table-meta 1.0.0

Universal class that created to be a middleware, universal mapping for data from different parsers - simple-ddl-parser and py-models-parser

Table Meta

It's a universal class that created to be a middleware, universal mapping for data from different parsers - simple-ddl-parser and py-models-parser.

Python 3.9+.

Based on this middleware 2 libraries are worked - omymodels & fakeme.

It's allow create 1 adapter for different inputs and produce output only on one standard - easy to maintain ad add different output variants.

All classes - Pydantic classes, so you can do with them anything that you can with Pydantic classes.

Library contains 2 different classes - TableMeta - main class to convert input relative to models or tables. Second - Type, for Enum types data.

How it use

Install

    pip install table-meta

Usage

from table_meta import TableMeta

data = {your_table_definition}

table_data = TableMeta(**data)

Convert simple-ddl-parser input to TableMeta

Simple-ddl-parser: https://github.com/xnuinside/simple-ddl-parser

Pay attention that TableMeta expected data from simple-ddl-parser , that created with flag 'group_by_type=True' Example: result = DDLParser(ddl).run(group_by_type=True, output_mode="bigquery")

To convert simple-ddl-parser output to TableMeta - use method: ddl_to_meta()

Usage example:

    from simple_ddl_parser import DDLParser
    from table_meta import ddl_to_meta

    ddl = "your ddl"
    parser_result = DDLParser(ddl).run(group_by_type=True, output_mode="bigquery")
    data = ddl_to_meta(parser_result)

    # ddl_to_meta returns Dict with 2 keys {"tables": [], "types": []} inside lists you will have Table Meta a models

    print(data)

Convert py-model-parser input to TableMeta

Py-models-parser: https://github.com/xnuinside/py-models-parser

Usage example:

    from py_models_parser import parse
    from table_meta import models_to_meta

    model_from = "your python models, supported by parser"
    result = parse(model_from)
    data = models_to_meta(result)

    # models_to_meta returns Dict with 2 keys {"tables": [], "types": []} inside lists you will have a Table Meta models

    print(data)

Changelog

See CHANGELOG.md.