Performance Testing - Tools

Find the Best Load Testing Tool — Image generated from Microsoft Designer

Choosing the right performance testing tool is an investment in the success and user experience of your application. There is no one-size fits all solution ever. By understanding your needs and carefully evaluating available options, you can become a performance testing maestro, ensuring your application thrives under any load..

I have compiled a report on the Load Testing tools I have investigated, and I would like to present their key features to you. You can choose the tool that best fits your needs from JMeter, K6, Gatling, Locust or others. Please read on to learn about the differences in JMeter, K6, Gatling, and Locust OSS offerings.

The key metrics for the comparison I took as a baseline are as follows

• Generate large loads and load models to suit real-world needs
• Ease of writing scripts, maintain them and execute them
• Distributed Load Testing options and Reporting Test Results
• Load Generator Resource consumption

JMeter:

• Java-based, which can be resource-intensive, especially with large user loads.
• Seamless distributed testing capabilities using Master-Slave architecture and automatically aggregates the test results. However, you need to install Jmeter and copy the scripts manually to each slave instance
• Should only Navigate through UI, to generate test scripts and are cumbersome to maintain or track changes in Version Control Systems.
• Supports most protocols. For a complete list look here. No native Support for gRPC and WebSockets, only available through Third Party Plugins
• Provide IP Spoofing — The JMeter host must have multiple IP addresses (i.e. IP aliases, network interfaces, devices)
• Inbuilt support to control traffic to different endpoints in each Thread group using Logic Controllers

Gatling:

• Based on Scala, typically consumes more memory than k6 or Locust, but less than JMeter
• Domain-Specific Language — Scala, Java and Kotlin to write test scripts
• Native support for HTTP, WebSockets, Server-Sent Events, JMS, and MQTT protocols. Third-party integrations for gRPC, AMQP etc
• Distributed Testing: Gatling open-source doesn’t have a cluster mode, but you can achieve similar results manually. You will have to configure all your load injectors and aggregate the results manually.
• Can shape the load in terms of throughput/requests per second instead of virtual users. You can choose the latter too depending on your use case
• Load model to suit how your application handles traffic
• I searched for IP Spoofing in Gatling, This is what they said 🤭. I can’t disagree though!

Locust:

• Python-based and use Python to write test scripts
• Locust only comes with built-in support for HTTP/HTTPS but it can be extended to test almost any system
• Ability to run simple Load Tests from Web UI
• Custom load shapes can be scripted in Python
• Supports Distributed Testing using Master-Slave architecture but with only one worker instance per processor core. Also, you will need to copy the load test scripts to each worker node too.
• Automated aggregated Results from Distributed Testing.
• No Support for IP Spoofing! 🤦🏻 Okay, No support for having the load generator use multiple source IP addresses

K6:

• Built on Go, known for efficiency and low memory footprint.
• Write test script in JS and Less Resource Intensive compared to other tools as it’s built using Go and can generate a huge load just from a single machine
• You can have custom Load shapes to suit your real-time traffic patterns using stages in Scenarios
• Easy to integrate custom implementations using xk6
• Native Support for HTTP/1.1, HTTP/2, WebSockets, gRPC
• Cumbersome to set up Distributed Testing. Supported through k6-operator and only runs on Kubernetes
• No Automated test result aggregation in Distributed Testing, but the hack is to output test results to an external entity like Prometheus or Influx DB and visualize results in Grafana
• The USP for K6 is its ability to directly specify the required throughput rather than specifying the number of Concurrent Users, although you could choose an executor to specify no. of concurrent users only
• Supports having the load generator use multiple source IP addresses
• For throughput control capabilities look here

For Reporting the Test Results, All of the above tools have different ways from CLI to HTML reports and can be integrated with Prometheus or InfluxBD for Real-Time Monitoring

If you have some money to shed for your Performance testing, you could run them in the cloud too. This might also reduce some extra effort that you need to take for Distributed Testing when generating large load, Aggregating results at the end of the test and visualizing results for the tools above.

Here are some options below

• JMeter — BlazeMeter, Flood.io
• Gatling — Gatling Enterprise, Flood.io
• K6 — Grafana Cloud K6
• Provision Locust using Infrastructure as Code/IaC

And finally…. Recommendations:

• Prioritise k6 or Locust for resource efficiency, especially for high-concurrency tests
• Consider Gatling for a balance of features and performance, but be mindful of its higher memory usage
• Choose JMeter for its extensive features and flexibility, but be prepared for greater resource needs

Don’t hesitate to try demos. Hands-on experience is key to making an informed decision

A few other tools you might want to explore are

• Vegeta
• Siege

Disclaimer: This blog serves as a platform for me to share my thoughts and experiences. While I may reference my work or industry, the content represents my perspective and is not endorsed by or affiliated with my employer.