What Your Team Should Know About Load Testing vs Performance Testing

You need to stress out your software. People like to avoid pressure. But it’s the only way to build resilience. Every great person has overcome great adversity. And every memorable product is one that you subject to professional scrutiny.

Today, we talk about how to turn targeted struggle into an asset. We’ll discuss performance vs load vs stress testing, their distinct benefits, and use cases.

Are Load Testing and Performance Testing the Same?

No. The terms are often used interchangeably. That’s just what happens in the IT chaos. And that’s why you may also come across terms like load performance testing or performance load testing. This duo seemed to have amalgamated together. But we need to make sure there’s no confusion. So, let’s get a closer look at what is load and performance testing separately.

What Is Performance Testing?

Performance testing is an umbrella term. It covers procedures related to checking your app’s operations under various conditions. To determine the exact QA services needed, “performance” was divided into different types of tests.

• Load testing – investigating performance under expected load conditions.
• Stress testing – going beyond maximum load to find breaking points.
• Spike testing – checking the system’s response to sudden load spikes.
• Endurance (soak) testing – subjecting the system to normal loads for long periods.
• Scalability testing – evaluating how the system scales as load increases.
• Volume testing – working with large amounts of data.
• Capacity testing – finding the maximum capacity before performance degrades.
• Baseline testing – establishing performance benchmarks for comparison.
• Configuration testing – testing different system configurations for optimal performance.

That’s how the performance vs load testing conundrum came to be. The latter is the most common type. So, the two kind of became synonymous.Let’s stop dragging load testing with us for a bit and focus on the pioneer of software resilience.

The Goal of Performance Testing

The goal of performance testing is to measure overall system behavior in terms of speed, reliability, resource usage, and scalability. The insights you obtain from these tests help you:

• Identify performance bottlenecks.
• Validate system scalability.
• Ensure that the system meets the defined performance requirements.
• Determine app readiness for high user loads, etc.

When to Run Performance Testing

Performance testing is typically run after functional testing and before deployment. You should always execute it in environments that closely resemble production. Doing so means carrying out an authentic examination. From it, you can get the most accurate, real-world view of your product.

You should use performance testing:

• Before the launch of a new application or a major update.
• After infrastructure changes (e.g., server upgrades).
• After implementing performance-related fixes.
• When expanding the user base or preparing for increased traffic.
• During periodic system evaluations to maintain long-term performance standards.

Performance Testing Metrics

When you carry out performance testing, you need to pay attention to metrics that let you determine your app’s productivity. There’s quite a lot of them. So, if you don’t feel like going through the complete list, scroll to the end of this section for a summary of the most used and relevant metrics.

Response Metrics

• Average response time – the mean time taken to respond to all requests.
• Peak response time – the longest time taken for a single request.
• Minimum response time – the shortest time taken for a single request.

Throughput Metrics

• Throughput – the number of requests processed by the system in a given amount of time.
• Transactions per second – number of completed transactions per second.
• Requests per second – number of client requests processed per second.

Resource Utilization Metrics

• CPU usage – the percentage of CPU capacity used during the test.
• Memory usage – the amount of memory consumed by the application under test.
• Disk I/O – the rate at which data is read from or written to disk (input/output operations).
• Network I/O – measures the data transmission rate over the network in terms of data sent and received.

Scalability Metrics

• Max concurrent users – the maximum number of users the system can handle before performance degrades.
• Max throughput – the maximum throughput (requests/transactions per second) the system can achieve before it becomes bottlenecked.
• Resource scaling – how well the system scales with added resources.

Error Metrics

• Error rate – the percentage of requests that result in an error, including server errors (5xx), client errors (4xx), or other failures.
• Failed transactions/requests – the absolute number of requests that failed or returned incorrect results.

Latency Metrics

• Latency – the delay between a request being sent and the beginning of the system’s response.
• Network latency – time taken for data to travel across the network.
• Server latency – time taken for the server to process a request.

Concurrency Metrics

• Concurrent users – the number of users interacting with the system simultaneously.
• Concurrent transactions – the number of transactions being processed concurrently by the system.

Average Wait Time

• The time that a request spends in a queue waiting for processing.

Data Volume Metrics

• Data throughput – the amount of data transmitted over the network during the performance test, often measured in megabytes or gigabytes per second.
• Payload size – the size of the data being processed, which can impact performance metrics like response time and throughput.

Connection Metrics

• Connection pool usage – measures how many database or service connections are used out of the available connection pool.
• Session duration – the average time a user session remains open, which can affect resource consumption.
• Open connections – number of concurrent open connections at any given time during the test.

Queue Metrics

• Queue length – the number of requests waiting in a queue for processing.
• Queue time – time spent by requests waiting in the queue before being processed.

Throughput Time

• Total throughput time – total time taken to process a specific amount of work (from request to response).

System Stability Metrics

• Mean time between failures – the average time between system failures during a performance test.
• Mean time to recovery – the time it takes for the system to recover from a failure or crash and return to normal operation.
• Uptime percentage – the percentage of time the system remains operational without crashing or needing a restart.

User Experience Metrics

• Apdex (application performance index) – a user satisfaction metric that measures the ratio of satisfactory, tolerating, and frustrating response times.
• First byte time – the time it takes for the server to send the first byte of a response.

Caching Metrics

• Cache hit ratio – the percentage of requests served from the cache compared to the total number of requests.
• Cache utilization – how well the cache is being used to store frequently accessed data.

Core Performance Metrics

The above are metrics used across all performance testing services. We’ll talk about those unique to load testing vs stress testing, too. For now, here’s a brief list of core performance metrics:

1. Response time (especially average and peak response times).
2. Throughput (requests/transactions per second).
3. Resource utilization (CPU, memory, disk I/O, network).
4. Error rate (failed requests or transactions).
5. Latency (first byte time and network delays).
6. Concurrent users (system capacity).
7. Peak load (maximum load before degradation).
8. Wait time (queue and processing delays).
9. Transaction rate (speed of key business transactions).
10. Apdex score (user satisfaction index).

Whether your testing is handled by an internal team or a QA company, you ought to make sure they know when to apply what metrics. Sometimes, some of them may be redundant or not needed. And if you use each criterion all the time, you’ll see your money quickly going down without getting much in return.

Manual and Automated Performance Testing

There’s another thing our QA engineers highlight as exceptionally beneficial. Since performance testing is a mammoth of QA resources, it’s a good idea to add automated software testing services to your development rather soon. They will allow you to cover bigger areas quicker and with much better accuracy.

But don’t be too rash to dismiss manual QA. It’s often time-consuming and somewhat error-prone, yes. Yet, if you’re like our team and treat quality as a deity, then manual testing services are indispensable.

For instance, only they can help you locate obscure issues, which would further refine your app. Consider exploratory performance and ad-hoc testing. Both are impossible with automation, yet they’re arguably most valuable since they give your system a fine polish, adding to user satisfaction.

Overall, before getting to performance testing, assess your crew’s skills. Without good expertise, no insights, tips, or best practices will have a positive impact on your product.

What Is Load Testing?

To end the performance testing vs load testing perplexity once and for all, we now move on to the second piece of the puzzle.

Load testing is a type of performance testing. It measures how a system acts under an expected or normal load. The goal is to determine how well your app handles concurrent users or transactions and identify performance issues.

Briefly, load testing checks whether your software can handle the performance it was designed to operate within. That’s why it’s better to use it:

• As part of the development process.
• Prior to launching new features or scaling the system.
• For capacity planning to understand how the system will behave with increasing numbers of users or transactions.
• To validate that the system meets performance benchmarks required by service level agreements.
• During hardware upgrades, software updates, or cloud migrations.

Load testing and performance testing rely on the same metrics. But that doesn’t mean all performance scores are applicable to load tests as well. There are a few that are unique, or rather, most relevant, to load testing.

• Concurrent users – measures the number of users interacting with the system at the same time.
• Throughput – measures how many requests or transactions the system can handle per second.
• Request queuing time – time requests wait to be processed, showing potential delays at high loads.
• Session/connection count – number of active sessions or connections during the test.
• Hits per second – the rate at which the system receives requests.
• Bandwidth utilization – the amount of network bandwidth used under load.
• Resource utilization under load – the use of CPU, memory, and other resources under expected traffic.
• Peak load capacity – the maximum load the system can handle before performance degrades.

To sum it all up, comparing load testing vs performance testing is like comparing the functions of the entire body with what a liver does. The organ is a part of the system. It contributes to it. Yes, it has some special roles. But it’s just a segment of something bigger.

Performance vs Load Testing: Why You Need to Know Their Differences

Ultimately, it doesn’t matter what you call performance and load testing. You can even give them cute nicknames if that’s your style. The main thing is that you know how and when to use each. Carrying out performance tests doesn’t always mean running load tests and vice versa.

Basically, whether you use load or performance testing depends on your goal and available resources, of course. Performance testing encompasses multiple types of tests. Thus, it needs more effort, diverse skills, and potentially specific infrastructure. Load testing is only one of many performance tests. So, it often requires less work, objectively.

If you want to ensure your app behaves as you want it to under adequate traffic – use load testing. If you want to comprehensively assess your system’s productivity – go with performance testing. But do remember that it includes several kinds of investigations. And you may not need all of them.

Your main task is to determine what you want to check within your product and apply performance testing’s facets based on your needs. That’s precisely why you should fundamentally understand the differences between performance testing and load testing. It’s the only way for you to get the most out of each of them.

Performance Testing vs Load Testing vs Stress Testing

Now that we’re done with load vs performance testing, it’s to introduce another rascal to the mix. Why would we do that? To guarantee each type’s maximized value for you. The better you know something, the more you can get out of it.

Stress testing is another child of performance testing. It investigates how a system behaves under extreme or excessive load conditions. In other words, it goes outside of what your app considers “normal traffic.”

By pushing the system to its limits, stress testing reveals weaknesses, bottlenecks, or failures that could arise during real-world usage, especially during peak traffic periods. It ensures that the system either maintains performance under high stress or recovers gracefully from failures.

Stress testing’s use cases and metrics are very similar to load testing. The big difference is that stress tests crank everything up considerably. For instance, this testing the ideal time to run such testing would be during, say, Black Friday, when tons of people are guaranteed to flood your app.

And that’s about it for load test vs stress test vs performance testing. We’re finally done with this bewildering trio. So, it’s high time to look at tools that will make executing each of them easier.

Performance and Load Testing Tools

There’s lots of software for performance and load testing. And before you settle on a particular option, be sure to conduct a detailed evaluation where you consider:

• The features a tool offers.
• How user-friendly it is, and what its learning curve is.
• Whether your team is familiar with the tool or they’ll need training.
• How will the software scale as your project grows.
• How it integrates with your existing toolkit and workflows.

Overall, don’t jump on the most popular or trusted options. Tools with a good reputation have earned their status, no one is arguing that. But they simply might not be the ideal variant for your particular case.

• Apache JMeter – a popular and versatile tool for load testing web applications, APIs, and databases.
• Locust – a Python-based tool that is easy to use and highly scalable.
• Gatling – a Scala-based tool with a strong focus on performance and scalability.
• K6 – a JavaScript-based tool that is designed for modern performance testing.
• NeoLoad – a powerful and flexible tool for load testing complex applications.
• WebLOAD – a robust tool for load testing web applications and APIs.
• LoadRunner – a comprehensive tool for load testing enterprise applications.
• LoadNinja – a cloud-based tool that allows you to record and replay user sessions to simulate real-world traffic.
• BlazeMeter – a cloud-based platform for load testing web apps and APIs.
• Dynatrace – a full-stack performance monitoring platform that leverages AI for insight generation and analysis.

In case you struggle to find one tool that covers all your needs, don’t be afraid to mix and match a few options. This way, you can often save costs and give your testing what it actually requires.

To Sum Up

People build character naturally. They go through life, encountering difficulties. And they either overcome or succumb to them. That approach wouldn’t work with software. If you allow it to sort of develop naturally, you’d be rushing to your business goals at one millimeter per hour.

So, to guarantee your product’s timely success, you need to use performance testing and do so smartly. Remember, a stress-free existence will turn your app into a slug. And too much pressure will split it in half.

To find that perfect balance, assemble a team that can expertly deal with IT adversity.