Meshery CRD Model Generation: Filtering Vs. Grouping Explained
When working with Meshery and generating models from Kubernetes Custom Resource Definitions (CRDs), understanding the difference between filtering and grouping is crucial. While the current documentation might use these terms interchangeably, there's a distinct behavior happening under the hood that we need to clarify. This article aims to set the record straight, ensuring you know exactly how Meshery handles your CRDs and how you can best leverage its capabilities.
Understanding Component Grouping into Models
Let's start by talking about component grouping into models. When you're using Meshery to generate models from various sources like GitHub repositories, Helm charts, Artifact Hub, or directly from your Kubernetes clusters, Meshery employs an automatic grouping mechanism. This process isn't about you meticulously selecting which CRDs to include at this stage; instead, Meshery intelligently organizes components based on their origin. Think of it as Meshery saying, "All these CRDs came from this specific GitHub repository, so let's group them together into a model representing that repository." Similarly, if you're importing from a Helm chart, all the CRDs within that chart will be grouped under a model named after the chart. This source-based grouping is the default and most common behavior for direct import methods. The backend API, specifically the generationPayloadItem in server/handlers/component_generation.go, doesn't even have a parameter for user-specified group filters during these direct import types. It's designed to be straightforward: point Meshery to a source, and it creates a model based on that source. This makes the initial import process seamless, allowing you to quickly get a foundational model representing your specified source without needing to pre-filter anything. The power here lies in Meshery's ability to infer structure from the source itself, saving you time and effort. This automatic grouping ensures that related CRDs are kept together, making it easier to manage and understand the components within your Meshery environment. It’s a core part of how Meshery streamlines the integration of Kubernetes resources into its management platform, providing a logical organization right from the start. The emphasis is on the source as the primary organizational principle, which simplifies the user experience for most common import scenarios. This default behavior is robust and covers a vast majority of use cases, providing a solid starting point for any model generation task.
The Nuance of Group Filtering: Exclusive to CSV/Spreadsheet Imports
Now, let's delve into the specific capability that is often confused with the automatic grouping we just discussed: group filtering. It's important to understand that group filtering is not a universal feature across all import methods in Meshery. Instead, this advanced functionality is exclusively available when you are importing CRDs via a CSV (Comma Separated Values) file or a spreadsheet. When you choose the CSV import route, Meshery provides you with a powerful tool to precisely control which CRDs are included in your model based on their spec.group field. This is where the term **
