Skip to Content

Priority‑Based Message Processing System on AWS

8 March 2026 by
Suraj Barman

Priority‑Based Message Processing System on AWS

A priority‑aware pipeline routes urgent business events ahead of routine work while preserving reliability for all messages. It combines Amazon MQ for ordered queues, DynamoDB for state storage, and AWS App Runner for scalable compute.

Core AWS Services

This layer supplies the managed components that keep the system running without manual server upkeep.

  • Amazon MQ - provides JMS‑compatible brokers with built‑in persistence and dead‑letter queues.
  • Amazon DynamoDB - stores message metadata, status flags, and supports Streams for change capture.
  • AWS App Runner - hosts the processing microservice with automatic scaling and HTTPS endpoints.
  • AWS KMS - encrypts data at rest for both MQ and DynamoDB resources.

Priority Queue Configuration

Messages are assigned JMS priority levels (High = 9, Medium = 4, Low = 0) so the broker orders delivery automatically.

  • Define three priority classes in the producer code.
  • Configure the broker to respect JMS priority flags.
  • Map high‑priority traffic to an express lane that bypasses delay logic.
  • Low‑priority items follow a delayed‑entry path to smooth load spikes.

Delay & Retry Mechanisms

Application‑level delays and a two‑stage retry strategy ensure that routine work does not block urgent processing.

  • Use JMS asynchronous send with CompletableFuture for non‑blocking delays.
  • Configure per‑message delay intervals (e.g., 0 s for High, 30 s for Medium, 120 s for Low).
  • Implement exponential back‑off retries on processing failures.
  • Route permanently failed items to a dead‑letter queue for manual inspection.

Real‑time UI with WebSockets

The front‑end receives live status updates through a WebSocket gateway backed by DynamoDB Streams.

  • Publish change events from DynamoDB Streams to an App Runner WebSocket endpoint.
  • React components subscribe to the socket for instant feedback on each message lifecycle.
  • Display priority, current state, and retry count in a concise dashboard.
  • Leverage WebSocket for full‑duplex communication.

Infrastructure as Code with CDK

The entire stack is defined in AWS CDK (Python) to enable repeatable deployments across environments.

  • Provision KMS key, DynamoDB table, and Global Secondary Index.
  • Deploy an ActiveMQ broker with encrypted storage and audit logging.
  • Generate App Runner service linked to the processing container image.
  • Export outputs (table name, broker endpoint) for application configuration.

For a step‑by‑step implementation, see the priority‑based processing guide. Additional patterns for real‑time orchestration are available in the real‑time orchestration example.