugging steps to diagnose and fix the issue.
This article describes an experiment using SpringBoot 2.5.4 and JMeter to measure the heap memory allocated per HTTP and RPC request, detailing the setup, GC logging configuration, results showing average memory consumption around 34 KB per request, and recommendations for logging and performance optimization.
This article presents a comprehensive guide to PHP memory optimization, covering strategies such as using unset() to free variables, processing data in chunks, manual garbage collection, monitoring usage, optimizing loops, generators, lazy class loading, database tuning, profiling tools, serialization practices, Opcache, session handling, large file processing, and Composer dependency management.
This article details a practical experiment using SpringBoot 2.5.4 and JMeter to measure the heap memory allocated by individual HTTP and RPC requests, analyzes GC logs, and demonstrates how request size and logging affect memory consumption, providing insights for backend performance optimization.
The article investigates a mysterious "prevent gc" comment in RocketMQ's source, explains how Thread.sleep(0) forces a JVM safepoint to mitigate long GC pauses, and demonstrates that changing loop counters from int to long eliminates the need for the hack.
This guide walks through identifying Java processes that cause high CPU load, extracting the hottest threads with top and jstack, analyzing JVM memory regions, interpreting GC logs, and applying practical JVM tuning parameters and tools such as jmap, jstat, and MAT to resolve performance bottlenecks.
This article presents a systematic experiment that uses JMeter to generate HTTP requests against a SpringBoot 2.5.4 service, records detailed GC logs, and calculates that each HTTP call consumes roughly 34 KB of heap memory, while logging and payload size can significantly increase the allocation.
This article explains the limitations of Go's runtime.SetFinalizer, introduces the new runtime.AddCleanup function in Go 1.24, demonstrates proper usage with code examples, and highlights important pitfalls and best‑practice tips for reliable resource management.
A Java service experienced occasional five‑minute latency spikes despite similar provider response times, prompting a systematic investigation of container memory usage, page‑cache behavior, young‑generation GC pauses, and network bottlenecks, ultimately revealing and mitigating the root causes.
This article demonstrates how to experimentally determine the heap memory allocated by individual HTTP and RPC requests in a SpringBoot application using JMeter load testing, GC logging, and code analysis, providing insights into memory consumption, GC frequency, and optimization strategies for backend performance.
The article details a SpringBoot experiment that uses JMeter to send 20,000 HTTP requests, captures detailed GC logs, and demonstrates that each request consumes roughly 34 KB of heap memory, with larger payloads and logging increasing the usage, highlighting the importance of memory‑aware coding and log management.
During performance testing of a Netty-based service, occasional message sends exceeded one second despite low CPU usage, prompting an investigation that identified a blocking ChannelHandler caused by a conditional sleep, leading to NioEventLoop thread stalls and delayed transmissions.
This article demonstrates how to quantify the heap memory allocated by individual HTTP and RPC calls in a SpringBoot application by creating a test service, configuring JMeter for load, capturing detailed GC logs, and analyzing the results to guide JVM and logging optimizations.
This article presents a SpringBoot-based experiment measuring the heap memory allocated per HTTP request, detailing the setup with JMeter, JVM GC logging, observed memory usage of roughly 34 KB per request, analysis of GC frequency, and concludes with recommendations on logging and memory optimization, while also promoting related AI services and community resources.
The author recounts a sudden CPU spike on a Kubernetes pod caused by excessive JVM garbage collection, walks through step‑by‑step diagnostics using top, thread inspection, jstack, and network file transfer, identifies a flawed Excel export loop, and shares the fix and lessons learned.
This article investigates the mysterious "prevent gc" comment in RocketMQ source code, explains how Thread.sleep(0) interacts with JVM safepoints and counted loops, and demonstrates practical fixes by changing loop indices or inserting explicit safepoint calls.
The article walks through real interview scenarios where MySQL or Java processes consume 900% CPU, detailing step‑by‑step diagnosis with top, processlist, jstack, and concrete remediation such as adding indexes, using caches, fixing busy loops, and tuning memory.
This article documents the author's practical migration from JDK 8 to JDK 11, covering motivations such as performance, compatibility, and industry trends, detailed upgrade steps, GC tuning, resolved issues, and showcases significant performance gains and new Java 11 language features with code examples.
This article explains how using an off‑heap local cache (OHC) can dramatically lower GC pauses and cut interface latency by up to tenfold, covering the underlying principles, configuration, custom serializers, performance testing, monitoring metrics, and practical optimization recommendations for high‑traffic Java backend services.
This article explains why and how the Go runtime’s garbage collector can skip scanning objects that contain no pointers, describes the noscan flag and its implementation in the GC pipeline, shows benchmark results, and offers practical tips for leveraging this optimization in real‑world code.
The article describes a production OOM incident caused by downloading a 2.4 GB file, walks through GC log and heap‑dump analysis using MAT, identifies the problematic use of FileUtils.readFileToByteArray, and presents streaming and external storage solutions to prevent memory exhaustion.
This guide walks through diagnosing excessive CPU load and memory consumption in Java applications, using tools like top, jstack, jmap, and jstat, and provides step‑by‑step solutions and JVM tuning parameters to reduce GC pauses and prevent full garbage collections.
This article explains six typical OutOfMemoryError situations in Java—heap, native thread, stack overflow, direct buffer, GC overhead, and Metaspace—provides code examples that trigger each issue, shows the resulting exception messages, and offers practical tips such as using thread pools, limiting recursion depth, adjusting GC settings, and configuring Metaspace to avoid these memory problems.
This article demonstrates how to quickly locate and resolve frequent full GC and CPU spikes in Java backend services by combining Arthas‑integrated async‑profiler flame graphs with VisualVM, GCEasy analysis, and practical step‑by‑step deployment procedures.
This comprehensive guide presents a systematic, step‑by‑step process for diagnosing JVM memory problems—including heap, Metaspace, DirectMemory, JNI memory, and stack issues—using Alibaba Cloud ARMS, ATP, standard JDK tools, and best‑practice commands to quickly locate root causes and apply effective solutions.
This guide walks through systematic troubleshooting of Java applications by checking CPU, disk, memory, and network layers, using tools like jstack, jmap, vmstat, iostat, and tcpdump to pinpoint and resolve performance and stability problems.
This article details the systematic investigation of intermittent availability drops in a high‑throughput Go service, covering hypothesis formulation, extensive profiling with pprof, gctrace, strace, fgprof, go trace, heap analysis, the discovery of a gcache LFU bug, and the final remediation steps.
This article explains how to monitor, interpret, and optimize Java Garbage Collection by using JDK flags, IDE configurations, and the gceasy.io visualizer, providing step‑by‑step guidance, sample logs, and performance metrics for effective JVM tuning.
When a production container suddenly hit over 90% CPU and excessive JVM garbage collection, the author walks through entering the pod, using top and top‑H to locate the offending thread, extracting its stack with jstack, downloading the data via a simple HTTP server, and ultimately discovering an Excel export routine that caused massive object allocation, fixing the code and restoring stability.
Switching a Java Netty gateway from GZIP to ZSTD compression using zstd‑jni doubled GC time and introduced heap and Netty off‑heap memory leaks, which were resolved by employing the library’s direct off‑heap API with a NoFinalizer compressor, promptly releasing ByteBufs, avoiding finalize(), and adopting jemalloc to reduce fragmentation.
This article walks through a real‑world investigation of a pod whose CPU spiked to over 90%, detailing how abnormal JVM garbage‑collection patterns were identified, traced to a specific Java thread, and resolved by fixing an inefficient Excel export routine.
This article explains how the HeapTaskDaemon thread drives garbage collection in Android's ART runtime, analyzes its source code and related HeapTask classes, and presents two practical methods—adding custom HeapTasks or pausing existing ones—using ELF symbol lookup, ndk_dlopen, and virtual‑function hooking to temporarily suppress GC for better performance.
This article explains the common Java OutOfMemoryError types—including heap space, GC overhead limit exceeded, Metaspace, CodeCache, Direct Memory, and Linux OOM Killer—detailing their causes, symptoms, and step‑by‑step troubleshooting techniques for backend developers.
This article explains the various Java OutOfMemoryError types—including Java heap space, GC overhead limit exceeded, Metaspace, CodeCache, Direct Memory, and Linux OOM Killer—describes their causes, typical symptoms, and provides step‑by‑step troubleshooting and mitigation strategies for developers.
The article details Gaode’s journey of building a high‑performance Go ecosystem that scaled from zero to tens of millions of QPS, comparing Go with Java and Erlang, outlining cloud‑native serverless architecture, and sharing real‑world refactoring and optimization case studies such as a million‑QPS rendering gateway and a Go‑based sharding middleware.
This article explains why upgrading from JDK 8 to JDK 11 is worthwhile, details performance gains, new language and API features, garbage‑collector improvements, modularization concepts, step‑by‑step migration procedures, common pitfalls, and practical verification methods.
After receiving an alarm that a production container’s CPU usage surged past 90%, I investigated the JVM metrics, discovered excessive young and full GCs in a single pod, and walked through the detailed troubleshooting steps—including top, thread analysis, jstack, and code fixes—that resolved the issue.
This article explains why upgrading from JDK 8 to JDK 11 is beneficial, details performance improvements—especially GC behavior—lists the new language and API features, provides step‑by‑step migration guidance, highlights common pitfalls such as module‑system issues, and offers practical recommendations for production environments.
This article documents a comprehensive migration from JDK 8 to JDK 17, detailing new language and JVM features, step‑by‑step upgrade procedures, Maven and SpringBoot version changes, performance benchmarking, and ZGC‑based garbage‑collection tuning to dramatically reduce pause times.
The article details a production incident where a containerized Java service experienced a CPU surge due to frequent young and full garbage collections, describing step‑by‑step diagnostics using Linux tools, jstack analysis, and code fixes that ultimately resolved the issue.
This article documents a comprehensive upgrade from JDK 8 to JDK 17, detailing new language and JVM features, evaluation criteria for selecting services, step‑by‑step migration of Maven settings, dependencies, SpringBoot and middleware, performance testing results, and ZGC‑focused garbage‑collector tuning.
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.
The article explains Go’s runtime finalizer mechanism, shows how a single blocked finalizer can stall all cleanup callbacks, and recounts a real‑world memory‑leak incident where an unrelated goroutine’s blocked finalizer for struct B prevented struct A’s resources from being released.
The article details a real‑world incident where a Java backend pod experienced 90% CPU usage and excessive young and full garbage collections, describing step‑by‑step diagnostics, stack analysis, and code fixes that resolved the performance problem.
This article walks through JVM GC log analysis and tuning, covering monitoring steps, parameter settings, log interpretation, and using the free GCEasy visualizer to identify performance issues and optimize Java applications.
A high‑traffic Go service suffered intermittent availability drops below 99.9% due to timeouts, and after extensive profiling, tracing, and heap analysis the root cause was identified as a memory‑leak bug in the third‑party gcache LFU implementation, which was fixed by upgrading the library.
The article recounts a production incident where a Java service’s CPU surged to 1200%, detailing how top, jstack, and jmap were used to trace the issue to a missing parameter that caused a full‑table scan, prolonged GC pauses, and ultimately a system freeze.
During a major sales promotion, an API suffered timeouts due to Full GC pauses over 500 ms, which were traced to stale MySQL connections in the DBCP pool accumulating in the old generation; the article details the investigation steps, root cause, and mitigation strategies such as switching to G1 GC and adjusting eviction settings.
This article explains the risks of using default JVM thread and memory parameters in containers, presents experimental data on ParallelGCThreads, and provides concrete recommendations for heap, off‑heap, and Metaspace settings across different container CPU and memory sizes.
This article analyzes a production incident where excessive Full GC pauses during a high‑traffic promotion were traced to stale database connections in the DBCP pool, explains the investigation steps, root cause, and presents several JVM and connection‑pool configuration solutions to prevent similar performance degradations.
This article explains the trade‑off between throughput and pause time in JVM garbage collection, details how the ParallelGCThreads, ConcGCThreads, and CICompilerCount parameters are calculated and affect performance, presents experimental results, and provides concrete configuration recommendations for both on‑heap and off‑heap memory in containerized Java applications.
During a high‑traffic promotion, an interface experienced frequent timeouts and Full GC pauses over 500 ms, which were traced to a misbehaving DBCP connection pool that failed to keep connections alive, causing massive old‑generation garbage and severe GC latency.
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.
The article describes analysis and resolution of full GC issues in the inventory center online business, detailing incident review, emergency measures like memory expansion and restart, root‑cause discovery of a memory leak caused by loading massive procurement out‑stock data and blocked threads, and mitigation through thread‑pool tuning, data migration, and process optimization to prevent future pauses.
