adding a quickfix for saving expeectation suites that have use dateti…

…me objects in the evaluation parameters (#1957)

Co-authored-by: Eugene Mandel <[email protected]>

great_expectations/marshmallow__shade/schema.py

@@ -584,6 +584,12 @@ def dumps(self, obj: typing.Any, *args, many: bool = None, **kwargs):
             if ``obj`` is invalid.
         """
         serialized = self.dump(obj, many=many)
+
+        def datetime_serializer(o):
+            if isinstance(o, dt.datetime):
+                return o.__str__()
+        if "default" not in kwargs:
+            kwargs.update({"default": datetime_serializer})
         return self.opts.render_module.dumps(serialized, *args, **kwargs)
 
     def _deserialize(

tests/data_asset/test_datetime_evaluation_parameter.py

@@ -0,0 +1,25 @@
+"""
+Test the ability to use datetime objects as run time evaluation parameters.
+"""
+
+import pandas as pd
+
+from great_expectations.dataset import PandasDataset
+
+
+def test_pandas_datetime_evaluation_parameter():
+    evaluation_params = {"now": pd.Timestamp.now(), "now_minus_48h": pd.Timestamp.now() - pd.to_timedelta(2, unit="d")}
+
+    test_data = {
+        "data_refresh": [pd.Timestamp.now(), (pd.Timestamp.now() - pd.to_timedelta(1, unit="d"))]
+    }
+    _df = pd.DataFrame(test_data)
+    df = PandasDataset(_df)
+
+    for param in evaluation_params:
+        df.set_evaluation_parameter(param, evaluation_params[param])
+    df.expect_column_max_to_be_between(column="data_refresh", min_value={"$PARAMETER": "now_minus_48h"})
+
+    result = df.validate()
+
+    assert result.success

tests/data_context/test_pandas_datetime_suites.py

@@ -0,0 +1,69 @@
+import pandas as pd
+from tempfile import TemporaryDirectory
+import great_expectations as ge
+from great_expectations.dataset import PandasDataset
+from great_expectations.data_context.data_context import ExpectationSuite
+import os
+import datetime
+
+
+def test_save_expectation_suite_with_datetime_objects(data_context_parameterized_expectation_suite):
+    # create datetime evaluation parameters
+    evaluation_params = {"now": datetime.datetime.now(), "now_minus_48h": datetime.datetime.now() - datetime.timedelta(days=2)}
+    test_data = {"data_refresh": [datetime.datetime.now(), datetime.datetime.now() - datetime.timedelta(days=1)]}
+    test_df = pd.DataFrame(test_data)
+    dataset_name = "test_pandas_source"
+
+    with TemporaryDirectory() as tempdir:
+        ge_path = os.path.join(tempdir, "great_expectations")
+        ge.DataContext.create(tempdir, usage_statistics_enabled=False)
+        context = ge.DataContext(ge_path)
+
+        context.add_datasource(dataset_name, class_name="PandasDatasource")
+
+        batch_kwargs = {"dataset": test_df, "datasource": dataset_name, "PandasInMemoryDF": True, "ge_batch_id": "test_id",}
+
+        empty_suite = context.create_expectation_suite("test_suite")
+
+        batch = context.get_batch(batch_kwargs=batch_kwargs, expectation_suite_name=empty_suite)
+        for param in evaluation_params:
+            batch.set_evaluation_parameter(param, evaluation_params[param])
+
+        # Add expectation that will succeed using the datetime in a $PARAMETER
+        batch.expect_column_max_to_be_between(column="data_refresh", min_value={"$PARAMETER": "now_minus_48h"})
+        result = batch.validate()
+        assert result.success
+        batch.save_expectation_suite()
+        assert isinstance(batch, PandasDataset)
+
+        # Check that we can load the saved expectation suite
+        reloaded_expectation_suite = context.get_expectation_suite("test_suite")
+        assert isinstance(reloaded_expectation_suite, ExpectationSuite)
+
+        # Run validation via the action_list_operator
+        run_id = {
+            "run_name": f"{dataset_name}_{datetime.datetime.now()}",
+            "run_time": datetime.datetime.now(),
+        }
+        results = context.run_validation_operator(
+            "action_list_operator", assets_to_validate=[batch], run_id=run_id,
+            evaluation_parameters=evaluation_params
+        )
+        assert results.success
+
+        # Check that we can build Data Docs
+        index_page_locator_infos = context.build_data_docs()
+        assert index_page_locator_infos["local_site"] == f"file://{ge_path}/uncommitted/data_docs/local_site/index.html"
+
+        # Check that we can reload the expectation suite and validate
+        reloaded_batch = context.get_batch(batch_kwargs=batch_kwargs, expectation_suite_name=reloaded_expectation_suite)
+
+        run_id = {
+            "run_name": f"reloaded_{dataset_name}_{datetime.datetime.now()}",
+            "run_time": datetime.datetime.now(),
+        }
+        reloaded_results = context.run_validation_operator(
+            "action_list_operator", assets_to_validate=[reloaded_batch], run_id=run_id,
+        )
+
+        assert reloaded_results.success