Scaling PicDB: Handling Millions of Image Assets with Go and Next.js
An inside look at the backend infrastructure that powers Arkynox's flagship storage platform.
Introduction
When building PicDB, our primary goal was to create an image database that could ingest, process, and serve assets globally with sub-50ms latency. Leveraging the Go programming language for our microservices and Next.js for the client interface allowed us to hit these aggressive targets.
"The true bottleneck of any storage service isn't disk I/O—it's how efficiently you can route the network requests before they hit the disk."
— Akkil M G, Lead Developer
The Architecture
Our architecture uses a distributed routing layer built in Go. Let's look at the basic struct for our node configuration:
package core
type StorageNode struct {
ID string `json:"id"`
Region string `json:"region"`
Capacity uint64 `json:"capacity_bytes"`
IsActive bool `json:"is_active"`
}Performance Metrics
After migrating from our initial Node.js implementation to Go, the metrics improved drastically across all regions.
| Region | Previous Latency | Current Latency (Go) | Improvement |
|---|---|---|---|
| US-East | 120ms | 35ms | 70% |
| EU-Central | 145ms | 42ms | 71% |
| AP-South | 180ms | 65ms | 63% |
Next Steps
As we continue to scale PicDB, we are looking into edge caching mechanisms and WebAssembly for on-the-fly image manipulation before serving.