FastAPI Microservice Platform

Overview

During my internship at Predulive Labs, I owned the backend API layer for the core product platform. The challenge was to replace fragile, hand-rolled endpoint logic with a consistent, well-typed, and observable API surface that could be maintained by multiple developers.

Problem

The existing backend lacked:

• Consistent request/response schemas — different endpoints returned data in different shapes
• Observability — no structured logging, no error classification
• Reliable deployment — manual SSH deploys with no rollback story

Architecture

I chose FastAPI as the framework for its native async support, automatic OpenAPI documentation, and first-class Pydantic integration. Each endpoint is modelled as a typed Pydantic schema, which enforces validation at the boundary rather than deep inside business logic.

class ProjectCreateRequest(BaseModel):
    title: str = Field(..., min_length=1, max_length=200)
    description: str | None = None
    tags: list[str] = Field(default_factory=list)

@router.post("/projects", response_model=ProjectResponse, status_code=201)
async def create_project(body: ProjectCreateRequest, db: AsyncSession = Depends(get_db)):
    ...

Database Optimization

The application used both PostgreSQL (relational data) and MongoDB (document store for unstructured content). I profiled slow queries with EXPLAIN ANALYZE and added:

• Covering indexes on high-frequency read paths
• Pagination using keyset pagination instead of OFFSET to avoid full scans at large offsets
• Connection pooling via asyncpg to reduce per-request latency

This improved average API response time by 30% across core data retrieval pipelines.

CI/CD Pipeline

I introduced a GitHub Actions workflow that runs lint, type-check, and tests on every pull request, and deploys to staging automatically on merge to main. The pipeline reduced release turnaround from 2 days to under 4 hours.

on:
  push:
    branches: [main]
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build and push Docker image
        run: docker build -t app . && docker push registry/app

Results

• 12+ endpoints delivered with consistent Pydantic-validated schemas
• 40% reduction in integration overhead reported by the frontend team
• 30% improvement in average response time on core read paths
• Release cycle shortened from 2 days to under 4 hours