🍃 MongoDB Masterclass Laboratory

Welcome to the MongoDB Masterclass Repository. This is a premium, engineering-focused curriculum designed to take you from basic document storage to mastery of distributed clusters, advanced modeling, and the Aggregation Framework.

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🧠 The "High Friction" Philosophy

This repository is built on the High Friction Learning principle. Unlike other tutorials that hide complexity behind "magic scripts," we require you to interact directly with the tools.

• No Magic Scripts: We do not use setup.sh or reset.sh. You will use docker-compose, npm, and mongosh directly.
• TDD-Driven: Each lab is a puzzle. You are given a specification and a failing test suite; you succeed only when your database state satisfies the code's expectations.
• Atomic & Ephemeral: Every lab runs in an isolated Docker container. When you're done, you destroy the environment to keep your system clean.

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🗺️ Curriculum Overview

The journey is divided into 8 Progressive Modules. For the full roadmap, see docs/syllabus.md.

Module	Focus
01: Foundations	Core Engine, BSON Types, and Server Configuration.
02: CRUD Mastery	Advanced Operations, Write Concerns, and Specialized Data (GridFS).
03: Schema Design	Relationships, Advanced Patterns, and Schema Evolution/Versioning.
04: Performance	Core Indexing (ESR), Geospatial, and Atlas Search.
05: Aggregation	The Pipeline Framework, Analytics, and Data Transformations.
06: Security	Authentication, RBAC, and Encryption at Rest.
07: Integration	Java/Node Drivers, Real-time Change Streams, and ACID Transactions.
08: Scale	Replica Sets, Sharding, and Cloud-Native Atlas.

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🚀 Getting Started

1. Prerequisites

• Docker & Docker Compose
• Node.js (v18+) & NPM
• MongoDB Database Tools (mongodump, mongorestore, mongofiles)

2. Global Installation

Run this once at the root to prepare the workspaces:

npm install

3. Lab Workflow

The standard educational path is to navigate into each laboratory directory, study the theory, and execute the implementation steps manually. Makefiles are provided as optional utility shortcuts for experienced users but are not mandatory.

Standard Path (Manual)

1. Understand: Enter the lab folder (e.g., cd labs/007-specialized-data-gridfs) and read CONCEPT.md.
2. Setup: Follow the README.md instructions to spin up the environment:

   docker-compose up -d

3. Implement: Solve the challenges described in the README.
4. Validate: Run the tests locally:

   npm test

5. Cleanup: Tear down the environment:

   docker-compose down -v

Alternative Path (Makefile Shortcuts)

From the root directory, you can use these shortcuts:

• make setup LAB=007
• make test LAB=007
• make clean LAB=007

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🤖 Agent Workflows (Automation & Quality)

If you are using an AI coding assistant (like Antigravity), this repository includes native Agent Workflows to automate quality gates and implementation steps. These are invoked via / commands:

• /lab-init: Initiates a new laboratory by generating DRAFT artifacts and folder structure.
• /lab-architect: Validates planning artifacts (spec, plan, tasks). Moves a lab to READY status.
• /lab-builder: Automates the technical implementation (TDD, scaffolding, and docs) based on an approved plan.
• /lab-auditor: Performs a final pedagogical review to detect conceptual gaps before marking a lab as Completed.
• /lab-governor: Manages global changes to the Constitution and ensures consistency across all labs.
• /lab-refiner: Retrospectively audits and upgrades an already delivered laboratory to ensure compliance with the latest standards.

Master Workflows (Full Cycles)

• /lab-master-plan: Executes the full design cycle (Init + Architect) to reach READY status.
• /lab-master-build: Executes the full implementation cycle (Builder + Auditor) to reach AUDITED status.

For a detailed example of the workflow in action, see docs/LAB_LIFECYCLE.md.

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📜 Contribution & Rules

Please read our Constitution before contributing or attempting to automate the learning process. We value precision and manual mastery over convenience.

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Created by the MongoDB Masterclass Team.