E-Commerce GraphQL Architecture
Led redesign of backend GraphQL services for a high-traffic e-commerce platform serving 50k+ daily requests at Speeler Technologies.
Rationale
Why GraphQL? At Speeler Technologies, I was incredibly focused on modernizing their core e-commerce platform specifically for a printing enterprise. The main challenge was that their shopping cart needed to be radically faster to handle peak traffic. Rebuilding it with AWS AppSync and GraphQL was a huge win—it actually cut data retrieval time by about 30%.
The Hardest Challenge: The database design. I used a strict Single-Table Design in DynamoDB to keep queries lightning fast, but setting up the fine-grained access control with Cognito/IAM alongside it took extensive, careful planning to enforce exactly right.
Tech Stack
Key Highlights
- ▹Reduced P95 latency by 30% by implementing resolver batching and AppSync caching.
- ▹Designed a multi-tenant DynamoDB Single-Table Architecture, utilizing sparse indexes and GSI sharding to eliminate hot partitions and support high-cardinality access patterns.
- ▹Engineered a fault-tolerant, event-driven pipeline using Lambda and SQS Dead Letter Queues (DLQ) to process images, reducing operational infrastructure costs by 70%.
- ▹Architected the frontend synchronization layer using React and WebSockets, implementing UI updates to mask network latency.
- ▹Orchestrated the migration to AWS ECS Fargate, implementing Blue/Green deployments for zero-downtime releases.
Architecture Details
This outlines the cloud-native, serverless approach taken to redesign the backend services at Speeler Technologies.
1. AppSync & DynamoDB Single-Table Design
- Rebuilt the monolithic shopping cart into a lightning-fast AWS AppSync / GraphQL schema.
- Optimized database reads executing a strict DynamoDB Single-Table Design, keeping latency flat even at massive scale while strictly scoping identity and access via AWS Cognito and IAM.
2. Lambda Image Pipeline Automation
- The enterprise required processing hundreds of massive printing images daily.
- I built a fully automated system: as soon as an image hits an S3 bucket, it triggers an event-driven AWS Lambda function to process it instantly. Moving to this serverless architecture eliminated hours of manual work and saved the company 70% in operational costs.
3. Docker to Fargate
- Packaged the entire React frontend application into Docker containers.
- Orchestrated the deployment onto AWS ECS Fargate, allowing the application to completely automatically scale horizontally up and down based strictly on heavy traffic spikes.