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.
This article shares 360 Group's systematic Kafka capacity‑planning methodology, covering hardware performance analysis, disk I/O benchmarking, cluster configuration, load‑testing procedures, observed write‑read dynamics, and practical recommendations for reliable Kafka deployments.
This chapter introduces advanced Java multithreading concepts, explaining thread safety, the synchronized keyword, various synchronization techniques, and practical code examples such as object‑level, class‑level, method‑level synchronization and double‑checked locking to help solve concurrency problems in performance testing.
This article presents a comprehensive overview of payment system architecture, detailing core transaction and payment modules, service governance, data consistency, asynchronous processing, performance testing, and practical production practices for building a robust backend payment platform.
This article recounts the end‑to‑end design, testing, and iterative optimization of a volunteer registration platform that required tens of thousands of QPS, covering requirement analysis, middleware benchmarking, data partitioning, compression, connection‑limit handling, and final deployment lessons.
This article describes a Java performance‑testing script that uses concurrent user initialization, explains how overlooking atomicity in thread‑safe classes caused some users to miss initialization, and presents three practical solutions such as improving stop logic, tracking completed users, and using concurrent collections.
This article presents a Go‑based dynamic QPS load‑testing framework that leverages a custom goroutine pool, explains the rationale behind reusing goroutines, provides core functions for task execution and console command handling, and includes a complete runnable demo illustrating high‑throughput performance testing.
This article explains how to verify and measure micro‑level CPU usage in client applications by selecting appropriate performance‑testing tools, demonstrating two validation methods—Process Explorer and the Windows Performance Toolkit (WPT)—and providing step‑by‑step installation and usage guidance.
This article details a real‑world multi‑cloud cluster migration, covering preparation, testing strategies, traffic replay, performance validation, latency simulation, and communication practices that enabled a successful two‑hour cutover without impacting critical services.
This article demonstrates how to use the Go-based FunTester framework to conduct performance tests—select, insert, update, and delete—on MySQL through GORM, providing code examples and explaining the setup of the testing harness.
This article evaluates twelve Java object‑property conversion libraries—ranging from manual getters/setters to MapStruct—by measuring their performance across 100 to one‑million conversions, discussing implementation details, rating each tool, and recommending the most efficient solution for backend developers.
This article details a Java‑based log replay solution that re‑implements Goreplay functionality using Disruptor, covering design concepts, log extraction, implementation steps, performance metrics, identified risks, and provides full source code examples for a scalable load‑testing engine.
This article demonstrates how to accurately measure and restrict the CPU consumption of a CPU‑intensive third‑party SDK by creating and configuring cgroup and cpuset groups, binding the process to a single core, and verifying the limits with stress testing tools.
This article explains the fundamentals of microservices architecture, outlines its core characteristics and key benefits, and discusses why specialized testing strategies—including system‑level and service‑level performance testing—are essential, while recommending several load‑testing tools for effective evaluation.
This article explains the core concepts of microservices, outlines their key benefits such as rapid delivery and scalability, discusses the need for specialized testing strategies, and reviews popular load‑testing tools to ensure reliable performance in distributed systems.
The article details a Java reflection issue where invoking a static test method via ExecuteSource caused IllegalAccessException, explains the root cause related to method visibility, and shows how adding the public modifier fixes the problem, enabling successful distributed performance testing.
This article examines common sources of performance‑testing errors—including hidden locking‑resource issues, inadequate local machine configuration, inefficient regex‑based data extraction, costly exception handling, and unsynchronized thread termination—while offering practical Java‑centric mitigation strategies.
This article presents a systematic performance test of the DTLE data replication tool, using sysbench, tc, and iperf3 to simulate different network latency and bandwidth scenarios, and compares DTLE's replication delay and bandwidth usage against native MySQL replication across three test cases.
This article walks through configuring JMeter 5.12 to run Groovy scripts inside JSR223 assertions, demonstrating how to validate structured JSON responses, log request details, and handle custom failure messages, while providing step‑by‑step screenshots and a complete script example.
This article describes how Tubi’s engineering team built and optimized a real‑time recommendation backend, using ScalaMeter microbenchmarks and wrk2 load testing to measure latency, throughput and error rates, and demonstrates scaling the service across multiple machines with custom scripts.
This article provides a curated collection of links covering performance testing of message queues, stress testing password interfaces, probabilistic algorithm testing, notable bug analyses, and various web crawling implementations using httpclient and Groovy, offering valuable resources for backend developers and testers.
This report presents a comprehensive performance‑testing plan for a Kafka cluster, detailing objectives, test scope, JVM and broker tuning, producer and consumer parameter experiments, extensive benchmark results, and practical recommendations for achieving high throughput and stability in large‑scale message processing.
This article introduces the fundamentals of performance testing, covering its definition, testing methods, timing, perspectives, test types, key metrics, and practical scripting examples to simulate high concurrency loads for reliable software evaluation.
This article outlines a curriculum covering Python3 basics, essential Linux commands, MySQL database usage, performance testing tools like JMeter and Gatling, introductory Selenium automation, and the setup of development environments including JD, Tomcat, Jenkins, Ant, and ADB.
This article investigates the feasibility of improving Locust's HTTP request performance by replacing urllib3 with PycURL, explores protocol extensions, examines the impact of gevent's monkey patching, and concludes that the substitution does not yield significant gains despite a modest single‑user speedup.
This article outlines a comprehensive performance testing workflow, introduces layered and segment diagnostic methods, presents a detailed Apache/Tomcat/Linux/Oracle case study with LoadRunner and Nmon, and discusses monitoring metrics, analysis results, and practical recommendations for optimizing system performance.
When a performance test that simulates 100 concurrent HTTP GET requests triggers a “Too many open files” error, the article explains why the default 1024 file‑descriptor limit causes the failure and provides step‑by‑step commands to permanently increase the limit on Linux systems.
This article explains core performance‑testing concepts such as TPS, response time, think time, and threading, then details how to identify CPU, memory, and disk bottlenecks on Linux using tools like top, vmstat, iostat, and pidstat, linking system resources to overall application performance.
