Key Methods to Perform Microservices Load Testing In 2022
Load tests are the most basic routines that software teams carry out but performing load testing for Microservices is a challenge. Distributed applications come across changes in terms of scale and resource consumption. This results into app vulnerability to performance issues and continuous monitoring is difficult. Strategy for microservices specific load testing can lower this risk, but testing teams still face challenges when they need to make a practical plan.
There are no shortcuts to perform load testing for microservices. The right way to address this is to account for software testing services during the planning stage of a microservices-based application. By following the below principles, development and testing teams can perform a comprehensive load testing strategy for microservices-based applications.
Steps to perform microservices load testing
1. Start with APM
Microservices load testing should begin with application performance monitoring (APM) rather than simulated data loads. Highly componentized applications, including microservices-based applications, are challenging to analyze. Even with complete load testing, it is tough to determine overall performance or to identify the reason of any performance issues.
2. Work on observability
Observability is essential for effective load testing. IT teams establish performance and scalability parameters for microservices. If the data important to monitor these parameters isn’t present, it’s time to identify new types of APM tools. Otherwise, load testing company will have to add automated monitoring processes to the code.
Kubernetes offers strong monitoring tools that not need developers to manually add monitoring routines. Moreover, a service mesh will offer a full spectrum of load testing parameters and observability.
3. Analyze scaling patterns
When testing microservices, it’s important to evaluate the time it takes to connect workloads, load balance across instances, spin up new instances and scale back when workload volume reduces. Visibility within operational patterns, as well as the application’s overall behavior under certain scaling conditions, is important.
To run load testing on a microservices-based application within simulated scenarios, don’t ramp up workloads. Rather, introduce the similar sort of sporadic demand and fluctuating workflow that would happen in a production environment.
4. Test hosting domains
In hybrid and multi-cloud environments, traffic move across different functional boundaries and hosting domains. This movement result into congestion and network latency, leading to lengthy deployment times for application components in another domain.
While not all applications will span different hosting domains, things including cloud-bursting or failover between domains will result into crossover. Hence, it will be tough to measure load test results without centralized monitoring across all hosting domains.
5. Work distribution framework
The final microservices load testing method is to select the framework for workflow and load distribution. Examples of work distribution frameworks comprise of things like API gateways and service mesh implementations as they both play a vital role in scaling and load balancing process.
Before teams choose a particular framework, they need to identify whether they’re adding complexity to the movement of work between the microservices that build an application.
Wrapping Up
There is no doubt that load testing is an important phase and is one of the most effective means to achieve testing goals. However, businesses must understand that load testing has its own set of stages that needs to be followed properly. It allows achieving testing goals within stipulated timelines. Following each stage helps Load Testing Company India achieve the desired outcome, without needing any manual intervention. A well-planned testing leads to successful software development.
