YAML Validator
What are YAML Files?
YAML files are used for configuring Kubernetes clusters. They consist of information about containers and pods, which direct them to operate, interact, and perform functions in a specific way mentioned in the YAML manifest files.
What are the issues with YAML File in Kubernetes?
YAML files form a large part of the Kubernetes structure because it defines several aspects of the Kubernetes workloads. However, YAML files are susceptible to errors, and it is difficult to map the errors that occur within the manifest files which are connected with each other.
Developers use static validation tools, but even with those, it is difficult to identify issues such as misconfiguration, faulty syntax, misinformation about API versions, end ports, and more. These issues can be prevented and solved with a YAML validator.
What is a YAML Validator?
In simple terms, a YAML validator or a Kubernetes validator is used for validating the data within the YAML manifest files. Overall, the validator will check for the issues mentioned in the previous section and more. All inconsistencies and errors come under the following types of validation.
● API Validation: Here, the validator checks if the API mentioned within the manifest files is functioning properly.
● Security Checking: Checking security elements like misconfigurations, flagging anomalies and suggesting more secure ways of coding the file.
● Syntax Checking: Checking the syntax of the file, flagging errors and suggesting corrections.
● Custom Validation: The third and most complex type of validation involves writing custom protocols in a programming language of one’s choice to ensure various facets of the K8 ecosystem are functioning properly. These facets might be coded in custom languages such as JavaScript.
Overall, there are three types of YAML validation that exist, and there are several tools that perform these. Some of the best tools are:
● Kubeval
● Polaris
There are others such as Copper and Conftest, which can be used too. Each validator functions differently. For instance, Kube-score checks for security and best practices which are in-app checks. On the other hand, Kubescape checks for security misconfigurations in various phases and locations such as Visual Studio (When writing a YAML file), code repositories and inside the cluster.
How the Kubernetes YAML File is Validated
The validation process can differ from company to company. This is because developers have a different mental map of the validation process and what kinds of checks will occur in what place and which tools will perform them.
One of the easy-to-understand ways is the pipeline method. Here it can consist of three stages. Each one is distinct from the other and enables validation of one aspect of the manifest file in the K8 ecosystem.
Structural validation
To begin with, the syntax of the YAML file is validated. This is the first stage where minor issues and errors within the file are flagged. This allows developers to know of such errors soon and enables them to prevent them.
Security validation
Security validation is the second stage where tools such as Kubescape are used. The tool is based on an OPA engine. It retrieves K8 objects and scans them with the Rego files. These check the policies that are defined and find out which data instances violate the expected state of the system.
Overall, Kubescape enables the above functions and aligns the checks with multiple frameworks available.
Semantic validation
This checks the correctness of the K8 schema. It ensures that the particular YAML file and the purpose it serves are correct. Although Kubernetes’ back-end accomplishes the task automatically, it does so much later in the process. Thus, tools such as Kubeval can be used at this stage.
Pragmatic validation
The final validation stage involves looking at the same file from different perspectives and contexts. It checks for adherence to best practices, versioning schemes, performance issues, and more. Furthermore, this type of validation that uses policies can also extend to the fourth stage.
Custom Validation
The optional fourth stage can be custom validation, where one can use tools such as Validkube, Copper, Conftest, and Config-lint, to perform checks at the most intricate levels using the policies’ developer’s code. While Validkube is good for custom validation it is also good for the previous stages because it encompasses the capabilities of kubeval, kubectl-neat, and Trivy.
Overall these are the steps involved in the Kubernetes YAML validation process.
Benefits of Using a YAML Validator
The benefits of using a YAML Validator in Kubernetes include:
● Identifying errors and mapping them across various manifest files.
● Understanding and predicting what kind of output to expect when YAML files are written a certain way.
● Increasing efficiency of K8 clusters by facilitating error-free YAML files that aren’t misconfigured and adhere to best practices.
● Adapting to specific clusters to get in-depth details about their operations.
YAML Validator Example
Sample YAML File:
InsuranceCompanies:
Time: “Feb 2019
“Top Insurance Companies”:
–
No: 1
Name: “Berkshire Hathaway ( BRK.A)”
“Market Capitalization”: “$308 billion”
source: “investopedia.com”
url: “https://www.investopedia.com/articles/active-trading/111314/top-10-insurance-companies-metrics.asp”
When you run this file, you’ll receive the following errors:
Error : Unexpected characters near “Top Insurance Companies”:”.
Line : 3 “Top Insurance Companies”:
The correct YAML file then would be:
InsuranceCompanies:
Time: “May 2021”
“Top Insurance Companies”:
–
No: 1
Name: “Berkshire Hathaway ( BRK.A)”
“Market Capitalization”: “$655 billion”
source: “investopedia.com”
url: “https://www.investopedia.com/articles/active-trading/111314/top-10-insurance-companies-metrics.asp”
Overall, this is how a YAML validator functions with a Kubernetes ecosystem and infrastructure.
Summary
When using YAML files, many common errors can negatively affect operations. By using a YAML File Validator errors can be detected and fixed ensuring faster more efficient Kubernetes performance.
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