the_information_nexus/tech_docs/CUE_your_new_friend.md

The Complete Technical Guide to CUE (Configure-Unify-Execute)

A structured, practical, and comprehensive introduction to CUE—covering core concepts, real-world applications, and best practices.

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1. Introduction to CUE

What is CUE?

CUE (Configure-Unify-Execute) is an open-source data constraint language developed at Google. It is designed for:

• Configuration management (Kubernetes, Terraform, CI/CD)
• Data validation (API schemas, input sanitization)
• Code generation (Dockerfiles, Kubernetes manifests, SQL schemas)
• Policy enforcement (security rules, compliance checks)

Unlike YAML/JSON, CUE merges types, values, and constraints into a single model, enabling deterministic, reproducible configurations.

Why CUE Over Alternatives?

Tool	Strengths	Weaknesses vs. CUE
YAML	Human-readable, widely used	No validation, no logic, fragile
Jsonnet	Templating, code reuse	Slower, no built-in validation
HCL	Terraform integration	Less flexible for non-TF use
Pulumi	Full programming model	Complex, runtime-dependent
CUE	Validation + unification	Steeper initial learning curve

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2. Core Concepts

The CUE Lattice

CUE’s power comes from its lattice structure, where:

• _ (Top): Accepts any value (e.g., string, int, {}).
• _|_ (Bottom): An invalid/conflicting value (error state).
• Unification (&) combines constraints (like logical AND).
• Disjunction (|) allows alternatives (like logical OR).

Example:

#Schema: {
    name:  string & =~"^[A-Z][a-z]+$"  // Must start with uppercase
    age:   int & >0 & <120             // Age between 0 and 120
    role?: "admin" | "user"            // Optional role
}

valid: #Schema & { name: "Alice", age: 30 }  // ✅ Valid
invalid: #Schema & { name: "alice", age: 150 }  // ❌ Fails (name, age)

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3. Getting Started

Installation

# Install CUE
brew install cue-lang/tap/cue  # macOS
sudo apt-get install cue       # Debian/Ubuntu
go install cuelang.org/go/cmd/cue@latest  # Go users

# Verify
cue version

Basic Workflow

1. Define schemas (.cue files).
2. Validate data (cue vet).
3. Export to JSON/YAML (cue export).

Example:

// schema.cue
#Person: {
    name: string
    age:  int & >=18
}

// data.json
{ "name": "Bob", "age": 25 }

# Validate
cue vet schema.cue data.json  # ✅ Passes

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4. Real-World Use Cases

Case 1: Kubernetes Configuration

Problem: YAML files are brittle and hard to validate.
Solution: Define a reusable schema.

// k8s.cue
#Deployment: {
    apiVersion: "apps/v1"
    kind:       "Deployment"
    metadata: name: string
    spec: {
        replicas: int & >=1
        template: spec: containers: [{
            name:  string
            image: string & =~".+:.+"
        }]
    }
}

myapp: #Deployment & {
    metadata: name: "webapp"
    spec: replicas: 3
    spec: template: spec: containers: [{ name: "app", image: "nginx:latest" }]
}

# Export to YAML
cue export k8s.cue -e myapp --out yaml > deploy.yaml

Case 2: Terraform Guardrails

Problem: Prevent invalid cloud configurations.
Solution: Enforce AWS/GCP policies.

#AWS: {
    region: "us-east-1" | "us-west-2"
    instanceType: "t2.micro" | "t3.medium"
}

my_vm: #AWS & {
    region: "us-east-1"
    instanceType: "t2.micro"  # ✅ Valid
}

invalid_vm: #AWS & {
    region: "eu-west-1"  # ❌ Fails (invalid region)
}

Case 3: CI/CD Pipeline-as-Code

Problem: YAML-based pipelines are hard to maintain.
Solution: Define pipelines in CUE.

#Pipeline: {
    steps: [...{
        name: string
        cmd:  string
    }]
}

my_pipeline: #Pipeline & {
    steps: [
        { name: "build", cmd: "go build" },
        { name: "test",  cmd: "go test" },
    ]
}

# Generate GitHub Actions YAML
cue export pipeline.cue --out yaml > .github/workflows/ci.yml

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5. Debugging & Best Practices

Debugging Unification Errors

• Use cue eval -v to trace unification steps.
• Check for _|_ (bottom) in output—indicates conflicts.

Best Practices

1. Modularize: Split schemas into files (schemas/, configs/).
2. Reuse: Leverage CUE’s import system (import "acme.com/schemas").
3. Validate Early: Run cue vet in CI/CD.

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6. Advanced Topics

Go + CUE Integration

Embed CUE in Go programs for dynamic validation:

package main

import (
	"cuelang.org/go/cue/cuecontext"
	"fmt"
)

func main() {
	ctx := cuecontext.New()
	schema := ctx.CompileString(`
		#Person: {
			name: string
			age:  int & >=18
		}`)
	data := ctx.CompileString(`{ name: "Alice", age: 30 }`)

	unified := schema.Unify(data)
	if err := unified.Validate(); err != nil {
		fmt.Println("Validation failed:", err)
	} else {
		fmt.Println("Valid!")
	}
}

Generating Code

Convert CUE to:

• OpenAPI: cue export --out openapi
• Go Structs: cue get go
• SQL: Use templates (text/template).

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7. Learning Resources

• Official Docs: cuelang.org
• Playground: cuelang.org/play
• Community: CUE Slack

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Conclusion

CUE replaces ad-hoc YAML/JSON with structured, validated, and reusable configurations. By mastering:

1. Lattice logic (unification, constraints),
2. Real-world workflows (K8s, Terraform, CI/CD),
3. Tooling integration (Go, OpenAPI),

You can eliminate configuration errors and enforce policies declaratively.

Next Step: Try the CUE Tutorial or experiment in the playground! 🚀

Here’s a Mermaid.js diagram that visually explains the CUE lattice (Top, Bottom, Unification, and Disjunction) in a way that’s both accurate and appealing for visual learners. You can embed this directly in your Gitea Markdown files (if Mermaid rendering is enabled):

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Diagram Explanation:

1. ⊤ (Top)

   • The root of all possible values (e.g., string, int, {}).
   • Branches into concrete values (like "Alice") and constraints (like >=18).

2. Unification (&)

   • Merges constraints (like a strict AND gate).
   • Outputs either a valid value (if constraints are met) or ⊥ (Bottom) (if conflicts exist).

3. Disjunction (|)

   • Represents alternatives (like a flexible OR gate).
   • Outputs the first valid alternative (e.g., "admin" | "user").

4. ⊥ (Bottom)

   • The "error state" (e.g., name: 42 when string is required).

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Why This Works:

• Visualizes the Lattice: Shows how CUE narrows possibilities from ⊤ to valid outputs.
• Color-Coded: Green (Top), Blue (Valid), Red (Error), Orange (Alternatives).
• Includes Examples: Embedded real CUE snippets for clarity.

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Alternate Version (Simpler):

If you prefer a minimalistic view:

graph LR
    T["⊤ (Any Value)"] -->|Unification (&)| U["Valid"]
    T -->|Conflict| B["⊥ (Invalid)"]
    T -->|Disjunction (|)| D["A | B | C"]

%% CUE Lattice Diagram
graph TD
    A["⊤ (Top)"] --> B["Concrete Values"]
    A --> C["Constraints"]
    B --> D["Unification (&)"]
    C --> D
    D --> E["Valid Output"]
    D --> F["⊥ (Bottom, Invalid)"]
    B --> G["Disjunction (|)"]
    C --> G
    G --> H["Valid Alternative"]

    style A fill:#4CAF50,stroke:#388E3C
    style F fill:#F44336,stroke:#D32F2F
    style E fill:#2196F3,stroke:#1976D2
    style H fill:#FF9800,stroke:#F57C00

    %% Examples
    I["Example:\nname: string & =~'^[A-Z]'"] --> D
    J["Example:\nrole: 'admin' | 'user'"] --> G