The Monorepo That Runs 29 Services — Full Architecture Breakdown

The Monorepo That Runs 29 Services

"Controlling complexity is the essence of computer programming." — Brian Kernighan, co-author of The C Programming Language

In the last post, I explained why I built this platform from scratch. Now let's open the hood.

This monorepo contains two applications — a FastAPI backend and a Next.js frontend — plus shared infrastructure configuration. Everything lives in one repository, deploys via one docker-compose.yml, and runs on one $12/month VPS.

Here's the directory structure, explained layer by layer.

Top-Level Layout

blog_platform/
├── fastapi_backend/     # Python API server (29 routers, 21 models)
├── blog_frontend/       # Next.js 16 app (React 19, Turbopack)
├── nginx/               # Reverse proxy config
├── docs/                # Architecture decisions, content plans
├── conductor/           # Feature specs and bug fixes
├── docker-compose.yml   # Single-file deployment
├── docker-compose.prod.yml
└── deploy.sh            # Zero-downtime deploy script

The two applications communicate exclusively through HTTP. The frontend never imports Python code, and the backend never references React components. The contract is the API schema — documented automatically by FastAPI's OpenAPI generation at /docs.

FastAPI Backend — fastapi_backend/

fastapi_backend/
├── app/
│   ├── main.py              # App entry, middleware, router registration
│   ├── config.py            # Settings from environment variables
│   ├── database.py          # Async SQLAlchemy engine + session factory
│   ├── dependencies.py      # Shared dependency injection (auth, db)
│   ├── core/                # Cross-cutting concerns
│   │   ├── limiter.py       # Rate limiting (slowapi + Redis)
│   │   ├── redis.py         # Redis connection pool
│   │   ├── scheduler.py     # APScheduler background jobs
│   │   ├── uploads.py       # File upload handling
│   │   └── exceptions.py    # Custom exception classes
│   ├── models/              # SQLAlchemy ORM models (21 files)
│   ├── schemas/             # Pydantic request/response schemas
│   ├── routers/             # API endpoint handlers (29 files)
│   └── services/            # Business logic layer
├── alembic/                 # Database migrations
├── tests/                   # Pytest test suite
├── uploads/                 # User-uploaded images, PDFs, data files
├── scripts/                 # Maintenance scripts
├── requirements.txt
├── Pipfile
└── Dockerfile

Entry Point — main.py

The application is assembled in main.py. Here's the skeleton:

app = FastAPI(title="Madhu Dadi — AI, Python & Analytics Hub API", lifespan=lifespan)

app.add_middleware(TrustedHostMiddleware, allowed_hosts=settings.ALLOWED_HOSTS)
app.add_middleware(CORSMiddleware, ...)
app.add_middleware(SessionMiddleware, ...)

V1 = "/api/v1"
app.include_router(auth.router,        prefix=V1)
app.include_router(posts.router,       prefix=V1)
app.include_router(series.router,      prefix=V1)
app.include_router(comments.router,    prefix=V1)
# ... 25 more routers

Explanation

• Initializes a FastAPI application with a specified title and lifespan management.
• Adds middleware for security and session management, including TrustedHostMiddleware and CORS support.
• Defines a versioned API prefix (/api/v1) for organizing routes.
• Includes multiple routers for handling different functionalities such as authentication, posts, series, and comments, enhancing modularity.
• Prepares the application to scale by allowing the addition of more routers as needed.

The lifespan context manager initializes Redis and starts the background scheduler on startup, then tears them down on shutdown.

Settings — config.py

All configuration comes from environment variables via Pydantic's BaseSettings:

class Settings(BaseSettings):
    APP_NAME: str = "Madhu Dadi API"
    DEBUG: bool = False
    DATABASE_URL: str
    REDIS_URL: str
    SECRET_KEY: str
    CORS_ORIGINS: list[str]
    ALLOWED_HOSTS: list[str]
    # ... 30+ more settings

Explanation

• This code defines a Settings class that inherits from BaseSettings, which is part of the Pydantic library for data validation and settings management.
• The class includes several attributes such as APP_NAME, DEBUG, DATABASE_URL, REDIS_URL, SECRET_KEY, CORS_ORIGINS, and ALLOWED_HOSTS, which represent various configuration options for an application.
• Each attribute is type-annotated, ensuring that the values assigned to them conform to the specified types (e.g., str, bool, list[str]).
• The DEBUG attribute is set to False by default, indicating that the application is in production mode unless explicitly changed.
• The class is designed to easily manage and validate application settings, potentially loading values from environment variables or configuration files.

No hardcoded secrets. No .env files committed to git. Every deployment environment (dev, staging, production) supplies its own values through environment variables or Docker secrets.

Database — database.py

Async SQLAlchemy 2.0 with session-per-request pattern:

engine = create_async_engine(settings.DATABASE_URL, pool_size=20, max_overflow=10)
AsyncSessionLocal = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)

async def get_db() -> AsyncGenerator[AsyncSession, None]:
    async with AsyncSessionLocal() as db:
        yield db

Explanation

• Creates an asynchronous engine for database connections using a specified URL and connection pool settings.
• Defines a session factory AsyncSessionLocal that produces instances of AsyncSession, allowing for asynchronous database operations.
• Implements an asynchronous generator function get_db that yields a database session for use in asynchronous contexts.
• Ensures that the database session is properly managed and closed after use by utilizing an asynchronous context manager.

The expire_on_commit=False is intentional — it prevents lazy-loading issues after commit, which is a common pitfall in async SQLAlchemy.

The 29 Routers

Every API endpoint lives in app/routers/. Here's what each one does:

That's 29 routers serving approximately 120 individual endpoints. Each router is between 50 and 300 lines. The admin.py router is the largest at ~500 lines because it handles post CRUD with all the tag/series/difficulty associations.

The 21 Database Models

All models inherit from SQLAlchemy's DeclarativeBase and live in app/models/:

class Base(DeclarativeBase):
    pass

Explanation

• This code snippet creates a base class named Base using SQLAlchemy's DeclarativeBase, which is essential for defining ORM models.
• The Base class serves as a foundation for all ORM-mapped classes, allowing them to inherit common functionality.
• By using this base class, developers can easily create database tables and map them to Python classes.
• This approach simplifies the process of managing database schemas and relationships in a Python application.

Key models and their relationships:

The most interesting table is RagChunk. It stores post content split into chunks, each with a 1536-dimensional vector embedding. The search query is: find chunks whose embedding is closest to the query embedding, filtered by the user's premium tier. This is the core of the "Ask AI" feature.

Redis as the Glue Layer

Redis isn't a cache in this architecture — it's a service bus. It handles five distinct concerns:

1. Rate limiting — slowapi uses Redis as its backing store. Each endpoint family has its own limit (100/hr for anonymous, 500/hr for authenticated). The key is rate_limit:{ip}:{route_group} with a sliding window counter.

2. OAuth state — Google OAuth uses a redirect-based flow. The state parameter (a random token) is stored in Redis with a 10-minute TTL. After the callback, the token is verified and deleted. This prevents CSRF on the OAuth handshake.

3. Embedding cache — When a user asks the RAG system a question, the query is first checked against a Redis set of recent queries. If found within 5 minutes, the cached embedding is reused. This saves ~200ms per query on repeated questions.

4. Task queue — Redis pub/sub dispatches background tasks: email digests, content revalidation (purging Cloudflare cache), and maintenance jobs. The publisher pushes to a channel, and the APScheduler subscriber picks it up.

5. Leaderboard — XP rankings use Redis sorted sets (ZADD, ZREVRANK, ZRANGE). The leaderboard is recomputed every 5 seconds from a materialized PostgreSQL view, then stored in Redis for fast reads. This avoids sorting 10,000+ users on every page load.

Background Scheduler

app/core/scheduler.py runs four recurring jobs:

def start_scheduler():
    scheduler = AsyncIOScheduler()
    scheduler.add_job(send_daily_digests, "cron", hour=8, minute=0)
    scheduler.add_job(regenerate_sitemap, "cron", hour=2, minute=0)
    scheduler.add_job(clean_expired_tokens, "cron", hour=3, minute=0)
    scheduler.add_job(check_stripe_subscriptions, "interval", hours=1)
    scheduler.start()

Explanation

• Defines a function start_scheduler that sets up an asynchronous job scheduler using AsyncIOScheduler.

• Adds a job to send daily digests at 8:00 AM using a cron schedule.

• Schedules a job to regenerate the sitemap at 2:00 AM, also using a cron schedule.

• Sets up a job to clean expired tokens every hour on the hour with an interval schedule.

• Starts the scheduler to begin executing the scheduled jobs.

• Daily digests — queries the Post table for posts published in the last 24 hours, assembles an HTML email, and sends via SMTP to subscribed users.

• Sitemap regeneration — queries all published posts and series, generates a fresh sitemap.xml, and pings Google/Bing.

• Token cleanup — deletes expired refresh tokens from the database.

• Stripe sync — checks for subscriptions that should have expired and marks them accordingly.

The scheduler runs inside the same Python process as the FastAPI app. No separate Celery worker needed.

Frontend — blog_frontend/

blog_frontend/
├── src/
│   ├── app/                  # Next.js App Router pages
│   │   ├── blog/             # Blog posts (dynamic routes)
│   │   ├── admin/            # Admin dashboard (protected)
│   │   ├── login/            # Auth pages
│   │   ├── register/
│   │   ├── profile/          # User profiles, settings
│   │   ├── series/           # Series index + detail
│   │   ├── tags/             # Tag index + filtered posts
│   │   ├── search/           # Full-text + vector search UI
│   │   ├── ask/              # RAG chat interface
│   │   ├── challenge/        # Daily coding challenge
│   │   ├── leaderboard/      # XP rankings
│   │   ├── milestones/       # Badges, progress, knowledge graph
│   │   ├── bookmarks/        # Saved posts
│   │   ├── layout.tsx        # Root layout with metadata defaults
│   │   ├── robots.ts         # Dynamic robots.txt
│   │   └── sitemap.ts        # Dynamic sitemap.xml
│   ├── components/           # Reusable React components
│   │   ├── admin/            # PostEditor, AnalyticsChart, etc.
│   │   ├── blog/             # MarkdownRenderer, PostCard, etc.
│   │   ├── layout/           # Navbar, Footer, CommandPalette
│   │   ├── ui/               # Button, Input, Spinner, GlassCard
│   │   ├── user/             # BadgeGrid, UserStats, KnowledgeGraph
│   │   ├── premium/          # PremiumGate, PlanSelectionModal
│   │   └── rag/              # RagChat overlay
│   ├── contexts/             # AuthContext, ThemeContext, LearningContext
│   ├── lib/                  # API client, utilities, types
│   └── workers/              # Web Workers (Python runner)
├── public/                   # Static assets
├── e2e/                      # Playwright tests
├── next.config.ts
├── tailwind.config.ts
├── vitest.config.ts
└── postcss.config.js

The API Client — src/lib/api.ts

The frontend communicates with the backend through a typed API client. Every endpoint is a function:

export const postsApi = {
  get: (slug: string, token?: string) => 
    apiFetch<PostResponse>(`/posts/${slug}`, { auth: true, token }),
  list: (params?: PostListParams) => 
    apiFetch<PaginatedResponse<PostListItem>>(`/posts?${toQuery(params)}`),
  create: (payload: CreatePostPayload) =>
    apiFetch<PostResponse>("/posts", { method: "POST", body: payload, auth: true }),
  // ... 5 more methods
};

The apiFetch wrapper handles:

• Automatic JWT token injection from cookies or localStorage
• 401 → token refresh → retry (with debounce to avoid race conditions)
• Error normalization (FastAPI validation errors → user-friendly messages)
• Request deduplication for concurrent identical calls

Server Components vs. Client Components

Every page is a server component by default. Client components are only used where interactivity is required:

• PostEditor — markdown editing, tag selection, image upload
• RagChat — streaming chat UI with source citations
• KnowledgeGraph — D3.js force-directed graph
• Navbar/CommandPalette — user menu, keyboard shortcuts
• ThemeToggle — dark/light mode

Server components handle everything else: data fetching, metadata generation, static params, and most of the rendering. This means the average page ships ~40KB of HTML instead of ~200KB of JavaScript.

Shared Infrastructure — nginx/

server {
    listen 80;
    server_name madhudadi.in;

    location /blog {
        proxy_pass http://frontend:3000;
        proxy_set_header Host $host;
    }

    location /api/ {
        proxy_pass http://backend:8000;
        proxy_set_header Host $host;
    }
}

Nginx handles:

• Routing — /blog → Next.js frontend, /api → FastAPI backend
• Caching — static assets (CSS, JS, images) cached for 1 year with hashed filenames
• Compression — brotli for modern browsers, gzip fallback
• Security headers — HSTS, X-Content-Type-Options, CSP, Referrer-Policy
• Cloudflare integration — real IP headers, cache purging via API

The key insight: Nginx is the only entry point. There's no Kubernetes ingress, no cloud load balancer, no API gateway. One Nginx config handles everything.

What I'd Change

Looking back, there are three things I'd do differently:

1. Use a task queue from day one. The Redis pub/sub approach works, but it's not durable. If the app crashes mid-job, the task is lost. A proper queue (ARQ, Celery with Redis broker) would give retries, dead-letter queues, and job persistence.

2. Split the admin router. app/routers/admin.py at 500+ lines handles too many concerns. Post CRUD, user management, analytics queries, and task management should be separate routers. The file grew organically and never got refactored.

3. Add OpenAPI types to the frontend. The API client in src/lib/api.ts is manually typed. There's no code generation from the FastAPI OpenAPI schema. This means when the backend adds a field, the frontend type needs a manual update. Using openapi-typescript or orval would eliminate this class of bugs.

What's Next

In the next post, I'll dive into the RAG chat system — how embeddings are generated, how hybrid search works, and how the streaming response pipeline is built.

Built with FastAPI, Next.js 16, PostgreSQL, Redis, and zero third-party CMS. Deployed on a $12/month VPS.

Next in this series: Building a RAG Chat System: From Zero to Production →