Added validate_df task to CustomerGaugeToADLS flow

tests/integration/flows/test_customer_gauge_to_adls.py

@@ -5,6 +5,7 @@
 import pytest
 
 from viadot.flows import CustomerGaugeToADLS
+from viadot.exceptions import ValidationError
 
 DATA = {
     "user_name": ["Jane", "Bob"],
@@ -38,5 +39,56 @@ def test_customer_gauge_to_adls_run_flow(mocked_class):
     )
     result = flow.run()
     assert result.is_successful()
+    assert len(flow.tasks) == 10
     os.remove("test_customer_gauge_to_adls_flow_run.parquet")
     os.remove("test_customer_gauge_to_adls_flow_run.json")
+
+@mock.patch(
+    "viadot.tasks.CustomerGaugeToDF.run",
+    return_value=pd.DataFrame(data=DATA),
+)
+@pytest.mark.run
+def test_customer_gauge_to_adls_run_flow_validation_success(mocked_class):
+    flow = CustomerGaugeToADLS(
+        "test_customer_gauge_to_adls_flow_run",
+        endpoint="responses",
+        total_load=False,
+        anonymize=True,
+        columns_to_anonymize=COLUMNS,
+        adls_dir_path=ADLS_DIR_PATH,
+        adls_file_name=ADLS_FILE_NAME,
+        overwrite_adls=True,
+        validate_df_dict={"column_size": {"user_address_state": 2}},
+    )
+    result = flow.run()
+    assert result.is_successful()
+    assert len(flow.tasks) == 11
+
+    os.remove("test_customer_gauge_to_adls_flow_run.parquet")
+    os.remove("test_customer_gauge_to_adls_flow_run.json")
+
+@mock.patch(
+    "viadot.tasks.CustomerGaugeToDF.run",
+    return_value=pd.DataFrame(data=DATA),
+)
+@pytest.mark.run
+def test_customer_gauge_to_adls_run_flow_validation_failure(mocked_class):
+    flow = CustomerGaugeToADLS(
+        "test_customer_gauge_to_adls_flow_run",
+        endpoint="responses",
+        total_load=False,
+        anonymize=True,
+        columns_to_anonymize=COLUMNS,
+        adls_dir_path=ADLS_DIR_PATH,
+        adls_file_name=ADLS_FILE_NAME,
+        overwrite_adls=True,
+        validate_df_dict={"column_size":{"user_name":5}},
+    )
+    try:
+        flow.run()
+    except ValidationError:
+        pass
+
+    os.remove("test_customer_gauge_to_adls_flow_run.parquet")
+    os.remove("test_customer_gauge_to_adls_flow_run.json")
+

viadot/flows/customer_gauge_to_adls.py

@@ -17,6 +17,7 @@
     df_to_parquet,
     dtypes_to_json_task,
     update_dtypes_dict,
+    validate_df,
 )
 from viadot.tasks import AzureDataLakeUpload, CustomerGaugeToDF
 
@@ -52,6 +53,7 @@ def __init__(
         adls_sp_credentials_secret: str = None,
         overwrite_adls: bool = False,
         if_exists: str = "replace",
+        validate_df_dict: dict = None,
         timeout: int = 3600,
         *args: List[Any],
         **kwargs: Dict[str, Any]
@@ -91,6 +93,7 @@ def __init__(
                 ACCOUNT_NAME and Service Principal credentials (TENANT_ID, CLIENT_ID, CLIENT_SECRET) for the Azure Data Lake.
                 Defaults to None.
             overwrite_adls (bool, optional): Whether to overwrite files in the lake. Defaults to False.
+            validate_df_dict (Dict[str], optional): A dictionary with optional list of tests to verify the output dataframe. If defined, triggers the `validate_df` task from task_utils. Defaults to None.
             if_exists (str, optional): What to do if the file exists. Defaults to "replace".
             timeout (int, optional): The time (in seconds) to wait while running this task before a timeout occurs. Defaults to 3600.
         """
@@ -105,6 +108,9 @@ def __init__(
         self.end_date = end_date
         self.customer_gauge_credentials_secret = customer_gauge_credentials_secret
 
+        # validate_df
+        self.validate_df_dict = validate_df_dict
+
         # anonymize_df
         self.anonymize = anonymize
         self.columns_to_anonymize = columns_to_anonymize
@@ -169,6 +175,12 @@ def gen_flow(self) -> Flow:
             flow=self,
         )
 
+        if self.validate_df_dict:
+            validation_task = validate_df.bind(
+                customerg_df, tests=self.validate_df_dict, flow=self
+            )
+            validation_task.set_upstream(customerg_df, flow=self)
+
         if self.anonymize == True:
             anonymized_df = anonymize_df.bind(
                 customerg_df,