Complete Web Performance Testing using Jmeter + AWS module

Examine how performance shapes user experience and revenue by analyzing incidents from five crore concurrent users and server crashes, highlighting cloud and microservices in performance testing.

Differentiate functional testing from non-functional testing with real-world examples of correctness, usability, and security, and highlight performance testing as the focus on speed and scalability.

Analyze how the Mybank launch failed in production after extensive design, development, and testing, highlighting missing process steps that caused login failures for 600 users.

Explore a revised performance testing approach for a bank app, validating it for 600 users and showcasing iterative tuning of queries, web services, and code to ensure production readiness.

Visualize the production setup for my bank application, including web and database servers, real-time transactions, batch jobs, 500 customers, 100 employees, ATMs, and external systems.

Simulate production-like workloads for the bank online application using Jmeter, with scripts and test data to model user and external system traffic, and monitor memory, CPU, and response times.

Explore the performance test life cycle, from requirements capture and test planning to deployment, detailing workload models, environment design, data strategies, monitoring, scripting, execution, analysis, and post-deployment review.

Explore how performance affects revenues and user behavior, showing real-life examples where reduced wait times boosted signups and conversions, while slower pages increased abandonment and reduced visits.

Identify scenarios where performance testing adds value, including software and hardware upgrades, hotfixes, new implementations, major code changes, migrations, and architectural shifts to prevent production degradation.

Identify possible career paths in performance testing from trainee to architect, with timelines, required skills, and growth through multiple tools, protocols, workload modeling, monitoring, scripting, and sql.

Explore real-world pan-India demand for performance testing and hiring challenges, and review tools like Microfocus Loadrunner, Apache Jmeter, Tricentis Neo Load, Blazemeter, Rational Performance tester by IBM, and VSTS.

Revisit core performance testing concepts, emphasizing production-like simulations and reproducible workload models to uncover bottlenecks that impact revenue.

Master performance testing terminology and concepts to build a solid foundation. Learn essential definitions like requirement, test case, and test plan in a clear, logically sequenced session.

Define response time as the system's time to respond to user actions, from clicking to display. Examine network time, server processing, and browser rendering with login and search examples.

Define throughput as the number of transactions or data a system processes per unit time, while bandwidth is the network's transmission capacity and latency the time delay to reach destination.

This session explains average (mean) and the 90th percentile for response times in performance testing, showing how 90% of samples fall below the percentile and how to calculate percentiles.

Identify performance requirements from a user perspective, defining target response times, scalability, and throughput, such as search completing in up to five seconds and 500 concurrent users.

Learn what a performance test environment includes: hardware, software, network tools, test data, configuration settings, test harness, and load generator machines that create the load on the application under test.

Understand how performance test scripts are generated by recording with tools like jmeter, loadrunner, or neoload, and contain user actions such as login, search, flight, payment, and log off.

Understand how a workload model represents real production usage to simulate peak load during performance testing, including transaction volume, transaction mix, user statistics, and OLTP and batch jobs.

Rendering time measures browser side processing from network packet receipt to visible content, including HTML parsing, CSS parsing, JavaScript interpretation, and painting; it is separate from response time.

Explore basic architecture types for performance testing: two-tier and three-tier structures, plus thin versus thick clients, and how presentation, business, and data layers affect validation and processing, including microservice patterns.

identify daily, monthly, or yearly peak loads to simulate during performance tests. learn how load generators, memory leaks, and heap memory impact Java applications.

Identify the four types of performance tests—load, soak (endurance), stress, and spike tests—and learn how each validates performance and probes memory management and system behavior under various workloads.

Analyze the load profile and peak load to design and execute a load test that reproduces steady-state traffic with ramp up and ramp down, using production data and workload model.

Perform a stress test to determine the application's breaking point and how performance degrades under high load. Increment load in small steps to observe breakpoints.

Perform endurance or soak testing to evaluate memory management and uncover memory leaks during long-duration runs, revealing issues that surface under sustained load.

Explore how four performance tests relate: load, stress, endurance, and spike, by visualizing a time versus users graph and comparing magnitude and duration.

Master performance testing terminology and its building blocks to speak confidently as a tester. Revisit the terms and stay updated through this course to jump start your understanding.

Explore the complete web performance testing lifecycle from requirements analysis to deployment and production monitoring. Learn the key deliverables, including performance requirement document, test plan, execution report, and lessons learned.

Identify and categorize performance requirements to meet SLAs and user experience, distinguishing explicit, user-visible response times from implicit throughput and infrastructure targets managed by IT.

Explore the importance of response time as a key performance requirement, and how percentile-based measurement using tools like browser dev tools and VI slow HTTP watch reveals end-user performance.

Identify implicit performance requirements, including throughput, CPU and memory utilization, and error rate targets. Learn how headroom and saturation influence end user response times.

Decide which performance tests to run by risk and context, selecting load, stress, endurance (soak), and spike tests as appropriate for greenfield, maintenance, and external use.

Learn how to build a practical performance test environment by sizing to 50–60% of production, leveraging UAT for testing, and managing data and time-sharing to avoid clashes.

Develop a workload model that captures transaction mix, concurrency, and background jobs to simulate production load, using logs and interviews; apply Pareto analysis for greenfield or maintenance scenarios.

Plan test data preparation for performance testing by creating large fresh transactional datasets and using scripted data generation or selective production dumps for planned and unplanned executions.

Protect production data by masking, scrambling, or anonymizing it before moving 1 million records to a performance test environment, using tools like Oracle, Informatica, or Delphix.

Prepare performance test scripts by understanding functional flows through walkthroughs and manual validation with the testing team. Then record, debug, parameterize, and correlate scripts in an aligned test environment.

Plan load generation strategy by selecting where to generate load from and how much capacity to simulate, aligning with geographic user bases and load generator memory footprints.

Execute and analyze load, soak, endurance, and stress tests with dry runs and progressive load, validate twice, document results, and raise defects before deployment.

Explore why the arithmetic mean can misrepresent data with outliers, using salary and response-time examples, and introduce standard deviation as the measure of data spread.

Learn the fundamental performance laws: utilization, Little's law, and forced flow—and how they relate throughput, service time, think time, and validation in performance testing.

Explore how browser caching stores resources in memory to speed page loads. Compare caching on and off with developer tools and observe impacts on load times during testing.

Explore http requests such as get, post, put, delete, options, and head, and learn how get retrieves resources with 200 responses and post creates resources with 201.

Explore how http headers convey information in requests and responses using name-value pairs. Examine headers such as accept-encoding, accept-language, and content-type to understand values, q factors, and server communication.

Understand URL encoding and decoding through a search query like mike stand, where plus signs become %2B in the URL and are decoded back by the server.

Solve hands-on exercises to calculate mean, standard deviation, and 50th to 99th percentiles for two load-test data sets, compare dispersion, and discuss uses of standard deviation.

Explore how jmeter loads tests and measures performance across web, soap and rest web services, and databases, using a protocol level tool with a test ide, cli, and plugins.

Discover the Apache Software Foundation, a volunteer-driven, open source community established in 1999 that maintains Jmeter and 320 active projects with 8400 committers, illustrating open source life cycle and contributions.

Explore Apache Jmeter, an open source 100% Java tool for load and performance testing of static and dynamic resources, including http/https, API testing, multithreading, and plugins.

Compare Selenium and JMeter at protocol level, highlighting how Selenium operates at the DOM/UI level with webdrivers, while JMeter sends and analyzes server requests without launching a browser.

Explore the Apache Jmeter website to access downloads, release notes, documentation, user manual, and tutorials, and learn to leverage components, functions, properties, and plugins for effective distributed testing.

Install the Java Development Kit 1.8, configure JAVA_HOME and PATH, and verify the Java installation for use with JMeter in web performance testing.

Learn to download, extract, and install Apache JMeter 5.6.3, launch the GUI via the jmeter.bat file, and create a desktop shortcut for quick access.

Explore the jmeter folder structure: bin contains startup scripts and templates, docs holds documentation, lib stores jar files and plugins, and an extra subfolder hosts ant files and jmeter archives.

Navigate the Jmeter UI to explore menus, look and feel, log viewer, and language options, then use the left explorer and right details pane to run and debug tests.

Explore the key Jmeter components, including test plan, thread group, samplers, controllers, listeners, timers, assertions, config elements, and pre and post processors, and how they model load tests.

Build and run an apache jmeter script to test the jmeter apache.org home page and user manual, using a thread group, http samplers, and a view results tree listener.

Build JMeter scripts by combining thread group and HTTP sampler to access the JMeter website home and functions pages, and compare response codes, messages, and load times.

Demonstrates solving assignment part 1 by launching jmeter, building a test plan with thread group and http samplers, and using view results tree to capture response code and load time.

Leverage the JMeter HTTP sampler to analyze a component reference page, recording size in bytes, header and body sizes, request body and headers, and latency versus load time.

Demonstrates building and validating a JMeter post request to purchase.php with flight, price, airline, and port parameters, comparing JMeter output with browser payload to ensure accuracy.

Learn why assertions matter in JMeter scripts and implement a flight booking test on Blaze Demo Dotcom with http samplers, a thread group, a test plan, and a listener.

Learn to validate functional flow in jmeter using assertions, including response and text assertions, to verify server responses contain expected text like 'flights from Paris to London'.

Apply JMeter assertions to verify loaded pages by checking text presence on blaze demo and workhorse pages, including response code, duration, and size checks, with custom error messages for failures.

Solve part one of the assignment by building a JMeter test with a thread group, HTTP request, and a response assertion for the blaze demo home page.

Learn how to use duration assertion in jmeter by setting a milliseconds threshold, applying to main samples, and validating that load time stays under the expected value.

Learn to validate server payloads with size assertion in JMeter, comparing expected byte counts to actual responses, and exploring greater than, less than, and equal conditions.