JMeter vs. Locust: Choosing The Right Tool -  Part 1 | Loadium

JMeter vs. Locust | Choosing The Right Load Testing Tool — Part 1

Two of the most well-known and extensively used load testing tools used by IT professionals and organizations are JMeter and Locust. We’ll…

JMeter vs. Locust | Choosing The Right Load Testing Tool — Part 1

Two of the most well-known and extensively used load testing tools used by IT professionals and organizations are JMeter and Locust. We’ll compare JMeter and Locust side by side in this blog, concentrating on the frameworks’ most important features. We hope it proves useful in choosing which tool is appropriate for your load test case and other load testing scenarios.

A Brief Overview of JMeter and Locust

Apache JMeter

JMeter is one of the most well-known performance frameworks, having been launched over two decades ago. It’s written entirely in Java and has a long history of versions. JMeter was originally created to do load testing on Web and FTP applications. It now allows users to write tests using a desktop program that is compatible with every OS platform, allowing them to test practically any application and protocol.


P: It is a relatively young Python performance framework that has acquired a lot of attention in the previous five years. The main benefit of this framework is that it allows you to write complete performance routines in Python. Due to its event-based methodology and “test as code” capabilities, Locust is very scalable. Locust has a huge and constantly growing group of users that prefer it to JMeter because of these reasons.

1. Both Are Open Source

One of the most crucial questions to ask is what a tool’s licensing scope is. If a tool is an open source, you can achieve nearly any performance test objective you choose without paying anything extra. JMeter and Locust, both open source, are no exception.

Locust was built by a small group of community-driven developers and is released under the MIT license, whereas JMeter was produced by Apache and is licensed under the Apache 2.0 license. Both of these tools are open-source, which means you are free to use them.

2. Load Test Creation And Maintenance

A performance test pipeline has three basic steps: create, execute, and analyze. It’s no secret that the initial step generally takes the most time. There may be exceptions to this rule, but if your application is well-written, you should not spend more time on test execution and analysis than on test development. As a result, this stage is critical to our comparison. Because being familiar with a performance tool necessitates first and foremost being comfortable with the process of creating tests.

The GUI mode of JMeter is the most common way to write a load test. The JMeter GUI mode is a desktop client that lets you build tests quickly and simply without writing a single line of code (until you need to create a tricky test). As an example, consider the following scenario:

JMeter is extremely easy to use, and even a novice engineer can read and write basic scenarios with ease. You can, however, use Java to run code in both GUI and non-GUI mode if necessary. However, this way is not popular across the JMeter community due to the complexity of script implementation (as JMeter was designed to be used with GUI mode) and the lack of documentation on how to make such scripts.

On the other side, Locust is all about code. You’ll need at least some basic Python coding skills to feel comfortable developing performance tests. This is the same test scenario as before, only this time Locust is used:

Scripts created in Locust appear to be quite clear, but when you’re working on a large, complex test, it might be difficult to review.

Having all of your tests in code, on the other hand, is a huge benefit since it allows you to immediately correct them without having to use the UI. If you’re running your script on the server and don’t have access to a desktop client, this may be really useful. Using a version control system like Git, coding also allows you to validate any test changes made by your peers.

3. Supported Protocols

It’s always easier to use the same tool to run load tests for different parts of your system/product. You should be able to test everything using the fewest tools possible as long as the quality of your testing isn’t affected. That’s why this step of comparison is so important: if you need to use several tools to test different protocols, you’ll have to spend more time on support, find engineers with more experience, and create additional hooks to execute these tools in integration tests.

With JMeter, you can create performance tests for anything in one location using a full arsenal of built-in functions and third-party plugins. Without any code, you may test various protocols and databases. JDBC, FTP, LDAP, SMTP, and a slew of additional protocols are among them.

Locust was designed primarily for HTTP web-based testing, according to the documentation. You may, however, enhance its default capabilities by writing your own Python function to test anything that can be tested with the Python programming language. To summarize, you can test everything you want, but each new script will necessitate extra effort and Python programming knowledge.

4. Number of Concurrent Users

This is one of the most important sections of the comparison because a performance tool should allow you to run as many users as you need to meet your testing objectives. It is necessary to simulate hundreds or millions of people in some circumstances and tests, while it is sufficient to run only a hundred users in others.

The number of concurrent users a program can handle is mostly determined by the resources needed to run each user in a load test. JMeter and Locust approach machine resources in very different ways. JMeter uses a thread-based approach, which means that each user gets their own thread. Each of these stages, such as thread allocation and benchmarking, consumes a significant amount of resources, which is why JMeter is restricted in the number of people it can simulate on a single engine. The number of users you can run on a single system is determined by a variety of criteria such as script complexity, hardware, response size, and so on. Loadium eliminates this kind of limits and provides you to reach millions of users easily.

Locust, on the other hand, uses an entirely new user simulation model based on events and an async approach, with the event coroutine serving as the backbone of the whole process. This solution allows the Locust framework to perform tests with many stages while simulating thousands of concurrent users on a single computer.

Please keep in mind that if you have a master that handles results aggregation and a number of slave nodes that handle target requests, you may easily use both technologies in a distributed way.

5. Ramp-up Flexibility

For modeling all potential use scenarios of your application, flexible ramp-up capabilities are critical. In addition to the progressive load, you may wish to construct a spike load during load testing to see how the system handles unexpected surges.

By default, both programs create loads in a similar way: you may define how many people you want to employ and how quickly they should join the performance test.

In JMeter, you may set up a load in the “Thread Group” controller by filling out the following fields:

JMeter, on the other hand, offers extra plugins that allow you to create a very flexible load. One of the most effective methods to do so is to employ the Ultimate Thread Group, which enables users to create highly precise load patterns:

Locust takes a different approach. When you execute a performance script, Locust creates a server with a web interface on http://localhost:8089 that allows you to specify just a linear load.

Happy testing!

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