Despite the benefits of APIs, having a working one is not easy. 40% of API users have experienced some sort of API brokerage. API load testing helps ensure API’s robust performance under different traffic situations.
In this article, we will explain API load testing, its different types, common failures of load testing, and what they mean for QA specialists and testing experts.
What is API load testing?
API load testing examines if your API can handle high requests. API load testing simulates real-world conditions and determines performance requirements ahead of time.
API load testing shows:
- The number of requests per second that an API can handle.
- How efficiently the API behaves under high traffic.
For high-traffic applications, load testing is critical to ensure the user experience remains consistent. Exercising your application under different conditions will help you find weaknesses before they affect the end user.
PULSE is an automated AI-based API test automation tool created by TESTIFI. PULSE can reduce cycle time and cost by 50%, and improve documentation. It is integrated with the CI/CD pipeline and aligns with shift-left testing. Well-known companies like BMW and Amazon use TESTIFI services.
4 Types of load test
1-Standard load testing
API is tested against the standard and expected load traffic that it expects to receive in the production stage. The aim is to understand the API behavior under normal situations and if it fulfills the minimum performance requirements ( see Figure 1).
Figure1. Standard load testing
In this testing, API receives an average load level with sudden and significant increases ( spikes) that can last for a short or long duration. The aim is to validate if the API satisfies its requirement during spike times ( see Figure 2).
Figure 2. Spike testing
This test will aim to break the API by increasing load in a short period. The aim is to understand the API’s behavior when facing excessive load (see Figure 3). An API under stress can behave differently, like :
- Slowing down,
- Blocking incoming requests,
- Showing HTTP 420/429 error,
- Prioritizing certain operations over others,
- Losing data.
Figure 3. Stress testing
Many APIs, such as e-commerce and stock exchange APIs, have to be run consistently. The soak test is about how the API behaves under a moderate load but in a long time frame. This test aims to identify if the API suffers from memory and resource leakage ( see Figure 4).
Figure 4. Soak testing
Source : Loadster
3 Common load test failures
1. Scalability bottleneck
The API response time may lengthen as the load grows. Up to a particular level of load, the API response time can be within the desired range, but once that level is reached, it increases (see Figure 5). This is an instance of an API with a scalability bottleneck brought on by the exhaustion of finite server resources.
Figure 5. Scalability bottleneck
The cause of scalability bottleneck can be :
- Database connection pool,
- Server connection,
- Hardware limitation.
Most APIs have a scalability bottleneck at some point, but it is essential to know if the API reaches this bottleneck at what load level. If the load required to hit the bottleneck is much higher than the expected average load, then there might not be a need for fixing it.
2. Sustain load error problems
This problem can arise when doing a soak test. Initially, there are no problems, but suddenly, many errors arise in different places. However, the load is steady, so a high load does not cause the problem.
The cause can be :
- Memory leakage
- Resource leakage
Sustain load error problems must be taken seriously as this can also happen in the production stage.
3. Script-related problems
Not every failure is related to the API. If there are script problems in the test or the data used for testing is faulty, an error can arise.
If the error is generated in a specific step or operation and the error is happening consistently under a low load level then the problem can be related to your testing script. You should review your script for possible mistakes and bugs.
4 Best practices for API load testing
1- Automate load testing
Load testing can be automated and have substantial benefits, such as reducing the time and effort required for testing.
For more on test automation, read Automation Testing: Types, Frameworks, Tools & Best Practices.
2- Test early
In traditional testing approaches, API load testing is done after the development stage. However, If testing is left until the end of the software development cycle, it can result in last-minute failures that can delay the API deployment and result in higher costs.
3- Validate service level agreement (SLA)
SLA set a minimum performance requirement. The load testing result should meet SLA requirements; if it doesn’t, the code must be reviewed to satisfy the SLA requirements.
4- Reuse functional testing
Look for opportunities to reuse functional tests you created for load testing. This is called API functional load testing. This method involves writing API functionality tests and then running them under different load scenarios. This practice has 2 main benefits :
- Saving time on test development & execution
- Evaluating if and how well an API can handle different loads
- API Security Testing: Importance, Automation & Common Issues
- Top 10 Best Practices for Software Testing
If you need more information on how to use API load testing, you can reach us:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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