API has definitely become a main source of building the business logic of any product. It serves as an intermediary that allows different software systems to communicate and interact with each other either by externally sourced APIs or crafting our own in-house solutions.
WHY DO WE NEED API PERFORMANCE TESTING?
Imagine you are building a streaming application; you would need to check how fast your movie loads when your users click on it.
You would need to assess the scalability of APIs, ensuring they can handle growing user bases and increased demand without degradation in performance. No one would want to be watching it like
Now, let's take a deep dive into the world of API performance testing – an aspect that can make or break the user experience.
List of types of API performance testing:
Objective: Assesses how fast or resource efficient your test object is, providing insights into its overall responsiveness and improve API performance
- API Load Testing:
Objective: Examines how a test object behaves under specific loads, evaluating its performance when subjected to varying levels of demand.
- API Stress Testing:
Objective: Pushes a test object to its limits by continuously increasing the load, determining the point at which it experiences failure or performance degradation.
Now, with these distinctions clarified, let's explore API performance testing, a critical aspect of ensuring the optimal functionality and reliability of APIs.
- Volume Testing:
Objective: Scrutinizes an application's behaviour when confronted with the simultaneous processing of substantial data volumes. It comes into play during scenarios like data migration or when executing bulk uploads/downloads from databases.
WHAT IS API PERFORMANCE TESTING?
API performance testing involves evaluating how efficiently an API functions under different conditions sometimes including various loads and stress scenarios and to improve api performance. This testing is essential to guarantee that APIs can meet user expectations and handle the demands placed on them.
List of different types of API performance testing tools:
- K6:
Key Features:
- Open-source performance testing tool has a reliable community to ask.
- Reduces the need for distributed load testing since you can supposedly generate serious load on a single generator.
- Allows for scalability testing with a cloud-based execution option.
- Provides scripting flexibility for complex scenarios.
Cons:
1.Accessing other critical script options such as thresholds, exec, executor, tags, and env through the API is limited. Limited attribute visibility hinders users seeking a straightforward method for runtime verifications, linting, and unit tests without requiring additional frameworks like JEST.
2.It has very limited compatibility with Async Protocols. You can bundle the socket.io client library with Webpack but the k6 JS runtime lacks a global event loop which is crucial for handling asynchronous protocols.
- Apache JMeter:
Key Features:
- Open-source and has great community support.
- Supports various protocols, including HTTP, HTTPS, JDBC, and more.
- Provides a user-friendly GUI for test creation.
- Enables load testing, stress testing, and performance testing.
Cons:
1.Absence of Browser rendering support: In comparison to specific alternative performance testing tools, JMeter is devoid of intrinsic browser interaction capabilities however you can add WebDriver to JMeter in order to interact with a fully rendered webpage.
2.Complex and not scalable: The utilization of JMeter introduces complexity into these processes, as it mandates dedicated hardware and network resources. Consequently, conducting large-scale testing with JMeter becomes unfeasible.
- LoadRunner (Micro Focus):
Key Features:
- Comprehensive performance testing tool.
- Supports a wide range of protocols, including HTTP, SOAP, REST, and more.
- Offers detailed analysis and reporting features.
- Suitable for large-scale, enterprise-level testing.
Cons:
1.However, LoadRunner has a wide range of support, Vugen - the scripting language that it runs on has very little options compared to the other testing tools.
2.In LoadRunner, arranging scripts takes up more time and resources. Creating test scripts in LoadRunner is also more complex because it requires managing different agents.
4.Postman:
Key Features:
- Originally known for API development, Postman has expanded to include testing.
- User-friendly interface for creating and executing API tests.
- Supports automated testing with scripts.
- Allows collaboration among team members.
Cons:
1.Postman, despite having a very seamless integration with the different version control, can still have a chance of conflicts raised when working on a collaborative and large project. This could get worse with, well you know, merge conflicts :)
2.Doesn’t have a lot of workarounds while working with SOAP API’s or non-RESTful API’s.
3.Postman does its requests only sequentially, one by one by default. Although concurrent iterations can be configured, the tool may not provide the same level of concurrency control and load distribution as specialized performance testing tools.
- Gatling:
Key Features:
- Open-source and designed for high-performance testing.
- Uses a scenario-based approach for test creation.
- Provides real-time analytics and reports.
Cons:
1.Not a very active community in comparison to other testing tools which can pose a problem when you are working around to integrate a lot of third-party applications. Moreover, it uses a domain specific language which would take time to set into if you are new to Scala.
2.Additional load on the testing machine may influence the accuracy of performance measurements and should be carefully managed to ensure reliable and meaningful test outcomes.
Now that you have an idea about the different API performance testing tools to optimize API speed, you can choose what fits well based on your project.
Top comments (1)
Great list!
I never thought about using Postman for API performance tests. I knew that you could write some tests in Postman, but it never occurred to me to use it for performance tests.
I’ve used JMeter a little bit and started learning about Gatling. The other ones are new to me, and I’ll take a look at those.
Question: Do you think it’s valid to use these tools to test an application locally on a Docker container or a VM, with the third-party APIs mocked and a database running in another container?
Despite the fact that running locally won’t have the same specs that your production or homologation systems would have, nor the network latency from the communication between the components, and not having the same data volume.
I asked this question because it’s a discussion that I’ve been having with my coworkers, and we haven’t reached a conclusion yet.
We understand the limitations of not having a local scenario that matches the entire system. However, I still think you can use these tools to identify other gaps in the application, like a bad query, poor use of a data structure, or a bad implementation. We can always use statistics to calculate the probability of what the behavior is going to be in the real scenario, but as I said, it’s something that we haven’t concluded yet.
Apologies for the long commentary; this subject excites me!