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Here's how to get the most out of your membership:

The Course

• 19 lessons across 6 chapters — from async Python fundamentals to a full capstone project

• Lessons are sequential — complete each one before moving to the next

• You'll earn a certificate when you finish all 19 lessons

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Drop questions in the Community Chat anytime. Post code snippets, share your project progress, and help others debug.

What's Coming

• Live Q&A sessions

• Guest lectures from production FastAPI teams

• New lessons on WebSockets, GraphQL federation, and multi-region deployments

Start with Chapter 1, Lesson 1: Python Async Fundamentals and let's build something production-grade.

Most FastAPI tutorials show you the happy path. Here are the five issues I see repeatedly in production microservices — and the fixes that actually work.

1. Blocking the event loop with sync database calls

FastAPI runs on uvicorn with an async event loop. One synchronous psycopg2 call blocks every concurrent request.

Fix: Use asyncpg or databases with encode/databases. If you must use SQLAlchemy, use the 2.0 async engine:

from sqlalchemy.ext.asyncio import create_async_engine

engine = create_async_engine("postgresql+asyncpg://user:pass@host/db")

2. No connection pooling between services

Each service instance opening its own database connections leads to connection exhaustion under load.

Fix: Use PgBouncer in front of Postgres and set pool_size + max_overflow explicitly:

engine = create_async_engine(
    DATABASE_URL,
    pool_size=20,
    max_overflow=10,
    pool_timeout=30
)

3. Serializing everything through a single HTTP gateway

Synchronous request chains (Service A → B → C) multiply latency. A 50ms call through 4 services = 200ms minimum.

Fix: Use async messaging for non-blocking workflows. RabbitMQ or Kafka for event-driven communication. Reserve synchronous HTTP/gRPC for queries that need immediate responses.

4. Missing structured logging and correlation IDs

When a request touches 5 services, print() statements are useless for debugging.

Fix: Use structlog with a correlation ID middleware:

import structlog
from uuid import uuid4

@app.middleware("http")
async def add_correlation_id(request, call_next):
    correlation_id = request.headers.get("X-Correlation-ID", str(uuid4()))
    structlog.contextvars.bind_contextvars(correlation_id=correlation_id)
    response = await call_next(request)
    response.headers["X-Correlation-ID"] = correlation_id
    return response

5. No circuit breaker on inter-service calls

When Service B goes down, Service A keeps retrying and eventually crashes too. Cascading failures take out your entire system.

Fix: Use tenacity with exponential backoff + a circuit breaker pattern:

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, max=10))
async def call_service_b(client, payload):
    response = await client.post("http://service-b/api/process", json=payload)
    response.raise_for_status()
    return response.json()

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These five fixes alone will handle 80% of the performance issues I see in production FastAPI microservices. The full course goes deep on each of these patterns plus infrastructure, observability, and a capstone project where you build and deploy a complete distributed system.