from typing import Optional

from feast.constants import ConfigMeta

class ConfigOptions(metaclass=ConfigMeta):

#: Default Feast Job Service URL

JOB_SERVICE_URL: Optional[str] = None

#: Enable or disable TLS/SSL to Feast Job Service

JOB_SERVICE_ENABLE_SSL: str = "False"

#: Path to certificate(s) to secure connection to Feast Job Service

JOB_SERVICE_SERVER_SSL_CERT: str = ""

#: Enable or disable control loop for Feast Job Service

JOB_SERVICE_ENABLE_CONTROL_LOOP: str = "False"

#: If set to True, Control Loop will try to start failed streaming jobss

JOB_SERVICE_RETRY_FAILED_JOBS: str = "False"

#: Pause in seconds between starting new jobs in Control Loop

JOB_SERVICE_PAUSE_BETWEEN_JOBS: str = "5"

#: Port for which Prometheus metric server will be running on

JOB_SERVICE_PROMETHEUS_METRIC_PORT: int = 8080

#: Endpoint URL for S3 storage_client

S3_ENDPOINT_URL: Optional[str] = None

#: Spark Job launcher. The choice of storage is connected to the choice of SPARK_LAUNCHER.

#:

#: Options: "standalone", "dataproc", "emr"

SPARK_LAUNCHER: Optional[str] = None

#: Feast Spark Job ingestion jobs staging location. The choice of storage is connected to the choice of SPARK_LAUNCHER.

#:

#: Eg. gs://some-bucket/output/, s3://some-bucket/output/, file:///data/subfolder/

SPARK_STAGING_LOCATION: Optional[str] = None

#: Feast Spark Job ingestion jar file. The choice of storage is connected to the choice of SPARK_LAUNCHER.

#:

#: Eg. "dataproc" (http and gs), "emr" (http and s3), "standalone" (http and file)

SPARK_INGESTION_JAR: str = "https://storage.googleapis.com/feast-jobs/spark/ingestion/feast-ingestion-spark-develop.jar"

#: Spark resource manager master url

SPARK_STANDALONE_MASTER: str = "local[*]"

#: Directory where Spark is installed

SPARK_HOME: Optional[str] = None

#: The project id where the materialized view of BigQuerySource is going to be created

#: by default, use the same project where view is located

SPARK_BQ_MATERIALIZATION_PROJECT: Optional[str] = None

#: The dataset id where the materialized view of BigQuerySource is going to be created

#: by default, use the same dataset where view is located

SPARK_BQ_MATERIALIZATION_DATASET: Optional[str] = None

#: Dataproc cluster to run Feast Spark Jobs in

DATAPROC_CLUSTER_NAME: Optional[str] = None

#: Project of Dataproc cluster

DATAPROC_PROJECT: Optional[str] = None

#: Region of Dataproc cluster

DATAPROC_REGION: Optional[str] = None

#: No. of executor instances for Dataproc cluster

DATAPROC_EXECUTOR_INSTANCES = "2"

#: No. of executor cores for Dataproc cluster

DATAPROC_EXECUTOR_CORES = "2"

#: No. of executor memory for Dataproc cluster

DATAPROC_EXECUTOR_MEMORY = "2g"

# namespace to use for Spark jobs launched using k8s spark operator

SPARK_K8S_NAMESPACE = "default"

# expect k8s spark operator to be running in the same cluster as Feast

SPARK_K8S_USE_INCLUSTER_CONFIG = "True"

# SparkApplication resource template

SPARK_K8S_JOB_TEMPLATE_PATH = None

# SparkApplication resource template for Batch Ingestion Jobs

SPARK_K8S_BATCH_INGESTION_TEMPLATE_PATH: Optional[str] = ""

# SparkApplication resource template for Stream Ingestion Jobs

SPARK_K8S_STREAM_INGESTION_TEMPLATE_PATH: Optional[str] = ""

# SparkApplication resource template for Historical Retrieval Jobs

SPARK_K8S_HISTORICAL_RETRIEVAL_TEMPLATE_PATH: Optional[str] = ""

#: Default Redis host to Redis Instance which stores Spark Ingestion Job metrics

SPARK_METRICS_REDIS_HOST: Optional[str] = None

#: Default Redis port to Redis Instance which stores Spark Ingestion Job metrics

SPARK_METRICS_REDIS_PORT: Optional[str] = None

#: Host to Redis Instance which stores locks for job management

LOCK_MGR_REDIS_HOST: Optional[str] = None

#: Port to Redis Instance which stores locks for job management

LOCK_MGR_REDIS_PORT: Optional[str] = None

#: TTL for locks for job management

LOCK_EXPIRY: Optional[str] = "60"

#: File format of historical retrieval features

HISTORICAL_FEATURE_OUTPUT_FORMAT: str = "parquet"

#: File location of historical retrieval features

HISTORICAL_FEATURE_OUTPUT_LOCATION: Optional[str] = None

#: Default Redis host

REDIS_HOST: Optional[str] = ""

#: Default Redis port

REDIS_PORT: Optional[str] = ""

#: Redis credentials

REDIS_PASSWORD: Optional[str] = ""

#: Enable or disable TLS/SSL to Redis

REDIS_SSL: Optional[str] = "False"

#: BigTable Project ID

BIGTABLE_PROJECT: Optional[str] = ""

#: BigTable Instance ID

BIGTABLE_INSTANCE: Optional[str] = ""

#: Cassandra host. Can be a comma separated string

CASSANDRA_HOST: Optional[str] = ""

#: Cassandra port

CASSANDRA_PORT: Optional[str] = ""

#: Enable or disable StatsD

STATSD_ENABLED: str = "False"

#: Default StatsD port

STATSD_HOST: Optional[str] = None

#: Default StatsD port

STATSD_PORT: Optional[str] = None

#: Ingestion Job DeadLetter Destination. The choice of storage is connected to the choice of SPARK_LAUNCHER.

#:

#: Eg. gs://some-bucket/output/, s3://some-bucket/output/, file:///data/subfolder/

DEADLETTER_PATH: str = ""

#: Ingestion Job Checkpoint Location. Format same as for DeadLetter path

CHECKPOINT_PATH: str = ""

#: ProtoRegistry Address (currently only Stencil Server is supported as registry)

#: https://github.com/odpf/stencil

STENCIL_URL: str = ""

#: Bearer token used for authentication with Stencil Server

STENCIL_TOKEN: str = ""

#: If set to true rows that do not pass custom validation (see feast.contrib.validation)

#: won't be saved to Online Storage

INGESTION_DROP_INVALID_ROWS: str = "False"

#: EMR cluster to run Feast Spark Jobs in

EMR_CLUSTER_ID: Optional[str] = None

#: Region of EMR cluster

EMR_REGION: Optional[str] = None

#: Template path of EMR cluster

EMR_CLUSTER_TEMPLATE_PATH: Optional[str] = None

#: Log path of EMR cluster

EMR_LOG_LOCATION: Optional[str] = None

#: Whitelisted Feast Job Types

WHITELISTED_JOB_TYPES: Optional[str] = None

#: Whitelisted Feast projects

WHITELISTED_PROJECTS: Optional[str] = None

#: File path to a whitelist containing all the feature tables allowed for ingestion.

#: Each line in the file should be in the format of <project>:<feature table>

WHITELISTED_FEATURE_TABLES_PATH: Optional[str] = None

#: If set - streaming ingestion job will be consuming incoming rows not continuously,

#: but periodically with configured interval (in seconds).

#: That may help to control amount of write requests to storage

SPARK_STREAMING_TRIGGERING_INTERVAL: Optional[str] = None

#: GCP project of the BigQuery dataset used to stage the entities during historical

#: feature retrieval. If not set, the GCP project of the feature table batch source

#: will be used instead.

BQ_STAGING_PROJECT: Optional[str] = None

#: BigQuery dataset used to stage the entities during historical feature retrieval.

# If not set, the BigQuery dataset of the batch source will be used

#: instead.

BQ_STAGING_DATASET: Optional[str] = None

def defaults(self):

return {

k: getattr(self, k)

for k in self.__config_keys__

if getattr(self, k) is not None

}