Table Meta standard that used in fakeme library & OmyModels to unify data mapping for ORM, Python Models & ddl data
MIT License
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.
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.
pip install table-meta
from table_meta import TableMeta
data = {your_table_definition}
table_data = TableMeta(**data)
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)
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)
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