How We Scaled Our Platform to Handle 1 Million Contracts

Reaching 1 million contracts on our platform was a significant milestone that came with its share of technical challenges. In this post, we'll share the journey of how we scaled our infrastructure and the lessons we learned along the way.

The Challenge

When we first launched, our architecture was designed to handle thousands of contracts. As our user base grew exponentially, we started hitting performance bottlenecks:

• Database queries were taking longer
• PDF generation was consuming too much memory
• Search functionality was becoming sluggish
• API response times were increasing

Our Approach

1. Database Optimization

We implemented several database optimizations:

• Sharding: We sharded our database by customer ID, distributing the load across multiple servers
• Caching: Implemented Redis for frequently accessed data
• Query Optimization: Rewrote complex queries and added appropriate indexes

2. Microservices Architecture

We transitioned from a monolithic architecture to microservices:

• PDF generation became a separate service
• Search functionality moved to Elasticsearch
• Authentication was extracted to a dedicated service

3. Infrastructure Improvements

• Migrated to Kubernetes for better container orchestration
• Implemented auto-scaling based on traffic patterns
• Added CDN for static assets

Results

The improvements were dramatic:

• 80% reduction in API response times
• 99.99% uptime over the past 6 months
• 10x improvement in search performance
• 50% reduction in infrastructure costs

Lessons Learned

1. Monitor Everything: Comprehensive monitoring helped us identify bottlenecks early
2. Incremental Changes: Small, incremental improvements were more effective than large rewrites
3. Cache Strategically: Not everything needs to be cached, but when done right, it's incredibly powerful

Scaling to 1 million contracts taught us valuable lessons about building resilient systems. We're now well-positioned to handle the next million!