def test_add_column_row_condition(spark_session):
df = pd.DataFrame({"foo": [1, 2, 3, 3, None, 2, 3, 4, 5, 6]})
df = spark_session.createDataFrame(
[
tuple(
None if isinstance(x, (float, int)) and np.isnan(x) else x
for x in record.tolist()
)
for record in df.to_records(index=False)
],
df.columns.tolist(),
)
engine = SparkDFExecutionEngine(batch_data_dict={tuple(): df})
domain_kwargs = {"column": "foo"}
new_domain_kwargs = engine.add_column_row_condition(
domain_kwargs, filter_null=True, filter_nan=False
)
assert new_domain_kwargs["row_condition"] == 'col("foo").notnull()'
df, cd, ad = engine.get_compute_domain(new_domain_kwargs, domain_type="table")
res = df.collect()
assert res == [(1,), (2,), (3,), (3,), (2,), (3,), (4,), (5,), (6,)]
new_domain_kwargs = engine.add_column_row_condition(
domain_kwargs, filter_null=True, filter_nan=True
)
assert new_domain_kwargs["row_condition"] == "NOT isnan(foo) AND foo IS NOT NULL"
df, cd, ad = engine.get_compute_domain(new_domain_kwargs, domain_type="table")
res = df.collect()
assert res == [(1,), (2,), (3,), (3,), (2,), (3,), (4,), (5,), (6,)]
new_domain_kwargs = engine.add_column_row_condition(
domain_kwargs, filter_null=False, filter_nan=True
)
assert new_domain_kwargs["row_condition"] == "NOT isnan(foo)"
df, cd, ad = engine.get_compute_domain(new_domain_kwargs, domain_type="table")
res = df.collect()
assert res == [(1,), (2,), (3,), (3,), (None,), (2,), (3,), (4,), (5,), (6,)]
# This time, our skip value *will* be nan
df = pd.DataFrame({"foo": [1, 2, 3, 3, None, 2, 3, 4, 5, 6]})
df = spark_session.createDataFrame(df)
engine = SparkDFExecutionEngine(batch_data_dict={tuple(): df})
new_domain_kwargs = engine.add_column_row_condition(
domain_kwargs, filter_null=False, filter_nan=True
)
assert new_domain_kwargs["row_condition"] == "NOT isnan(foo)"
df, cd, ad = engine.get_compute_domain(new_domain_kwargs, domain_type="table")
res = df.collect()
assert res == [(1,), (2,), (3,), (3,), (2,), (3,), (4,), (5,), (6,)]
new_domain_kwargs = engine.add_column_row_condition(
domain_kwargs, filter_null=True, filter_nan=False
)
assert new_domain_kwargs["row_condition"] == 'col("foo").notnull()'
df, cd, ad = engine.get_compute_domain(new_domain_kwargs, domain_type="table")
res = df.collect()
expected = [(1,), (2,), (3,), (3,), (np.nan,), (2,), (3,), (4,), (5,), (6,)]
# since nan != nan by default
assert np.allclose(res, expected, rtol=0, atol=0, equal_nan=True)
def _spark(
cls,
execution_engine: SparkDFExecutionEngine,
metric_domain_kwargs: Dict,
metric_value_kwargs: Dict,
metrics: Dict[str, Any],
runtime_configuration: Dict,
):
parse_strings_as_datetimes: bool = (
metric_value_kwargs.get("parse_strings_as_datetimes") or False)
if parse_strings_as_datetimes:
# deprecated-v0.13.41
warnings.warn(
"""The parameter "parse_strings_as_datetimes" is deprecated as of v0.13.41 in \
v0.16. As part of the V3 API transition, we've moved away from input transformation. For more information, \
please see: https://greatexpectations.io/blog/why_we_dont_do_transformations_for_expectations/
""",
DeprecationWarning,
)
# check if column is any type that could have na (numeric types)
column_name = metric_domain_kwargs["column"]
table_columns = metrics["table.column_types"]
column_metadata = [
col for col in table_columns if col["name"] == column_name
][0]
if isinstance(
column_metadata["type"],
(
sparktypes.LongType,
sparktypes.DoubleType,
sparktypes.IntegerType,
),
):
# if column is any type that could have NA values, remove them (not filtered by .isNotNull())
compute_domain_kwargs = execution_engine.add_column_row_condition(
metric_domain_kwargs,
filter_null=cls.filter_column_isnull,
filter_nan=True,
)
else:
compute_domain_kwargs = metric_domain_kwargs
(
df,
compute_domain_kwargs,
accessor_domain_kwargs,
) = execution_engine.get_compute_domain(
compute_domain_kwargs, domain_type=MetricDomainTypes.COLUMN)
# NOTE: 20201105 - parse_strings_as_datetimes is not supported here;
# instead detect types naturally
column = F.col(column_name)
if isinstance(column_metadata["type"],
(sparktypes.TimestampType, sparktypes.DateType)):
diff = F.datediff(
column,
F.lag(column).over(Window.orderBy(F.lit("constant"))))
else:
diff = column - F.lag(column).over(
Window.orderBy(F.lit("constant")))
diff = F.when(diff.isNull(), 1).otherwise(diff)
# NOTE: because in spark we are implementing the window function directly,
# we have to return the *unexpected* condition.
# If we expect values to be *strictly* increasing then unexpected values are those
# that are flat or decreasing
if metric_value_kwargs["strictly"] is True:
return (
F.when(diff <= 0, F.lit(True)).otherwise(F.lit(False)),
compute_domain_kwargs,
accessor_domain_kwargs,
)
# If we expect values to be flat or increasing then unexpected values are those
# that are decreasing
else:
return (
F.when(diff < 0, F.lit(True)).otherwise(F.lit(False)),
compute_domain_kwargs,
accessor_domain_kwargs,
)
def _spark(
cls,
execution_engine: SparkDFExecutionEngine,
metric_domain_kwargs: Dict,
metric_value_kwargs: Dict,
metrics: Dict[str, Any],
runtime_configuration: Dict,
):
parse_strings_as_datetimes: bool = (
metric_value_kwargs.get("parse_strings_as_datetimes") or False)
if parse_strings_as_datetimes:
warnings.warn(
f"""The parameter "parse_strings_as_datetimes" is no longer supported and will be deprecated in a \
future release. Please update code accordingly. Moreover, in "{cls.__name__}._spark()", types are detected naturally.
""",
DeprecationWarning,
)
# check if column is any type that could have na (numeric types)
column_name = metric_domain_kwargs["column"]
table_columns = metrics["table.column_types"]
column_metadata = [
col for col in table_columns if col["name"] == column_name
][0]
if isinstance(
column_metadata["type"],
(
sparktypes.LongType,
sparktypes.DoubleType,
sparktypes.IntegerType,
),
):
# if column is any type that could have NA values, remove them (not filtered by .isNotNull())
compute_domain_kwargs = execution_engine.add_column_row_condition(
metric_domain_kwargs,
filter_null=cls.filter_column_isnull,
filter_nan=True,
)
else:
compute_domain_kwargs = metric_domain_kwargs
(
df,
compute_domain_kwargs,
accessor_domain_kwargs,
) = execution_engine.get_compute_domain(compute_domain_kwargs,
MetricDomainTypes.COLUMN)
# NOTE: 20201105 - parse_strings_as_datetimes is not supported here;
# instead detect types naturally
column = F.col(column_name)
if isinstance(column_metadata["type"],
(sparktypes.TimestampType, sparktypes.DateType)):
diff = F.datediff(
column,
F.lag(column).over(Window.orderBy(F.lit("constant"))))
else:
diff = column - F.lag(column).over(
Window.orderBy(F.lit("constant")))
diff = F.when(diff.isNull(), -1).otherwise(diff)
# NOTE: because in spark we are implementing the window function directly,
# we have to return the *unexpected* condition
if metric_value_kwargs["strictly"]:
return (
F.when(diff >= 0, F.lit(True)).otherwise(F.lit(False)),
compute_domain_kwargs,
accessor_domain_kwargs,
)
# If we expect values to be flat or decreasing then unexpected values are those
# that are decreasing
else:
return (
F.when(diff > 0, F.lit(True)).otherwise(F.lit(False)),
compute_domain_kwargs,
accessor_domain_kwargs,
)
def _spark(
cls,
execution_engine: SparkDFExecutionEngine,
metric_domain_kwargs: Dict,
metric_value_kwargs: Dict,
metrics: Dict[Tuple, Any],
runtime_configuration: Dict,
):
# check if column is any type that could have na (numeric types)
column_name = metric_domain_kwargs["column"]
table_columns = metrics["table.column_types"]
column_metadata = [
col for col in table_columns if col["name"] == column_name
][0]
if isinstance(
column_metadata["type"],
(
sparktypes.LongType,
sparktypes.DoubleType,
sparktypes.IntegerType,
),
):
# if column is any type that could have NA values, remove them (not filtered by .isNotNull())
compute_domain_kwargs = execution_engine.add_column_row_condition(
metric_domain_kwargs,
filter_null=cls.filter_column_isnull,
filter_nan=True,
)
else:
compute_domain_kwargs = metric_domain_kwargs
(
df,
compute_domain_kwargs,
accessor_domain_kwargs,
) = execution_engine.get_compute_domain(
compute_domain_kwargs, domain_type=MetricDomainTypes.COLUMN)
# NOTE: 20201105 - parse_strings_as_datetimes is not supported here;
# instead detect types naturally
column = F.col(column_name)
if isinstance(column_metadata["type"],
(sparktypes.TimestampType, sparktypes.DateType)):
diff = F.datediff(
column,
F.lag(column).over(Window.orderBy(F.lit("constant"))))
else:
diff = column - F.lag(column).over(
Window.orderBy(F.lit("constant")))
diff = F.when(diff.isNull(), 1).otherwise(diff)
# NOTE: because in spark we are implementing the window function directly,
# we have to return the *unexpected* condition.
# If we expect values to be *strictly* increasing then unexpected values are those
# that are flat or decreasing
if metric_value_kwargs["strictly"] is True:
return (
F.when(diff <= 0, F.lit(True)).otherwise(F.lit(False)),
compute_domain_kwargs,
accessor_domain_kwargs,
)
# If we expect values to be flat or increasing then unexpected values are those
# that are decreasing
else:
return (
F.when(diff < 0, F.lit(True)).otherwise(F.lit(False)),
compute_domain_kwargs,
accessor_domain_kwargs,
)
