How do you plan a realistic workload model for jmeter testing?

As a QA Lead, planning a realistic workload model for JMeter testing always starts with clearly defining the test objectives. I once worked on a project where the client was concerned about their system's performance under heavy traffic. We defined our objectives to measure response time, throughput, and error rates across different load levels. Setting clear metrics and thresholds, like ensuring response times stayed under 3 seconds, guided our approach. This clarity not only helped us focus our efforts but also provided the client with actionable insights, ultimately leading to a more resilient system.

Understanding the objectives of the performance test from customer is the critical phase for a successful performance test. As a performance Engineer, you may need to work with Developers, business analysts and product owners to determine the objectives. The goal may look simple like I want to see if my application supports 1000 concurrent users without any issues, but you need to ask follow up questions like - How long the 1000 users should stay on the application to be considered supported - What % of errors are acceptable - what is the acceptable response time at 1000 users - Is it 1000 users navigating through the same use case or mix of use cases - Do you anticipate any concurrency for a given step

Evaluate system response times under different user loads to ensure that performance meets specified requirements. Identify and measure performance bottlenecks, such as slow database queries, inefficient code, or resource contention issues. Validate system scalability by determining the maximum number of concurrent users or transactions the system can handle without degradation in performance. Assess system stability and reliability under sustained load conditions to identify potential issues like memory leaks or crashes.

To plan a realistic workload model for JMeter testing: 1. Define Test Objectives: Start by setting clear goals. Decide what you’re trying to measure, like response time, throughput, or error rates, and establish acceptable limits for each, like a max response time of 2 seconds. 2. Understand User Behavior: Think about how users actually interact with the system, including peak traffic times and common actions. 3. Focus on Key Scenarios: Identify critical workflows that need testing. 4. Model User Load: Simulate real-world traffic by adjusting concurrent users, requests, and pacing to match expected conditions. 5. Consider Edge Cases: Plan for unusual situations, like traffic spikes or system crashes, to ensure stability under stress.

Planning a realistic workload model for JMeter testing involves understanding your application's usage patterns and simulating those patterns under different scenarios. Identify critical scenarios and estimate the number of concurrent users, considering peak load. Define ramp-up and ramp-down periods, incorporate realistic think times, and simulate geographically distributed users. Vary browser and device types, handle dynamic data, and include variations in data volume. Consider error scenarios, determine test duration, run iterative tests with different workload models, and establish baseline testing. Collaborate with stakeholders to align the workload model with realistic scenarios and user expectations.

Gather information about the application's users, their demographics, and their typical usage patterns. Understand how users interact with your application, including actions they perform, pages they visit, and workflows they follow. Identify key use cases and user journeys that represent typical interactions with the application. Break down each use case into individual steps or actions, such as logging in, searching for products, adding items to the cart, and checking out.

For any kind of performance modelling you need to understand the application workflow, application architecture and required business flow. To know all this you need to collaborate with client development team where they will give you the understanding of application architecture then if there is part of application that is already in production then you can analyse the business data for the modelling. Once you have clear picture then there are set of things that you need to understand and if in doubts then need to ask client or application team- 1. Identify use cases-Identify the primary use cases or user scenarios that you want to test. 2. Define User profile(if needed). 3. Determine workload pattern. 4 Set goals(Performance metrics)

This is a Key Step in the whole process. If the System is already live and running, Grab the Weblogs (Apache or similar) and analyze it using (Splunk, ELK Stack, WeblogExpert, GoAccess etc). List out the transaction profile in 3 categories 1. Most Frequently used scenarios 2. Most business critical scenarios 3. Neither 1 nor 2, But scenarios like reporting or other complex use cases that could consume system resources. Use Pareto Principle, and identify ~20% of this scenarios that could count around ~80% of total transactions. This would be best start that has worked for me 20+ years But if the System/Application is new, Discuss with Business, Dev, DBA and identify use cases based on the above outlined categories

When planning a realistic workload model for JMeter testing, I recall a time when we needed to identify the key scenarios for a complex e-commerce platform. As a QA lead, I guided my team to focus on the most critical user actions like login, product search, and the checkout process. We also included scenarios for edge cases, such as incorrect inputs during payment. This approach ensured our testing was thorough, capturing not only the common user paths but also potential points of failure. The insights gained from these scenarios helped us optimize the system's performance and ensure a seamless user experience.

It is helpful to have data from real users, knowing which is the usual behavior and load in a common day or when the system requires to do the extra mile and which are the reasons because this happens. Information from Google Analytics, monitoring and/or observability systems will bring details for modeling tests as close as possible to reality. For representing users' behavior it is high-priority to understand how flows work, when and how dynamics values are generated. Parametrizing and correlating are critical steps on this process, as well as data treatment. Smoke tests help validating script fitting, first with 1 user and later with multiple like 5 or 10 top, to verify behavior on concurrency before starting the planned executions.

Gathering information about the application's user base, including demographics, user roles, and typical usage patterns. Identify common user actions, workflows, and scenarios that users perform while interacting with the application. Determine the mix of transactions or requests associated with each user action or scenario. Estimate the frequency and distribution of each transaction type, such as HTTP requests, API calls, database queries, or file uploads. Calculate the total number of concurrent users and the rate of user arrivals for each user segment based on the estimated transaction rates. Determine the distribution of users across different user segments to reflect realistic usage patterns.

Usually load tests are the most known in performance testing but you may want to consider, depending on the suitable needs, scope and coverage, to do other test types as stress, endurance, scalability or peak. Also it is significant to choose the proper duration for each test, considering relevant information analyzed. Something common these days is to use a tools combination if tests must have more concurrency or distributed geographical access. JMeter scripts can be loaded to cloud providers like BlazeMeter or Octoperf allowing to scale them.

In JMeter, use the Thread Group to simulate concurrent users. Configure the number of threads, ramp-up period, and loop count based on the expected user load. Use CSV files or JMeter variables to parameterize test data, such as usernames, passwords, or input values, to make the test more realistic. Use timers to simulate think times between user actions. This helps in creating a more realistic load on the application. Use JMeter's built-in monitors or external tools to monitor the resource utilization of the application under test. This helps in identifying bottlenecks and optimizing the workload model.

The closer the Jmeter simulation represents the real application workload distribution, the test can be realistic and can help identify critical hotspots

First, make sure your virtual users cover all the steps required to complete a happy path. If it's an e-commerce application, this will be completing the purchase. Once that's covered, consider the unhappy paths - dropouts, shopping cart, abandonment, payment drop outs, etc. You should be able to determine the ratio of these actions from the application logs.

User behavior changes over time. Continuous monitoring and validation can help you cover the gaps in your test scenarios, leading to greater accuracy. To refine your model proactively, check for any upcoming new features or business events with stakeholders to make sure your tests cover new events and features. There will be more of them with the growth of the company.

The most common pitfalls of workload profiling is the infamous coordinated omission problem, mostly related to closed workload model. Implementing open workload model can give you more realistic and accurate measurements as your throughput will no longer be bound to response times of your application.