EMR launcher

sdk/python/feast/cli.py

@@ -364,11 +364,13 @@ def sync_offline_to_online(feature_table: str, start_time: str, end_time: str):
     """
     Sync offline store to online.
     """
-    import feast.pyspark.aws.jobs
+    from datetime import datetime
 
     client = Client()
     table = client.get_feature_table(feature_table)
-    feast.pyspark.aws.jobs.sync_offline_to_online(client, table, start_time, end_time)
+    client.start_offline_to_online_ingestion(
+        table, datetime.fromisoformat(start_time), datetime.fromisoformat(end_time)
+    )
 
 
 @cli.command()
@@ -424,5 +426,40 @@ def list_emr_jobs():
     )
 
 
+@cli.command()
+@click.option(
+    "--features",
+    "-f",
+    help="Features in feature_table:feature format, comma separated",
+    required=True,
+)
+@click.option(
+    "--entity-df-path",
+    "-e",
+    help="Path to entity df in CSV format. It is assumed to have event_timestamp column and a header.",
+    required=True,
+)
+@click.option("--destination", "-d", help="Destination", default="")
+def get_historical_features(features: str, entity_df_path: str, destination: str):
+    """
+    Get historical features
+    """
+    import pandas
+
+    client = Client()
+
+    # TODO: clean this up
+    entity_df = pandas.read_csv(entity_df_path, sep=None, engine="python",)
+
+    entity_df["event_timestamp"] = pandas.to_datetime(entity_df["event_timestamp"])
+
+    uploaded_df = client.stage_dataframe(
+        entity_df, "event_timestamp", "created_timestamp"
+    )
+
+    job = client.get_historical_features(features.split(","), uploaded_df,)
+    print(job.get_output_file_uri())
+
+
 if __name__ == "__main__":
     cli()

sdk/python/feast/client.py

@@ -76,6 +76,7 @@
 from feast.online_response import OnlineResponse, _infer_online_entity_rows
 from feast.pyspark.abc import RetrievalJob, SparkJob
 from feast.pyspark.launcher import (
+    stage_dataframe,
     start_historical_feature_retrieval_job,
     start_historical_feature_retrieval_spark_session,
     start_offline_to_online_ingestion,
@@ -885,9 +886,16 @@ def _get_feature_tables_from_feature_refs(
         return feature_tables
 
     def start_offline_to_online_ingestion(
-        self,
-        feature_table: Union[FeatureTable, str],
-        start: Union[datetime, str],
-        end: Union[datetime, str],
+        self, feature_table: Union[FeatureTable, str], start: datetime, end: datetime,
     ) -> SparkJob:
         return start_offline_to_online_ingestion(feature_table, start, end, self)  # type: ignore
+
+    def stage_dataframe(
+        self,
+        df: pd.DataFrame,
+        event_timestamp_column: str,
+        created_timestamp_column: str,
+    ) -> FileSource:
+        return stage_dataframe(
+            df, event_timestamp_column, created_timestamp_column, self
+        )

sdk/python/feast/constants.py

@@ -80,6 +80,16 @@ class AuthProvider(Enum):
 CONFIG_SPARK_HISTORICAL_FEATURE_OUTPUT_FORMAT = "historical_feature_output_format"
 CONFIG_SPARK_HISTORICAL_FEATURE_OUTPUT_LOCATION = "historical_feature_output_location"
 
+CONFIG_REDIS_HOST = "redis_host"
+CONFIG_REDIS_PORT = "redis_port"
+CONFIG_REDIS_SSL = "redis_ssl"
+
+CONFIG_SPARK_EMR_REGION = "emr_region"
+CONFIG_SPARK_EMR_CLUSTER_ID = "emr_cluster_id"
+CONFIG_SPARK_EMR_CLUSTER_TEMPLATE_PATH = "emr_cluster_template_path"
+CONFIG_SPARK_EMR_STAGING_LOCATION = "emr_staging_location"
+CONFIG_SPARK_EMR_LOG_LOCATION = "emr_log_location"
+
 
 # Configuration option default values
 FEAST_DEFAULT_OPTIONS = {

sdk/python/feast/pyspark/abc.py

@@ -5,6 +5,10 @@
 from enum import Enum
 from typing import Dict, List, Optional
 
+import pandas
+
+from feast.data_source import FileSource
+
 
 class SparkJobFailure(Exception):
     """
@@ -258,19 +262,31 @@ def __init__(
         start: datetime,
         end: datetime,
         jar: str,
+        redis_host: str,
+        redis_port: int,
+        redis_ssl: bool,
     ):
         self._feature_table = feature_table
         self._source = source
         self._start = start
         self._end = end
         self._jar = jar
+        self._redis_host = redis_host
+        self._redis_port = redis_port
+        self._redis_ssl = redis_ssl
 
     def get_name(self) -> str:
         return (
-            f"BatchIngestion-{self._feature_table['name']}-"
+            f"BatchIngestion-{self.get_feature_table_name()}-"
             f"{self._start.strftime('%Y-%m-%d')}-{self._end.strftime('%Y-%m-%d')}"
         )
 
+    def _get_redis_config(self):
+        return dict(host=self._redis_host, port=self._redis_port, ssl=self._redis_ssl)
+
+    def get_feature_table_name(self) -> str:
+        return self._feature_table["name"]
+
     def get_main_file_path(self) -> str:
         return self._jar
 
@@ -289,6 +305,8 @@ def get_arguments(self) -> List[str]:
             self._start.strftime("%Y-%m-%dT%H:%M:%S"),
             "--end",
             self._end.strftime("%Y-%m-%dT%H:%M:%S"),
+            "--redis",
+            json.dumps(self._get_redis_config()),
         ]
 
 
@@ -334,3 +352,18 @@ def offline_to_online_ingestion(
             IngestionJob: wrapper around remote job that can be used to check when job completed.
         """
         raise NotImplementedError
+
+    @abc.abstractmethod
+    def stage_dataframe(
+        self,
+        df: pandas.DataFrame,
+        event_timestamp_column: str,
+        created_timestamp_column: str,
+    ) -> FileSource:
+        """
+        Upload a pandas dataframe so it is available to the Spark cluster.
+
+        Returns:
+            FileSource: representing the uploaded dataframe.
+        """
+        raise NotImplementedError

sdk/python/feast/pyspark/historical_feature_retrieval_job.py

@@ -773,7 +773,7 @@ def _feature_table_from_dict(dct: Dict[str, Any]) -> FeatureTable:
     spark = SparkSession.builder.getOrCreate()
     args = _get_args()
     feature_tables_conf = json.loads(args.feature_tables)
-    feature_tables_sources_conf = json.loads(args.feature_tables_source)
+    feature_tables_sources_conf = json.loads(args.feature_tables_sources)
     entity_source_conf = json.loads(args.entity_source)
     destination_conf = json.loads(args.destination)
     start_job(

sdk/python/feast/pyspark/launcher.py

@@ -6,10 +6,18 @@
 
 from feast.config import Config
 from feast.constants import (
+    CONFIG_REDIS_HOST,
+    CONFIG_REDIS_PORT,
+    CONFIG_REDIS_SSL,
     CONFIG_SPARK_DATAPROC_CLUSTER_NAME,
     CONFIG_SPARK_DATAPROC_PROJECT,
     CONFIG_SPARK_DATAPROC_REGION,
     CONFIG_SPARK_DATAPROC_STAGING_LOCATION,
+    CONFIG_SPARK_EMR_CLUSTER_ID,
+    CONFIG_SPARK_EMR_CLUSTER_TEMPLATE_PATH,
+    CONFIG_SPARK_EMR_LOG_LOCATION,
+    CONFIG_SPARK_EMR_REGION,
+    CONFIG_SPARK_EMR_STAGING_LOCATION,
     CONFIG_SPARK_HOME,
     CONFIG_SPARK_INGESTION_JOB_JAR,
     CONFIG_SPARK_LAUNCHER,
@@ -50,7 +58,27 @@ def _dataproc_launcher(config: Config) -> JobLauncher:
     )
 
 
-_launchers = {"standalone": _standalone_launcher, "dataproc": _dataproc_launcher}
+def _emr_launcher(config: Config) -> JobLauncher:
+    from feast.pyspark.launchers import aws
+
+    def _get_optional(option):
+        if config.exists(option):
+            return config.get(option)
+
+    return aws.EmrClusterLauncher(
+        region=config.get(CONFIG_SPARK_EMR_REGION),
+        existing_cluster_id=_get_optional(CONFIG_SPARK_EMR_CLUSTER_ID),
+        new_cluster_template_path=_get_optional(CONFIG_SPARK_EMR_CLUSTER_TEMPLATE_PATH),
+        staging_location=config.get(CONFIG_SPARK_EMR_STAGING_LOCATION),
+        emr_log_location=config.get(CONFIG_SPARK_EMR_LOG_LOCATION),
+    )
+
+
+_launchers = {
+    "standalone": _standalone_launcher,
+    "dataproc": _dataproc_launcher,
+    "emr": _emr_launcher,
+}
 
 
 def resolve_launcher(config: Config) -> JobLauncher:
@@ -177,5 +205,17 @@ def start_offline_to_online_ingestion(
             feature_table=_feature_table_to_argument(client, feature_table),
             start=start,
             end=end,
+            redis_host=client._config.get(CONFIG_REDIS_HOST),
+            redis_port=client._config.getint(CONFIG_REDIS_PORT),
+            redis_ssl=client._config.getboolean(CONFIG_REDIS_SSL),
         )
     )
+
+
+def stage_dataframe(
+    df, event_timestamp_column: str, created_timestamp_column: str, client: "Client"
+) -> FileSource:
+    launcher = resolve_launcher(client._config)
+    return launcher.stage_dataframe(
+        df, event_timestamp_column, created_timestamp_column,
+    )

sdk/python/feast/pyspark/launchers/aws/__init__.py

@@ -0,0 +1,3 @@
+from .emr import EmrClusterLauncher, EmrIngestionJob, EmrRetrievalJob
+
+__all__ = ["EmrRetrievalJob", "EmrIngestionJob", "EmrClusterLauncher"]