Viewing partition recommendations
You can view and analyze the collected data, which includes recommendations for microservices to implement in place of the Java® monolithic application.
About this task
The AI engine for partition recommendations generates partition recommendations along with runtime invocation details by analyzing the runtime traces of the completed use cases for the Java monolithic application. These partitions are groupings of monolith Java classes that serve as starting points for potential microservices. The AI engine also generates class containment dependency details from the static code analysis of the Java monolithic application.
The generated partition recommendations, the class dependency analysis, and the runtime invocation analysis of a Java monolithic application are contained in the final_graph.json file in the mono2micro-output/oriole directory. Use the IBM® Mono2Micro™ workbench UI by running the mono2micro workbench command to view and customize these partition recommendations.
Prerequisites
Docker or Podman is required to run the workbench UI.
For information about terms in partition recommendations, see Concepts and terminology.
Procedure
What to do next
After you finish analyzing collected data in the workbench and save your partitions, you can use the Mono2Micro code generation tool to automatically generate the API services and related code to help create microservices from your partitions.