Performance Testing Mastery: Core Concepts & Best Practices

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., JavaScript, 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.

Overview

Section 1: Introduction to performance testing

Lecture 1 Introduction to performance testing

Section 2: Writing a performance testing script

Lecture 2 Writing a performance testing script - theoretical part

Lecture 3 Writing a performance testing script - practical part (1)

Lecture 4 Writing a performance testing script - practical part (2)

Section 3: Important considerations during performance testing

Lecture 5 Important considerations during performance testing

Section 4: Performance testing kubernetes applications

Lecture 6 Performance testing kubernetes applications

Section 5: Monitoring

Lecture 7 Monitoring

Section 6: Performance bottlenecks

Lecture 8 Performance bottlenecks

Section 7: Distributed performance testing with jmeter

Lecture 9 Distributed performance testing with jmeter - theoretical part

Lecture 10 Distributed performance testing with jmeter - practical part

Section 8: Test types

Lecture 11 Test types

Section 9: Reporting

Lecture 12 Reporting - theoretical part

Lecture 13 Reporting - practical part

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.