def test_catch_exceptions_with_bad_expectation_type():
# We want to catch degenerate cases where an expectation suite is incompatible with
my_df = PandasDataset({"x": range(10)})
my_df._expectation_suite.append_expectation(
ExpectationConfiguration(expectation_type="foobar", kwargs={})
)
result = my_df.validate(catch_exceptions=True)
# Find the foobar result
idx = 0
for idx, val_result in enumerate(result.results):
if val_result.expectation_config.expectation_type == "foobar":
break
assert result.results[idx].success is False
assert result.results[idx].expectation_config.expectation_type == "foobar"
assert result.results[idx].expectation_config.kwargs == {}
assert result.results[idx].exception_info["raised_exception"] is True
assert (
"AttributeError: 'PandasDataset' object has no attribute 'foobar'"
in result.results[idx].exception_info["exception_traceback"]
)
with pytest.raises(AttributeError):
result = my_df.validate(catch_exceptions=False)
def validate_expectations(cls, df_ge: PandasDataset,
specs: SchemaParserResult) -> defaultdict[list]:
"""
Validates the dynamic expectations from the schema via the
great expectations library.
"""
invalid_elements = defaultdict(list)
suite = ExpectationSuite(
expectation_suite_name="custom_specifications")
for column in specs.expectation_definitions.keys():
for expectation in specs.expectation_definitions[column]:
kwargs_extended = dict(expectation['kwargs'])
kwargs_extended['column'] = column
suite.append_expectation(
ExpectationConfiguration(
expectation_type=expectation['expectation_type'],
kwargs=kwargs_extended))
# noinspection PyTypeChecker
result = df_ge.validate(expectation_suite=suite, result_format="BASIC")
for expectation_result in result.results:
if expectation_result.exception_info['raised_exception']:
continue
column_name = expectation_result.expectation_config.kwargs[
"column"]
n_invalid = expectation_result.result['unexpected_count']
invalid_elements[column_name].append(n_invalid)
return invalid_elements
def test_pandas_datetime_evaluation_parameter():
evaluation_parameters = {
"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_parameters:
df.set_evaluation_parameter(param, evaluation_parameters[param])
df.expect_column_max_to_be_between(
column="data_refresh", min_value={"$PARAMETER": "now_minus_48h"})
result = df.validate()
assert result.success
def test_validation_with_ge(feast_client: Client, kafka_server):
kafka_broker = f"{kafka_server[0]}:{kafka_server[1]}"
topic_name = f"avro-{uuid.uuid4()}"
entity, feature_table = create_schema(kafka_broker, topic_name,
"validation_ge")
feast_client.apply_entity(entity)
feast_client.apply_feature_table(feature_table)
train_data = generate_train_data()
ge_ds = PandasDataset(train_data)
ge_ds.expect_column_values_to_be_between("num", 0, 100)
ge_ds.expect_column_values_to_be_in_set("set", ["a", "b", "c"])
expectations = ge_ds.get_expectation_suite()
udf = create_validation_udf("testUDF", expectations, feature_table)
apply_validation(feast_client,
feature_table,
udf,
validation_window_secs=1)
job = feast_client.start_stream_to_online_ingestion(feature_table)
wait_retry_backoff(
lambda: (None, job.get_status() == SparkJobStatus.IN_PROGRESS), 120)
wait_retry_backoff(
lambda: (None, check_consumer_exist(kafka_broker, topic_name)), 120)
test_data = generate_test_data()
ge_ds = PandasDataset(test_data)
validation_result = ge_ds.validate(expectations, result_format="COMPLETE")
invalid_idx = list({
idx
for check in validation_result.results
for idx in check.result["unexpected_index_list"]
})
entity_rows = [{"key": key} for key in test_data["key"].tolist()]
try:
ingested = ingest_and_retrieve(
feast_client,
test_data,
avro_schema_json=avro_schema(),
topic_name=topic_name,
kafka_broker=kafka_broker,
entity_rows=entity_rows,
feature_names=["validation_ge:num", "validation_ge:set"],
expected_ingested_count=test_data.shape[0] - len(invalid_idx),
)
finally:
job.cancel()
test_data["num"] = test_data["num"].astype(np.float64)
test_data["num"].iloc[invalid_idx] = np.nan
test_data["set"].iloc[invalid_idx] = None
pd.testing.assert_frame_equal(
ingested[["key", "validation_ge:num", "validation_ge:set"]],
test_data[["key", "num", "set"]].rename(columns={
"num": "validation_ge:num",
"set": "validation_ge:set"
}),
)
def test_validation_reports_metrics(feast_client: Client, kafka_server,
statsd_server: StatsDServer):
kafka_broker = f"{kafka_server[0]}:{kafka_server[1]}"
topic_name = f"avro-{uuid.uuid4()}"
entity, feature_table = create_schema(kafka_broker, topic_name,
"validation_ge_metrics")
feast_client.apply_entity(entity)
feast_client.apply_feature_table(feature_table)
train_data = generate_train_data()
ge_ds = PandasDataset(train_data)
ge_ds.expect_column_values_to_be_between("num", 0, 100)
ge_ds.expect_column_values_to_be_in_set("set", ["a", "b", "c"])
expectations = ge_ds.get_expectation_suite()
udf = create_validation_udf("testUDF", expectations, feature_table)
apply_validation(feast_client,
feature_table,
udf,
validation_window_secs=10)
job = feast_client.start_stream_to_online_ingestion(feature_table)
wait_retry_backoff(
lambda: (None, job.get_status() == SparkJobStatus.IN_PROGRESS), 120)
wait_retry_backoff(
lambda: (None, check_consumer_exist(kafka_broker, topic_name)), 120)
test_data = generate_test_data()
ge_ds = PandasDataset(test_data)
validation_result = ge_ds.validate(expectations, result_format="COMPLETE")
unexpected_counts = {
"expect_column_values_to_be_between_num_0_100":
validation_result.results[0].result["unexpected_count"],
"expect_column_values_to_be_in_set_set":
validation_result.results[1].result["unexpected_count"],
}
invalid_idx = list({
idx
for check in validation_result.results
for idx in check.result["unexpected_index_list"]
})
entity_rows = [{"key": key} for key in test_data["key"].tolist()]
try:
ingest_and_retrieve(
feast_client,
test_data,
avro_schema_json=avro_schema(),
topic_name=topic_name,
kafka_broker=kafka_broker,
entity_rows=entity_rows,
feature_names=[
"validation_ge_metrics:num", "validation_ge_metrics:set"
],
expected_ingested_count=test_data.shape[0] - len(invalid_idx),
)
finally:
job.cancel()
expected_metrics = [(
f"feast_feature_validation_check_failed#check:{check_name},"
f"feature_table:{feature_table.name},project:{feast_client.project}",
value,
) for check_name, value in unexpected_counts.items()]
wait_retry_backoff(
lambda: (
None,
all(
statsd_server.metrics.get(m) == v
for m, v in expected_metrics),
),
timeout_secs=30,
timeout_msg="Expected metrics were not received: " +
str(expected_metrics) + "\n"
"Actual received metrics" + str(statsd_server.metrics),
)
def test_validation_with_ge(feast_client: Client, kafka_server):
entity = Entity(name="key", description="Key", value_type=ValueType.INT64)
kafka_broker = f"{kafka_server[0]}:{kafka_server[1]}"
topic_name = f"avro-{uuid.uuid4()}"
feature_table = FeatureTable(
name="validation_test",
entities=["key"],
features=[
Feature("num", ValueType.INT64),
Feature("set", ValueType.STRING)
],
batch_source=FileSource(
event_timestamp_column="event_timestamp",
file_format=ParquetFormat(),
file_url="/dev/null",
),
stream_source=KafkaSource(
event_timestamp_column="event_timestamp",
bootstrap_servers=kafka_broker,
message_format=AvroFormat(avro_schema()),
topic=topic_name,
),
)
feast_client.apply_entity(entity)
feast_client.apply_feature_table(feature_table)
train_data = generate_train_data()
ge_ds = PandasDataset(train_data)
ge_ds.expect_column_values_to_be_between("num", 0, 100)
ge_ds.expect_column_values_to_be_in_set("set", ["a", "b", "c"])
expectations = ge_ds.get_expectation_suite()
udf = create_validation_udf("testUDF", expectations)
apply_validation(feast_client,
feature_table,
udf,
validation_window_secs=1)
job = feast_client.start_stream_to_online_ingestion(feature_table)
wait_retry_backoff(
lambda: (None, job.get_status() == SparkJobStatus.IN_PROGRESS), 120)
wait_retry_backoff(
lambda: (None, check_consumer_exist(kafka_broker, topic_name)), 120)
test_data = generate_test_data()
ge_ds = PandasDataset(test_data)
validation_result = ge_ds.validate(expectations, result_format="COMPLETE")
invalid_idx = list({
idx
for check in validation_result.results
for idx in check.result["unexpected_index_list"]
})
entity_rows = [{"key": key} for key in test_data["key"].tolist()]
try:
ingested = ingest_and_retrieve(
feast_client,
test_data,
avro_schema_json=avro_schema(),
topic_name=topic_name,
kafka_broker=kafka_broker,
entity_rows=entity_rows,
feature_names=["validation_test:num", "validation_test:set"],
expected_ingested_count=test_data.shape[0] - len(invalid_idx),
)
finally:
job.cancel()
test_data["num"] = test_data["num"].astype(np.float64)
test_data["num"].iloc[invalid_idx] = np.nan
test_data["set"].iloc[invalid_idx] = None
pd.testing.assert_frame_equal(
ingested[["key", "validation_test:num", "validation_test:set"]],
test_data[["key", "num", "set"]].rename(columns={
"num": "validation_test:num",
"set": "validation_test:set"
}),
)