How I Built a System That Tracks 900,000 Real-Time Events Per Day

Source: Dev.to

Introduction

A few months ago I started building a system to collect and analyze real‑time event data. What began as a small experiment quickly grew into something much larger. The system now processes roughly 900,000 new records per day and has accumulated over 7 million total events so far.

Architecture Decisions

Scaling Challenges

The biggest challenge wasn’t collecting the data; it was ensuring the system could continue scaling without slowing down.

Incremental Metric Updates

Instead of querying the entire dataset repeatedly, the system updates metrics incrementally as new events arrive. This keeps performance consistent even as the dataset continues growing.

Rolling Time Windows

Whenever possible, queries are limited to rolling time windows. This reduces database load and keeps response times fast.

Database Choice

I chose PostgreSQL as the primary database because of its reliability and performance at scale. Early on, proper indexing made the biggest difference—queries that were instant with thousands of rows became noticeably slower with millions of rows unless the correct columns were indexed.

Live Demo

The system is currently running live here:
https://spindex.net/

It continuously ingests and processes new data in real time.

Lessons Learned

• Scaling problems usually stem from early architecture decisions, not traffic itself.
• A system designed correctly from the beginning can handle millions of records without major issues.
• A poorly designed system will struggle much sooner.

As the dataset continues to grow, efficiency becomes more important with every additional million records.