jtd-codebuild 0.2.0

Tool for generating language specific schemas and interfaces code from JSON Type Definition IDL files in yaml format. Powered by jtd-codegen.

JSON Type Definition Code Build

jtd-codebuild is a tool for generating language specific schemas and interfaces code from JSON Type Definition IDL files in yaml format.

This tool is built on top of jtd-codegen so check out the documentation if you don't have a clue about JSON Type Definition.

Prerequisites

• Python
  • pyyaml
• jtd-codegen

Installation

pip install jtd-codebuild

Usage

jtd-codebuild path/to/the/folder/where/jtd-codebuild.json/is/located

Required conventions

Configuration

The script will find jtd-codebuild.json which is the configuration file of this tooling.

root-schema-path

The path to the root schema file. Root schema file will be the entry point of the code generation, where every definition files will be merged into.

Defaults to schema.jtd.yaml

definitions-path

The path to the definitions directory. This directory will be recursively searched for definition files.

Definition file is a file that contains a single or multiple definitions. Checkout the documentation below for more information.

Defaults to definitions

output-schema-path

The path for the merged schema file converted in json format.

Defaults to gen/schema.jtd.json

includes

Array of JTD package paths to include.

The path should have jtd-codebuild.json file in it so that this tool can find the codebuild configuration.

If you specifiy a package path, it will reference the package's schema definitions when generating schema file you are working on.

Defaults to []

targets

Compile targets.

It's a JSONRecord contains the object having following properties:

• language (string): The language of the target. We essentially inject this value to jtd-codegen as target language option which is provided as a flag which is like --{language}-out. Available languages can be found in the documentation of jtd-codegen. (See: https://jsontypedef.com/)
• path (string): The path to the directory where the generated code will be placed.

targets - Language Specific Options - Python

• use-pydantic (boolean): Whether to use pydantic as a dataclass decorator provider. If this is set to true, the generated code will use pydantic.dataclasses.dataclass as a dataclass decorator so that you can use pydantic's validation features. Otherwise, the generated code will be plain python dataclasses. Defaults to false.
• subsriptable (boolean): Whether to make the generated class subscriptable. If this is set to true, the generated class will be subscriptable so that you can access the properties of the class like obj["property"]. Otherwise, the generated class will not be subscriptable. Defaults to false.

targets - Language Specific Options - TypeScript

• tsconfig-path (string): The path to the tsconfig file. This will be used to compile typescript code to javascript code and type declarations. If you want to automatically generate plain javascript artifact with type declarations, you should also provide this option.

Configuration Example

Example congfiguration file is provided below. Copy it and modify it to your needs.

{
  "root-schema-path": "schema.jtd.yaml",
  "definitions-path": "definitions",
  "output-schema-path": "gen/schema.jtd.json",
  "includes": ["../path/to/another/folder/that/contains/jtd-codebuild.json"],
  "targets": [
    {
      "language": "python",
      "path": "gen/python",
      "use-pydantic": true,
      "subscriptable": true
    },
    {
      "language": "typescript",
      "path": "gen/typescript",
      "tsconfig-path": "tsconfig.build.json"
    }
  ]
}

Root Schema File

Root schema file is the entry point of the code generation.

It will be the file where every definition files will be merged into.

If you don't need a root Schema type, you can just create an empty file.

Definition files

Definition files are sharable files of which each of them contains a single or multiple definitions.

Each declared keys as a root key in the definition file will be merged as a key of definitions object in the root schema file, and those symbols can be shared across the other definition files.

For example, let's say you have a definition file whose code is like below.

book:
  properties:
    id:
      type: string
    title:
      type: string

This can be referenced in other definition files like below.

user:
  properties:
    id:
      type: string
    name:
      type: string
    books:
      elements:
        ref: book

This will be merged as a single schema like below.

{
  "definitions": {
    "book": {
      "properties": {
        "id": {
          "type": "string"
        },
        "title": {
          "type": "string"
        }
      }
    },
    "user": {
      "properties": {
        "id": {
          "type": "string"
        },
        "name": {
          "type": "string"
        },
        "books": {
          "elements": {
            "ref": "book"
          }
        }
      }
    }
  }
}

Checkout more about ref if you don't have a clue about it. https://jsontypedef.com/docs/jtd-in-5-minutes/#ref-schemas

Manual dependency management

Since IDL files are basically just a bunch of JSON objects, we need to manually manage the dependency between the definition files.

For example, assume you have a folder structure like the below:

definitions
├── book
│   └── book.jtd.yaml
└── user
    └── user.jtd.yaml

And assume that book.jtd.yaml and user.jtd.yaml are the root definition files of each module.

In this case, you need to annotate that user.jtd.yaml depends on book.jtd.yaml like below.

# user.jtd.yaml
#
# Depends on:
#   - book (at ../book/book.jtd.yaml)
user:
  properties:
    id:
      type: string
    name:
      type: string
    books:
      elements:
        ref: book