Vision AI V1 API - Class Google::Cloud::VisionAI::V1::BigQueryConfig (v1.2.0)

Reference documentation and code samples for the Vision AI V1 API class Google::Cloud::VisionAI::V1::BigQueryConfig.

Message of configurations for BigQuery processor.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#cloud_function_mapping

def cloud_function_mapping() -> ::Google::Protobuf::Map{::String => ::String}
Returns
  • (::Google::Protobuf::Map{::String => ::String}) —

    Data Schema By default, Vision AI Application will try to write annotations to the target BigQuery table using the following schema:

    ingestion_time: TIMESTAMP, the ingestion time of the original data.

    application: STRING, name of the application which produces the annotation.

    instance: STRING, Id of the instance which produces the annotation.

    node: STRING, name of the application graph node which produces the annotation.

    annotation: STRING or JSON, the actual annotation protobuf will be converted to json string with bytes field as 64 encoded string. It can be written to both String or Json type column.

    To forward annotation data to an existing BigQuery table, customer needs to make sure the compatibility of the schema. The map maps application node name to its corresponding cloud function endpoint to transform the annotations directly to the google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or proto_rows should be set). If configured, annotations produced by corresponding application node will sent to the Cloud Function at first before be forwarded to BigQuery.

    If the default table schema doesn't fit, customer is able to transform the annotation output from Vision AI Application to arbitrary BigQuery table schema with CloudFunction.

    • The cloud function will receive AppPlatformCloudFunctionRequest where the annotations field will be the json format of Vision AI annotation.
    • The cloud function should return AppPlatformCloudFunctionResponse with AppendRowsRequest stored in the annotations field.
    • To drop the annotation, simply clear the annotations field in the returned AppPlatformCloudFunctionResponse.

#cloud_function_mapping=

def cloud_function_mapping=(value) -> ::Google::Protobuf::Map{::String => ::String}
Parameter
  • value (::Google::Protobuf::Map{::String => ::String}) —

    Data Schema By default, Vision AI Application will try to write annotations to the target BigQuery table using the following schema:

    ingestion_time: TIMESTAMP, the ingestion time of the original data.

    application: STRING, name of the application which produces the annotation.

    instance: STRING, Id of the instance which produces the annotation.

    node: STRING, name of the application graph node which produces the annotation.

    annotation: STRING or JSON, the actual annotation protobuf will be converted to json string with bytes field as 64 encoded string. It can be written to both String or Json type column.

    To forward annotation data to an existing BigQuery table, customer needs to make sure the compatibility of the schema. The map maps application node name to its corresponding cloud function endpoint to transform the annotations directly to the google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or proto_rows should be set). If configured, annotations produced by corresponding application node will sent to the Cloud Function at first before be forwarded to BigQuery.

    If the default table schema doesn't fit, customer is able to transform the annotation output from Vision AI Application to arbitrary BigQuery table schema with CloudFunction.

    • The cloud function will receive AppPlatformCloudFunctionRequest where the annotations field will be the json format of Vision AI annotation.
    • The cloud function should return AppPlatformCloudFunctionResponse with AppendRowsRequest stored in the annotations field.
    • To drop the annotation, simply clear the annotations field in the returned AppPlatformCloudFunctionResponse.
Returns
  • (::Google::Protobuf::Map{::String => ::String}) —

    Data Schema By default, Vision AI Application will try to write annotations to the target BigQuery table using the following schema:

    ingestion_time: TIMESTAMP, the ingestion time of the original data.

    application: STRING, name of the application which produces the annotation.

    instance: STRING, Id of the instance which produces the annotation.

    node: STRING, name of the application graph node which produces the annotation.

    annotation: STRING or JSON, the actual annotation protobuf will be converted to json string with bytes field as 64 encoded string. It can be written to both String or Json type column.

    To forward annotation data to an existing BigQuery table, customer needs to make sure the compatibility of the schema. The map maps application node name to its corresponding cloud function endpoint to transform the annotations directly to the google.cloud.bigquery.storage.v1.AppendRowsRequest (only avro_rows or proto_rows should be set). If configured, annotations produced by corresponding application node will sent to the Cloud Function at first before be forwarded to BigQuery.

    If the default table schema doesn't fit, customer is able to transform the annotation output from Vision AI Application to arbitrary BigQuery table schema with CloudFunction.

    • The cloud function will receive AppPlatformCloudFunctionRequest where the annotations field will be the json format of Vision AI annotation.
    • The cloud function should return AppPlatformCloudFunctionResponse with AppendRowsRequest stored in the annotations field.
    • To drop the annotation, simply clear the annotations field in the returned AppPlatformCloudFunctionResponse.

#create_default_table_if_not_exists

def create_default_table_if_not_exists() -> ::Boolean
Returns
  • (::Boolean) — If true, App Platform will create the BigQuery DataSet and the BigQuery Table with default schema if the specified table doesn't exist. This doesn't work if any cloud function customized schema is specified since the system doesn't know your desired schema. JSON column will be used in the default table created by App Platform.

#create_default_table_if_not_exists=

def create_default_table_if_not_exists=(value) -> ::Boolean
Parameter
  • value (::Boolean) — If true, App Platform will create the BigQuery DataSet and the BigQuery Table with default schema if the specified table doesn't exist. This doesn't work if any cloud function customized schema is specified since the system doesn't know your desired schema. JSON column will be used in the default table created by App Platform.
Returns
  • (::Boolean) — If true, App Platform will create the BigQuery DataSet and the BigQuery Table with default schema if the specified table doesn't exist. This doesn't work if any cloud function customized schema is specified since the system doesn't know your desired schema. JSON column will be used in the default table created by App Platform.

#table

def table() -> ::String
Returns
  • (::String) — BigQuery table resource for Vision AI Platform to ingest annotations to.

#table=

def table=(value) -> ::String
Parameter
  • value (::String) — BigQuery table resource for Vision AI Platform to ingest annotations to.
Returns
  • (::String) — BigQuery table resource for Vision AI Platform to ingest annotations to.