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所在平台: Udemy |
课程主页: https://www.udemy.com/course/performance-testing-mastery-core-concepts-best-practices/
课程评论：没有评论
课程名称：性能测试精通：核心概念与最佳实践 课程概述：在这门综合课程中，深入了解性能测试的核心原则和实践。课程开始时，你将掌握基础知识，包括关键术语、入口和出口标准、测试目标，以及负载测试、压力测试、浸泡测试和尖峰测试的时机与背后原理。在此基础上，你将学习如何设定有意义的指标（吞吐量、响应时间、错误率）并撰写清晰、可操作的报告，以便于通知利益相关者并推动持续改进。 接下来，你将设计和实施复杂的JMeter脚本，以模拟真实世界的流量模式。课程中将应用经过验证的参数化和关联技术，以实现可预测、可测量的每秒事务数（TPS），确保你的负载场景准确反映用户行为，并理解虚拟用户与每秒事务之间的关系。你还将解决一些关键问题，如数据库中的数据人口、缓存对性能的影响以及动态数据源，以使你的测试更具现实性和重复性。 随着现代应用向容器化微服务的转变，本课程探讨了性能测试在单体应用和基于Kubernetes的环境之间的不同。你将学习如何使用JMeter设置分布式测试，并清楚理解pod/服务器的CPU和内存资源设置如何影响测试结果和负载下的整体应用行为。 监控是任何性能测试的核心要素。你将学习如何自行配置监控，收集和可视化来自应用服务器、容器和基础设施的指标，并学习如何识别CPU、内存、I/O和网络层的瓶颈。课程中将提供实际演示和样本测试计划，逐步引导你。到课程结束时，你将拥有一套技术和模板工具，能够自信地编写测试计划、识别性能问题、优化资源使用，并在负载下交付高性能的应用程序。
Dive into the core principles and hands‑on practices of performance testing in this comprehensive course. You'll begin by mastering the fundamentals-key terminology, entry and exit criteria, test objectives, and the when and why of load, stress, soak, and spike tests. With these foundations in place, you'll learn to set meaningful targets (throughput, response time, error rates) and craft clear, actionable reports that inform stakeholders and drive continuous improvement.Next, you'll design and implement complex JMeter scripts that mirror real‑world traffic patterns. You'll apply proven techniques for parameterization and correlation to achieve predictable, measurable Transactions Per Second (TPS), ensuring your load scenarios accurately reflect user behavior and you will understand the correlation between virtual users and transactions per second. You'll also tackle critical considerations-population of data in databases, effect of caching on performance, and dynamic data feeds-to make your tests both realistic and repeatable.As modern applications shift to containerized microservices, this course explores how performance testing differs between monolithic and Kubernetes‑based environments. You will learn how to set up distributed testing with JMeter and gain a clear understanding of how pod/server CPU and memory resource settings influence test results and overall application behavior under load.Monitoring is a keystone of any performance test. You will learn to configure monitoring by yourself-collecting and visualizing metrics from application servers, containers, and infrastructure-and learn to detect bottlenecks in CPU, memory, I/O, and network layers. Along the way, practical demos and sample test plans will guide you step by step. By the end of the course, you'll have a toolkit of techniques and templates to confidently write test plans, identify performance issues, optimize resource usage, and deliver high‑performing applications under load.
