samla

Fetches and parses MSSQL schema metadata (tables, views, columns) into structured Python objects.

Install

pip install samla

Usage

From a database connection

Connects directly to MSSQL and fetches schema metadata:

import pyodbc
from samla import get_and_parse_tables, FilterGroup

filters = [
    FilterGroup(catalog_patterns=["NorthernVault"], schema_patterns=["Reporting"]),
]

conn = pyodbc.connect("<your connection string>")
tables = get_and_parse_tables(conn=conn, source_system="ACME", filters=filters)

The SQL query used to fetch metadata from INFORMATION_SCHEMA is available here.

From a CSV file

If you cannot connect to the database directly, load from a pre-exported CSV file instead:

from pathlib import Path
from samla import load_tables_from_file

tables = load_tables_from_file(path=Path("tables.csv"))

To produce the CSV, save the output from a database run:

from samla import save_tables_to_file

save_tables_to_file(tables=tables, path=Path("tables.csv"))

Or from the command line

python main.py                        # fetch from database
python main.py --file tables.csv      # load from CSV file

Filtering

FilterGroup filters tables by catalog, schema, and/or table name using regex patterns. Multiple patterns within a field are OR'd; multiple FilterGroups are also OR'd between each other.

filters = [
    FilterGroup(schema_patterns=["Sales", "Person"]),
    FilterGroup(catalog_patterns=["AdventureWorks"], table_patterns=["^Product"]),
]

Data model

Table

SourceColumn

Development

pip install -e ".[dev]"
pytest