def test_failing_type_eval_column():
ntype = create_structured_dataframe_type(
"NumericType",
columns_validator=column_validator,
columns_aggregate_validator=aggregate_validator,
dataframe_validator=dataframe_validator,
)
@op(out={"basic_dataframe": Out(dagster_type=ntype)})
def create_dataframe(_):
yield Output(
DataFrame({
"foo": [1, "a", 7],
"bar": [9, 10, 11]
}),
output_name="basic_dataframe",
)
@graph
def basic_graph():
return create_dataframe()
result = basic_graph.execute_in_process(raise_on_error=False)
output = [
item for item in result.all_node_events if item.is_successful_output
][0]
output_data = output.event_specific_data.type_check_data
output_metadata = output_data.metadata_entries
assert len(output_metadata) == 1
column_const = output_metadata[0]
assert column_const.label == "columns-constraint-metadata"
column_const_data = column_const.entry_data.data
assert column_const_data["expected"] == {
"foo": {
"in_range_validation_fn": in_range_validator.__doc__.strip(),
"dtype_in_set_validation_fn":
dtype_is_num_validator.__doc__.strip(),
}
}
assert column_const_data["offending"] == {
"foo": {
"dtype_in_set_validation_fn": ["row 1"],
"in_range_validation_fn": ["row 1", "row 2"],
}
}
assert column_const_data["actual"] == {
"foo": {
"dtype_in_set_validation_fn": ["a"],
"in_range_validation_fn": ["a", 7]
}
}
def test_failing_type_eval_aggregate():
ntype = create_structured_dataframe_type(
"NumericType",
columns_validator=column_validator,
columns_aggregate_validator=aggregate_validator,
dataframe_validator=dataframe_validator,
)
@solid(output_defs=[
OutputDefinition(name='basic_dataframe', dagster_type=ntype)
])
def create_dataframe(_):
yield Output(
DataFrame({
'foo': [1, 2, 3],
'bar': [9, 10, 10]
}),
output_name='basic_dataframe',
)
@pipeline
def basic_pipeline():
return create_dataframe()
result = execute_pipeline(basic_pipeline, raise_on_error=False)
output = [
item for item in result.step_event_list if item.is_successful_output
][0]
output_data = output.event_specific_data.type_check_data
output_metadata = output_data.metadata_entries
assert len(output_metadata) == 1
column_const = output_metadata[0]
assert column_const.label == 'column-aggregates-constraint-metadata'
column_const_data = column_const.entry_data.data
assert column_const_data['expected'] == {
'bar': {
'all_unique_validator': all_unique_validator.__doc__.strip()
}
}
assert column_const_data['offending'] == {
'bar': {
'all_unique_validator': 'a violation'
}
}
assert column_const_data['actual'] == {
'bar': {
'all_unique_validator': [10.0]
}
}
def test_failing_type_eval_dataframe():
ntype = create_structured_dataframe_type(
"NumericType",
columns_validator=column_validator,
columns_aggregate_validator=aggregate_validator,
dataframe_validator=dataframe_validator,
)
@solid(output_defs=[
OutputDefinition(name="basic_dataframe", dagster_type=ntype)
])
def create_dataframe(_):
yield Output(
DataFrame({
"foo": [1, 2, 3],
"baz": [9, 10, 10]
}),
output_name="basic_dataframe",
)
@pipeline
def basic_pipeline():
return create_dataframe()
result = execute_pipeline(basic_pipeline, raise_on_error=False)
output = [
item for item in result.step_event_list if item.is_successful_output
][0]
output_data = output.event_specific_data.type_check_data
output_metadata = output_data.metadata_entries
assert len(output_metadata) == 1
column_const = output_metadata[0]
assert column_const.label == "dataframe-constraint-metadata"
column_const_data = column_const.entry_data.data
assert column_const_data["expected"] == ["foo", "bar"]
assert column_const_data["actual"] == {
"extra_columns": ["baz"],
"missing_columns": ["bar"]
}
def test_failing_type_eval_dataframe():
ntype = create_structured_dataframe_type(
"NumericType",
columns_validator=column_validator,
columns_aggregate_validator=aggregate_validator,
dataframe_validator=dataframe_validator,
)
@op(out={"basic_dataframe": Out(dagster_type=ntype)})
def create_dataframe(_):
yield Output(
DataFrame({
"foo": [1, 2, 3],
"baz": [9, 10, 10]
}),
output_name="basic_dataframe",
)
@graph
def basic_graph():
return create_dataframe()
result = basic_graph.execute_in_process(raise_on_error=False)
output = [
item for item in result.all_node_events if item.is_successful_output
][0]
output_data = output.event_specific_data.type_check_data
output_metadata = output_data.metadata_entries
assert len(output_metadata) == 1
column_const = output_metadata[0]
assert column_const.label == "dataframe-constraint-metadata"
column_const_data = column_const.entry_data.data
assert column_const_data["expected"] == ["foo", "bar"]
assert column_const_data["actual"] == {
"extra_columns": ["baz"],
"missing_columns": ["bar"]
}
def test_successful_type_eval():
ntype = create_structured_dataframe_type(
"NumericType",
columns_validator=column_validator,
columns_aggregate_validator=aggregate_validator,
dataframe_validator=dataframe_validator,
)
@op(out={"basic_dataframe": Out(dagster_type=ntype)})
def create_dataframe(_):
yield Output(
DataFrame({
"foo": [1, 2, 3],
"bar": [9, 10, 11]
}),
output_name="basic_dataframe",
)
@graph
def basic_graph():
return create_dataframe()
result = basic_graph.execute_in_process()
assert result.success
def test_failing_type_eval_multi_error():
ntype = create_structured_dataframe_type(
"NumericType",
columns_validator=column_validator,
columns_aggregate_validator=aggregate_validator,
dataframe_validator=dataframe_validator,
)
@solid(output_defs=[
OutputDefinition(name='basic_dataframe', dagster_type=ntype)
])
def create_dataframe(_):
yield Output(
DataFrame({
'foo': [1, 'a', 7],
'baz': [9, 10, 10],
'bar': [9, 10, 10]
}),
output_name='basic_dataframe',
)
@pipeline
def basic_pipeline():
return create_dataframe()
result = execute_pipeline(basic_pipeline, raise_on_error=False)
output = [
item for item in result.step_event_list if item.is_successful_output
][0]
output_data = output.event_specific_data.type_check_data
output_metadata = output_data.metadata_entries
assert len(output_metadata) == 3
agg_data = output_metadata[0]
assert agg_data.label == 'column-aggregates-constraint-metadata'
agg_metadata = agg_data.entry_data.data
assert agg_metadata['expected'] == {
'bar': {
'all_unique_validator': all_unique_validator.__doc__.strip()
}
}
assert agg_metadata['offending'] == {
'bar': {
'all_unique_validator': 'a violation'
}
}
assert agg_metadata['actual'] == {'bar': {'all_unique_validator': [10.0]}}
column_const = output_metadata[1]
assert column_const.label == 'columns-constraint-metadata'
column_const_data = column_const.entry_data.data
assert column_const_data['expected'] == {
'foo': {
'in_range_validation_fn': in_range_validator.__doc__.strip(),
'dtype_in_set_validation_fn':
dtype_is_num_validator.__doc__.strip(),
}
}
assert column_const_data['offending'] == {
'foo': {
'dtype_in_set_validation_fn': ['row 1'],
'in_range_validation_fn': ['row 1', 'row 2'],
}
}
assert column_const_data['actual'] == {
'foo': {
'dtype_in_set_validation_fn': ['a'],
'in_range_validation_fn': ['a', 7]
}
}
df_data = output_metadata[2]
assert df_data.label == 'dataframe-constraint-metadata'
df_metadata = df_data.entry_data.data
assert df_metadata['expected'] == ['foo', 'bar']
assert df_metadata['actual'] == {
'extra_columns': ['baz'],
'missing_columns': []
}
def test_failing_type_eval_multi_error():
ntype = create_structured_dataframe_type(
"NumericType",
columns_validator=column_validator,
columns_aggregate_validator=aggregate_validator,
dataframe_validator=dataframe_validator,
)
@solid(output_defs=[
OutputDefinition(name="basic_dataframe", dagster_type=ntype)
])
def create_dataframe(_):
yield Output(
DataFrame({
"foo": [1, "a", 7],
"baz": [9, 10, 10],
"bar": [9, 10, 10]
}),
output_name="basic_dataframe",
)
@pipeline
def basic_pipeline():
return create_dataframe()
result = execute_pipeline(basic_pipeline, raise_on_error=False)
output = [
item for item in result.step_event_list if item.is_successful_output
][0]
output_data = output.event_specific_data.type_check_data
output_metadata = output_data.metadata_entries
assert len(output_metadata) == 3
agg_data = output_metadata[0]
assert agg_data.label == "column-aggregates-constraint-metadata"
agg_metadata = agg_data.entry_data.data
assert agg_metadata["expected"] == {
"bar": {
"all_unique_validator": all_unique_validator.__doc__.strip()
}
}
assert agg_metadata["offending"] == {
"bar": {
"all_unique_validator": "a violation"
}
}
assert agg_metadata["actual"] == {"bar": {"all_unique_validator": [10.0]}}
column_const = output_metadata[1]
assert column_const.label == "columns-constraint-metadata"
column_const_data = column_const.entry_data.data
assert column_const_data["expected"] == {
"foo": {
"in_range_validation_fn": in_range_validator.__doc__.strip(),
"dtype_in_set_validation_fn":
dtype_is_num_validator.__doc__.strip(),
}
}
assert column_const_data["offending"] == {
"foo": {
"dtype_in_set_validation_fn": ["row 1"],
"in_range_validation_fn": ["row 1", "row 2"],
}
}
assert column_const_data["actual"] == {
"foo": {
"dtype_in_set_validation_fn": ["a"],
"in_range_validation_fn": ["a", 7]
}
}
df_data = output_metadata[2]
assert df_data.label == "dataframe-constraint-metadata"
df_metadata = df_data.entry_data.data
assert df_metadata["expected"] == ["foo", "bar"]
assert df_metadata["actual"] == {
"extra_columns": ["baz"],
"missing_columns": []
}
