RAPID-ML puts data modeling at the center of API design.
Describe data types naturally with an expressive, technology-independent modeling language.
Share canonical data models and adapt them to each API with just the right flexibility to bend the model, not break it.
Unify APIs at any scale from microservices to cloud ecosystems. Free your client developers from API mismatch, and integrate faster.
Describe data structures, relationships, simple types, enums and constraints using a natural, concise language.
Code generators for JSON and XML Schema translate your definitions to physical message formats.
Import shared data models from the filesystem, github or any http repository.
Expose data structures through your API by binding them directly into object resources and collection resources.
To reference the bound data from a message payload, just say 'this'!
Bind template and method parameters to properties to enable richer validation and code generation.
Use property sets to choose which properties to include, and add constraints to fine-tune them to your API.
Specify a property set by explicit inclusion or exclusion.
Tighten cardinality and primitive type constraints with built-in validations to protect the underlying data contract.
Finally, canonical data models are a practical reality!
Reference properties connect one data structure to another. With RAPID-ML, you can express each reference as a hyperlink, embedded structure, or both.
Use the referenceEmbed keyword to embed the referenced data structure, or just the properties you want.
A referenceLink models the reference as a hyperlink to another resource, bound to the target data structure. Optionally "decorate" the hyperlink with embedded target properties to optimize client round-trips.