⚡️ Resolve issues and add test of new functions

tests/integration/flows/test_adls_to_azure_sql.py

@@ -6,8 +6,30 @@
 from prefect.engine import signals
 
 from viadot.flows import ADLSToAzureSQL
-from viadot.flows.adls_to_azure_sql import check_dtypes_sort, df_to_csv_task
-
+from viadot.flows.adls_to_azure_sql import check_dtypes_sort, df_to_csv_task, len_from_dtypes, check_hardcoded_dtypes_len, get_real_sql_dtypes_from_df
+
+test_df = pd.DataFrame(
+    {
+        "Date": ["2023-01-01", "2023-01-02", "2023-01-03", "2023-01-04", "2023-01-05"],
+        "User ID": ["1a34", "1d34$56", "1a3456&8", "1d3456789!", "1s3"],  # max length = 10
+        "Web ID": ["4321", "1234$56", "123", "0", "12"],  # max length = 7
+        "User name": ["Ada", "aaaaadAA", "Adulkaaa", "A", " "],  # max length = 8
+        "User country": ["Poland", "USA", "Norway", "USA", "USA"],  # max length = 6
+        "All Users": [1234, 123456, 12345678, 123456789, 123],
+        "Age": [0, 12, 123, 89, 23],
+        "Last varchar": ["Last", " ", "varchar", "of this ", "df"],  # max length =8
+    }
+)
+Real_Sql_Dtypes = {
+    "Date": "DATE",
+    "User ID": "VARCHAR(10)",
+    "Web ID": "VARCHAR(7)",
+    "User name": "VARCHAR(8)",
+    "User country": "VARCHAR(6)",
+    "All Users": "INT",
+    "Age": "INT",
+    "Last varchar": "VARCHAR(8)",
+}
 
 def test_get_promoted_adls_path_csv_file():
     adls_path_file = "raw/supermetrics/adls_ga_load_times_fr_test/2021-07-14T13%3A09%3A02.997357%2B00%3A00.csv"
@@ -101,3 +123,73 @@ def test_check_dtypes_sort():
         assert False
     except signals.FAIL:
         assert True
+
+
+def test_get_real_sql_dtypes_from_df():
+    assert get_real_sql_dtypes_from_df(test_df) == Real_Sql_Dtypes
+
+
+def test_len_from_dtypes():
+    real_df_lengths = {
+        "Date": "DATE",
+        "User ID": 10,
+        "Web ID": 7,
+        "User name": 8,
+        "User country": 6,
+        "All Users": "INT",
+        "Age": "INT",
+        "Last varchar": 8,
+    }
+    assert len_from_dtypes(Real_Sql_Dtypes) == real_df_lengths
+
+
+def test_check_hardcoded_dtypes_len_userid(caplog):
+    smaller_dtype_userid = {
+        "Date": "DateTime",
+        "User ID": "varchar(1)",
+        "Web ID": "varchar(10)",
+        "User name": "varchar(10)",
+        "User country": "varchar(10)",
+        "All Users": "int",
+        "Age": "int",
+        "Last varchar": "varchar(10)",
+    }
+    with pytest.raises(ValueError):
+        check_hardcoded_dtypes_len(test_df, smaller_dtype_userid)
+        assert (
+            "The length of the column User ID is too big, some data could be lost. Please change the length of the provided dtypes to 10"
+            in caplog.text
+        )
+
+
+def test_check_hardcoded_dtypes_len_usercountry(caplog):
+    smaller_dtype_usercountry = {
+        "Date": "DateTime",
+        "User ID": "varchar(10)",
+        "Web ID": "varchar(10)",
+        "User name": "varchar(10)",
+        "User country": "varchar(5)",
+        "All Users": "int",
+        "Age": "int",
+        "Last varchar": "varchar(10)",
+    }
+    with pytest.raises(ValueError):
+        check_hardcoded_dtypes_len(test_df, smaller_dtype_usercountry)
+        assert (
+            "The length of the column User country is too big, some data could be lost. Please change the length of the provided dtypes to 6"
+            in caplog.text
+        )
+
+
+def test_check_hardcoded_dtypes_len():
+    good_dtypes = {
+        "Date": "DateTime",
+        "User ID": "varchar(10)",
+        "Web ID": "varchar(10)",
+        "User name": "varchar(10)",
+        "User country": "varchar(10)",
+        "All Users": "int",
+        "Age": "int",
+        "Last varchar": "varchar(10)",
+    }
+    assert check_hardcoded_dtypes_len(test_df, good_dtypes) == None

viadot/flows/adls_to_azure_sql.py

@@ -29,7 +29,14 @@ def union_dfs_task(dfs: List[pd.DataFrame]):
     return pd.concat(dfs, ignore_index=True)
 
 def get_real_sql_dtypes_from_df(df: pd.DataFrame) -> Dict[str, Any]:
-    """Obtain SQL data types from a pandas DataFrame"""
+    """Obtain SQL data types from a pandas DataFrame
+    and the lengths of the columns based on the real maximum lengths of the data in them.
+    Args:
+        df (pd.DataFrame): Data Frame from original ADLS file.
+    Returns:
+        Dict[str, Any]: Dictionary with data types of columns and their real maximum length. 
+    """
+
     typeset = CompleteSet()
     dtypes = infer_type(df.head(10000), typeset)
     dtypes_dict = {k: str(v) for k, v in dtypes.items()}
@@ -58,18 +65,17 @@ def get_real_sql_dtypes_from_df(df: pd.DataFrame) -> Dict[str, Any]:
 
     return dict_dtypes_mapped
 
-def len_from_dtypes(dtypes) -> Dict[str, Any]:
+def len_from_dtypes(dtypes: Dict[str, Any]) -> Dict[str, Any]:
     """Function that turns a dictionary of column names and their dtypes into a dictionary
-    of column names and either the lengths of the varchars or the dtypes.
-
+    of column names and either the lengths of the varchars (of 'int' type) or the dtypes (of 'string' type).
 
     Args:
         dtypes (Dict[str, Any], optional): Dictionary of columns and data type to apply
             to the Data Frame downloaded.
 
     Returns:
         Dict[str, Any]: Dictionary of the columns and their dtypes as strings
-            or the lengths of their varchars, as ints
+            or the lengths of their varchars, as ints.
     """
     dtypes_lens = {}
     for k, v in dtypes.items():
@@ -80,7 +86,7 @@ def len_from_dtypes(dtypes) -> Dict[str, Any]:
             dtypes_lens[k] = str(v)
     return dtypes_lens
 
-def check_hardcoded_dtypes_len(real_data_df, given_dtypes):
+def check_hardcoded_dtypes_len(real_data_df: pd.DataFrame, given_dtypes: Dict[str, Any]) -> None:
     """Function to check if the length of columns provided by the hard-coded dtypes are not too small
         compared to the real columns of the df.
 
@@ -91,17 +97,19 @@ def check_hardcoded_dtypes_len(real_data_df, given_dtypes):
 
     Raises:
         ValueError: Raised whenever the length of the hardcoded dtypes is too small to contain the full data.
+    
+    Returns:
+        None
     """
-    real_data_len = len_from_dtypes( get_real_sql_dtypes_from_df(real_data_df) )
-    given_data_len = len_from_dtypes(given_dtypes)
+    real_column_lengths = len_from_dtypes( get_real_sql_dtypes_from_df(real_data_df) )
+    given_column_lengths = len_from_dtypes(given_dtypes)
 
-    for (column_given, len_given), (column_real, len_real) in zip(given_data_len.items(), real_data_len.items()):
-        #check if both of them are varchars
-        if isinstance(given_data_len[column_real], int) and isinstance(real_data_len[column_real], int):
+    for (column_given, len_given), (column_real, len_real) in zip(given_column_lengths.items(), real_column_lengths.items()):
+        #checking only the columns with lengths of varchars
+        if isinstance(given_column_lengths[column_real], int) and isinstance(real_column_lengths[column_real], int):
             if len_real > len_given:
                 logger.error(f"The length of the column {column_real} is too big, some data could be lost. Please change the length of the provided dtypes to {len_real}")
-                raise ValueError("Dtype length is incorrect!")
-
+                raise ValueError("Datatype length is incorrect!")         
 
 @task(timeout=3600)
 def map_data_types_task(json_shema_path: str):
@@ -189,22 +197,17 @@ def check_dtypes_sort(
                 new_dtypes = dict()
                 for key in df.columns:
                     new_dtypes.update([(key, dtypes[key])])
-
-                check_hardcoded_dtypes_len(df, new_dtypes)
-
             else:
                 new_dtypes = dtypes.copy()
-                check_hardcoded_dtypes_len(df, new_dtypes)
-
         else:
             logger.error("There is a discrepancy with any of the columns.")
             raise signals.FAIL(
                 "dtype dictionary contains key(s) that not matching with the ADLS file columns name, or they have different length."
             )
     else:
         new_dtypes = dtypes.copy()
-        check_hardcoded_dtypes_len(df, new_dtypes)
-
+
+    check_hardcoded_dtypes_len(df, new_dtypes)
     return new_dtypes