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Whether you’re building web applications, mobile applications, or something else entirely, performance and scalability go hand in hand. You can expect a system that scales poorly to eventually be outperformed and replaced by a competing system that scales well. So, how do you ensure the longevity of your software system? The first step is to find out where in your software architecture your performance is being throttled.
This article will go over the basics of performance testing, common performance bottlenecks, and some strategies and tools for finding bottlenecks. We’ll also sprinkle in some questions to help you test your knowledge. Afterward, we’ll wrap up with some resources to check out if you want to learn more about some similar topics.
Let’s dive right in!
Try one of our 300+ courses and learning paths: Performance Test Automation 101: Gatling, Lighthouse, and Jenkins.
Performance testing is primarily concerned with testing a system or application to ensure that it maintains its performance under increased load and edge conditions. Performance testing is typically handled by a quality assurance (QA) team or by your development and operations (DevOps) team.
Performance testing is less about finding bugs in the software and more about identifying and removing performance bottlenecks. Bottlenecks are individual points that limit an application’s performance. These bottlenecks are usually due to inappropriate architecture, poor hardware choices, or faulty implementations.
“I say an hour lost at a bottleneck is an hour out of the entire system. I say an hour saved at a non-bottleneck is worthless. Bottlenecks govern both throughput and inventory.”
Eliyahu M. Goldratt
Without performance testing, your system can suffer from slow runtimes, poor scalability, prolonged response times, and other inconsistencies that contribute to a negative user experience.
A system that runs suboptimally or is prone to failures will eventually need to resolve issues by reviewing code and codebases. Performance testing is necessary, and it’s also something that you should be doing regularly for each build to make sure that your system runs reliably. Even short periods of downtime can be costly.
Performance testing can demonstrate that a system meets a standard of performance or as a comparison between two systems to see which one performs better.
Reliability: “How consistently does this system perform under various conditions?”
Scalability: “What is the maximum workload or user load that this system can handle?”
Speed: “How fast is this system running?”
Stability: “Is this system resilient to failure in different conditions?”
Scalability Thought Exercise
How many end-users will your application need to support at release?
Now, think about how many users it’ll need to support a year or two from then.
These are some common metrics used for performance testing.
Great performance testing starts with great workload modeling. Aim to mirror how real users behave, not just how many requests your tool can fling.
Identify critical user journeys: sign-in, search, product view, checkout, report export, API hot paths. Weight them by real traffic share.
Concurrency, RPS, and think time: relate active users (N), average response time (R), and throughput (X) with Little’s Law: N ≈ X × R. Don’t delete think time; model it. Removing it inflates throughput and hides server-side contention.
Ramp-up and ramp-down: increase load gradually to avoid artificial spikes in connection setup, TLS handshakes, JVM warm-up, or autoscaling noise.
Warm-up and cache effects: run a short warm-up to JIT/hot paths and prime caches, then measure steady state. Also run a cold-start pass to understand worst case.
Data correlation and parameterization: avoid replaying identical requests. Correlate dynamic tokens, vary inputs, and ensure unique data where the system expects it (user IDs, order numbers) to surface realistic contention and lock behavior.
Test data management: seed representative volumes, indexes, and cardinality. A tiny toy dataset can make slow queries look fast.
Environment parity: if you can’t mirror prod, at least keep tier counts, network hops, and scaling policies consistent. Then apply scaling factors when interpreting results.
Performance testing methodologies can vary depending on your specific needs, but for the most part, they tend to follow the same basic steps.
Ideally, you’ll want to be able to choose an appropriate performance testing tool and testing environment. If you don’t have control over these factors, you’ll want to know as much as you can about your testing environment before designing tests. During this stage, you’ll need to identify relevant information related to the:
Knowing what environment you’ll be working in and what tools will be available will help you identify potential roadblocks before testing begins.
Next, you’ll want to identify which key performance indicators will provide the information you need. You will also need to identify realistic benchmarks, constraints, and other performance-related criteria.
At this stage, you should be thinking about what “good performance” actually means for your application. There isn’t an industry-standard definition of what good performance entails, so you’ll have to base your goals around what good performance would look like from the perspective of your end-users.
Here are some questions to consider when defining your KPI benchmarks:
In this phase, you’ll be thinking about how usage may vary among end users, as well as typical use cases. Consider different scenarios that reflect real-world platform traffic and traffic spike conditions, and then design your tests to examine how a system performs in these situations.
Then, create a plan for how the performance testing will take place. Once relevant stakeholders have approved the proposed method and designs, you can move on to configuring the test environment and script development.
During this stage, you or your business should configure and prepare any tools or resources needed to perform the test.
Ideally, your performance test environment would be a 1:1 mirror of the deployment environment, but that’s often impractical due to the cost and complexity required to reproduce your server content and architecture.
In general, you do want to make sure that the number of application tiers in your performance testing environment is identical to the live tier deployment model. It’s also better if the size of the application database is a close approximation of the live one.
Develop and test your performance test. You want to run a pilot test to check and see if your performance test can measure the targeted KPIs.
You can now execute your performance tests. During the performance tests, you will be monitoring and recording performance data.
Once you’re done gathering performance data, you can start analyzing it and identify different ways to improve platform performance.
The results are the most critical deliverable for performance testing. Create a descriptive and concise test report. Share your findings with the developers, along with any recommended solutions. Then, retest!
Prefer percentiles over averages: report p50/p90/p95/p99. A low average can hide a painful long tail that users actually feel.
Watch the curve shape: as you raise load, healthy systems show throughput rising and latency staying flat until a knee point, then latency climbs quickly and errors appear. That knee is your practical capacity for the tested scenario.
Track saturation signals: rising CPU steal, GC time, run-queue depth, connection pool waits, DB lock/wait events, thread pool saturation, 5xx/timeout rates. These point directly at the limiting resource.
Separate client and server time: DNS, connect, TLS, TTFB, content download. Knowing where time is spent prevents misattribution.
Control coordinated omission: make sure your tool keeps scheduling new requests on time even when the system slows, otherwise the worst latencies won’t be measured.
Compare to a baseline: always keep a recent baseline run. Regressions pop out when you diff percentiles and error rates at the same load.
Deliver a brief narrative with plots: what you tested, the knee point, the bottleneck indicators you observed, and the smallest change likely to move the needle. Then retest to confirm.
Whether you’re testing in-house or working for a client, you’ll need to select and use a testing tool that is compatible with the application you’re testing. This is an important step that is often overlooked and can lead to unnecessary problems down the road.
Here are some of the most popular performance testing tools used today.
LoadRunner is a highly efficient industry-standard performance testing tool that supports performance for Windows, Mac, and web-based applications. It specializes in finding bottlenecks before the application is implemented or deployed. LoadRunner can be a somewhat costly tool to use but comes with a slew of features.
Key features:
LoadRunner supports the following integrations:
WebLOAD is a flexible performance testing tool that would be a solid choice for small, medium, and large enterprises. WebLOAD is capable of simulating hundreds of thousands of concurrent users.
Key features:
It also provides the following integrations:
JMeter is a popular open-source load and performance testing tool. It supports various applications, servers, and protocols, including HTTP, TCP, SOAP, Web, LDAP, etc. It is one of the most popular performance testing tools available but is less efficient at detecting threats in large-scale applications than in small applications.
Key features:
JMeter is compatible with the following integrations:
Your program runs, software bugs have been addressed, and the internal logic of the program is solid. However, your program is just not performing as well as it needs to for some reason. Software systems can be complex, and that complexity tends to scale as a company grows. Where complexity abounds, so do performance bottlenecks.
A software system can become so complex that the source of these bottlenecks can become obscured to even the most experienced engineers. Luckily, profiling can make it much easier to locate bottlenecks.
Performance testing is most often a proactive measure to address bottlenecks and other performance issues in your application. On the other hand, profiling is a reactive measure that helps finetune the performance testing process.
If your test data shows a problem with your system, you need to find out where that performance problem is. Finding out where a system is running slow is difficult, especially for modern applications with hundreds of thousands of lines of code and numerous components.
Profilers help programmers see which sections of a program use up the most time and resources. Profilers measure the duration and frequency of function calls while considering the time complexity and available memory of a program. If performance issues occur, be prepared to diagnose the issue with a profiling tool like Prefix or Scalene before jumping into optimization.
“Rushing to optimize before the bottlenecks are known may be the only error to have ruined more designs than feature creep.”
Eric S. Raymond
Bake performance testing into your delivery pipeline so regressions never surprise you in production.
Define a baseline: record percentiles and error rate for a stable build at a fixed load profile. Store as the reference.
Set a performance budget: for each API or page, declare targets like p95 ≤ 300 ms, error rate ≤ 0.1%, CPU ≤ 70%.
Gate the build: run a short smoke load on every PR and a deeper scenario nightly. Fail the pipeline when budgets are exceeded.
Tie to SLOs/SLIs: align budgets to user-visible objectives (availability, latency). Your tests should prove you’re on track to meet them.
Version tests with code: keep test scripts and test data under source control. Changes to endpoints or payloads travel together with the application.
Report where developers live: post summaries and percentiles to pull requests, with links to the full run and graphs.
This turns performance testing from an ad-hoc event into a habitual safety net.
Test your knowledge!
A system has low latency and high throughput for 10,000 users running concurrent sessions. If this system is scalable, what would happen to its performance when increasing the number of concurrent users to 100,000?
Performance would be disrupted for all users
Performance would be disrupted temporarily, but return to baseline after a period of time
There would be no impact on the overall performance of the system whatsoever
Performance would be disrupted for some users, but not all
Try one of our 300+ courses and learning paths: Performance Test Automation 101: Gatling, Lighthouse, and Jenkins.
By now you should have a rough idea of what performance testing entails, and why it can be helpful for software development. Thinking about system performance can get you to consider a solution’s reliability, robustness, ease of maintenance, and potential for scalability. So, if you’re just starting your journey to becoming a software engineer, or are considering a career in DevOps, you’re already ahead of the game!
To continue learning about these concepts and more, check out this Educative course on Performance Test Automation 101.
Happy learning!
