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.

---

Alternate Version (Simpler):

If you prefer a minimalistic view:

graph LR
    T["⊤ (Top)"] -->|Unification &| U["Valid"]
    T -->|Conflict| B["⊥ (Bottom)"]
    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

---

It looks like you're describing concepts related to type systems or constraint solving in programming languages. Let's break down each term and how they might be used in such a context:

1. Top ((_))

• Definition: The "Top" value is a universal type or constraint that can accept any value. It represents the most general or unrestricted type.
• Example: In a type system, the "Top" type might be something like Any or Object, which can hold any kind of value (e.g., strings, integers, objects, etc.).

2. Bottom ((_|_))

• Definition: The "Bottom" value represents an invalid or conflicting state. It is often used to indicate an error or an impossible condition.
• Example: In a type system, the "Bottom" type might be something like Never or Void, which represents a type that can never be instantiated. It is used to signal errors or contradictions.

3. Unification ((&))

• Definition: Unification combines constraints in a way that is similar to a logical AND. It finds the most specific type or constraint that satisfies all given constraints.
• Example: If you have two constraints (A) and (B), (A & B) will result in a new constraint that is the intersection of (A) and (B). For example, if (A) is int and (B) is number, (A & B) might result in int because int is a more specific type of number.

4. Disjunction ((|))

• Definition: Disjunction allows alternatives, similar to a logical OR. It represents a type or constraint that can be one of several possible values.
• Example: If you have two constraints (A) and (B), (A | B) will result in a new constraint that can be either (A) or (B). For example, if (A) is int and (B) is string, (A | B) represents a type that can be either an int or a string.

Example in Practice

Let's consider a simple example in a hypothetical type system:

Type A = int
Type B = string
Type C = A & B
Type D = A | B

• Type C: Since int and string are incompatible types, (A & B) would result in the "Bottom" type ((_|_)), indicating an error or conflict.
• Type D: (A | B) would result in a type that can be either int or string.

These concepts are fundamental in type inference and constraint solving in programming languages, particularly in statically typed languages like TypeScript, OCaml, or Haskell.