There are many tools leveraged for software testing, QA testing, and performance testing on an existing server. You would think that with all these tools at your disposal, these forms of testing would be easy to manage. However, benchmark testing to ascertain a server's performance under different load conditions can be challenging.
It's often a result of clunky interfaces, the inability to replicate real-life scenarios (for accurate metrics like requests per second, throughput, average request time, and 499 requests), and efficient monitoring to see where you're running into bottlenecks quickly.
That’s where Locust comes into play.
Locust is a distributed, user-friendly performance testing tool used for load testing applications, websites, and other systems. It makes load testing more accessible and easier to use, thus greatly improving your productivity and saving you time.
Before we talk about Locust and how it can help you, let's first define what load testing is and examine its role in performance testing.
Load Testing Defined
According to Intersog, load testing is a non-functional application testing process that helps IT teams test the server’s ability to handle different load conditions. This subset of performance or stress testing helps create artificial usage of systems that mimics real usage.
Most often, load tests are conducted using virtual machines and instantiated on demand. These virtual machines are built on cloud servers, as they’re cost-effective and easier to terminate once the stress test is concluded. This approach enables testers to simulate user demand in a variety of load testing scenarios.
Examples of Load Testing Tools
There are a variety of load testing tools (both free and paid) that are commonly used for website load testing. Some of the leading ones are as follows:
- Apache JMeter
- Rational Performance Tester
IT teams with the knowledge and technical prowess to configure a tool have the option of leveraging a free tool. Those without the necessary skill sets and those who simply don’t want to bother with instantiating and terminating servers go with paid tools.
Some typical load testing scenarios are as follows:
- Check the performance of CPU and memory under peak load conditions.
- Check network delay issues between the client and the server.
- Check software design issues.
- Load test an application.
- Stress test an application.
- Test if web pages load within an acceptable time frame.
- Test the execution time of database queries.
- Test response times under a variety of load conditions.
- Webpage load time on slow connections.
The objective here is to ensure stability and seamless functioning when the website or application is live. Therefore, IT teams demand powerful load testing tools to eliminate potential bottlenecks in the system. One such tool is Locust.
Locust is a load testing tool that’s both scriptable and scalable. All testing scenarios are coded in pure Python, negating the need to use domain-specific language or complex user interfaces (UIs).
As it’s easy to use, and you can quickly run performance tests distributed across multiple machines leveraging gevent. Furthermore, request libraries—including its documentation—are used as prompts to debug tests.
However, like anything in life, there are some drawbacks. The primary disadvantage here is the fact that you have to engage in capture and replay manually. Intersog also states that while the ability to copy and paste an example makes Locust highly user-friendly, it can fall short when there’s a need to deploy advanced testing techniques.
According to the Locust website, it gets its name from the insect because of its swarming behavior mimicked by the tool. Some of the terminology used by Locust, like attacking, hatching, and swarming, are all inspired and borrowed from nature.
Each simulated user is like a locust and applies significant pressure on websites and applications. This behavior is then monitored in real time on a web-based UI.
Each Locust in the swarm runs its own process (or greenlet), and you can develop highly expressive Python scenarios without compromising your code with callbacks. This means that this tool is entirely event-based, making it possible to support thousands of users from a single system. Some of the other load testing tools fall short because they don’t support callbacks and only leverage lightweight processes in gevent.
What Are the Pros and Cons of Using Locust?
The key advantages of load testing with Locust are as follows:
- All tests are coded in pure Python.
- Doesn’t demand extensive resources.
- Easy to set up.
- Highly user-friendly.
- Test any scenario.
Here are the key disadvantages of load testing with Locust:
- Demands an in-depth understanding of HTTP protocols.
- Lacks ramp-up capabilities.
- No capture and replay.
- Only uses a linear load model.
- You don’t need to be a programmer to use Locust.
To determine if Locust is the right load testing tool for you, you'll have to determine whether the pros outweigh the cons. For example, if you have limited knowledge of HTTP protocols, you'll likely have a steep learning curve ahead of you. However, if you don't have the required budget for a paid tool, Locust is probably your best option.
How Does the Locust Framework Help?
In short, Locust makes performance testing easy. You can simply code test scenarios with Python and run the test without working with a clunky UI. By switching from callbacks to coroutines, you can ensure that your code looks and behaves like normal blocking Python code.
Locust has a web-based UI that can be easily extended across platforms. You leverage it to upload distributed performance tests across several machines that are set up to simulate millions of simultaneous users. This makes engaging in performance testing effortless.
The HTML+JS UI also displays relevant test details and results in real time. Although it’s web-based, you can utilize Locust to test just about any system.
When it comes to performance testing, Locust is still a relatively new testing framework based on Python. In fact, Python is the primary trademark of this framework, as you can write performance scripts with pure Python.
Because of Locust’s extensively event-based implementation, the testing framework is also highly scalable, not to mention its “test as code” feature. As a result, this tool's popularity is growing exponentially and is now preferred to Apache JMeter (which is written in pure Java).
However, you can also use testing tools like JMeter in Locust to engage in performance testing of HTTP and FTP protocols. Furthermore, if your codebase is Python, Locust transforms into a one-size-fits-all solution to pull data, domain logic, or models from your existing codebase. But if you’re not using Python, Locust can still be integrated to test the performance of enterprise systems.
Locust Is Free and Makes Load Testing Easier
In conclusion, when testing an enterprise server's performance under different load conditions, the open-source load testing tool Locust delivers. However, it's essential to consider its shortcomings before committing.
It's a tool that makes the load testing of applications, websites, and other systems more accessible, and it’s easily extended across platforms. Furthermore, it can be a breeze if you're already a Python programmer.
Given that Python is an extremely popular programming language with a low barrier to entry, and Locust is an open-source testing tool, the odds are stacked in its favor.