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Task Management API

Production-grade FastAPI backend with async architecture, advanced filtering, rate limiting, and built-in performance monitoring.

FastAPIPostgreSQLSQLAlchemyDockerAlembicSlowAPI
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Overview

This project focuses on building a production-grade backend API, going beyond basic CRUD to support advanced filtering, performance monitoring, and real-world API constraints.

The system is built using FastAPI with a layered architecture (router → service → repository) and fully async database operations using SQLAlchemy. Authentication uses JWT with secure password hashing, while rate limiting is enforced per endpoint to protect critical routes.

The API supports complex filtering across multiple parameters (status, priority, category, date range, and search) using composable query construction, ensuring scalability without sacrificing maintainability.

A lightweight monitoring layer tracks request counts, latency, and slow requests, exposed via a /metrics endpoint—reinforcing observability as a core part of system design rather than an afterthought.

87%Test coverage
<200msAvg response time
8Filter parameters supported
/metricsPerformance monitoring
Tech Stack

TOOLS &
TECHNOLOGIES.

TECH.01
FastAPI
TECH.02
PostgreSQL
TECH.03
SQLAlchemy
TECH.04
Docker
TECH.05
SlowAPI
Key Highlights

WHAT MAKES IT
STAND OUT.

87%Test coverage across API layers
<200msAverage API latency
8Dynamic query filters supported
/metricsBuilt-in observability endpoint
Challenges & Solutions

WHAT WAS HARD.
HOW I SOLVED IT.

Critical Challenge

Supporting multiple dynamic filters without creating complex, unmaintainable query logic.

Engineered Solution

Built composable SQLAlchemy queries with conditional clauses, enabling scalable filtering while keeping the repository layer clean.

Critical Challenge

Applying different rate limits across endpoints while maintaining consistent response behavior.

Engineered Solution

Used per-route rate limiting with centralized configuration and a custom handler to standardize 429 responses and headers.

Critical Challenge

Adding performance monitoring without coupling it to business logic.

Engineered Solution

Implemented lightweight middleware to track latency and request metrics, exposed via a /metrics endpoint with minimal overhead.

Core Architecture

HOW IT'S
ALL WIRED UP.