Building a Scalable SaaS Mockup with Python, Docker, and Async Processing

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Alexander Lifanov demonstrated how to create a UI-less SaaS mockup using Python, Docker, and asynchronous processing—handling 500 parallel connections with ease. Here’s how you can replicate and expand on this setup.

You Should Know:

1. Python Async Server Setup

Use `asyncio` and `aiohttp` for high-performance async processing:

import asyncio 
from aiohttp import web

async def handle_request(request): 
data = await request.json() 
 Process transaction here 
await asyncio.sleep(5)  Simulate processing 
return web.json_response({"status": "success"})

app = web.Application() 
app.router.add_post("/process", handle_request)

web.run_app(app, port=8080)

2. Load Testing with Locust

Simulate 6,000 users with Locust:

pip install locust

Create `locustfile.py`:

from locust import HttpUser, task, between

class SaaSUser(HttpUser): 
wait_time = between(1, 5)

@task 
def process_transaction(self): 
self.client.post("/process", json={"data": "test"})

Run Locust:

locust -f locustfile.py --host http://localhost:8080

3. Docker Compose for Mass Deployment

Deploy multiple clients with Docker:

version: '3' 
services: 
server: 
image: python:3.9 
command: python server.py 
ports: 
- "8080:8080" 
volumes: 
- ./server.py:/server.py

client: 
image: python:3.9 
command: locust -f /locustfile.py --host http://server:8080 
volumes: 
- ./locustfile.py:/locustfile.py 
depends_on: 
- server

Run with:

docker-compose up --scale client=10

4. Monitoring with Grafana + Loki

  • Deploy Grafana & Loki for logging:
    docker run -d --name=loki -p 3100:3100 grafana/loki 
docker run -d --name=grafana -p 3000:3000 grafana/grafana
  • Configure Loki as a data source in Grafana.

5. Cost Optimization

Use spot instances on AWS/GCP:

aws ec2 run-instances --instance-type t3.micro --spot-price "0.02"

What Undercode Say:

This approach proves that lightweight Python async servers, combined with Docker and cloud optimizations, can handle thousands of transactions at minimal cost. Future enhancements could include:
– Kubernetes for auto-scaling
– Redis for rate limiting
– Prometheus for real-time metrics

Prediction:

As async Python and microservices evolve, expect even lighter frameworks (e.g., FastAPI + Uvicorn) to dominate scalable SaaS prototyping.

Expected Output:

  • A fully scalable SaaS mockup
  • 500+ parallel connections tested
  • Cloud cost: $0.10/day
  • Full observability via Grafana/Loki

URLs (if needed):

IT/Security Reporter URL:

Reported By: Alexander Lifanov – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

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