Building a Scalable Finance Tracker: Lessons in Performance Optimization

When I started building the Finance Tracker, I knew performance would be critical. Users need to see their spending trends instantly, and slow load times would kill the user experience.

The Challenge

Initial load times were around 2-3 seconds, especially when users had hundreds of transactions. The spending visualizations were particularly slow because I was fetching all transactions and calculating aggregations on the client side.

The Solution

I moved the heavy lifting to the backend using MongoDB aggregation pipelines. Instead of fetching all transactions and processing them in React, I created a pipeline that:

1. Groups transactions by category 2. Calculates totals and percentages 3. Returns only the aggregated data

This reduced data transfer by 90% and cut load times to under 500ms.

Key Learnings

• Database aggregation is powerful: Moving calculations to MongoDB reduced client-side processing significantly
• React optimization matters: Using useMemo for expensive calculations prevented unnecessary re-renders
• Measure everything: Using React DevTools Profiler helped identify the exact bottlenecks

Results

• 40% faster page load times
• 90% reduction in data transfer
• Sub-second response times even with 1000+ transactions

This project taught me that performance optimization is an ongoing process, not a one-time task.