SQLift
Released: Apr 2026
Description
SQLift is a full-stack workout tracking web application built as a CS-5200 Database Management course project. It lets users plan custom workouts from a library of 1000+ exercises, track live sessions set-by-set with weights and reps, and review progress through analytics graphs and personal records. A social layer allows users to befriend each other and compete on global and friend leaderboards ranked by total workout volume, while an achievements system rewards fitness milestones.
Key Responsibilities
Designed the relational database schema, including the multi-level session of record set hierarchy, friendship graph, achievements, and goals tables.
Built and maintained backend Flask API modules for workout CRUD, live session tracking, analytics, leaderboard calculations, and achievements.
Wrote the Python web scraper to populate the exercise library with 1000+ entries, muscle group mappings, and equipment data from the Wger API.
Implemented session-based authentication with bcrypt password hashing and contributed to profile and friend management endpoints.
Contributed to the React frontend, including the live session page, stats visualizations, and leaderboard views.
Configured the GitHub Actions CI/CD pipeline for automated deployment to Vercel with smoke tests against the live backend.
Technology Used
React
Python
Flask
PostgreSQL
Supabase
Features
Live Session Tracking
During an active workout, users log every set in real time, with details like weight, reps, RPE, rest time, and set type (warm-up, working, drop) and with auto-fill from the previous set to speed up logging.
Exercise Library
A pre-populated library of 1000+ exercises sourced from the Wger API, each tagged with targeted muscle groups and required equipment, supports filtering and browsing by category.
Progress Analytics
The stats page visualizes exercise progression over configurable time ranges, tracks body measurements, displays muscle group volume breakdowns, and surfaces hero stats like total volume, heaviest set, and session streaks.
Social & Leaderboards
Users can send and manage friend requests, set friendship visibility levels, and compete on both a global and friends-only leaderboard ranked by cumulative workout volume.
Achievements & Goals
An achievements system awards badges for fitness milestones, and a goals feature lets users set personal objectives with target completion dates and track them over time.
Screenshots
Additional Information
This project was built as the final project for CS-5200 Database Management Systems at Northeastern University, with a focus on relational database design and complex SQL querying.
The database schema spans 15+ tables with multi-level relational hierarchies, covering users, workouts, sessions, set logs, exercises, muscle groups, equipment, measurements, friendships, goals, and achievements.
Supabase transaction pooler (port 6543) is used for efficient connection management, allowing the stateless Vercel serverless backend to interact with PostgreSQL without exhausting connections.
The exercise library was populated using a custom Python scraper that fetched and normalized data from the Wger open-source fitness API across multiple pages.
Additional features were planned, such as multiple user roles (for trainers) as well as more achievements and a way to upload profile pictures, but were scrapped due to time constraints.
The project was built collaboratively by a three-person team (Shaad Qureshi, Ben O'Neil, Sudaiv Shetty) with Git-based workflows and feature branches.