A Guide to Apache JMeter Alternatives

Load testing is a crucial part of modern software development, particularly in the era of cloud computing and microservices. With increasingly complex online platforms and growing user expectations, the need for effective load-testing tools has never been greater.

This article primarily focuses on Apache JMeter, a load-testing tool that has demonstrated its reliability over the years. It has established itself as a robust tool for testing applications, databases, and various other systems. However, it’s important to note that it does have its limitations.

As we explore the capabilities and constraints of JMeter, we will also touch upon Multiple, a modern SaaS alternative in the field of load testing. Its standout features include a user-friendly interface and intuitive scripting, among others.

By the end of this article, you will have a thorough understanding of JMeter as a load-testing tool and an introduction to Multiple as an emerging contender in the market.

Summary of key JMeter strengths and limitations

JMeter offers several notable advantages, which are summarized in the table below.

Despite JMeter’s numerous advantages, it is essential to acknowledge the following limitations as well.

Strengths of JMeter

JMeter has a number of essential advantages that contribute to its popularity.

Open-source design and community support

JMeter is licensed under the Apache License 2.0, which allows it to be freely used and modified. The open-source nature of JMeter provides several significant benefits, including the following.

Community support

JMeter is a popular open-source load testing tool with a thriving community of developers and users. This community support is one of JMeter’s key strengths. Furthermore, extensive documentation is available for JMeter, providing in-depth insights into its usage, components, functions, and properties.

Continuous improvement

The open-source model allows for continuous improvement of the tool. Developers from around the world contribute to the codebase, making it more robust and keeping it up to date with the latest technological trends.

This collaborative environment allows developers to contribute code, identify issues, and submit pull requests, with a global community ready to tackle these issues.

Extensibility

JMeter’s open-source nature has led to the creation of a vast ecosystem of plugins and extensions. These plugins enhance JMeter’s capabilities, allowing users to extend its functionality for various testing scenarios, protocols, and integrations. Whether it’s testing web services, databases, or custom protocols, chances are that there’s a JMeter plugin available to simplify the task.

For example, the JMeter Plugins Manager is a custom plugin that extends the functionality of JMeter by enabling support for various custom samplers not initially included in the tool. Over 60,000 monthly installations emphasize its wide-reaching popularity and usage.

Scripting capabilities

Apache JMeter stands out among performance testing tools due to its powerful scripting capabilities. These features empower technical users to create complex and realistic test scenarios, customize behavior, and ensure comprehensive performance testing.

JMeter supports a variety of scripting languages, including these:

• Groovy: Groovy is the recommended scripting language for JMeter as of 2023. It can be used to automate a wide range of tasks in JMeter.
• BeanShell: BeanShell is a lightweight scripting language that is interpreted by the Java Virtual Machine. BeanShell can be used to automate tasks in JMeter in a way similar to Groovy, but it is not as widely used.
• JavaScript: JMeter also supports JavaScript, which can be used to automate tasks in JMeter using the Rhino JavaScript engine. However, JavaScript is not as widely used as Groovy in JMeter for performance reasons.

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Dynamic test logic

JMeter’s dynamic test logic is one of its key advantages, allowing users to create dynamic load tests that can adapt to changing conditions. This is achieved through the use of scripting languages and regular expressions.

Scripting languages such as Groovy and BeanShell can be used to create custom samplers, assertions, and pre- and post-processors. These custom components can be used to implement dynamic test logic using elements such as the following:

• Conditional logic: Technical users can incorporate conditional statements in their test scripts to simulate various user behaviors based on specific conditions. For instance, they can create conditional loops or dynamically adjust request parameters based on server responses.
  
  Here’s an example of a JMeter Thread Group with a While Controller for conditional testing:

Thread Group
├── HTTP Request
└── While Controller
    ├── Condition: ${__javaScript("${extractedData}" != "desiredValue")}
    │   └── HTTP Request
    └── Debug Sampler

• Parameterization: JMeter’s scripting capabilities allow for the dynamic parameterization of requests. For instance, in ecommerce testing, users can simulate product searches, views, and cart additions using extracted data for thorough performance evaluation using regular expressions.
  
  This is an example of a JMeter Regular Expression Extractor for extracting a value from a response:

HTTP Request (Search for a Product)
├── Regular Expression Extractor (Extract "productID")
│   ├── Apply to: Main sample and sub-samples
│   ├── Field to check: Response Body
│   ├── Regular Expression: "productID":"(.*?)"
│   ├── Template: $1$
│   ├── Match No.: 1
│   └── Default Value: Not found
└── Another HTTP Request (Add Product to Cart using the extracted "productID")

Distributed testing

JMeter excels at distributed testing. By harnessing the power of multiple remote servers or “nodes,” JMeter allows testers to easily simulate massive user loads.

JMeter uses a master-slave architecture for distributed testing. The master machine controls the test and distributes the load to the slave machines, which execute the test and report the results back to the master.

In this setup, a master node controls multiple slave nodes, distributing the load among them. This architecture allows for the simultaneous execution of test scripts on multiple machines, enabling the simulation of a large number of concurrent users.

There are several technical benefits to using JMeter for distributed testing:

• Scalability: Technical users can easily scale their performance tests by adding more slave nodes to the distributed setup. This scalability ensures that even the most resource-intensive tests can be conducted efficiently, accommodating a growing user load.

• Load balancing: Distributed testing in JMeter intelligently balances the test load across the slave nodes. This ensures that the load on each machine remains within its capacity, preventing overload and resource exhaustion.

• Geographical load distribution: JMeter’s distributed testing is not confined to a single data center or location. Technical users can set up slave nodes across different geographical regions to simulate users accessing their applications from various parts of the world.

Platform independence

JMeter is platform-independent, granting it another distinct edge in the world of performance testing. This remarkable strength allows technical users to seamlessly execute tests across diverse operating systems and environments, facilitating thorough cross-platform testing. This is made possible by the following factors:

• Use of Java: JMeter is written in Java and runs on the Java Runtime Environment (JRE). The JRE is available for a wide range of operating systems, including Windows, Linux, macOS, and Unix.
• RMI communication protocol: JMeter uses the RMI protocol to communicate between the master and slave servers. RMI is a platform-independent protocol that allows Java objects to be invoked remotely.
• Platform-independent test elements: Users can create test plans on one operating system and execute them seamlessly on different operating systems without modifications.

There are a number of benefits to platform independence in software testing, including these:

• Uniform test results: JMeter’s platform independence guarantees uniformity in test results across different platforms. This consistency is crucial for assessing how an application performs under varying operating systems and configurations.
• Containerization: JMeter can be containerized using technologies like Docker, which further extends its compatibility with diverse platforms, including cloud environments like AWS.
• Lower cost: Platform independence can help reduce the cost of software testing by eliminating the need to maintain separate test suites for different platforms.

Limitations of JMeter

While JMeter is a powerful tool, it does have its constraints. Here are some key challenges users face when using JMeter.

High resource usage

While powerful, JMeter can be resource-intensive in terms of CPU, memory, and disk space, especially when running large-scale tests.

Here are some additional details about the resource-intensive aspects of this product.

High memory and CPU usage

JMeter simulates user behavior using threads, where each thread represents a virtual user or request. For every thread, JMeter creates a separate Java thread to execute the requests concurrently. As the number of threads increases, so does the overhead associated with managing these threads. This can be memory-intensive, particularly when running tests with a large number of concurrent users or long test durations.

The exact memory consumption depends on test complexity and configuration as well as the usage of specialized components such as listeners and samplers. Memory usage can range from hundreds of megabytes to several gigabytes of RAM, depending on these factors.

GUI mode overhead

JMeter’s GUI is built using the Java Swing framework, which is a mature and versatile GUI toolkit. However, Java Swing is known for performance issues, especially when dealing with complex and dynamic user interfaces, such as large results sets or rendering graphics.

For actual load testing, it is recommended to use JMeter in non-GUI mode to avoid the performance overhead of the GUI. However, non-GUI mode is not very intuitive to use and can be difficult to set up and configure for complex tests.

Need for significant infrastructure setup

JMeter does not offer a cloud-based testing solution that could simplify the scaling of the test environment. Users are required to establish their own infrastructures and configure the network settings, firewalls, load balancers, etc., needed to run distributed testing with JMeter, which can be time-consuming and costly.

Here are some examples of the infrastructure that users may need to set up for JMeter load testing:

• Dedicated machines: To ensure reliable testing and prevent resource contention, it’s essential to have dedicated machines (VMs or physical systems) for your JMeter infrastructure. This allows for precise measurement of application performance.
• Load balancers: Implementing a load balancer in your JMeter infrastructure ensures scalability, high availability, and accurate performance metrics for comprehensive testing.
• Resource scaling: Adequate hardware resources (CPU, RAM, storage) must be provisioned for load generators to create the desired load.
• Firewall configuration: This involves collaboration with network and security teams to ensure that the testing traffic does not trigger security alerts or get blocked.
• Monitoring infrastructure: Monitoring tools like InfluxDB and Grafana may need to be set up to gather real-time performance metrics during tests.

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Steep learning curve

The steep learning curve associated with JMeter primarily stems from its powerful and feature-rich nature, which can be both an advantage and a challenge for new users.

Here, we will delve into the reasons behind JMeter’s steep learning curve.

Abundance of features

JMeter offers a wide range of features and capabilities for performance testing, including various samplers, listeners, controllers, and configuration options. Learning how to effectively use these features and understanding when and how to configure them can be overwhelming for beginners.

Unintuitive UI

JMeter’s user interface, while functional, is not always considered intuitive by new users. For example, the test plan structure is hierarchical and nested, which can be confusing. Additionally, configuration settings are dispersed across various elements, making it difficult to find specific options. For instance, thread group settings are within the thread group element, while HTTP request configurations are in separate samplers. This makes it difficult for beginners to know where to set specific options.

Performance tuning

Optimizing JMeter for high concurrency and large-scale testing requires a deep understanding of Java Virtual Machine (JVM) settings, heap sizes, and system resource management. Fine-tuning JMeter to maximize performance can be a complex task. And while consistently monitoring JMeter’s memory usage and garbage collection is crucial for sustaining robust performance, it can also add to the tool’s overall complexity.

Lack of collaboration features

JMeter lacks built-in communication features, so users must rely on external tools or platforms to collaborate effectively with colleagues and stakeholders. This can be a drawback for teams that need to work together on load testing.

The lack of collaboration tools means that multiple users cannot work on the same test plan simultaneously, so they need to manually merge their changes, which can be error-prone and time-consuming.

Sharing test results among team members can also be challenging. While JMeter does provide various reporting options, sharing these reports in a collaborative manner is not straightforward.

Stack limitations

While JMeter is proficient at handling a wide range of protocols, there are certain protocols specific to particular applications or industries that it may not support. This can affect its ability to test certain applications and environments effectively:

• WebSockets: JMeter lacks support for WebSockets, which makes it challenging to test web applications that use WebSockets like chat applications and streaming video players.
• gRPC: JMeter does not cater to gRPC, which can make it difficult to test applications that use gRPC, such as microservices-based applications.
• MQTT: JMeter does not facilitate MQTT, making it difficult to test applications that use MQTT like IoT devices or smart home appliances.

It is important to note that although there are plugins and custom samples available to overcome these limitations, they might not deliver full functionality. They could also require additional arrangements, such as procuring a license from a third-party vendor, entering into an agreement, or referring to documentation.

Introducing Multiple as an alternative to JMeter

Multiple is a modern, cloud-based load testing tool that addresses many of the shortcomings of JMeter and introduces several advanced features. Here is an overview of its primary features:

Exploring Multiple’s key features in depth

In this section, we provide a more detailed look at the key features of Multiple, including functionality, benefits, and real-world use cases.

Intuitive scripting

Multiple distinguishes itself from other tools with its developer-focused scripting approach. It utilizes JavaScript, a programming language familiar to many developers. Multiple also seamlessly incorporates NPM packages, so users can leverage a vast ecosystem of open-source libraries and modules to enhance their load tests. This integration streamlines activities like performing HTTP requests, generating artificial data, and handling dependencies.

Let’s take a look at how this works using an example.

Test creation using JavaScript and NPM packages

Creating a new test in Multiple is as easy as clicking the + NEW TEST button in the top-right corner.

Configure test settings

The next step involves defining the parameters and dependencies necessary for the load test, as shown below.

Here’s an example of how you can create a GET and POST request using Multiple:

async function vuLoop(ctx) {
  // Retrieve the apiClient from VU Init
  const { apiClient } = ctx.vuInitData;

  // Generate random user data using faker
  const userData = {
    name: faker.person.firstName(),
    job: faker.person.jobTitle(),
  };

  // Send a POST request to create a new user
  let startTime = Date.now();
  let postRes = await apiClient.post('/api/users', userData);
  ctx.log('POST response:', postRes.data);

  // Capture the time taken for the POST request
  ctx.metric('POST /api/users', Date.now() - startTime, 'ms (API)');

  // Now, let's retrieve user data with a GET request
  startTime = Date.now();
  let getRes = await apiClient.get('/api/users');
  ctx.log('GET response:', getRes.data.data);

  // Capture the time taken for the GET request
  ctx.metric('GET /api/users', Date.now() - startTime, 'ms (API)');
}

This code simulates the process of creating and retrieving user data in a load-testing scenario by using a random data generation library and measuring request times.

Run load test

To start a load test in Multiple, simply click START TEST and configure the test parameters.

View results

During test execution, Multiple provides an option to observe the results, which are updated in real time. Upon completion of the test, a comprehensive report becomes available for you to scrutinize and interpret the outcomes.

Debug test

Multiple provides a debugging feature called “Debug Run” that is an efficient tool for identifying and correcting compilation errors in real time. It offers instant feedback on any discrepancies in your code, enabling you to rectify them immediately.

This feature plays a crucial role in problem detection, validation of test logic, and ensuring the correctness of your load tests.

Context-aware virtual users

The Multiple load testing tool offers a unique feature known as context-aware virtual users. This feature simulates virtual user journeys that accurately reflect real-world user interactions with your application.

Technically speaking, a virtual user (VU) is a simulated user or thread that mimics the behavior of a real user accessing a system or application. In Multiple, VUs are independent, concurrent JavaScript bots that execute the load test scripts.

Key features of virtual users in Multiple include the following:

• Identity: Each VU is allocated a unique numeric ID, accessible as ctx.info.vuId.
• Context awareness: Virtual users possess knowledge about the ongoing test, including details like who initiated it (ctx.info.startedbyEmail), when it started (ctx.info.startedAt), and associated test result information.
• Memory: Virtual users can store and retrieve information in their memory. This feature is helpful for managing and persisting data, such as authorization tokens.

With these options, developers can simulate complex scenarios and multi-step user flows. For example, consider a user commenting, liking, and engaging with a live-streamed concert on a social media platform, as shown below. The diagram below provides a visual representation of this scenario, helping us understand the intricacies of Multiple’s capabilities in emulating such multi-step user interactions.

Streamlined scalability and maintenance

Multiple is fully SaaS-based, so users are freed from the hassles of setting up and managing hardware, software, or infrastructure. This significantly reduces the time, cost, and effort required to start load testing.

The platform manages all the scaling aspects related to large-scale load testing, allowing users to focus on analyzing the results and improving their applications’ performance. Users can also easily start any load test using a simple configuration interface, eliminating the need to go into the complexities of backend infrastructure setup or the requirement for deep technical knowledge.

Wide protocol support

Multiple supports various protocols, including REST, MongoDB, GraphQL, Redis, and gRPC. This is made possible by Multiple’s utilization of JavaScript and the NPM package ecosystem for scripting.

For instance, if you need to conduct a load test using gRPC, you can simply write an import statement in your script like this one:

"dependencies": {
  "@grpc/grpc-js": "^1.7.3",
  "protobufjs": "^6.11.4"
}

This is then followed by a simple connection configuration in the vuInit function.

async function vuInit(ctx) {
  // Load the protobuf definition file for your gRPC service
  const root = loadSync('your_service.proto');
  const YourService = root.lookupType('YourService');
  
  // Create a gRPC client with your service and endpoint
  const client = new YourService(ctx.env.GRPC_ENDPOINT, credentials.createInsecure());
  
  return { client };
}

The advantage of supporting such a wide range of protocols and services is that it enables more precise load testing and closely mirrors real-world scenarios.

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Reporting

The importance of test results in load testing cannot be overstated. They provide valuable insights into how your application or service performs under load, helping you identify bottlenecks and areas for improvement.

Multiple takes reporting further by offering on-demand custom metrics with real-time reporting features. Users can define which metrics are important to them and capture them with just a single line of code, providing a high degree of flexibility and allowing you to tailor your load tests to your needs.

Here’s an example of a custom metric for /api/users to track the response time in seconds instead of milliseconds for GET calls. To see this reflected in the metrics, users need only incorporate the following code into the load test:

  ctx.metric(
    'GET /api/users in seconds',
    (Date.now() - startTime) / 1000,
    'seconds (API)',
  );

Users can also download files containing test result data by selecting the Export Data option on the Test Results page. This file contains valuable data, including the JavaScript test scripts, run parameters, metrics, and any errors that occurred during the test run.

Collaboration and teamwork features

Collaboration is a crucial aspect of modern software development. Multiple makes it easy for team members to work together on load testing thanks to a variety of features that enhance the load testing experience.

Collaboration

• Test and result sharing: Load tests and results can be easily shared with team members by sending them the URL of the test or result.
• Watch live runs: The platform enables users to observe a real-time load test, offering live metrics and graph updates. It also allows users to join a live run initiated by another team member to monitor progress and performance.
• Historical test tracking: The system keeps track of the test results history. Users can view the date and status of previous test runs in a list.

Team management

• Manage users: Invite new members or admins to join your team or remove users if needed.

• Cross-organization participation: When more than one team or organization uses Multiple for load testing, they can be joined together. Different organizations can be switched from the account settings.

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Conclusion

JMeter is a powerful and versatile load-testing tool that has been used by developers and testers for many years. It offers a wide range of features and capabilities, making it a good choice for a variety of load testing needs. However, it can be resource-intensive and requires some technical expertise to use.

Multiple, a modern cloud-based load testing tool, addresses these limitations and offers several advanced features, such as streamlined scalability and maintenance, intuitive scripting, diverse protocol support, and collaborative features.

Both tools offer unique advantages. However, Multiple’s advanced features and user-friendly approach make it a strong competitor in the load testing domain. Ultimately, the choice of tool depends on your specific needs and circumstances. We encourage you to explore Multiple further and try it for yourself. You can sign up for a free account or explore the documentation to learn more about how Multiple can enhance your load-testing process.