def _spark(cls, column_A, column_B, **kwargs):
value_pairs_set = kwargs.get("value_pairs_set")
if value_pairs_set is None:
# vacuously true
return column_A == column_B
value_pairs_set = [(x, y) for x, y in value_pairs_set]
conditions = [
(column_A.eqNullSafe(F.lit(x)) & column_B.eqNullSafe(F.lit(y)))
for x, y in value_pairs_set
]
row_wise_cond = reduce(lambda a, b: a | b, conditions)
return row_wise_cond
def _spark(
cls,
execution_engine: "SparkDFExecutionEngine",
metric_domain_kwargs: Dict,
metric_value_kwargs: Dict,
metrics: Dict[Tuple, Any],
runtime_configuration: Dict,
):
ignore_row_if = metric_value_kwargs["ignore_row_if"]
compute_domain_kwargs = copy.deepcopy(metric_domain_kwargs)
if ignore_row_if == "both_values_are_missing":
compute_domain_kwargs["row_condition"] = (
F.col(metric_domain_kwargs["column_A"]).isNotNull()
& F.col(metric_domain_kwargs["column_B"]).isNotNull())
compute_domain_kwargs["condition_parser"] = "spark"
elif ignore_row_if == "either_value_is_missing":
compute_domain_kwargs["row_condition"] = (
F.col(metric_domain_kwargs["column_A"]).isNotNull()
| F.col(metric_domain_kwargs["column_B"]).isNotNull())
compute_domain_kwargs["condition_parser"] = "spark"
(
df,
compute_domain_kwargs,
accessor_domain_kwargs,
) = execution_engine.get_compute_domain(
compute_domain_kwargs, domain_type=MetricDomainTypes.COLUMN_PAIR)
df = df.withColumn(
"combined",
F.array(
F.col(metric_domain_kwargs["column_A"]),
F.col(metric_domain_kwargs["column_A"]),
),
)
value_set_df = (SQLContext(df._sc).createDataFrame(
metric_value_kwargs["value_pairs_set"], ["col_A", "col_B"]).select(
F.array("col_A", "col_B").alias("set_AB")))
df = df.join(value_set_df, df["combined"] == value_set_df["set_AB"],
"left").withColumn(
"__success",
F.when(F.col("set_AB").isNull(),
F.lit(False)).otherwise(F.lit(True)),
)
return df["__success"], compute_domain_kwargs, accessor_domain_kwargs
def _spark(
cls,
column,
value_set,
**kwargs,
):
# no need to parse as datetime; just compare the strings as is
parse_strings_as_datetimes: bool = (
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,
)
if value_set is None:
# vacuously true
return F.lit(True)
return column.isin(value_set)
def _spark(
cls,
execution_engine,
metric_domain_kwargs,
metric_value_kwargs,
metrics,
runtime_configuration,
):
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.StringType)):
column = F.col(column_name).cast(sparktypes.IntegerType())
else:
raise TypeError(
"Column must be a string-type capable of being cast to int.")
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)
if any(np.array(df.select(column.isNull()).collect())):
raise TypeError(
"Column must be a string-type capable of being cast to int.")
diff = column - F.lag(column).over(Window.orderBy(F.lit("constant")))
diff = F.when(diff.isNull(), 1).otherwise(diff)
if metric_value_kwargs["strictly"] is True:
diff = F.when(diff <= 0, F.lit(False)).otherwise(F.lit(True))
else:
diff = F.when(diff < 0, F.lit(False)).otherwise(F.lit(True))
return (
np.array(df.select(diff).collect()).reshape(-1)[1:],
compute_domain_kwargs,
accessor_domain_kwargs,
)
def _spark(cls, column_list, **kwargs):
sum_total = kwargs.get("sum_total")
expression = "+".join([
f"COALESCE({column_name}, 0)"
for column_name in column_list.columns
])
row_wise_cond = F.expr(expression) == F.lit(sum_total)
return row_wise_cond
def _spark(
cls,
execution_engine: "SqlAlchemyExecutionEngine",
metric_domain_kwargs: Dict,
metric_value_kwargs: Dict,
metrics: Dict[Tuple, Any],
runtime_configuration: Dict,
):
return F.count(F.lit(1)), metric_domain_kwargs, dict()
def _spark(
cls,
execution_engine: SparkDFExecutionEngine,
metric_domain_kwargs,
metric_value_kwargs,
metrics,
runtime_configuration,
):
(
selectable,
compute_domain_kwargs,
accessor_domain_kwargs,
) = execution_engine.get_compute_domain(metric_domain_kwargs,
MetricDomainTypes.COLUMN)
column_name = accessor_domain_kwargs["column"]
column = F.col(column_name)
query = F.when(column == 3, F.lit(False)).otherwise(F.lit(True))
return (query, compute_domain_kwargs, accessor_domain_kwargs)
def _spark(cls, column, **kwargs):
center_point = kwargs.get("center_point")
unit = kwargs.get("unit")
range = kwargs.get("range")
projection = kwargs.get("projection")
if projection == "fcc":
if unit == "kilometers":
distances = F.udf(
lambda x, y=center_point: fcc_projection(x, y),
sparktypes.FloatType(),
)
elif unit == "miles":
distances = F.udf(
lambda x, y=center_point: fcc_projection(x, y) * 1.609344,
sparktypes.FloatType(),
)
range = range * 1.609344
return F.when(distances(column) < range,
F.lit(True)).otherwise(F.lit(False))
elif projection == "pythagorean":
if unit == "kilometers":
distances = F.udf(
lambda x, y=center_point: pythagorean_projection(x, y),
sparktypes.FloatType(),
)
elif unit == "miles":
distances = F.udf(
lambda x, y=center_point: pythagorean_projection(x, y) *
1.609344,
sparktypes.FloatType(),
)
range = range * 1.609344
return F.when(distances(column) < range,
F.lit(True)).otherwise(F.lit(False))
def _spark(
cls,
column,
value_set,
**kwargs,
):
# no need to parse as datetime; just compare the strings as is
parse_strings_as_datetimes: bool = (
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()", it is not used.
""",
DeprecationWarning,
)
if value_set is None:
# vacuously true
return F.lit(True)
return column.isin(value_set)
def _spark(cls, column, **kwargs):
return F.count(F.lit(1)).over(Window.partitionBy(column)) <= 1
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[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, column, value_set, **kwargs):
if value_set is None:
# vacuously true
return F.lit(True)
return column.isin(value_set)
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,
)
def _spark(cls, column_list, **kwargs):
column_names = column_list.columns
row_wise_cond = (F.count(F.lit(1)).over(
Window.partitionBy(F.struct(*column_names))) <= 1)
return row_wise_cond
