• views: 222
• date: 23 June 2025
• posted by: 0nelove

Performance testing: scripts, Kubernetes, monitoring & bottlenecks
What you'll learn
Understand the fundamentals of performance testing, including key terminology, test types (load, stress, soak, spike), and when to apply each.
Understand different entry criteria, exit criteria, targets of performance testing and reporting
Design and implement complex performance testing scripts, leveraging best practices and understand the correlation between virtual users and TPS
Understand important considerations in performance testing like data population in database, caching and etc.
Gain a basic understanding of Kubernetes and learn how testing differs between monolithic and microservices-based applications
Understand the importance of monitoring during performance testing, use cases of monitoring and how it can be set up
Learn about different performance bottlenecks that can be present in tested applications and how they can be detected
Learn how to set up distributed testing with JMeter, understand its advantages, and explore important considerations
Requirements
Basic Software Testing Knowledge: Familiarity with manual or functional testing concepts.
Scripting Fundamentals: Comfort with at least one scripting language (e.g., jаvascript, Python, Groovy) for parameterization and correlation.
Command‑Line Experience: Ability to navigate a Unix/Linux or Windows CLI for installing tools and running tests.
Description
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.
Who this course is for
QA & Test Engineers looking to add performance testing skills to their toolkit.
DevOps / SRE Practitioners who need to validate application scalability and reliability under load.
Backend / Full‑Stack Developers interested in building more resilient, high‑performance services.
Technical Project Managers & Architects who want to understand performance risks and interpret test results.
Anyone aiming to break into performance engineering or broaden their software testing expertise.




   
   
   

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