Beyond the Basics: Insights That Revamp Your API Performance Testing

Reading Time: 10 minutes

Software, just like humans, is a social creature. It can’t exist in isolation, or it would be like a single grain of rice for a meal instead of a fulfilling plate. That’s why APIs exist. They help connect your app with others, amplify its features, and make it overall more valuable to users.

APIs act as bridges, linking your product with the outside world. From this, you can clearly see the point of performance testing for APIs. You’ve got to make sure your “bridge” can support lots of people, withstand storms and degradation, and offer a comfortable crossing experience.

Today, we talk about how to transform your API from a wobbly rope overpass into a Sydney Harbor bridge.

The Fundamentals of API Performance Testing

API performance testing is a lot like being a star, followed by hundreds of interviewers and press workers. Questions and screams flying at you. Flashes of light distract you from getting your point across. And you just want to run away and hide under a rock.

To ensure your APIs don’t crumble down under this pressure, you’ll have to brief and train them. This will let you:

Ensure API reliability and stability.
Enhance user experience with swift responses and flawless performance.
Improve scalability as your project develops.
Optimize resource utilization (e.g., CPU, memory, and bandwidth).
Meet service level agreements (SLAs), such as response time and uptime guarantees.
Identify security vulnerabilities when APIs behave abnormally under high loads, for example.
Foster continuous improvement by regularly running performance testing for APIs.

Now, let’s discuss how you can do that.

Types of API Performance Testing

First, you’ll want to include specific evaluations into your performance testing services. The following testing types and activities will help you get a holistic investigation for your APIs.

Load testing evaluates how an API performs under normal traffic conditions.
Stress testing determines the API’s breaking point. It helps identify how the API behaves under extreme traffic and whether it recovers gracefully.
Endurance testing assesses how well an API performs over an extended period under a continuous load. Here, you can check for performance degradation, memory leaks, etc.
Volume testing examines how the API handles large amounts of data. It evaluates the API’s ability to manage “heavy data and its impact on performance, memory, and throughput.
Spike testing simulates sudden, sharp increases in traffic. The goal is to ensure the API can cope with rapid traffic spikes without issues.
Active monitoring involves continuously testing the API’s performance and checking response times, error rates, and availability in real time.
Passive monitoring observes actual API usage from real users or systems. It collects and analyzes real-world performance data to detect authentic issues.

In case you came across resources comparing performance vs load testing, we’ll have to end this “argument” right here. Performance testing is an umbrella term. Load testing falls under it and serves as a specific kind of evaluation. That’s it.

Blurry definitions and terms are a staple of the IT sector. While we’re at it, let’s take a look at a famous trio. These aren’t testing types per se. They are specific approaches or focus areas within API performance testing.

Web API Performance Testing

Web API performance testing centers on web-based APIs. It checks how well the API responds under real-world traffic and identifies bottlenecks in server response times, network latency, and data processing.

REST API Performance Testing

REST API performance testing focuses on testing APIs that adhere to the REST architecture. It ensures that the REST API can handle different HTTP methods (GET, POST, PUT, DELETE) efficiently.

If you’re interested specifically in how to do performance testing for REST APIs, you can check out our other resources.

End-to-End API Performance Testing

End-to-end API performance testing examines the performance of the entire system or workflow that involves multiple APIs. This type of testing ensures that from the initial user request to the final response, all APIs work well together.

Now that we’re on the same page about what you need for productive API testing services, we’ll discuss how you should use the above practices.

API Performance Testing Metrics

When testing your APIs, you need to take note of particular metrics. You’ll use them to assess the health of the application programming interface.

Response time – the time taken for the API to process a request and return a response. This is often measured in milliseconds (ms) and is critical for user satisfaction.
Throughput – the number of requests the API can handle per unit of time, typically measured in requests per second (RPS). Higher throughput indicates better performance under load.
Error rate – the percentage of failed requests compared to total requests. This helps identify reliability issues and ensures that the API meets quality standards.
Latency – the delay between sending a request and receiving the first byte of the response. This is important for understanding the network performance and responsiveness.
Resource utilization – metrics related to the usage of server resources during the test, including CPU usage, memory consumption, disk I/O, and network bandwidth.
Concurrent users – the number of simultaneous users or sessions accessing the API during testing. This helps evaluate how well the API scales under load.
Connection time – the time it takes to establish a connection to the server before the actual request is sent. This can affect overall response time, especially in high-latency environments.
Data transfer rate – the amount of data transferred in a specific time frame, often measured in kilobytes per second (KB/s) or megabytes per second (MB/s). This reflects the efficiency of data handling.
Error response codes – tracking the types of HTTP error codes returned (e.g., 4xx for client errors, 5xx for server errors) helps identify specific issues and areas for improvement.
Session duration – the average time a user spends interacting with the API during a session, providing insights into user engagement and interaction patterns.

How to Use Metrics for Optimal Performance Analysis

All these metrics will provide you with a comprehensive evaluation of your APIs. But, sometimes, you can skip some of them or dedicate less attention to particular variants.

For example, you can skip resource utilization during initial development and unit testing. At this stage, the focus is on functionality and basic performance. Resource utilization metrics may not provide meaningful insights until the API is integrated and subjected to realistic loads.

Omitting throughput is reasonable when testing an internal API used by a small team with predictable, low traffic. Since the API isn’t expected to handle high volumes of requests, measuring throughput may not be critical.

Latency can be overlooked for an established API in a stable production environment with consistent performance. If response times and error rates are consistently acceptable, latency metrics may not need close attention unless there’s a specific issue or degradation reported.

Overall, the amount of metrics you use will depend on the testing phase and the context of your project. To not risk your app when selectively applying performance metrics, make sure your team is well-versed in the nuances of software testing services. If your crew lacks experience to balance efficiency with quality, you can always partner with a QA company that knows how to do API performance testing right.

API Performance Testing Process

Now, we move on to the practical, fun stuff – API performance testing requirements. These are the things you’ll have to do to secure productive API evaluation.

#1 Define Your Objectives

Determine what you want to achieve with the performance tests (e.g., response time targets, throughput requirements).
Establish benchmarks and acceptable performance thresholds.

#2 Plan the Tests

Decide which performance metrics to monitor (e.g., response time, error rate, throughput).
Select appropriate performance testing tools that fit the API’s architecture.

#3 Create Test Scenarios

Outline realistic user scenarios that mimic expected usage patterns, including different load conditions (normal, peak, and stress).
Prepare test data that reflects real-world usage, including edge cases.

#4 Set Up the Test Environment

Create a testing environment that closely resembles the production environment to ensure accurate results.
Ensure that the API and any necessary dependencies (like databases and services) are accessible.

#5 Execute the Tests

Execute the defined test scenarios under controlled load conditions.
Use monitoring tools to gather real-time data on the defined metrics during the test.

#6 Analyze Results

Gather all performance metrics from the test execution.
Compare the results against the success criteria to identify any issues or areas for improvement.

#7 Identify Bottlenecks

Look for patterns in the data to identify any performance bottlenecks or failure points.
Analyze specific metrics to pinpoint the root causes of any performance issues.

#8 Optimize and Retest

Work with the development team to address identified issues (e.g., optimizing code, database queries, or server configurations).
Execute the tests again to verify that performance improvements have been effective.

#9 Document Findings

Document the testing process, results, and any optimizations made.
Communicate findings with relevant stakeholders to inform future development and testing efforts.

#10 Strive for Continuous Monitoring and Advancement

Set up performance monitoring for the API in production to track performance over time and detect issues proactively.
Don’t stay satisfied with how things are. Strive to progressively improve your APIs.Well, the testing process is fairly straightforward. But you can improve it by correctly applying manual and automated QA.

#11 Enhancing QA with Automated Testing

We always say that manual and automated testing (AT) shouldn’t be separated or viewed as competitors. They best work together. Here are the cases that benefit most from combining API performance testing with automated software testing services.

High-traffic APIs for efficient, consistent, and scalable load simulations that provide immediate feedback and detailed insights.
CI/CD environments to integrate API tests into the CI/CD pipeline, enabling early issue detection with every code change.
Microservices architectures to check the performance of individual services as well as their interactions.
APIs undergoing frequent changes to quickly verify that alterations don’t affect performance.
Enterprise apps to swiftly cover complex interactions, testing across various scenarios.
APIs with complex logic or business rules to thoroughly validate performance under various conditions.
Applications requiring real-time data processing to make use of AT’s speed and accuracy for prompt insights into response times and system behavior.

And here’s when you better stick with manual testing services for API performance testing.

Initial development phase to understand the API’s basic functionality, identify obvious performance issues, and establish a baseline.
Exploratory testing for a more flexible and intuitive exploration of the API to locate unexpected issues.
Ad-Hoc testing to quickly assess the impact of minor changes or investigate specific performance issues.
Non-standard APIs to grasp specific nuances and performance characteristics of APIs (automated tools may struggle to adapt here).
During debugging for a more hands-on approach to investigate the root cause of problems and test potential fixes.
User acceptance testing to assess performance from a real user perspective.

Don’t forget that these are general recommendations. If you think your project requires manual or automated QA for different reasons/situations – do what you think is right for your project. Always.

API Performance Testing Best Practices

To help you refine your API performance testing even further, we took it up with our QA engineers. Our team of 150+ specialists has completed countless projects and can definitely share useful insights from their practice.

Here’s what our crew says will help you build advanced performance testing for APIs.

Define Clear Benchmarks & Metrics

Before testing begins, it’s essential to establish performance goals. Defining benchmarks such as response time and error rate helps measure the API’s performance accurately. These metrics should align with user expectations and business needs, providing a clear basis for evaluation.

Use Real User Data

Simulating real-world scenarios is key to gaining meaningful insights. By using actual user data, you ensure that the API is tested under conditions that closely resemble its live environment. This reduces the chances of discovering performance issues later in production.

Test for Different Types of Performance & Loads

APIs should be tested not only for average performance but also under stress, peak loads, and during prolonged usage. This includes testing for speed, scalability, and stability to ensure that the API can handle both normal traffic and sudden spikes.

Test Across Different Environments

Since APIs interact with diverse systems, it’s important to test them in multiple environments. Variations in configurations or network conditions across environments can reveal obscure performance issues.

Check Error Handling

API performance isn’t just about how fast responses are. It’s also about how the system behaves under failure. Test how well the API handles different types of errors, such as timeouts or incorrect inputs. Good error handling ensures resilience and a better user experience, even in adverse conditions.

Monitor Resource Utilization

During testing, monitoring the API’s resource utilization helps identify performance bottlenecks. High resource consumption can indicate inefficiencies or scalability issues that should be addressed before deployment.

Evaluate Third-Party Dependencies

Many APIs rely on third-party services. Their performance can impact your own API’s effectiveness. So, you should test how the API performs when these dependencies slow down or fail to ensure your system is still functional.

Automate

Automation lets you run tests consistently and frequently. It helps in detecting regressions early and provides a reliable framework for continuous improvement.

Test for Security Performance

Another key aspect to evaluate during API performance testing is security. This includes how the API handles encryption, authentication, and authorization processes under various loads. Efficient security mechanisms ensure that the API maintains performance without compromising on protection.

Best practices are definitely valuable. Yet, they’re pretty much nothing without a team who can implement them properly. Now that you have lots of practical tips, you need to think whether your crew can handle the needed QA services productively. Just remember one thing: great experts will do a great job despite the hurdles the IT realm throws at you (and it almost always does).

API Performance Testing Tools

When it comes to performance testing tools for APIs, there’s nothing simple. It may seem like picking your “support software” is as easy as looking at the features it offers. But there’s more nuance to the process.

First and foremost, you do need to look at how well a tool will be able to handle your needs.
You should also understand whether your team knows how to use it or if you’ll have to train them.
Then, it’s better to examine the learning curve and UI. In other words, how easy the tool is to work with.
Consider the tool’s scalability. You don’t want to spend money again when your project grows, yet the current tool can’t handle the workflow anymore.
Examine whether the tool integrates with your existing development and testing kit.
Last but not least, pay attention to the cost, hidden fees, etc.We’ve selected a few good options you could look into (both free and paid). Also, the list includes REST API performance testing tools and web API performance testing tools.

Open-Source Tools

Open-source tools are ideal if you’re working on a tighter budget. They’re perfect if you want more flexibility and customization and prioritize transparency.

JMeter

Can simulate various load patterns and protocols.
Supports custom plugins and scripting for complex scenarios.
Provides graphical reports and data analysis.

Gatling

Designed for high-performance load testing.
Offers a domain-specific language for defining tests, improving readability and maintainability.
Integrates with CI/CD pipelines.

Locust

Easy-to-use syntax for defining test scenarios.
Leverages Python’s ecosystem for scripting and analysis.
Provides real-time performance metrics and visualizations.

SoapUI

Handles both REST and SOAP APIs.
Combines performance testing with functional testing.
Supports data-driven testing for various scenarios.

Apache Bench

Simple and easy to use.
Provides essential metrics like response times and request rates.
Ideal for scripting and automation with its command line interface.

Commercial Tools

Commercial tools have guaranteed support and maintenance. They often provide advanced features and scalability. And, of course, they’re usually more user-friendly.

LoadNinja
- Simulates load from real browsers, ensuring accurate results.
- Is cloud-based, making it easy to scale.
- Allows for scriptless test creation, reducing complexity.
LoadView
- Supports various load testing scenarios, including API, website, and web application testing.
- Simulates load from different geographic locations.
- Provides in-depth performance reports with visualizations.
Postman
- Combines API development and performance testing in a single tool.
- Organizes API requests into collections for efficient testing.
- Manages environment variables for different testing environments.
SmartBear ReadyAPI
- Covers functional, security, and performance testing.
- Simulates APIs for testing before they’re ready.
- Supports test automation for continuous testing.
Apigee
- Offers API management features in addition to performance testing.
- Provides a scalable and reliable platform.
- Integrates with other tools in the Apigee ecosystem.
Let’s finish up with one last piece of advice. Don’t hesitate to combine tools. You can mix open-source and paid options as you wish. This is the simplest way to save costs and cover more of your testing needs. Just make sure your team is comfortable with your selections and can operate both of them properly.

To Sum Up

We’re confident this article will advance your API performance testing. The QA Madness team uses all of the above practices and tips daily for dozens of clients. So far, the results are nothing but impressive. And if you need help setting up and implementing the discussed processes, our crew is always here to help.

Let certified QA experts take care of your APIs

Contact us