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a/analytics/pyproject.toml +++ b/analytics/pyproject.toml @@ -20,6 +20,7 @@ pydantic = "^2.0.3" python = "^3.11" slack-sdk = "^3.23.0" typer = { extras = ["all"], version = "^0.9.0" } +sqlalchemy = "^2.0.30" [tool.poetry.group.dev.dependencies] black = "^23.7.0" diff --git a/analytics/src/analytics/datasets/base.py b/analytics/src/analytics/datasets/base.py index deb730d9d..919e7c56f 100644 --- a/analytics/src/analytics/datasets/base.py +++ b/analytics/src/analytics/datasets/base.py @@ -3,6 +3,7 @@ from typing import Self import pandas as pd +from sqlalchemy import Engine class BaseDataset: @@ -22,6 +23,78 @@ def from_dict(cls, data: list[dict]) -> Self: """Load the dataset from a list of python dictionaries representing records.""" return cls(df=pd.DataFrame(data)) + def to_sql( + self, + output_table: str, + engine: Engine, + *, + replace_table: bool = True, + ) -> None: + """Writes the contents of a pandas DataFrame to a SQL table. + + This function takes a pandas DataFrame (`self.df`), an output table name (`output_table`), + and a SQLAlchemy Engine object (`engine`) as required arguments. It optionally accepts + a `replace_table` argument (default: True) that determines how existing data in the + target table is handled. + + **Parameters:** + + * self (required): The instance of the class containing the DataFrame (`self.df`) + to be written to the database. + * output_table (str, required): The name of the table in the database where the + data will be inserted. + * engine (sqlalchemy.engine.Engine, required): A SQLAlchemy Engine object representing + the connection to the database. + * replace_table (bool, default=True): + * If True (default), the function will completely replace the contents of the + existing table with the data from the DataFrame. (if_exists="replace") + * If False, the data from the DataFrame will be appended to the existing table. + (if_exists="append") + + **Returns:** + + * None + + **Raises:** + + * Potential exceptions raised by the underlying pandas.to_sql function, such as + database connection errors or errors related to data type mismatches. + """ + if replace_table: + self.df.to_sql(output_table, engine, if_exists="replace", index=False) + else: + self.df.to_sql(output_table, engine, if_exists="append", index=False) + + @classmethod + def from_sql( + cls, + source_table: str, + engine: Engine, + ) -> Self: + """Reads data from a SQL table into a pandas DataFrame and creates an instance of the current class. + This function takes a source table name (`source_table`) and a SQLAlchemy Engine object (`engine`) as required arguments. It utilizes pandas.read_sql to retrieve the data from the database and then creates a new instance of the current class (`cls`) initialized with the resulting DataFrame (`df`). + + **Parameters:** + + * cls (class, required): The class that will be instantiated with the data from the + SQL table. This allows for creating objects of the same type as the function is called on. + * source_table (str, required): The name of the table in the database from which the + data will be read. + * engine (sqlalchemy.engine.Engine, required): A SQLAlchemy Engine object representing + the connection to the database. + + **Returns:** + + * Self: A new instance of the current class (`cls`) initialized with the DataFrame + containing the data from the SQL table. + + **Raises:** + + * Potential exceptions raised by the underlying pandas.read_sql function, such as + database connection errors or errors related to data type mismatches. + """ + return cls(df=pd.read_sql(source_table, engine)) + def to_csv( self, output_file: Path, diff --git a/analytics/src/analytics/integrations/db.py b/analytics/src/analytics/integrations/db.py new file mode 100644 index 000000000..357e60f23 --- /dev/null +++ b/analytics/src/analytics/integrations/db.py @@ -0,0 +1,21 @@ +# pylint: disable=invalid-name +"""Get a connection to the database using a SQLAlchemy engine object.""" + +from sqlalchemy import Engine, create_engine + +from config import settings + + +def get_db() -> Engine: + """ + Get a connection to the database using a SQLAlchemy engine object. + + This function retrieves the database connection URL from the configuration + and creates a SQLAlchemy engine object. + + Yields + ------ + sqlalchemy.engine.Engine + A SQLAlchemy engine object representing the connection to the database. + """ + return create_engine(settings.database_url, pool_pre_ping=True) diff --git a/analytics/tests/datasets/test_base.py b/analytics/tests/datasets/test_base.py index 2928fe76f..43b27c498 100644 --- a/analytics/tests/datasets/test_base.py +++ b/analytics/tests/datasets/test_base.py @@ -5,6 +5,10 @@ from analytics.datasets.base import BaseDataset +from analytics.integrations.db import get_db + +from config import settings + TEST_DATA = [ {"Col A": 1, "Col b": "One"}, {"Col A": 2, "Col b": "Two"}, @@ -12,6 +16,27 @@ ] +# add a test for sql +def test_to_and_from_sql(): + """BaseDataset should write to and load from SQL table with to_sql() and from_sql().""" + # Setup - create sample dataframe and instantiate class + test_df = pd.DataFrame(TEST_DATA) + dataset_in = BaseDataset(test_df) + + # Setup - configure SQL connection + db_url = settings.database_url + # Assert the value of db_url + assert db_url == "sqlite:///mock.db" + engine = get_db() + table_name = "your_table_name" # Replace with actual table name + + # Execution - write to SQL table and read from SQL table + dataset_in.to_sql(table_name, engine) + dataset_out = BaseDataset.from_sql(table_name, engine) + # Validation - check that datasets match + assert dataset_in.df.equals(dataset_out.df) + + def test_to_and_from_csv(tmp_path: Path): """BaseDataset should write to csv with to_csv() and load from a csv with from_csv().""" # setup - create sample dataframe and instantiate class