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tech_docs/CUE.md
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**CUE (Configure-Unify-Execute)** is an **open-source data constraint language** developed at Google, designed for **configuration management, validation, and templating**. It stands for **Configure, Unify, Execute**.
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### Purpose
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CUE simplifies tasks involving **defining, validating, and using data** in complex systems like Kubernetes. It combines **schemas, constraints, and values** into a unified framework, ensuring configurations adhere to predefined rules.
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### Key Features
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- **Validation First**: Unlike traditional languages, CUE prioritizes validation, ensuring data meets strict criteria.
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- **JSON Superset**: It builds upon JSON, adding logic like conditionals and loops.
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- **Constraint-Based**: Uses constraints instead of inheritance, avoiding complexity from order-dependent merging.
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- **Order Independence**: Merging configurations is **associative, commutative, and idempotent**, ensuring consistency regardless of merge order.
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- **Tooling**: Offers CLI tools (`cue` command), APIs for automation, and integrations with YAML/JSON.
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### Use Cases
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- **Kubernetes**: Validate and generate YAML configurations.
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- **Data Validation**: Define schemas for APIs or infrastructure.
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- **Code Generation**: Extract schemas from source code or generate code from CUE definitions.
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- **CI/CD Pipelines**: Ensure deployment consistency.
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### Example
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In CUE, you might define a schema for a Kubernetes deployment, enforce constraints (e.g., resource limits), and generate validated YAML—all in one workflow.
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### Summary
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CUE is a **robust, constraint-driven language** that unifies configuration, validation, and execution, particularly valuable for managing complex, policy-driven systems like cloud infrastructure.
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---
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Here are **six starter ideas** that feel more like “mini-projects” than “hello-world” exercises. Each one is scoped to be finished in an evening or weekend and leaves you with something useful you can actually commit to a repo or show a teammate.
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------------------------------------------------
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1. “Pet-Picture” API Contract
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• Goal: Design the request/response contract for a tiny REST service that lets users POST pictures of their pets.
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• CUE angle
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– Write a `#PetPhoto` schema with required fields (`id`, `petName`, `imageURL`), plus constraints (URL must start with `https://`, file size ≤ 5 MB, allowed MIME types).
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– Export the same CUE to JSON Schema so front-end folks can use it in Swagger / OpenAPI.
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• Quick win: `curl` a sample payload through `cue vet` in CI and watch it reject the 6 MB GIF someone tried to sneak in .
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2. Terraform Variable Guardrails
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• Goal: Stop teammates from fat-fingering Azure VM sizes or AWS regions in your Terraform repo.
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• CUE angle
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– Describe all valid regions, SKUs, and tag patterns in CUE.
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– Make a `variables.cue` that unifies with every `.tfvars` file in the repo; fail the plan if anything is wrong .
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• Quick win: open a PR that deliberately violates the policy and see GitHub Actions reject it in <30 s.
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3. Team Dotfiles Distribution
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• Goal: Ship a single YAML/JSON file per engineer that declares their preferred shell, VS Code extensions, and git aliases.
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• CUE angle
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– Provide a `#Workstation` schema with defaults (shell = `"zsh"`, extensions = `[ "GitLens", "k9s" ]`).
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– Write a 20-line Go program that ingests the unified CUE value and spits out the right Ansible or Home-Brew bundle per person .
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4. Kubernetes “Golden Path” Templates
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• Goal: Give devs a one-command way to generate a Deployment + Service + HPA that already meets security and resource standards.
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• CUE angle
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– Create a `#Microservice` definition that enforces labels, resource limits, and security contexts.
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– Provide optional overlays (staging vs prod) that inject different image registries or replica counts .
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• Quick win: `cue cmd gen prod my-app` writes `prod-my-app.yaml` with zero copy-paste.
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5. Resume-as-Data
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• Goal: Host your résumé as YAML but still be able to export to PDF, JSON, and HTML.
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• CUE angle
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– Model sections (`experience`, `education`, `skills`) with strict types and regex rules (company names must match LinkedIn URLs).
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– Use `cue export` to feed a Go template that renders LaTeX or Markdown.
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• Quick win: change one bullet in YAML, rerun `make` and see every output format refresh.
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6. Advent-of-Code Input Validator
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• Goal: Stop wasting time on malformed puzzle inputs.
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• CUE angle
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– For any AoC day, capture the input grammar in CUE (grids, ints, regex groups).
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– Add constraints (board size 100×100, numbers 0-9 only).
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• Quick win: paste your downloaded input into `input.txt`; `cue vet` tells you immediately if the site changed the format overnight.
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------------------------------------------------
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How to pick one
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• If you live in Terraform → start with #2.
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• If you just want to “see CUE do magic” → #1 or #5 finishes in <30 min.
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• If you’re already on K8s → #4 will save you real toil next sprint.
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Each repo can literally be a folder with a `schema.cue`, a `data.*` file, and a two-line GitHub Action that runs `cue vet`. Ship it, tag a friend for review, and you’ve officially gone from “reading about CUE” to “running CUE in production.”
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---
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Below is a **step-by-step brainstorm** that keeps the promise:
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“Everything is an object” → expressed in **CUE** → rendered into **one or more Docker containers** → **deployed on stock Debian**.
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We iterate *together*—after each numbered idea you can simply reply **“next”** or **“deep-dive <number>”** and I’ll expand only the one you like.
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------------------------------------------------
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1. Object–Music Player (OM-Player)
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• **Object model**: every track, playlist, device, volume-level, EQ preset is a CUE object.
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• **CUE → Docker trick**: `cue export om.cue | cue cmd gen docker-compose.yml` yields *three* containers (MPD server, web-UI, icecast stream).
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• **Deploy**: `./deploy.sh` on a Debian box auto-pulls images, mounts `/music`, and exposes port 8000.
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• *Hook*: you can change “cross-fade = 2.0s” in the CUE file, run `cue vet`, and the compose file re-renders without touching YAML by hand.
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2. Object–Photo Darkroom (OM-Darkroom)
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• **Object model**: raw image, transform pipeline (crop 1:1 → denoise → LUT), export profiles.
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• **CUE → Docker trick**: single multi-stage Dockerfile generated from CUE; includes conditional GPU layer if `gpu: true`.
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• **Deploy**: on Debian with or without NVIDIA Docker runtime .
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• *Hook*: `cue eval darkroom.cue` shows you the exact ImageMagick CLI that will run inside the container before you ever build.
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3. Object–Home-Lab Router (OM-Router)
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• **Object model**: interface objects, VLAN objects, nftables rule objects, WireGuard peer objects.
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• **CUE → Docker trick**: produces a *privileged* container that maps `/etc/wireguard`, `/etc/nftables`; plus a dry-run sidecar container that just prints the rules it *would* apply.
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• **Deploy**: systemd service on Debian host starts the container at boot.
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• *Hook*: `cue vet` guarantees that no two VLANs share the same ID, no overlapping subnets, etc.
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4. Object–FaaS Edge (OM-Edge)
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• **Object model**: every function is an object (`fn`, `runtime`, `env`, `memory`, `cpu`).
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• **CUE → Docker trick**: CUE spawns one container per function plus an nginx reverse-proxy container.
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• **Deploy**: Debian box becomes a mini Lambda—`curl /fn/hello` triggers the container of the moment.
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• *Hook*: horizontal scale is just changing `replicas: 3` in CUE and re-exporting.
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5. Object–Minecraft World (OM-Mine)
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• **Object model**: world seed, player objects, permission objects, mod objects.
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• **CUE → Docker trick**: Server container + optional RCON web UI container.
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• **Deploy**: on Debian; mods are baked into the image hash calculated from the CUE file.
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• *Hook*: roll back to yesterday’s world by reverting one Git commit to the CUE file and redeploying.
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6. Object–CI Matrix (OM-Matrix)
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• **Object model**: every job, step, runner label, secret ref is an object.
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• **CUE → Docker trick**: generates a lightweight Drone-CI server + runner containers.
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• **Deploy**: single `docker compose up` on Debian; self-hosted CI in < 2 min.
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• *Hook*: matrix expansion (`go: [1.21, 1.22]`) happens in CUE, not in YAML anchors.
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------------------------------------------------
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Your turn
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Reply with the **number** you want to pursue (or say “new batch”) and I’ll give:
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1. Minimal CUE schema
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2. Exact `docker-compose.yml` generator snippet
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3. One-liner to run it on a fresh Debian install
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