Python Microservices: Build, Scale, and Deploy like a Pro!

Install Python and Pip on Windows using the Python.org installer, add Python to PATH, verify with Python --version and pip --version, and install the requests package to confirm Pip works.

Learn to set up a python virtual environment on Windows with Visual Studio Code, install pip, create and activate a venv, install packages, and generate a requirements.txt.

Explore Python microservices frameworks, comparing Flask and Fast API for asynchronous performance, data validation, and built-in documentation. Choose the right framework for scalable microservices based on your project needs.

Build a simple microservice with FastAPI by installing FastAPI, creating main.py, and defining a get endpoint. Run with uvicorn locally to test in your browser.

Define routes and endpoints with FastAPI by combining a route with an http method, use dynamic path parameters like user_id, and return json responses.

Master json handling in FastAPI by formatting responses, validating data with Pydantic models, and designing get, post endpoints, including nested json and custom status codes.

Learn how to build a simple Flask microservice by creating an app, defining routes with decorators, and returning JSON responses through a home and hello endpoint.

Learn to build Flask microservices that handle HTTP methods—get, post, put, and delete—and process JSON data to create and test endpoints with curl.

Explore how containers simplify app packaging with Docker, compare containers to virtual machines, and learn core concepts like Docker image, Docker daemon, Docker client, and Dockerfile for portable, fast deployment.

Write a Dockerfile for a fastapi microservice by using a python 3.10 slim base, install fastapi and unicorn, copy code, expose port 8000, and run with unicorn via uvicorn.

Install and verify docker desktop, build a docker image tagged my fast API app from the current directory, and run a detached container with port 8000 mapped to localhost.

Learn to use docker compose to build and run a two-service microservice stack with a fast API app and Redis caching, including volumes and service-name networking.

Configure environment variables in docker to securely pass configuration options like database credentials and api keys to dockerized microservices, using dockerfile env, the -e flag, and dot env files.

Build a synchronous two-microservice workflow with fastapi using restful endpoints, where service a requests data from service b via http, with asynchronous read and fetch operations.

Set up RabbitMQ as a message broker to enable asynchronous communication between Python FastAPI microservices. Demonstrate building a producer and a consumer with Pika, durable queues, and message delivery.

Explore gRPC for FastAPI microservices, define proto contracts, generate Python code, and build a client-server data transfer with protocol buffers for low-latency inter-service communication.

Install and configure PostgreSQL locally, set the Postgres password, access via pgAdmin or the command line, and create a sample database to prepare for microservice integration.

Connect a FastAPI microservice to a PostgreSQL database using SQLAlchemy, defining a user model, Pydantic validation, and db sessions with post and get routes.

Apply the database per service pattern with fastapi and postgresql, using separate databases for users and orders, with corresponding tables and endpoints to create and fetch orders.

Boost Python microservice performance by caching product data with Redis in a FastAPI app; cache first requests and serve subsequent ones from memory to reduce database load.

Implement authentication and authorization in a FastAPI microservice with PostgreSQL, using Pydantic models and SQLAlchemy, JWT tokens, and admin-only routes for deleting users.

Build a microservice architecture with FastAPI, two services and an API gateway that enforces centralized security through token validation and Docker Compose orchestration.

Set up secure communication for fast API microservices using HTTPS and SSL/TLS by installing dependencies, generating a self-signed certificate with OpenSSL, configuring the PATH, and running Uvicorn.

Learn how kubernetes (k8s) automates deployment, scaling, and management of microservices, enabling service discovery, load balancing, and fault-tolerant, declarative deployment across environments.

Set up a local Kubernetes cluster with minikube on Windows, add it to your path, and start the cluster, ensuring Docker Desktop runs and minikube status is healthy.

Build a FastAPI web service to manage a product list, containerize with Docker, push to Docker Hub, and deploy to Kubernetes with two replicas for scalability and high availability.

Learn how to build and push a fast API product service as a Docker image, then deploy and scale it with a Kubernetes deployment and service using Minikube.

Manage pods and scale Kubernetes deployments by adjusting replicas, describing pods, and using the horizontal pod autoscaler for CPU-based autoscaling, with rolling updates and rollbacks to maintain availability.

Set up a fast api microservice that logs messages to logstash over udp via docker compose, with route and items/{id} endpoints emitting json logs.

Learn to monitor fast API microservices with Prometheus and Grafana by exposing metrics, scraping with Prometheus, and building interactive dashboards to visualize HTTP requests total and service health.

Learn to implement distributed tracing for debugging FastAPI microservices with Jaeger, using a Python client, OpenTracing, and a Jaeger backend to monitor traces end to end.