def test_validation_with_ge(feast_client: Client, kafka_server, pytestconfig):
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 = start_job(feast_client, feature_table, pytestconfig)
wait_retry_backoff(
lambda: (None, check_consumer_exist(kafka_broker, topic_name)), 300)
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:
stop_job(job, feast_client, feature_table)
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, pytestconfig):
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 = start_job(feast_client, feature_table, pytestconfig)
wait_retry_backoff(
lambda: (None, check_consumer_exist(kafka_broker, topic_name)), 300)
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:
stop_job(job, feast_client, feature_table)
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),
)