-
Notifications
You must be signed in to change notification settings - Fork 242
Expand file tree
/
Copy pathtest_spark_sql_source.py
More file actions
195 lines (167 loc) · 6.88 KB
/
test_spark_sql_source.py
File metadata and controls
195 lines (167 loc) · 6.88 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import os
from datetime import datetime, timedelta
from pathlib import Path
from feathr import (
BOOLEAN,
FLOAT,
INPUT_CONTEXT,
INT32,
DerivedFeature,
Feature,
FeatureAnchor,
TypedKey,
ValueType,
WindowAggTransformation,
)
import pytest
from feathr import FeathrClient
from feathr import FeatureQuery
from feathr import ObservationSettings
from feathr import TypedKey
from feathr import ValueType
from feathr.definition.materialization_settings import BackfillTime, MaterializationSettings
from feathr.definition.sink import HdfsSink
from feathr.utils.job_utils import get_result_df
from test_utils.constants import Constants
from feathr.definition.source import SparkSqlSource
def test_feathr_spark_sql_query_source():
test_workspace_dir = Path(__file__).parent.resolve() / "test_user_workspace"
config_path = os.path.join(test_workspace_dir, "feathr_config.yaml")
_get_offline_features(config_path, _sql_query_source())
_get_offline_features(config_path, _sql_table_source())
_materialize_to_offline(config_path, _sql_query_source())
def _get_offline_features(config_path: str, sql_source: SparkSqlSource):
client: FeathrClient = _spark_sql_test_setup(config_path, sql_source)
location_id = TypedKey(
key_column="DOLocationID",
key_column_type=ValueType.INT32,
description="location id in NYC",
full_name="nyc_taxi.location_id",
)
feature_query = FeatureQuery(feature_list=["f_location_avg_fare"], key=location_id)
settings = ObservationSettings(
observation_path="wasbs://public@azurefeathrstorage.blob.core.windows.net/sample_data/green_tripdata_2020-04.csv",
event_timestamp_column="lpep_dropoff_datetime",
timestamp_format="yyyy-MM-dd HH:mm:ss",
)
now = datetime.now()
if client.spark_runtime == "databricks":
output_path = "".join(
["dbfs:/feathrazure_cijob_materialize_offline_", "_", str(now.minute), "_", str(now.second), ""]
)
else:
output_path = "".join(
[
"abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/demo_data/feathrazure_cijob_materialize_offline_",
"_",
str(now.minute),
"_",
str(now.second),
"",
]
)
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)
return
def _materialize_to_offline(config_path: str, sql_source: SparkSqlSource):
client: FeathrClient = _spark_sql_test_setup(config_path, sql_source)
backfill_time = BackfillTime(start=datetime(2020, 5, 20), end=datetime(2020, 5, 20), step=timedelta(days=1))
now = datetime.now()
if client.spark_runtime == "databricks":
output_path = "".join(
["dbfs:/feathrazure_cijob_materialize_offline_sparksql", "_", str(now.minute), "_", str(now.second), ""]
)
else:
output_path = "".join(
[
"abfss://feathrazuretest3fs@feathrazuretest3storage.dfs.core.windows.net/demo_data/feathrazure_cijob_materialize_offline_sparksql",
"_",
str(now.minute),
"_",
str(now.second),
"",
]
)
offline_sink = HdfsSink(output_path=output_path)
settings = MaterializationSettings(
"nycTaxiTable",
sinks=[offline_sink],
feature_names=["f_location_avg_fare", "f_location_max_fare"],
backfill_time=backfill_time,
)
client.materialize_features(settings)
# assuming the job can successfully run; otherwise it will throw exception
client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)
# download result and just assert the returned result is not empty
# by default, it will write to a folder appended with date
res_df = get_result_df(client, "avro", output_path + "/df0/daily/2020/05/20")
assert res_df.shape[0] > 0
def _spark_sql_test_setup(config_path: str, sql_source: SparkSqlSource):
client = FeathrClient(config_path=config_path)
f_trip_distance = Feature(name="f_trip_distance", feature_type=FLOAT, transform="trip_distance")
f_trip_time_duration = Feature(
name="f_trip_time_duration",
feature_type=INT32,
transform="(to_unix_timestamp(lpep_dropoff_datetime) - to_unix_timestamp(lpep_pickup_datetime))/60",
)
features = [
f_trip_distance,
f_trip_time_duration,
Feature(name="f_is_long_trip_distance", feature_type=BOOLEAN, transform="cast_float(trip_distance)>30"),
Feature(name="f_day_of_week", feature_type=INT32, transform="dayofweek(lpep_dropoff_datetime)"),
]
request_anchor = FeatureAnchor(name="request_features", source=INPUT_CONTEXT, features=features)
f_trip_time_distance = DerivedFeature(
name="f_trip_time_distance",
feature_type=FLOAT,
input_features=[f_trip_distance, f_trip_time_duration],
transform="f_trip_distance * f_trip_time_duration",
)
f_trip_time_rounded = DerivedFeature(
name="f_trip_time_rounded",
feature_type=INT32,
input_features=[f_trip_time_duration],
transform="f_trip_time_duration % 10",
)
location_id = TypedKey(
key_column="DOLocationID",
key_column_type=ValueType.INT32,
description="location id in NYC",
full_name="nyc_taxi.location_id",
)
agg_features = [
Feature(
name="f_location_avg_fare",
key=location_id,
feature_type=FLOAT,
transform=WindowAggTransformation(
agg_expr="cast_float(fare_amount)", agg_func="AVG", window="90d", filter="fare_amount > 0"
),
),
Feature(
name="f_location_max_fare",
key=location_id,
feature_type=FLOAT,
transform=WindowAggTransformation(agg_expr="cast_float(fare_amount)", agg_func="MAX", window="90d"),
),
]
agg_anchor = FeatureAnchor(name="aggregationFeatures", source=sql_source, features=agg_features)
client.build_features(
anchor_list=[agg_anchor, request_anchor], derived_feature_list=[f_trip_time_distance, f_trip_time_rounded]
)
return client
def _sql_query_source():
return SparkSqlSource(
name="sparkSqlQuerySource",
sql="SELECT * FROM green_tripdata_2020_04_with_index",
event_timestamp_column="lpep_dropoff_datetime",
timestamp_format="yyyy-MM-dd HH:mm:ss",
)
def _sql_table_source():
return SparkSqlSource(
name="sparkSqlTableSource",
table="green_tripdata_2020_04_with_index",
event_timestamp_column="lpep_dropoff_datetime",
timestamp_format="yyyy-MM-dd HH:mm:ss",
)