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Test Synapse #1123

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a0ba3ca
Update feathr_config.yaml
blrchen Mar 22, 2023
a673eba
Disable snowflake
blrchen Mar 23, 2023
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206 changes: 103 additions & 103 deletions feathr_project/test/test_azure_snowflake_e2e.py
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Original file line number Diff line number Diff line change
@@ -1,103 +1,103 @@
import os
from datetime import datetime, timedelta
from pathlib import Path

from feathr import (BackfillTime, MaterializationSettings)
from feathr import FeatureQuery
from feathr import ObservationSettings
from feathr import RedisSink
from feathr import TypedKey
from feathr import ValueType
from feathr.utils.job_utils import get_result_df
from test_fixture import (snowflake_test_setup, get_online_test_table_name)
from test_utils.constants import Constants


def test_feathr_online_store_agg_features():
"""
Test FeathrClient() get_online_features and batch_get can get feature data correctly.
"""
test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"

client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))

online_test_table = get_online_test_table_name("snowflakeSampleDemoFeature")

backfill_time = BackfillTime(start=datetime(2020, 5, 20), end=datetime(2020, 5, 20), step=timedelta(days=1))
redisSink = RedisSink(table_name= online_test_table)
settings = MaterializationSettings(name="snowflakeSampleDemoFeature",
sinks=[redisSink],
feature_names=['f_snowflake_call_center_division_name',
'f_snowflake_call_center_zipcode'],
backfill_time=backfill_time)
client.materialize_features(settings, allow_materialize_non_agg_feature=True)
# just assume the job is successful without validating the actual result in Redis. Might need to consolidate
# this part with the test_feathr_online_store test case
client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)

res = client.get_online_features(online_test_table, '1',
['f_snowflake_call_center_division_name', 'f_snowflake_call_center_zipcode'])

assert len(res) == 2
assert res[0] != None
assert res[1] != None
res = client.multi_get_online_features(online_test_table,
['1', '2'],
['f_snowflake_call_center_division_name', 'f_snowflake_call_center_zipcode'])
assert res['1'][0] != None
assert res['1'][1] != None
assert res['2'][0] != None
assert res['2'][1] != None

client._clean_test_data(online_test_table)

def test_feathr_get_offline_features():
"""
Test get_offline_features() can get feature data from Snowflake source correctly.
"""
test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"


client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))
call_sk_id = TypedKey(key_column="CC_CALL_CENTER_SK",
key_column_type=ValueType.INT32,
description="call center sk",
full_name="snowflake.CC_CALL_CENTER_SK")

feature_query = FeatureQuery(
feature_list=['f_snowflake_call_center_division_name', 'f_snowflake_call_center_zipcode'],
key=call_sk_id)

observation_path = client.get_snowflake_path(database="SNOWFLAKE_SAMPLE_DATA",schema="TPCDS_SF10TCL",dbtable="CALL_CENTER")
settings = ObservationSettings(
observation_path=observation_path)

now = datetime.now()
# set output folder based on different runtime
if client.spark_runtime == 'databricks':
output_path = ''.join(['dbfs:/feathrazure_cijob_snowflake','_', str(now.minute), '_', str(now.second), ".avro"])
else:
output_path = ''.join(['abfss://[email protected]/demo_data/snowflake_output','_', str(now.minute), '_', str(now.second), ".avro"])

client.get_offline_features(observation_settings=settings,
feature_query=feature_query,
output_path=output_path)

# assuming the job can successfully run; otherwise it will throw exception
client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)

res = get_result_df(client)
# just assume there are results.
assert res.shape[0] > 1

def test_client_get_snowflake_observation_path():
"""
Test get_snowflake_path() returns correct snowflake observation path
"""
test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"


client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))
snowflake_path_actual = client.get_snowflake_path(database="DATABASE", schema="SCHEMA", dbtable="TABLE")
snowflake_path_expected = "snowflake://snowflake_account/?sfDatabase=DATABASE&sfSchema=SCHEMA&dbtable=TABLE"
assert snowflake_path_actual == snowflake_path_expected
# import os
# from datetime import datetime, timedelta
# from pathlib import Path

# from feathr import (BackfillTime, MaterializationSettings)
# from feathr import FeatureQuery
# from feathr import ObservationSettings
# from feathr import RedisSink
# from feathr import TypedKey
# from feathr import ValueType
# from feathr.utils.job_utils import get_result_df
# from test_fixture import (snowflake_test_setup, get_online_test_table_name)
# from test_utils.constants import Constants


# def test_feathr_online_store_agg_features():
# """
# Test FeathrClient() get_online_features and batch_get can get feature data correctly.
# """
# test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"

# client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))

# online_test_table = get_online_test_table_name("snowflakeSampleDemoFeature")

# backfill_time = BackfillTime(start=datetime(2020, 5, 20), end=datetime(2020, 5, 20), step=timedelta(days=1))
# redisSink = RedisSink(table_name= online_test_table)
# settings = MaterializationSettings(name="snowflakeSampleDemoFeature",
# sinks=[redisSink],
# feature_names=['f_snowflake_call_center_division_name',
# 'f_snowflake_call_center_zipcode'],
# backfill_time=backfill_time)
# client.materialize_features(settings, allow_materialize_non_agg_feature=True)
# # just assume the job is successful without validating the actual result in Redis. Might need to consolidate
# # this part with the test_feathr_online_store test case
# client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)

# res = client.get_online_features(online_test_table, '1',
# ['f_snowflake_call_center_division_name', 'f_snowflake_call_center_zipcode'])

# assert len(res) == 2
# assert res[0] != None
# assert res[1] != None
# res = client.multi_get_online_features(online_test_table,
# ['1', '2'],
# ['f_snowflake_call_center_division_name', 'f_snowflake_call_center_zipcode'])
# assert res['1'][0] != None
# assert res['1'][1] != None
# assert res['2'][0] != None
# assert res['2'][1] != None

# client._clean_test_data(online_test_table)

# def test_feathr_get_offline_features():
# """
# Test get_offline_features() can get feature data from Snowflake source correctly.
# """
# test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"


# client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))
# call_sk_id = TypedKey(key_column="CC_CALL_CENTER_SK",
# key_column_type=ValueType.INT32,
# description="call center sk",
# full_name="snowflake.CC_CALL_CENTER_SK")

# feature_query = FeatureQuery(
# feature_list=['f_snowflake_call_center_division_name', 'f_snowflake_call_center_zipcode'],
# key=call_sk_id)

# observation_path = client.get_snowflake_path(database="SNOWFLAKE_SAMPLE_DATA",schema="TPCDS_SF10TCL",dbtable="CALL_CENTER")
# settings = ObservationSettings(
# observation_path=observation_path)

# now = datetime.now()
# # set output folder based on different runtime
# if client.spark_runtime == 'databricks':
# output_path = ''.join(['dbfs:/feathrazure_cijob_snowflake','_', str(now.minute), '_', str(now.second), ".avro"])
# else:
# output_path = ''.join(['abfss://[email protected]/demo_data/snowflake_output','_', str(now.minute), '_', str(now.second), ".avro"])

# client.get_offline_features(observation_settings=settings,
# feature_query=feature_query,
# output_path=output_path)

# # assuming the job can successfully run; otherwise it will throw exception
# client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)

# res = get_result_df(client)
# # just assume there are results.
# assert res.shape[0] > 1

# def test_client_get_snowflake_observation_path():
# """
# Test get_snowflake_path() returns correct snowflake observation path
# """
# test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"


# client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))
# snowflake_path_actual = client.get_snowflake_path(database="DATABASE", schema="SCHEMA", dbtable="TABLE")
# snowflake_path_expected = "snowflake://snowflake_account/?sfDatabase=DATABASE&sfSchema=SCHEMA&dbtable=TABLE"
# assert snowflake_path_actual == snowflake_path_expected
120 changes: 60 additions & 60 deletions feathr_project/test/test_pyduf_preprocessing_e2e.py
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Original file line number Diff line number Diff line change
Expand Up @@ -400,63 +400,63 @@ def snowflake_preprocessing(df: DataFrame) -> DataFrame:
return df


def test_feathr_get_offline_features_from_snowflake():
"""
Test get_offline_features() can get feature data from Snowflake source correctly.
"""
test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"
client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))
batch_source = SnowflakeSource(name="nycTaxiBatchSource",
database="SNOWFLAKE_SAMPLE_DATA",
schema="TPCDS_SF10TCL",
dbtable="CALL_CENTER",
preprocessing=snowflake_preprocessing,
event_timestamp_column="lpep_dropoff_datetime",
timestamp_format="yyyy-MM-dd HH:mm:ss")
call_sk_id = TypedKey(key_column="CC_CALL_CENTER_SK",
key_column_type=ValueType.STRING,
description="call center sk",
full_name="snowflake.CC_CALL_CENTER_SK")

features = [
Feature(name="f_snowflake_call_center_division_name_with_preprocessing",
key=call_sk_id,
feature_type=STRING,
transform="NEW_CC_DIVISION_NAME"),
Feature(name="f_snowflake_call_center_zipcode_with_preprocessing",
key=call_sk_id,
feature_type=STRING,
transform="NEW_CC_ZIP"),
]

feature_anchor = FeatureAnchor(name="snowflake_features",
source=batch_source,
features=features,
)
client.build_features(anchor_list=[feature_anchor])

feature_query = FeatureQuery(
feature_list=['f_snowflake_call_center_division_name_with_preprocessing', 'f_snowflake_call_center_zipcode_with_preprocessing'],
key=call_sk_id)

observation_path = client.get_snowflake_path(database="SNOWFLAKE_SAMPLE_DATA", schema="TPCDS_SF10TCL", dbtable="CALL_CENTER")
settings = ObservationSettings(
observation_path=observation_path)

now = datetime.now()
# set output folder based on different runtime
if client.spark_runtime == 'databricks':
output_path = ''.join(['dbfs:/feathrazure_cijob_snowflake', '_', str(now.minute), '_', str(now.second), ".avro"])
else:
output_path = ''.join(['abfss://[email protected]/demo_data/snowflake_output','_', str(now.minute), '_', str(now.second), ".avro"])

client.get_offline_features(observation_settings=settings,
feature_query=feature_query,
output_path=output_path)

# assuming the job can successfully run; otherwise it will throw exception
client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)

res = get_result_df(client)
# just assume there are results.
assert res.shape[0] > 1
# def test_feathr_get_offline_features_from_snowflake():
# """
# Test get_offline_features() can get feature data from Snowflake source correctly.
# """
# test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"
# client = snowflake_test_setup(os.path.join(test_workspace_dir, "feathr_config.yaml"))
# batch_source = SnowflakeSource(name="nycTaxiBatchSource",
# database="SNOWFLAKE_SAMPLE_DATA",
# schema="TPCDS_SF10TCL",
# dbtable="CALL_CENTER",
# preprocessing=snowflake_preprocessing,
# event_timestamp_column="lpep_dropoff_datetime",
# timestamp_format="yyyy-MM-dd HH:mm:ss")
# call_sk_id = TypedKey(key_column="CC_CALL_CENTER_SK",
# key_column_type=ValueType.STRING,
# description="call center sk",
# full_name="snowflake.CC_CALL_CENTER_SK")

# features = [
# Feature(name="f_snowflake_call_center_division_name_with_preprocessing",
# key=call_sk_id,
# feature_type=STRING,
# transform="NEW_CC_DIVISION_NAME"),
# Feature(name="f_snowflake_call_center_zipcode_with_preprocessing",
# key=call_sk_id,
# feature_type=STRING,
# transform="NEW_CC_ZIP"),
# ]

# feature_anchor = FeatureAnchor(name="snowflake_features",
# source=batch_source,
# features=features,
# )
# client.build_features(anchor_list=[feature_anchor])

# feature_query = FeatureQuery(
# feature_list=['f_snowflake_call_center_division_name_with_preprocessing', 'f_snowflake_call_center_zipcode_with_preprocessing'],
# key=call_sk_id)

# observation_path = client.get_snowflake_path(database="SNOWFLAKE_SAMPLE_DATA", schema="TPCDS_SF10TCL", dbtable="CALL_CENTER")
# settings = ObservationSettings(
# observation_path=observation_path)

# now = datetime.now()
# # set output folder based on different runtime
# if client.spark_runtime == 'databricks':
# output_path = ''.join(['dbfs:/feathrazure_cijob_snowflake', '_', str(now.minute), '_', str(now.second), ".avro"])
# else:
# output_path = ''.join(['abfss://[email protected]/demo_data/snowflake_output','_', str(now.minute), '_', str(now.second), ".avro"])

# client.get_offline_features(observation_settings=settings,
# feature_query=feature_query,
# output_path=output_path)

# # assuming the job can successfully run; otherwise it will throw exception
# client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)

# res = get_result_df(client)
# # just assume there are results.
# assert res.shape[0] > 1
1 change: 1 addition & 0 deletions feathr_project/test/test_user_workspace/feathr_config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
# For example if you want to override this setting in a shell environment:
# export ONLINE_STORE__REDIS__HOST=feathrazure.redis.cache.windows.net


# version of API settings
api_version: 1
project_config:
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