11 Load Testing Best Practices

When your website gets hit with a traffic surge, can it handle the pressure, or will it crash and burn? Receiving the answer during the ongoing operation is not an option, as it can lead to huge revenue loss. For example, Costco lost an estimated $11 million in sales during a 16.5-hour website crash on Thanksgiving in 2019 because its system couldn’t handle the rush.

Is there a safety net that keeps your system running smoothly when demand spikes? Yes, it’s load testing that allows assessing system performance in advance and stands between you and a meltdown when it matters most. Let’s talk about how to do it right.

Ecommerce testing and QA services ensure that online stores are prepared for traffic surges and function without downtime during high-demand periods.

Quick Tips for Busy People

Here is a quick overview of the core load testing best practices:

• Define clear testing goals. Don’t test blindly. Know whether you are testing response time, scalability, or resilience.
• Base hypotheses on real data. Avoid assumptions. Test real scenarios that reflect your actual user behavior.
• Simulate real-world load. Load isn’t evenly distributed as users interact unpredictably. Test with realistic traffic patterns to capture true system performance.
• Build an accurate load profile. Some systems experience peaks, others sustain constant traffic. Know what applies to you.
• Pick the right tools. JMeter, Gatling, LoadRunner, Artillery — choose based on your tech stack and goals.
• Start with performance testing. Before applying load, check if your system works fast under normal conditions.
• Iterate, don’t overload. Don’t start with 100,000 users. Build up gradually to find breaking points before a complete meltdown.
• Watch for positive bias. Don’t trust the results if they only show good news. Hunt for failures.
• Avoid false causality. Just because something fails under load doesn’t mean the load itself caused the issue. Dig deeper.
• Test early, test often. A once-a-year load test won’t catch gradual performance decay. Make it a habit.
• Analyze beyond surface-level metrics. Inaccurate response time might not be an API issue, it could be a caching problem. Look at the whole picture.

And now, it’s time for a closer look.

Step 1: Preparation

Several load testing best practices begin before launching the initial virtual user. Getting JMeter installed and tests running is not the starting point. Without a strategy, it’s easy to overlook the most important aspect: a clear idea of what you’re actually testing.

Defining testing goals

Before launch tests, you and your team should answer the question: “What, exactly, are you testing?” Very often, the response is “performance,” which doesn’t actually mean anything until you define it.

Are you tracking response times to ensure that your service replies to HTTP requests quickly, regardless of system load? Maybe you’re watching how many requests per second your system can handle before falling over. Maybe you’re looking at resilience, simulating system failures and measuring how long it takes for your application to recover. For custom commerce solutions, load testing should reflect unique shopping behaviors, including batch checkouts and high search traffic.

Load testing without purpose is like debugging without logs — a guess in the dark. Clarity is always the first step.

Carefully choosing the testing hypothesis

So, you’ve defined what you’re testing. Now, it’s time to set a clear, measurable hypothesis. Without one, turning your load testing strategies into actionable insights can be a challenge.

Consider a traditional example. A team says, “The system won’t handle high traffic.” That’s not a hypothesis; that’s an assumption. High traffic when? How many users? A genuine hypothesis would be: “At 1,000 concurrent API requests, response time will exceed 2 seconds.” Now that’s testable.

A second frequent mistake is load testing the incorrect component. A team may assume, “More users will slow down our database,” when the actual bottleneck is caching or API rate limiting. To prevent this, always monitor the load on every system component, such as databases, APIs, and external dependencies.

And then there’s one more load distribution trap. Saying “Our system can handle 10,000 users per minute” is meaningless without context. Are the users coming in a steady stream, or is there a burst at the beginning? A system that can cope with steady traffic may still fall over with a flash flood, so you must divide tests into constant and peak loads, modeling different situations.

Simulating actual loads

One more load testing challenge is relying on averaged-out conditions that don’t very well reflect actual traffic patterns. In this way, important edge cases can be missed, creating blind spots in performance visibility.

When you’re testing an ecommerce website, it’s simple enough to simulate traffic in predictable intervals, but actual user behavior is much more erratic. Spikes in sales, last-minute purchases, and holiday rushes all generate patterns that an averaged plan might not be accurately modeling. All these scenarios should be taken into account to ensure resilience during operations.

For example, for a microservices-based ecommerce system with REST APIs, you can test the following user behavior:

A good load test mimics real users who hesitate, refresh pages, abandon carts, and return later. If your test is too neat, your system might pass but still fail spectacularly in production.

Building a load profile

Not all systems traffic alike. A property search engine might see constant, low-level searches throughout the day, but a concert ticketing website sees massive spikes the moment tickets go on sale. An ecommerce site has to deal with a mix: predictable orders every day and crazy flash sales.

To properly test, you need to understand your traffic patterns:

Load testing strategies with one-size-fits-all traffic patterns won’t prepare you for reality. A solid load profile makes the difference between a system that scales gracefully and one that buckles when it matters most.

Selecting testing tools

Selecting the appropriate load testing tool is based on your system design and testing requirements. Some of the most popular tools include:

There’s no universal tool since, as always, it all depends on your tech requirements.

Step 2: Execution

With a clear plan in place and an experienced team that offers back-end development services, it’s time to put the load testing strategy into action.

Start with performance testing

Set a baseline before you stress test your system to the breaking point. If it can’t support normal levels of traffic, scaling the load will simply replicate current issues.

Test key metrics first:

• How fast does your system handle simple GET requests?
• What happens when a single user makes multiple requests?
• Are slowdowns due to database queries, caching, or external APIs?

Typical errors are misconfigured test setups (e.g., incorrect Constant Throughput Timer settings) and underestimation of async requests, which may result in hidden bottlenecks.

To get meaningful results, ramp up the load gradually instead of overwhelming the system all at once. Only after a solid baseline should you increase traffic and simulate high-load scenarios.

Additionally, performance optimization techniques for SAP Commerce such as Apache JMeter load testing can help identify scalability bottlenecks in SAP Commerce, ensuring that the system maintains efficiency under increased demand.

Follow an iterative approach

Scaling to 100,000 users too soon will uncover latent performance problems, although, with the incorrect strategy, you might have trouble identifying the culprit.

Load testing should always be iterative. Start small to catch obvious weaknesses, then scale up.

This step-by-step approach enables you to detect failures as and when they arise rather than having to confront an entire breakdown.

Avoid positive bias issue

It happens all the time: teams execute a load test, get silky-smooth results, and decide everything is okay. But it’s not always that bright.
A test being stable doesn’t automatically mean that the system will be stable. Look at some examples below.

Mistake 1: testing under insufficient load. A team tests with 100 users, doesn’t notice any issues, and assumes the system will do equally well with 10,000. This is a classic case of inadequate load testing, leading to false confidence in stability.

To avoid this, employ ramp-up testing, wherein you gradually add user load and mimic real-world traffic surges.

Mistake 2: not considering asynchronous operations. API response times don’t capture enough on their own. Background tasks, like message queues in Kafka or RabbitMQ, can silently build up, piling on a backlog that isn’t always visible. The system might seem okay, but falling behind on processed tasks can cascade into failures.

The solution here is to look beyond API response time and monitor background process latency with tools like OpenTelemetry to track a request’s full lifecycle. That way, hidden bottlenecks won’t slow system performance.

Eliminate false causality

One of the dangers of load testing is confusing correlation with causation. Just because response time went down after you released a new feature doesn’t mean that the feature caused the slowdown. Explore some more examples.

Mistake 1: jumping to conclusions. A team sees increased latency following a server update and attributes the problem to the hardware. However, the actual problem may be caused by a configuration adjustment, a new database query, or latent code inefficiencies.

The solution is to look into all potential reasons first before you assume. Hunt for recent configuration changes, database performance issues, and code changes to determine the real cause of latency.

Mistake 2: misinterpreting performance bottlenecks. Traffic spikes, and database performance drops. The immediate assumption is that “More users are overloading the database.” But the actual reason could be JDBC connection limits, query locks, or poor indexing. Without examining database metrics, such as connection pools and query execution time, teams may prematurely optimize the incorrect system component.

To diagnose this, don’t just depend on user load analysis but also watch database metrics. e.g. connection pool utilization, query response time, and indexing efficiency to see where the actual bottleneck is.

Step 3: Analysis

Once your test is complete, the next pool of work begins. You get to figure out what the numbers really mean. You won’t know the truth based only on one test result. It’s the trends and patterns over time that inform you about system behavior.

Remember that good analysis isn’t just about collecting data, it’s about interpreting it correctly to make informed improvements.

To Sum Up

Load testing best practices is a checklist of technical tasks and a critical activity for identifying system weaknesses before they affect actual users. However, remember that a test well scripted on paper is useless if it doesn’t model reality.

Skipping gradual load increases? You might miss hidden bottlenecks. Ignoring async operations? Background operations can silently backlog. Assuming database slowdowns are due to high traffic? The real culprit could be elsewhere.

At Expert Soft, we help teams uncover these hidden risks before they turn into failures. Want to ensure your system can handle real-world traffic? Let’s talk load testing strategies!

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