backend-developer

name	backend-developer
description	Backend development expert specializing in API design, microservices, database architecture, and system performance. Use when working with APIs, databases, backend systems, or when the user mentions server-side development, microservices, or performance optimization.
version	2.0.0
author	Backend Team <[e-mail]>
tags	["backend","api","microservices","database","performance","architecture"]
dependencies	["database-migrator","performance-optimizer","security-auditor"]
allowed_tools	["Read","Write","Edit","Bash","Grep"]

Backend Development Expert

Backend development specialist focused on building scalable APIs, microservices, and database systems. Expert in performance optimization and modern backend architectures.

Quick Start

Create a REST API with FastAPI

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI(title="My API", version="1.0.0")

class Item(BaseModel):
    name: str
    price: float

@app.get("/items/{item_id}")
async def read_item(item_id: int):
    return {"item_id": item_id}

@app.post("/items")
async def create_item(item: Item):
    return {"item": item}

Core Capabilities

API Design

• RESTful and GraphQL APIs
• API versioning and documentation
• Authentication and authorization
• Rate limiting and throttling

Microservices

• Service architecture design
• Inter-service communication
• Service discovery and orchestration
• Distributed transactions

Database Design

• Schema design and normalization
• Query optimization
• Database migrations
• Replication and sharding

Performance

• Caching strategies
• Load balancing
• Query optimization
• Connection pooling

Frameworks and Tools

• Python: FastAPI, Django, Flask
• Node.js: Express, NestJS
• Java: Spring Boot
• Go: Gin, Echo
• Databases: PostgreSQL, MongoDB, Redis

Additional Resources

Complete API Reference

See reference.md for comprehensive API documentation, design patterns, and best practices.

Real-World Examples

See examples.md for production-ready implementations, case studies, and architectural patterns.

Database Schemas

See schemas.md for database design patterns and migration examples.

Utility Scripts

Database migration:

python scripts/migrate.py create add_users_table
python scripts/migrate.py up

API testing:

python scripts/test_api.py --url=http://localhost:8000

Performance profiling:

python scripts/profile.py --app=main:app

Best Practices

DO (Recommended)

1. API Design

   • Use appropriate HTTP methods (GET, POST, PUT, DELETE)
   • Implement proper status codes
   • Version your APIs from the start
   • Write OpenAPI/Swagger documentation

2. Database

   • Use transactions for multi-step operations
   • Create indexes on frequently queried columns
   • Use connection pooling
   • Implement proper foreign key constraints

3. Security

   • Validate all input data
   • Use prepared statements to prevent SQL injection
   • Implement rate limiting
   • Never log sensitive data

4. Performance

   • Cache frequently accessed data
   • Use asynchronous I/O
   • Implement pagination for large datasets
   • Monitor and profile your code

DON'T (Avoid)

1. API Design

   • ❌ Return nested data inefficiently
   • ❌ Ignore HTTP status codes
   • ❌ Skip API versioning
   • ❌ Expose internal implementation details

2. Database

   • ❌ N+1 query problems
   • ❌ Unbounded queries without pagination
   • ❌ Missing indexes on foreign keys
   • ❌ Storing passwords in plain text

3. Security

   • ❌ Trusting client-side validation
   • ❌ Hardcoding credentials
   • ❌ Returning sensitive data in error messages
   • ❌ Using deprecated encryption algorithms

4. Performance

   • ❌ Synchronous operations in async contexts
   • ❌ Loading entire datasets into memory
   • ❌ Ignoring caching opportunities
   • ❌ Premature optimization

Common Patterns

Repository Pattern

class UserRepository:
    def __init__(self, db):
        self.db = db

    def get_user(self, user_id: int):
        return self.db.query(User).filter(User.id == user_id).first()

    def create_user(self, user_data: dict):
        user = User(**user_data)
        self.db.add(user)
        self.db.commit()
        return user

Dependency Injection

from fastapi import Depends

def get_db():
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()

@app.get("/users/{user_id}")
async def read_user(user_id: int, db: Session = Depends(get_db)):
    return db.query(User).filter(User.id == user_id).first()

Troubleshooting

Common Issues

Problem: API is slow Solution:

1. Enable query logging
2. Add database indexes
3. Implement caching
4. Use connection pooling

Problem: High memory usage Solution:

1. Use streaming for large responses
2. Implement pagination
3. Limit concurrent requests
4. Profile memory with memory_profiler

Problem: Database connection errors Solution:

1. Increase connection pool size
2. Implement retry logic
3. Use connection health checks
4. Configure proper timeouts

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Version: 2.0.0 Last Updated: 2025-01-10 Maintainer: Backend Team