Case Studies
The 19-Second Dashboard: Real-Time Analytics for 340K Users
FleetPulse provided route optimization for 340K+ delivery drivers across North America. Their Angular-based dashboard was collapsing under load. Enterprise clients (paying $12K–$89K/year) were threatening churn because live tracking data took 19–45 seconds to populate on dashboard load. Customer success was hemorrhaging time on "why is the map blank?" tickets.
Strategic Implementation
& Digital Results
We rebuilt the dashboard as a MERN stack app with surgical backend optimization. The core fix: implemented Redis caching with a 90-second TTL for route snapshots, cutting MongoDB reads by 89%. For live tracking, we replaced polling with Socket.io bidirectional streams, pushing driver location updates only when coordinates changed (delta-based transmission).
Database layer: We created compound indexes on user_id + route_status + timestamp and sharded the MongoDB cluster by geographic region. For historical analytics (30-day trend charts), we pre-aggregated data nightly using a Node.js cron worker, storing results in a separate Mongo collection.
Deployed the Node.js backend on AWS ECS with auto-scaling—containerized microservices could now handle 6,800 concurrent users per instance (previous limit: 240).
Background
FleetPulse provided route optimization for 340K+ delivery drivers across North America. Their Angular-based dashboard was collapsing under load. Enterprise clients (paying $12K–$89K/year) were threatening churn because live tracking data took 19–45 seconds to populate on dashboard load. Customer success was hemorrhaging time on "why is the map blank?" tickets.
The Challenges
- MongoDB queries scanning 18M+ documents per user session with zero indexing strategy
- No caching layer: every dashboard refresh hit the database cold, creating cascade failures during peak hours (6–9 AM)
- Polling-based updates instead of WebSockets—clients hammered the API every 3 seconds, causing 340% server overhead
The Solution
We rebuilt the dashboard as a MERN stack app with surgical backend optimization. The core fix: implemented Redis caching with a 90-second TTL for route snapshots, cutting MongoDB reads by 89%. For live tracking, we replaced polling with Socket.io bidirectional streams, pushing driver location updates only when coordinates changed (delta-based transmission).
Database layer: We created compound indexes on user_id + route_status + timestamp and sharded the MongoDB cluster by geographic region. For historical analytics (30-day trend charts), we pre-aggregated data nightly using a Node.js cron worker, storing results in a separate Mongo collection.
Deployed the Node.js backend on AWS ECS with auto-scaling—containerized microservices could now handle 6,800 concurrent users per instance (previous limit: 240).
The Competitive
Edge Delivered.
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