How do you know if a solution is secure enough? How can you be sure your testing covers everything it should? The cloud-native architecture paradigm is a tried and true way to structure your company. It includes cohesive, independent components that are responsible for business agility, scalability, and resiliency—all contributing to accelerated time to market, competitive advantages, and optimized costs. The paradigm has been supported by the use of polyglot technology.
A complex solution created with a combination of tech architecture and other technologies is difficult to maintain and manage. There are a lot of components, requiring multiple technology frameworks, making it tough to do an excellent job. An underwhelming design or engineering practice exponentially increases the complexity risks of these types of solutions.
There is an engineering practice that is crucial to the success or failure of any digital transformation initiative: resiliency. Resiliency directly contributes to the availability and performance of your solution, and also impacts how your customer experiences it.
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Challenges in Testing Resiliency of Cloud-Native Applications
There are already a lot of materials detailing the implementation process for resiliency in cloud-native applications. There are also frameworks or tools that can be used to test your application’s resilience. But testing a solution for resilience is difficult, because how do we know for certain that the type of testing will expose all necessary and sufficient failure scenarios? How do we know what difficulties to introduce during the tests?
One way to do this is by identifying unique traversal paths.
Filter through our catalog of products by using a channel app that talks to the backend server to fetch data from a persistent database. We expect readers to be working with the latest technologies and innovations. One technology worth investment is APIs. The API provides clients with a gateway, enabling them to access services behind the API and consume data within the API.
Also, asking the people who use and love your product what they’re missing might be the key to success. There might be many reasons why each identified failure surface could fail. You’ll need to identify these causes next. The core microservice could fail just due to out-of-memory issues. API Gateway might become unresponsive because it doesn’t have the resources it needs to respond to requests (unbeknownst to us). If the network between the microservice and backend system goes down, the application may suffer data loss.
In order to prepare for problems, we recommend preparing this way:
- Prepare a plan of action.
- Look-out points to defend your perimeter.
- Escalation Procedures in case of “organized aggression.”
The above scenarios are not exhaustive, but are intended to help you see the problem with relying solely on failure testing. To simulate failure, you need to make sure that only one instance of the component is running at a time and make sure it fails along with any replica instances that are running. Additionally, if you don’t have a fully functional prototype, it may be worth investing in additional verification.
