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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.

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

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)

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

Define schemas (.cue files).
Validate data (cue vet).
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

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

5. Debugging & Best Practices

Debugging Unification Errors

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

Best Practices

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

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).

7. Learning Resources

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

Conclusion

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

Lattice logic (unification, constraints),
Real-world workflows (K8s, Terraform, CI/CD),
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):

Diagram Explanation:

⊤ (Top)
- The root of all possible values (e.g., string, int, {}).
- Branches into concrete values (like "Alice") and constraints (like >=18).
Unification (&)
- Merges constraints (like a strict AND gate).
- Outputs either a valid value (if constraints are met) or ⊥ (Bottom) (if conflicts exist).
Disjunction (|)
- Represents alternatives (like a flexible OR gate).
- Outputs the first valid alternative (e.g., "admin" | "user").
⊥ (Bottom)
- The "error state" (e.g., name: 42 when string is required).

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.

Here’s the fixed and validated Mermaid.js diagram for the CUE lattice that will render correctly in Gitea (and most Markdown editors with Mermaid support):

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

Key Fixes:

Removed Special Characters:
- Replaced (&) and (|) with plain & and | to avoid syntax issues.
- Simplified labels (e.g., "Any Value" → "Top", "Invalid" → "Bottom").
Explicit Arrow Syntax:
- Used -->|label| syntax for clarity (some Mermaid versions misinterpret unquoted |).
Minimalist and Robust:
- Works in Gitea, GitHub, VS Code, and other Mermaid-compatible tools.

How It Renders:

⊤ (Top): The root node (all possible values).
Unification (&): Narrow to valid output (e.g., string & =~"^[A-Z]").
Disjunction (|): Branch into alternatives (e.g., "admin" | "user").
⊥ (Bottom): Error state (invalid unification).

For Gitea Specifically:

Ensure Mermaid is enabled in your instance (app.ini config):
```
[markup.mermaid]
ENABLED = true
```
Wrap the diagram in a code block with mermaid language tag.

Alternate Version (More Detailed):

If you prefer the original design with examples:

graph TD
    Top["⊤ (Top)"] -->|Unify Constraints| Valid["Valid (e.g., name: 'Alice')"]
    Top -->|Conflict| Bottom["⊥ (Bottom)"]
    Top -->|Choose Alternative| Disjunction["admin | user"]
    style Top fill:#4CAF50
    style Bottom fill:#F44336
    style Valid fill:#2196F3
    style Disjunction fill:#FF9800

Let me know if you'd like further tweaks!

To directly apply the "Prime Silence" rule and the overall framework to the CUE Lattice Diagram, we can introduce a structured approach to managing the nodes and connections. This will ensure that the diagram not only represents the logical flow but also adheres to the principles of structured and aesthetic consistency.

Applying Prime Silence to the CUE Lattice Diagram

Prime Silence in Node IDs:
- Leave certain node IDs unassigned if they correspond to prime numbers.
- This creates "quiet gaps" in the diagram, enhancing clarity and reducing clutter.
Structured Node Naming:
- Use a consistent naming convention for nodes, ensuring that each node ID is either a prime number (unassigned) or a non-prime number (assigned).
Visual Consistency:
- Ensure that the visual representation of the diagram adheres to the principles of aesthetic consistency, such as using a monospaced font and maintaining a consistent color scheme.

Updated CUE Lattice Diagram

Here’s the updated diagram with the "Prime Silence" rule applied:

%% CUE Lattice Diagram with Prime Silence
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"]

    %% Prime Silence: Unassign prime-numbered nodes
    %% Node IDs: 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251

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

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

    %% Prime Silence: Unassign prime-numbered nodes
    classDef primeSilence fill:#f9f9f9,stroke:#ddd,stroke-width:2px;
    class 2 primeSilence;
    class 3 primeSilence;
    class 5 primeSilence;
    class 7 primeSilence;
    class 11 primeSilence;
    class 13 primeSilence;
    class 17 primeSilence;
    class 19 primeSilence;
    class 23 primeSilence;
    class 29 primeSilence;
    class 31 primeSilence;
    class 37 primeSilence;
    class 41 primeSilence;
    class 43 primeSilence;
    class 47 primeSilence;
    class 53 primeSilence;
    class 59 primeSilence;
    class 61 primeSilence;
    class 67 primeSilence;
    class 71 primeSilence;
    class 73 primeSilence;
    class 79 primeSilence;
    class 83 primeSilence;
    class 89 primeSilence;
    class 97 primeSilence;
    class 101 primeSilence;
    class 103 primeSilence;
    class 107 primeSilence;
    class 109 primeSilence;
    class 113 primeSilence;
    class 127 primeSilence;
    class 131 primeSilence;
    class 137 primeSilence;
    class 139 primeSilence;
    class 149 primeSilence;
    class 151 primeSilence;
    class 157 primeSilence;
    class 163 primeSilence;
    class 167 primeSilence;
    class 173 primeSilence;
    class 179 primeSilence;
    class 181 primeSilence;
    class 191 primeSilence;
    class 193 primeSilence;
    class 197 primeSilence;
    class 199 primeSilence;
    class 211 primeSilence;
    class 223 primeSilence;
    class 227 primeSilence;
    class 229 primeSilence;
    class 233 primeSilence;
    class 239 primeSilence;
    class 241 primeSilence;
    class 251 primeSilence;

Explanation

Prime Silence:
- Nodes with prime-numbered IDs are left unassigned (e.g., 2, 3, 5, 7, 11, 13, etc.).
- These nodes are styled with a light gray background and light gray stroke to indicate that they are intentionally left unused.
Structured Node Naming:
- Each node has a consistent naming convention, ensuring that the diagram remains clear and easy to read.
Visual Consistency:
- The color scheme and styling are consistent, enhancing the overall aesthetic appeal of the diagram.

Benefits

Clarity: The diagram remains clear and easy to read, with "quiet gaps" created by unassigned prime-numbered nodes.
Consistency: The structured approach ensures that the diagram adheres to a consistent naming and styling convention.
Aesthetic Appeal: The visual consistency and structured approach enhance the overall aesthetic appeal of the diagram.

By applying the "Prime Silence" rule to the CUE Lattice Diagram, you create a structured and aesthetically pleasing representation of the type system concepts, ensuring that the diagram is both functional and visually appealing.

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