AWS ECS Fargate Architecture

What this diagram shows

This diagram represents a production-grade containerised application running on AWS ECS Fargate. Traffic arrives at an Application Load Balancer, which routes requests to stateless Fargate tasks running in a cluster. Long-running or asynchronous work is decoupled via SQS queues, with separate Fargate services consuming and processing messages. Persistent state is stored in RDS for relational data and Redis for caching and session management. CloudWatch monitors container metrics and logs. The architecture scales automatically: the ALB distributes load, Fargate handles container orchestration without EC2 management, and services scale based on queue depth or request volume.

Key components

• Application Load Balancer (ALB) — Distributes incoming HTTP/HTTPS traffic across Fargate tasks and terminates TLS connections.
• ECS Fargate Cluster — Runs containerised services without requiring you to provision or manage underlying EC2 instances.
• Fargate Service (API/Web) — Maintains a desired number of task replicas running your application containers with automatic scaling.
• Fargate Service (Worker) — Consumes messages from SQS and processes asynchronous jobs or background tasks.
• SQS Queue — Decouples request producers from workers, allowing asynchronous job processing and load levelling.
• Amazon RDS — Provides managed relational database persistence for structured application data.
• Amazon ElastiCache (Redis) — Caches frequently accessed data and stores session state to reduce database load and latency.

When to use it

Use this diagram when documenting a containerised application that needs to handle variable traffic, process background jobs, and maintain persistent state. It suits microservices or monolithic apps that separate synchronous API workloads from asynchronous workers. Choose it when showing how to eliminate manual server management, leverage AWS managed services for scalability, or explain decoupling patterns via message queues. It is appropriate for architecture reviews, onboarding documentation, and capacity planning discussions.

Common mistakes

• Forgetting to show how tasks are scaled by target tracking on request count or SQS queue depth, leaving the scaling mechanism implicit.
• Omitting VPC security groups, subnets, and NAT gateways, which are essential for task networking and database access.
• Treating SQS as optional or assuming all work is synchronous, missing the value of decoupling and resilience that message queues provide in production systems.

Adapting it to your system

Start by identifying your application's synchronous and asynchronous workloads. Replace the generic API service with your actual microservices and update container image references. Adjust SQS queue names and worker task definitions to match your job types. For databases, substitute RDS engine choice (PostgreSQL, MySQL, MariaDB) based on your schema. If you cache session data in Redis, add TTL and eviction policies. Add CloudWatch alarms, log groups, and perhaps SNS for critical alerts. Document scaling policies: CPU and memory targets for web services, and queue depth thresholds for workers. Include VPC CIDR blocks, subnet availability zones, and security group rules if the diagram is used for infrastructure-as-code handoff.