def test_get_compute_domain_with_row_condition_alt(
spark_session, basic_spark_df_execution_engine):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
expected_df = df.where("b > 2")
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"row_condition": "b > 2",
"condition_parser": "spark"
},
domain_type="identity",
)
# Ensuring data has been properly queried
assert dataframes_equal(
data, expected_df), "Data does not match after getting compute domain"
# Ensuring compute kwargs have not been modified
assert ("row_condition" in compute_kwargs.keys()
), "Row condition should be located within compute kwargs"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_nonexistent_condition_parser(
spark_session, basic_spark_df_execution_engine):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
# Expect GreatExpectationsError because parser doesn't exist
with pytest.raises(GreatExpectationsError):
# noinspection PyUnusedLocal
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "nonexistent",
},
domain_type=MetricDomainTypes.IDENTITY,
)
def test_get_compute_domain_with_column_pair(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"column_A": "a",
"column_B": "b"
},
domain_type="column_pair")
# Ensuring that with no domain nothing happens to the data itself
assert dataframes_equal(
data, df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {
"column_A": "a",
"column_B": "b",
}, "Accessor kwargs have been modified"
def test_max_metric_spark_column_does_not_exist(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({"a": [1, 2, 1]}),
batch_id="my_id",
)
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
partial_metric = MetricConfiguration(
metric_name="column.max.aggregate_fn",
metric_domain_kwargs={"column": "non_existent_column"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
with pytest.raises(ge_exceptions.ExecutionEngineError) as eee:
# noinspection PyUnusedLocal
results = engine.resolve_metrics(
metrics_to_resolve=(partial_metric,), metrics=metrics
)
metrics.update(results)
assert (
str(eee.value)
== 'Error: The column "non_existent_column" in BatchData does not exist.'
)
def test_distinct_metric_spark(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame(
{"a": [1, 2, 1, 2, 3, 3, None]},
),
batch_id="my_id",
)
desired_metric = MetricConfiguration(
metric_name="column.value_counts",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"sort": "value", "collate": None},
)
metrics = engine.resolve_metrics(metrics_to_resolve=(desired_metric,))
assert pd.Series(index=[1, 2, 3], data=[2, 2, 2]).equals(metrics[desired_metric.id])
desired_metric = MetricConfiguration(
metric_name="column.distinct_values",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={"column.value_counts": desired_metric},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
assert results == {desired_metric.id: {1, 2, 3}}
def test_median_metric_spark(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame(
{"a": [1, 2, 3]},
),
batch_id="my_id",
)
desired_metric = MetricConfiguration(
metric_name="table.row_count.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
metrics = engine.resolve_metrics(metrics_to_resolve=(desired_metric,))
row_count = MetricConfiguration(
metric_name="table.row_count",
metric_domain_kwargs={},
metric_value_kwargs=dict(),
metric_dependencies={"metric_partial_fn": desired_metric},
)
metrics = engine.resolve_metrics(metrics_to_resolve=(row_count,), metrics=metrics)
desired_metric = MetricConfiguration(
metric_name="column.median",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={"table.row_count": row_count},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
assert results == {desired_metric.id: 2}
def test_dataframe_property_given_loaded_batch(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({"a": [1, 5, 22, 3, 5, 10]}, ),
batch_id="1234",
)
df = engine.dataframe
# Ensuring Data not distorted
assert engine.dataframe == df
def test_get_compute_domain_with_ge_experimental_condition_parser(
spark_session, basic_spark_df_execution_engine):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
# Filtering expected data based on row condition
expected_df = df.where("b == 2")
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
# Obtaining data from computation
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"column": "b",
"row_condition": 'col("b") == 2',
"condition_parser": "great_expectations__experimental__",
},
domain_type="column",
)
# Ensuring data has been properly queried
assert dataframes_equal(
data, expected_df), "Data does not match after getting compute domain"
# Ensuring compute kwargs have not been modified
assert ("row_condition" in compute_kwargs.keys()
), "Row condition should be located within compute kwargs"
assert accessor_kwargs == {
"column": "b"
}, "Accessor kwargs have been modified"
# Should react differently for domain type identity
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"column": "b",
"row_condition": 'col("b") == 2',
"condition_parser": "great_expectations__experimental__",
},
domain_type="identity",
)
# Ensuring data has been properly queried
assert dataframes_equal(data, expected_df.select(
"b")), "Data does not match after getting compute domain"
# Ensuring compute kwargs have not been modified
assert ("row_condition" in compute_kwargs.keys()
), "Row condition should be located within compute kwargs"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_unmeetable_row_condition_alt(
spark_session, basic_spark_df_execution_engine):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
expected_df = df.where("b > 24")
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "spark"
},
domain_type="identity",
)
# Ensuring data has been properly queried
assert dataframes_equal(
data, expected_df), "Data does not match after getting compute domain"
# Ensuring compute kwargs have not been modified
assert ("row_condition" in compute_kwargs.keys()
), "Row condition should be located within compute kwargs"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
# Ensuring errors for column and column_ pair domains are caught
with pytest.raises(GreatExpectationsError):
# noinspection PyUnusedLocal
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "spark",
},
domain_type="column",
)
with pytest.raises(GreatExpectationsError) as g:
# noinspection PyUnusedLocal
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "spark",
},
domain_type="column_pair",
)
def test_max_metric_spark_column_exists(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({"a": [1, 2, 1]}),
batch_id="my_id",
)
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
partial_metric = MetricConfiguration(
metric_name="column.max.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(partial_metric,), metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column.max",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"metric_partial_fn": partial_metric,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results == {desired_metric.id: 2}
def test_get_domain_records_with_unmeetable_row_condition_alt(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
expected_df = df.where("b > 24")
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data = engine.get_domain_records(domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "spark",
})
# Ensuring data has been properly queried
assert dataframes_equal(
data, expected_df), "Data does not match after getting compute domain"
# Ensuring errors for column and column_ pair domains are caught
with pytest.raises(ge_exceptions.GreatExpectationsError):
# noinspection PyUnusedLocal
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "spark",
},
domain_type="column",
)
with pytest.raises(ge_exceptions.GreatExpectationsError) as g:
# noinspection PyUnusedLocal
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "spark",
},
domain_type="column_pair",
)
def test_resolve_metric_bundle_with_nonexistent_metric(
spark_session, basic_spark_df_execution_engine):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 1, 2, 3, 3],
"b": [4, 4, 4, 4, 4, 4]
}, ),
batch_id="1234",
)
desired_metric_1 = MetricConfiguration(
metric_name="column_values.unique",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
desired_metric_2 = MetricConfiguration(
metric_name="column.min",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
)
desired_metric_3 = MetricConfiguration(
metric_name="column.max",
metric_domain_kwargs={"column": "b"},
metric_value_kwargs=dict(),
)
desired_metric_4 = MetricConfiguration(
metric_name="column.does_not_exist",
metric_domain_kwargs={"column": "b"},
metric_value_kwargs=dict(),
)
# Ensuring a metric provider error is raised if metric does not exist
with pytest.raises(MetricProviderError) as e:
# noinspection PyUnusedLocal
res = engine.resolve_metrics(metrics_to_resolve=(
desired_metric_1,
desired_metric_2,
desired_metric_3,
desired_metric_4,
))
print(e)
def test_get_compute_domain_with_column_domain_alt(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column": "a"}, domain_type="column")
# Ensuring that column domain is now an accessor kwarg, and data remains unmodified
assert dataframes_equal(
data, df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be empty"
assert accessor_kwargs == {
"column": "a"
}, "Accessor kwargs have been modified"
def test_get_compute_domain_with_nonexistent_condition_parser(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
# Expect GreatExpectationsError because parser doesn't exist
with pytest.raises(ge_exceptions.GreatExpectationsError):
# noinspection PyUnusedLocal
data = engine.get_domain_records(domain_kwargs={
"row_condition": "b > 24",
"condition_parser": "nonexistent",
}, )
def test_get_compute_domain_with_multicolumn(spark_session,
basic_spark_df_execution_engine):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame(
{
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None],
"c": [1, 2, 3, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column_list": ["a", "b", "c"]},
domain_type="multicolumn")
# Ensuring that with no domain nothing happens to the data itself
assert dataframes_equal(
data, df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be empty"
assert accessor_kwargs == {
"column_list": ["a", "b", "c"]
}, "Accessor kwargs have been modified"
# Checking for identity
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column_list": ["a", "b", "c"]}, domain_type="identity")
# Ensuring that with no domain nothing happens to the data itself
assert dataframes_equal(
data, df), "Data does not match after getting compute domain"
assert compute_kwargs == {
"column_list": ["a", "b", "c"]
}, "Compute domain kwargs should not change for identity domain"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
def test_map_unique_spark_column_does_not_exist(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame(
{
"a": [1, 2, 3, 3, 4, None],
"b": [None, "foo", "bar", "baz", "qux", "fish"],
}
),
batch_id="my_id",
)
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
condition_metric = MetricConfiguration(
metric_name="column_values.unique.condition",
metric_domain_kwargs={"column": "non_existent_column"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
with pytest.raises(ge_exceptions.ExecutionEngineError) as eee:
# noinspection PyUnusedLocal
metrics = engine.resolve_metrics(
metrics_to_resolve=(condition_metric,), metrics=metrics
)
assert (
str(eee.value)
== 'Error: The column "non_existent_column" in BatchData does not exist.'
)
def test_get_domain_records_with_row_condition_alt(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 3, 4],
"b": [2, 3, 4, None]
}, ),
batch_id="1234",
)
df = engine.dataframe
expected_df = df.where("b > 2")
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data = engine.get_domain_records(domain_kwargs={
"row_condition": "b > 2",
"condition_parser": "spark",
})
# Ensuring data has been properly queried
assert dataframes_equal(
data, expected_df), "Data does not match after getting compute domain"
def test_map_value_set_spark(spark_session, basic_spark_df_execution_engine):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame(
{"a": [1, 2, 3, 3, None]},
),
batch_id="my_id",
)
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
condition_metric = MetricConfiguration(
metric_name="column_values.in_set.condition",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"value_set": [1, 2, 3]},
metric_dependencies={
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(condition_metric,), metrics=metrics
)
metrics.update(results)
# Note: metric_dependencies is optional here in the config when called from a validator.
aggregate_partial = MetricConfiguration(
metric_name="column_values.in_set.unexpected_count.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"value_set": [1, 2, 3]},
metric_dependencies={"unexpected_condition": condition_metric},
)
results = engine.resolve_metrics(
metrics_to_resolve=(aggregate_partial,), metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.in_set.unexpected_count",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"value_set": [1, 2, 3]},
metric_dependencies={"metric_partial_fn": aggregate_partial},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results == {desired_metric.id: 0}
# We run the same computation again, this time with None being replaced by nan instead of NULL
# to demonstrate this behavior
df = pd.DataFrame({"a": [1, 2, 3, 3, None]})
df = spark_session.createDataFrame(df)
engine = basic_spark_df_execution_engine
engine.load_batch_data(batch_id="my_id", batch_data=df)
condition_metric = MetricConfiguration(
metric_name="column_values.in_set.condition",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"value_set": [1, 2, 3]},
metric_dependencies={
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(condition_metric,), metrics=metrics
)
metrics.update(results)
# Note: metric_dependencies is optional here in the config when called from a validator.
aggregate_partial = MetricConfiguration(
metric_name="column_values.in_set.unexpected_count.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"value_set": [1, 2, 3]},
metric_dependencies={"unexpected_condition": condition_metric},
)
results = engine.resolve_metrics(
metrics_to_resolve=(aggregate_partial,), metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.in_set.unexpected_count",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"value_set": [1, 2, 3]},
metric_dependencies={"metric_partial_fn": aggregate_partial},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results == {desired_metric.id: 1}
def test_sparkdf_batch_aggregate_metrics(caplog, spark_session):
import datetime
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame({
"a": [1, 2, 1, 2, 3, 3],
"b": [4, 4, 4, 4, 4, 4]
}, ),
batch_id="1234",
)
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
desired_metric_1 = MetricConfiguration(
metric_name="column.max.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
metric_dependencies={
"table.columns": table_columns_metric,
},
)
desired_metric_2 = MetricConfiguration(
metric_name="column.min.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
metric_dependencies={
"table.columns": table_columns_metric,
},
)
desired_metric_3 = MetricConfiguration(
metric_name="column.max.aggregate_fn",
metric_domain_kwargs={"column": "b"},
metric_value_kwargs=None,
metric_dependencies={
"table.columns": table_columns_metric,
},
)
desired_metric_4 = MetricConfiguration(
metric_name="column.min.aggregate_fn",
metric_domain_kwargs={"column": "b"},
metric_value_kwargs=None,
metric_dependencies={
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(
desired_metric_1,
desired_metric_2,
desired_metric_3,
desired_metric_4,
),
metrics=metrics,
)
metrics.update(results)
desired_metric_1 = MetricConfiguration(
metric_name="column.max",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
metric_dependencies={
"metric_partial_fn": desired_metric_1,
"table.columns": table_columns_metric,
},
)
desired_metric_2 = MetricConfiguration(
metric_name="column.min",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
metric_dependencies={
"metric_partial_fn": desired_metric_2,
"table.columns": table_columns_metric,
},
)
desired_metric_3 = MetricConfiguration(
metric_name="column.max",
metric_domain_kwargs={"column": "b"},
metric_value_kwargs=None,
metric_dependencies={
"metric_partial_fn": desired_metric_3,
"table.columns": table_columns_metric,
},
)
desired_metric_4 = MetricConfiguration(
metric_name="column.min",
metric_domain_kwargs={"column": "b"},
metric_value_kwargs=None,
metric_dependencies={
"metric_partial_fn": desired_metric_4,
"table.columns": table_columns_metric,
},
)
start = datetime.datetime.now()
caplog.clear()
caplog.set_level(logging.DEBUG, logger="great_expectations")
results = engine.resolve_metrics(
metrics_to_resolve=(
desired_metric_1,
desired_metric_2,
desired_metric_3,
desired_metric_4,
),
metrics=metrics,
)
metrics.update(results)
end = datetime.datetime.now()
print(end - start)
assert metrics[desired_metric_1.id] == 3
assert metrics[desired_metric_2.id] == 1
assert metrics[desired_metric_3.id] == 4
assert metrics[desired_metric_4.id] == 4
# Check that all four of these metrics were computed on a single domain
found_message = False
for record in caplog.records:
if (record.message ==
"SparkDFExecutionEngine computed 4 metrics on domain_id ()"):
found_message = True
assert found_message
def test_map_unique_spark_column_exists(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame(
{
"a": [1, 2, 3, 3, 4, None],
"b": [None, "foo", "bar", "baz", "qux", "fish"],
}
),
batch_id="my_id",
)
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
condition_metric = MetricConfiguration(
metric_name="column_values.unique.condition",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(condition_metric,), metrics=metrics
)
metrics.update(results)
# unique is a *window* function so does not use the aggregate_fn version of unexpected count
desired_metric = MetricConfiguration(
metric_name="column_values.unique.unexpected_count",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"unexpected_condition": condition_metric,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results[desired_metric.id] == 2
desired_metric = MetricConfiguration(
metric_name="column_values.unique.unexpected_values",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={
"result_format": {"result_format": "BASIC", "partial_unexpected_count": 20}
},
metric_dependencies={
"unexpected_condition": condition_metric,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results[desired_metric.id] == [3, 3]
desired_metric = MetricConfiguration(
metric_name="column_values.unique.unexpected_value_counts",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={
"result_format": {"result_format": "BASIC", "partial_unexpected_count": 20}
},
metric_dependencies={
"unexpected_condition": condition_metric,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results[desired_metric.id] == [(3, 2)]
desired_metric = MetricConfiguration(
metric_name="column_values.unique.unexpected_rows",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={
"result_format": {"result_format": "BASIC", "partial_unexpected_count": 20}
},
metric_dependencies={"unexpected_condition": condition_metric},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
assert results[desired_metric.id] == [(3, "bar"), (3, "baz")]
def test_z_score_under_threshold_spark(spark_session):
engine: SparkDFExecutionEngine = build_spark_engine(
spark=spark_session,
df=pd.DataFrame(
{"a": [1, 2, 3, 3, None]},
),
batch_id="my_id",
)
metrics: dict = {}
table_columns_metric: MetricConfiguration
results: dict
table_columns_metric, results = get_table_columns_metric(engine=engine)
metrics.update(results)
mean = MetricConfiguration(
metric_name="column.mean.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
stdev = MetricConfiguration(
metric_name="column.standard_deviation.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"table.columns": table_columns_metric,
},
)
desired_metrics = (mean, stdev)
results = engine.resolve_metrics(
metrics_to_resolve=desired_metrics, metrics=metrics
)
metrics.update(results)
mean = MetricConfiguration(
metric_name="column.mean",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={"metric_partial_fn": mean},
)
stdev = MetricConfiguration(
metric_name="column.standard_deviation",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"metric_partial_fn": stdev,
"table.columns": table_columns_metric,
},
)
desired_metrics = (mean, stdev)
results = engine.resolve_metrics(
metrics_to_resolve=desired_metrics, metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.map",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=dict(),
metric_dependencies={
"column.standard_deviation": stdev,
"column.mean": mean,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.under_threshold.condition",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"double_sided": True, "threshold": 2},
metric_dependencies={
"column_values.z_score.map": desired_metric,
"table.columns": table_columns_metric,
},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.under_threshold.unexpected_count.aggregate_fn",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"double_sided": True, "threshold": 2},
metric_dependencies={"unexpected_condition": desired_metric},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
metrics.update(results)
desired_metric = MetricConfiguration(
metric_name="column_values.z_score.under_threshold.unexpected_count",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs={"double_sided": True, "threshold": 2},
metric_dependencies={"metric_partial_fn": desired_metric},
)
results = engine.resolve_metrics(
metrics_to_resolve=(desired_metric,), metrics=metrics
)
assert results[desired_metric.id] == 0