feathr-ai · xiaoyongzhu · Sep 19, 2022 · Sep 13, 2022 · Sep 14, 2022 · Sep 14, 2022
diff --git a/build.sbt b/build.sbt
@@ -47,7 +47,8 @@ val localAndCloudCommonDependencies = Seq(
     "com.github.changvvb" %% "jackson-module-caseclass" % "1.1.1",
     "com.azure.cosmos.spark" % "azure-cosmos-spark_3-1_2-12" % "4.11.1",
     "org.elasticsearch" % "elasticsearch-spark-30_2.12" % "7.15.2",
-    "org.eclipse.jetty" % "jetty-util" % "9.3.24.v20180605"
+    "org.eclipse.jetty" % "jetty-util" % "9.3.24.v20180605",
+    "com.aerospike" % "aerospike-spark_2.12" % "3.3.0_spark3.1-allshaded"
 ) // Common deps
 
 val jdbcDrivers = Seq(

diff --git a/docs/dev_guide/aerospike_setup_guide.md b/docs/dev_guide/aerospike_setup_guide.md
@@ -64,4 +64,36 @@ Next we will verify the functionality of Aerospike database by performing basic
 
 	# View the config
 	asadm -e "show config"
-```
+```
+
+# Configure feathr core Spark to connect with Aerospike
+
+1. To connect to Aerospike (with Aerospike SDK, or Spark), username and password need to be configured. 
+Guidance for setting up username and password:
+https://docs.aerospike.com/server/operations/configure/security/access-control
+
+2. To connect to Aerospike with Spark, a spark conector jar needs to be submitted to your Spark runtime. 
+Link to spark connector: 
+https://docs.aerospike.com/connect/spark
+
+3. To use Aerospike as the online store, create `AerospikeSink` and add it to the `MaterializationSettings`, then use it with `FeathrClient.materialize_features`, e.g..
+
+```
+name = 'aerospike_output'
+os.environ[f"{name.upper()}_USER"] = "as_user_name"
+os.environ[f"{name.upper()}_PASSWORD"] = "some_magic_word"
+as_sink = AerospikeSink(name=name,seedhost="ip_address", port=3000, namespace="test", setname="test")
+client.materialize_features(..., materialization_settings=MaterializationSettings(..., sinks=[as_sink]))
+```
+
+
+# Known limitations for Aerospike:
+Aerospike has its own limitations on the data . 
+One limitation is that worth attention is, for any incoming data row, ANY column name should not be longer than 15 bytes.
+
+So when using feathr, do not define feature names longer than 15 ascii characters.
+
+Check 
+https://docs.aerospike.com/guide/limitations for more details.
+
+
diff --git a/feathr_project/feathr/__init__.py b/feathr_project/feathr/__init__.py
@@ -46,6 +46,7 @@
     'RedisSink',
     'HdfsSink',
     'MonitoringSqlSink',
+    'AerospikeSink',
     'FeatureQuery',
     'LookupFeature',
     'Aggregation',

diff --git a/feathr_project/feathr/definition/sink.py b/feathr_project/feathr/definition/sink.py
@@ -333,3 +333,24 @@ def get_required_properties(self) -> List[str]:
         if self.auth:
             return [self.name.upper() + "_USER", self.name.upper() + "_PASSWORD"]
         return []
+
+class AerospikeSink(GenericSink):
+    def __init__(self,name:str,seedhost:str,port:int,namespace:str,setname:str):
+        super().__init__(format="aerospike",mode="APPEND",options = {
+            "aerospike.seedhost":seedhost,
+            "aerospike.port":str(port),
+            "aerospike.namespace":namespace,
+            "aerospike.user":"${%s_USER}" % name.upper(),
+            "aerospike.password":"${%s_PASSWORD}" % name.upper(),
+            "aerospike.set":setname
+        })
+        self.name = name
+
+    def support_offline(self) -> bool:
+        return False
+
+    def support_online(self) -> bool:
+        return True
+
+    def get_required_properties(self) -> List[str]:
+        return [self.name.upper() + "_USER", self.name.upper() + "_PASSWORD"]
diff --git a/feathr_project/test/test_azure_spark_e2e.py b/feathr_project/test/test_azure_spark_e2e.py
@@ -1,7 +1,12 @@
 import os
 from datetime import datetime, timedelta
 from pathlib import Path
+from feathr import (BOOLEAN, FLOAT, INPUT_CONTEXT, INT32, STRING,
+                    DerivedFeature, Feature, FeatureAnchor, HdfsSource,
+                    TypedKey, ValueType, WindowAggTransformation)
+from feathr import FeathrClient
 from feathr.definition.sink import CosmosDbSink, ElasticSearchSink
+from feathr.definition.source import HdfsSource
 
 import pytest
 from click.testing import CliRunner
@@ -10,7 +15,7 @@
 from feathr import FeathrClient
 from feathr import FeatureQuery
 from feathr import ObservationSettings
-from feathr import RedisSink, HdfsSink, JdbcSink
+from feathr import RedisSink, HdfsSink, JdbcSink,AerospikeSink
 from feathr import TypedKey
 from feathr import ValueType
 from feathr.utils.job_utils import get_result_df
@@ -265,7 +270,6 @@ def test_feathr_materialize_to_cosmosdb():
     # assuming the job can successfully run; otherwise it will throw exception
     client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)
 
-
 @pytest.mark.skip(reason="Marked as skipped as we need to setup resources for this test")
 def test_feathr_materialize_to_es():
     """
@@ -295,7 +299,101 @@ def test_feathr_materialize_to_es():
     # assuming the job can successfully run; otherwise it will throw exception
     client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)
 
+@pytest.mark.skip(reason="Marked as skipped as we need to setup resources for this test")
+def test_feathr_materialize_to_aerospike():
+    """
+    Test FeathrClient() CosmosDbSink.
+    """
+    test_workspace_dir = Path(
+        __file__).parent.resolve() / "test_user_workspace"
+    # os.chdir(test_workspace_dir)
+    now = datetime.now()
+    # set workspace folder by time; make sure we don't have write conflict if there are many CI tests running
+    os.environ['SPARK_CONFIG__DATABRICKS__WORK_DIR'] = ''.join(['dbfs:/feathrazure_cijob','_', str(now.minute), '_', str(now.second), '_', str(now.microsecond)]) 
+    os.environ['SPARK_CONFIG__AZURE_SYNAPSE__WORKSPACE_DIR'] = ''.join(['abfss://[email protected]/feathr_github_ci','_', str(now.minute), '_', str(now.second) ,'_', str(now.microsecond)]) 
+
+    client = FeathrClient(config_path="feathr_config.yaml")
+    batch_source = HdfsSource(name="nycTaxiBatchSource",
+                              path="wasbs://[email protected]/sample_data/green_tripdata_2020-04.csv",
+                              event_timestamp_column="lpep_dropoff_datetime",
+                              timestamp_format="yyyy-MM-dd HH:mm:ss")
+
+    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="avgfare",
+                            key=location_id,
+                            feature_type=FLOAT,
+                            transform=WindowAggTransformation(agg_expr="cast_float(fare_amount)",
+                                                              agg_func="AVG",
+                                                              window="90d",
+                                                              )),
+                    Feature(name="maxfare",
+                            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=batch_source,
+                               features=agg_features)
+
+    client.build_features(anchor_list=[agg_anchor, request_anchor], derived_feature_list=[
+        f_trip_time_distance, f_trip_time_rounded])
 
+
+    backfill_time = BackfillTime(start=datetime(
+        2020, 5, 20), end=datetime(2020, 5, 20), step=timedelta(days=1))
+
+    now = datetime.now()
+    os.environ[f"aerospike_USER"] = "feathruser"
+    os.environ[f"aerospike_PASSWORD"] = "feathr"
+    as_sink = AerospikeSink(name="aerospike",seedhost="20.57.186.153", port=3000, namespace="test", setname="test")
+    settings = MaterializationSettings("nycTaxiTable",
+                                       sinks=[as_sink],
+                                       feature_names=[
+                                           "avgfare", "maxfare"],
+                                       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)
 if __name__ == "__main__":
+    test_feathr_materialize_to_aerospike()
     test_feathr_get_offline_features_to_sql()
     test_feathr_materialize_to_cosmosdb()
diff --git a/feathr_project/test/test_feature_registry.py b/feathr_project/test/test_feature_registry.py
@@ -62,6 +62,51 @@ def test_feathr_register_features_e2e():
                                 output_path=output_path)
     client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)
 
+def test_feathr_register_features_purview_e2e():
+    """
+    This test will register features, get all the registered features, then query a set of already registered features.
+    """
+
+    test_workspace_dir = Path(
+        __file__).parent.resolve() / "test_user_workspace"
+    client: FeathrClient = registry_test_setup(os.path.join(test_workspace_dir, "feathr_config_purview.yaml"))
+
+    # set output folder based on different runtime
+    now = datetime.now()
+    if client.spark_runtime == 'databricks':
+        output_path = ''.join(['dbfs:/feathrazure_cijob','_', str(now.minute), '_', str(now.second), ".parquet"])
+    else:
+        output_path = ''.join(['abfss://[email protected]/demo_data/output','_', str(now.minute), '_', str(now.second), ".parquet"])
+
+
+    client.register_features()
+    # Allow purview to process a bit
+    time.sleep(5)
+    # in CI test, the project name is set by the CI pipeline so we read it here
+    all_features = client.list_registered_features(project_name=client.project_name)
+    all_feature_names = [x['name'] for x in all_features]
+
+    assert 'f_is_long_trip_distance' in all_feature_names # test regular ones
+    assert 'f_trip_time_rounded' in all_feature_names # make sure derived features are there
+    assert 'f_location_avg_fare' in all_feature_names # make sure aggregated features are there
+    assert 'f_trip_time_rounded_plus' in all_feature_names # make sure derived features are there 
+    assert 'f_trip_time_distance' in all_feature_names # make sure derived features are there  
+
+    # Sync workspace from registry, will get all conf files back
+    client.get_features_from_registry(client.project_name)
+
+    feature_query = FeatureQuery(
+        feature_list=["f_location_avg_fare", "f_trip_time_rounded", "f_is_long_trip_distance"], 
+        key=TypedKey(key_column="DOLocationID",key_column_type=ValueType.INT32))
+    settings = ObservationSettings(
+        observation_path="wasbs://[email protected]/sample_data/green_tripdata_2020-04_with_index.csv",
+        event_timestamp_column="lpep_dropoff_datetime",
+        timestamp_format="yyyy-MM-dd HH:mm:ss")
+    client.get_offline_features(observation_settings=settings,
+                                feature_query=feature_query,
+                                output_path=output_path)
+    client.wait_job_to_finish(timeout_sec=Constants.SPARK_JOB_TIMEOUT_SECONDS)
+
 def test_feathr_register_features_partially():
     """
     This test will register full set of features into one project, then register another project in two partial registrations.
@@ -182,4 +227,3 @@ def test_feathr_get_features_from_registry():
         assert len(total_conf_files) == 3
 
 
-
diff --git a/feathr_project/test/test_user_workspace/feathr_config_purview.yaml b/feathr_project/test/test_user_workspace/feathr_config_purview.yaml
@@ -0,0 +1,113 @@
+# DO NOT MOVE OR DELETE THIS FILE
+
+# This file contains the configurations that are used by Feathr
+# All the configurations can be overwritten by environment variables with concatenation of `__` for different layers of this config file.
+# For example, `feathr_runtime_location` for databricks can be overwritten by setting this environment variable:
+# SPARK_CONFIG__DATABRICKS__FEATHR_RUNTIME_LOCATION
+# Another example would be overwriting Redis host with this config: `ONLINE_STORE__REDIS__HOST`
+# 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:
+  project_name: 'project_feathr_integration_test'
+  # Information that are required to be set via environment variables.
+  required_environment_variables:
+    # the environemnt variables are required to run Feathr
+    # Redis password for your online store
+    - 'REDIS_PASSWORD'
+    # client IDs and client Secret for the service principal. Read the getting started docs on how to get those information.
+    - 'AZURE_CLIENT_ID'
+    - 'AZURE_TENANT_ID'
+    - 'AZURE_CLIENT_SECRET'
+  optional_environment_variables:
+    # the environemnt variables are optional, however you will need them if you want to use some of the services:
+    - ADLS_ACCOUNT
+    - ADLS_KEY
+    - WASB_ACCOUNT
+    - WASB_KEY
+    - S3_ACCESS_KEY
+    - S3_SECRET_KEY
+    - JDBC_TABLE
+    - JDBC_USER
+    - JDBC_PASSWORD
+    - KAFKA_SASL_JAAS_CONFIG
+
+offline_store:
+  # paths starts with abfss:// or abfs://
+  # ADLS_ACCOUNT and ADLS_KEY should be set in environment variable if this is set to true
+  adls:
+    adls_enabled: true
+
+  # paths starts with wasb:// or wasbs://
+  # WASB_ACCOUNT and WASB_KEY should be set in environment variable
+  wasb:
+    wasb_enabled: true
+
+  # paths starts with s3a://
+  # S3_ACCESS_KEY and S3_SECRET_KEY should be set in environment variable
+  s3:
+    s3_enabled: true
+    # S3 endpoint. If you use S3 endpoint, then you need to provide access key and secret key in the environment variable as well.
+    s3_endpoint: 's3.amazonaws.com'
+
+  # jdbc endpoint
+  jdbc:
+    jdbc_enabled: true
+    jdbc_database: 'feathrtestdb'
+    jdbc_table: 'feathrtesttable'
+
+  # snowflake endpoint
+  snowflake:
+    snowflake_enabled: true
+    url: "dqllago-ol19457.snowflakecomputing.com"
+    user: "feathrintegration"
+    role: "ACCOUNTADMIN"
+
+spark_config:
+  # choice for spark runtime. Currently support: azure_synapse, databricks
+  # The `databricks` configs will be ignored if `azure_synapse` is set and vice versa.
+  spark_cluster: 'databricks'
+  # configure number of parts for the spark output for feature generation job
+  spark_result_output_parts: '1'
+
+  azure_synapse:
+    dev_url: 'https://feathrazuretest3synapse.dev.azuresynapse.net'
+    pool_name: 'spark3'
+    # workspace dir for storing all the required configuration files and the jar resources
+    workspace_dir: 'abfss://[email protected]/feathr_test_workspace'
+    executor_size: 'Small'
+    executor_num: 1
+    # Feathr Job configuration. Support local paths, path start with http(s)://, and paths start with abfs(s)://
+    # this is the default location so end users don't have to compile the runtime again.
+    # feathr_runtime_location: wasbs://[email protected]/feathr-assembly-LATEST.jar
+    feathr_runtime_location: "../../target/scala-2.12/feathr-assembly-0.7.2.jar"
+  databricks:
+    # workspace instance
+    workspace_instance_url: 'https://adb-2474129336842816.16.azuredatabricks.net/'
+    workspace_token_value: 'dapid8ddd83000dc2863763b7d47f0e8f3db'
+    # config string including run time information, spark version, machine size, etc.
+    # the config follows the format in the databricks documentation: https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/2.0/jobs
+    config_template: {"run_name":"FEATHR_FILL_IN","new_cluster":{"spark_version":"9.1.x-scala2.12","num_workers":1,"spark_conf":{"FEATHR_FILL_IN":"FEATHR_FILL_IN"},"instance_pool_id":"0403-214809-inlet434-pool-l9dj3kwz"},"libraries":[{"jar":"FEATHR_FILL_IN"}],"spark_jar_task":{"main_class_name":"FEATHR_FILL_IN","parameters":["FEATHR_FILL_IN"]}}
+    # Feathr Job location. Support local paths, path start with http(s)://, and paths start with dbfs:/
+    work_dir: 'dbfs:/feathr_getting_started'
+    # this is the default location so end users don't have to compile the runtime again.
+    feathr_runtime_location: "../../target/scala-2.12/feathr-assembly-0.7.2.jar"
+
+online_store:
+  redis:
+    # Redis configs to access Redis cluster
+    host: 'feathrazuretest3redis.redis.cache.windows.net'
+    port: 6380
+    ssl_enabled: True
+
+feature_registry:
+  # The API endpoint of the registry service
+    api_endpoint: "https://feathr-sql-registry.azurewebsites.net/api/v1"
+
+monitoring:
+  database:
+    sql:
+      url: 'jdbc:postgresql://featuremonitoring.postgres.database.azure.com:5432/postgres'
+      user: "demo"