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Orchestration vs Choreography: re-engineering the AWS Saga pattern sample

In this video, Yaron Schnieder (co-creator of Dapr and KEDA) illustrates different approaches to implementing the saga pattern on AWS; orchestration as in the AWS sample and a choreography approach with code that boost developer productivity.

The GitHub repo contains a Python app that shows you how to implement the AWS reference architecture for Saga orchestration using Catalyst Workflow.

Catalyst Workflow is a fully managed durable exection engine built on top of the open-source Dapr project. With Catalyst Workflow on AWS you can implement the saga pattern via orchestration and get the following benefits:

  • Use any AWS compute service available, including ECS, AppRunner, EC2 etc.
  • Use a fully managed Diagrid state store for workflow execution state, or bring your own
  • Use any messaging service for downstream notifications, from Diagrid's managed pub/sub to your own Kafka, SQS etc.
  • End to end observability with API logs, metrics and more
  • A workflow execution UI that allows you to dig into each activity's execution status
  • A single and consistent polyglot programming model that runs anywhere
  • Local debugging without needing emulators or Docker containers

Note: You can run Catalyst Workflow on any cloud or compute environment including your local machine

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