The Configuration Complexity Curse

TL;DR

Three different approaches to dynamic configuration:
- Templating
  - Examples: Helm, gomplate, etc.
  - Text templating very quickly becomes fragile, hard-to-understand, and hard-to-maintain.
  - Template writers lack the tools to build abstractions around the data.
- Layering
  - Examples: kustomize
  - Data layering breaks down when configurations grow in complexity and scale.
  - Template writers lack abstraction and type validation.
  - For large scale projects, inheritance creates deep layers of abstractions.
  - Semantics are locked into an opaque tool and not exposed as language features.
- Data Configuration Language (DCL)
  - Examples: jsonnet, ksonnet kubecfg, etc.
Any declarative systems and tools that grow in size and complexity need to be abstracted.
CUE is a language for defining, generating, and validating all kinds of data.
CUE is designed around graph unification.
- Sets of types and values can be modeled as directed graphs and then unified to the most specific representation of all graphs.
CUE makes tasks like validation, templating, querying, and code generation first class features.
CUE’s type system is expressive. For a given field, you can specify the type as well as optionality and constraints from other fields.
CUE also has a declarative scripting layer built on top of the configuration language.