Load testing service is a key step in development. It helps to identify bottlenecks and improve performance. It helps to make sure that the software works without a hitch, even when the traffic is high.
It’s easy to make common mistakes during load testing. This can lead to bad test results. Of course, in this way, you’ll miss the real problems until the real users find them. Here are the top mistakes to watch out for — and avoid.
1. Not Simulating Real-World Traffic
One of the biggest mistakes in load testing is not using realistic traffic. Many companies test their systems with fake or overly simple patterns, which don’t show how they would behave with real users.
For example, if the test just sends a steady stream of requests without any changes, it won’t reflect how real people act, because real users do things randomly and at different times. In the real world, user traffic is unpredictable, varied, with varying types of requests, sessions, and behavior.
To avoid making this mistake, ensure that your load testing includes the following:
- Find behavioral differences in users. Test things like browsing, searching, adding items to a cart, or clicking on multiple pages.
- Test traffic spikes, like during a big sale or promotion.
- Change how long users stay, since not everyone browses for the same amount of time.
- Use different devices, like phones, tablets, and computers.
When you mimic real-world user behavior like this, you’ll have a better idea of how your system will handle actual traffic. With the help of professional services for load testing by teams like PFLB, you can avoid these things, too.
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2. Ignoring Resource Bottlenecks
A common mistake is focusing only on how fast the system responds. While response times are important, they don’t tell you the whole story. You also need to monitor other critical resources, like CPU, memory, and network usage. If any of those parts get overloaded or run out of resources, the system can slow down, too.
If you only look at speed, you might miss hidden problems. Here’s what you should keep your eye on:
- CPU usage. Watch for high spikes, which could mean the system is struggling.
- Memory usage. Check for signs of memory being used up too fast or not being released properly.
- Network bandwidth. Make sure your system can handle sending and receiving a lot of data without slowing down.
- Disk I/O. Keep an eye on how often the system reads from or writes to the disk, as this can affect speed.
If you keep track of these resources or get load testing professional services from teams like PFLB, you will be able to spot the bottlenecks.
3. Insufficient Test Coverage
Some teams perform load testing with limited coverage. In this way, they can miss out on issues in less frequently accessed areas.
Load testing is generally limited to verifying basic functionality. However, more advanced features can suffer from performance degradation under heavy load, too. Testing only the most accessed functionalities can lead to an inaccurate representation of the real performance.
Make sure to cover all areas of interest, including:
- Critical user journeys. Validate all key workflows, such as login, checkout, and registration.
- Edge cases. Simulate unexpected but plausible user behavior that might stress the system.
- Third-party integrations. Integrate third-party APIs, payment gateways, or external services into the tests.
- Backend processes. Test database queries, data processing, and other server-side operations.
The more comprehensive your load test coverage, the better you can identify hidden performance issues and ensure the system can handle all types of traffic.
4. Overlooking Scalability
As important as it is to test how the system holds up versus current traffic levels, it is also essential to know how the system will hold up when the traffic increases.
Most teams only perform load testing to determine whether the system can handle projected traffic when it launches. They don’t account for future expansion or increased load from holiday surges, campaigns, or new feature rollouts.
To avoid this mistake, perform load tests simulating the following:
- Growth scenarios. Load test the system at increasing amounts of traffic to simulate future user growth.
- Stress testing. Load test the system above its anticipated capacity to observe its breaking point.
- Auto-scaling behavior. Test the system’s behavior when resources are scaled up or down automatically.
- Seasonal spikes. Simulate peak-demand periods like Black Friday sales or holiday shopping.
By scalability testing, you can make sure that your application will keep performing well as traffic grows and get ready for future demand.
5. Not Analyzing Test Results Thoroughly
Load testing generates a huge amount of data, yet rarely is the result read in great detail by organizations. One of the pitfalls is to focus on metrics like response time or throughput but not to pay attention to other important measures, such as error rates, resource utilization, and server logs.
When analyzing load test results, keep in mind the following:
- Error rates. Check for high error rates (e.g., 500 errors) at high loads.
- Utilization of resources. Monitor CPU, memory, and disk usage to identify resource bottlenecks.
- Server logs. Inspect server logs for warnings, exceptions, or failures that occurred during testing.
- Response times and throughput. While response time is important, throughput and request success rates are helpful too.
A close examination of the data will provide a clearer understanding of the system’s performance and enable you to spot elusive issues.
6. Omitting Load Testing in the Staging Environment
A staging environment should mimic the production environment so that teams can test under circumstances as similar to real-world use as possible. Testing under load in staging allows you to catch and fix performance issues before they have an impact on users.
To avoid skipping load testing in staging:
- Duplicate production conditions. Make sure that your staging environment is as close to production as possible in infrastructure and configurations.
- Test with realistic data. Create an environment as realistic as possible using production-like data.
- Check scalability. Make sure your staging environment can mimic high traffic and growth scenarios.
By load testing in staging, you reduce surprises in production and ensure that your system is performance-optimized before going live.
Conclusion
Load testing helps you make sure applications can handle high traffic. Good load testing will catch emerging performance problems in advance, mitigate the risk of downtime, and provide a satisfying user experience during peak usage levels. Make sure to include these best practices in your testing to avoid costly errors and to maximize application performance.
