Origin
Have you ever been troubled by project architecture choices? As a Python developer, I deeply relate to this. Last year, when I took over an e-commerce project's reconstruction, facing the original monolithic application, the team decided to adopt a microservices architecture. When selecting our technology stack, we chose Python as the primary development language, a decision that brought unexpected benefits.
Today, I want to share my experience and insights from using Python to build microservices in real projects. We'll explore Python's application in microservices from multiple dimensions, including development efficiency, architectural design, and production deployment.
Technology Selection
When choosing our tech stack, we faced an important question: Is Python really suitable for building microservices?
Honestly, this question troubled our team for a long time. Some colleagues were concerned about Python's performance, while others doubted its concurrent processing capabilities. However, after thorough research and practice, we discovered Python's unique advantages in microservices architecture.
First is development efficiency. Python's syntax is concise and elegant, as you probably know well. In our project, a new developer typically only needs 1-2 days to fully understand existing code and start contributing. In comparison, teams using Java or Go often require a longer adaptation period.
Second is ecosystem support. Python has rich third-party libraries and frameworks. In our project, the FastAPI framework alone saved us at least 40% of development time. Its automatic API documentation generation feature made frontend-backend collaboration exceptionally smooth.
Implementation
Let's look at how Python microservices work through a specific example.
In our e-commerce system, the order service is a core module. Here's what the order service built with FastAPI looks like:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List
import asyncio
app = FastAPI()
class Order(BaseModel):
id: str
user_id: str
items: List[dict]
total_amount: float
status: str
class OrderCreate(BaseModel):
user_id: str
items: List[dict]
@app.post("/orders/", response_model=Order)
async def create_order(order: OrderCreate):
# Order creation logic
try:
# Verify inventory
inventory_check = await check_inventory(order.items)
if not inventory_check:
raise HTTPException(status_code=400, detail="Insufficient inventory")
# Calculate order amount
total = calculate_total(order.items)
# Generate order ID
order_id = generate_order_id()
# Create order object
new_order = Order(
id=order_id,
user_id=order.user_id,
items=order.items,
total_amount=total,
status="pending"
)
# Send message to message queue
await publish_order_created_event(new_order)
return new_order
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
Would you like to understand the specific implementation of this code?
Challenges
During implementation, we did encounter some challenges. The most prominent was performance issues.
I remember once our order service hit a performance bottleneck during the Singles' Day shopping festival. Through performance analysis, we found the main issue was with database queries. This prompted us to rethink our service design.
We took the following measures:
- Introduced Redis to cache hot data, reducing database query time from an average of 150ms to 5ms
- Used asynchronous programming for I/O-intensive operations, improving concurrent processing capacity by 30%
- Optimized database indexes, reducing complex query response time by 60%
These optimization measures were very effective. During last year's December 12 shopping festival, our system easily handled peak loads of 3,000 orders per second, maintaining service response times within 100ms.
Learnings
After more than a year of practice, I have gained a deeper understanding of Python's application in microservices architecture.
Python's advantage lies in allowing developers to focus on business logic rather than tedious technical details. Through proper architectural design and performance optimization, Python can fully meet the needs of most business scenarios.
However, we must also acknowledge that Python isn't perfect. In some extreme performance-demanding scenarios, using Go or Rust might be a better choice. This requires us to be more rational in technology selection and make appropriate judgments based on actual needs.
I remember a colleague asked me early in the project: "Why choose Python instead of Go to build microservices?" My answer was: Choosing a technology stack isn't about choosing the "best," but choosing the "most suitable." For our team, the benefits of Python's development efficiency and ecosystem far outweighed its slight performance disadvantages.
Looking Forward
Looking ahead, Python still has great potential in the microservices field. With the popularization of asynchronous programming and development of JIT compilation technology, Python's performance shortcomings are gradually improving.
What are your thoughts or experiences with Python microservices development? Feel free to share your views in the comments. If you have any questions, you can also leave a message for discussion.
Let's explore more possibilities in Python microservices development together. After all, technology keeps advancing, and learning never ends.