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Universal class that created to be a middleware, universal mapping for data from different parsers - simple-ddl-parser and py-models-parser
Table Meta ^^^^^^^^^^
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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.
How it use ^^^^^^^^^^
.. code-block:: bash
pip install table-meta
.. code-block:: python
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:
.. code-block:: python
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:
.. code-block:: python
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)
v0.2.1
#. Added support for parsing 'dataset' from data as 'table_schema' also added fields like 'project' (to support BigQuery metadata) #. Depencencies updated #. Added HQL Table Properties
v0.1.5
#. field 'attrs' added to Type to store values from py-models-parser output
v0.1.3
#. 'parents' added to Type and to Table
v0.1.1
#. Fix dependencies for python 3.6
v0.1.0
#. Table Meta moved from O!MyModels to separate library. To make it re-usebale in fakeme library.
FAQs
Universal class that created to be a middleware, universal mapping for data from different parsers - simple-ddl-parser and py-models-parser
We found that table-meta demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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