In today's software industry, microservices architecture has revolutionized by offering significant benefits over the traditional monolithic approach. This architecture enables developers to easily integrate new features or updates without impacting other services, making it simpler to scale their application services.
To ensure smooth interactions between services, as well as responsiveness, reliability, stability, and scalability of the application, the microservices performance testing is pivotal, much like other types of testing for this architecture.
In addition to the traditional approach of performance testing, artificial intelligence plays a crucial role in streamlining testing processes, reducing both testing time and complexity that can be achieved by simulating real-world usage scenarios, where predictive analysis mitigates performance bottlenecks, and auto-scaling configurations adapt dynamically to changing workloads.
Let's first examine the projected growth of microservices architecture, expected to increase from USD 5.49 billion to USD 21.61 billion at an 18.66% CAGR from 2021 to 2030:
Microservices Architecture Market Size Trend
CAGR of 18.66%
Due to distributed nature of microservices architecture, the way data flows through different microservices is complex due to the service paths being dynamic and depending upon user choice of using a service or functionality. So, this needs a different test strategy and inclusion of AI performance testing tools help in making testing more efficient.
Microservices Performance Testing With AI
Below are some important areas where AI can help us in:
Below are the major scenarios that need to be covered while preparing the test plan for performance testing of microservices application:
There are several factors that can make a service to perform slow or fast depending upon supporting resources and total traffic on the network. A few common problems are mentioned below:
With the adoption of AI capabilities by existing tools like NeoLoad, Functionize, New Relic, and AppDynamics, you can get better results and more insights for your application. Below are some useful KPIs to measure during load testing:
In order to conduct the Performance testing properly for microservices-based applications, here are some best practices that need to be followed:
High risk or high usage micro-services should be load tested first
Use service virtualization for mocking the dependent functionality still under development
Components like 'Identity Provider', 'Service Discovery' and 'CDN' should be taken into consideration
Thorough knowledge of dependencies between the microservices and plan the testing accordingly
Test performance at UI level as well rather than just focusing on back end architecture
Core objective of microservices performance testing is to expose the components that are not optimized for scalability and prevent the crashes due to heavy load in production. So, performance testing is vital for microservices-based applications and need to be conducted at efficiently to ensure scalability, reliability, and responsiveness.
QASource team has good experience in performance testing for different domains. With powers of AI, they can assist in your performance testing needs to ensure the best user experience, test data preparation with generative AI and real-time monitoring into play using right set of tools. To learn more, get in touch now.
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