Package com.google.cloud.visionai.v1

Interface BigQueryConfigOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
BigQueryConfig, BigQueryConfig.Builder
@Generated public interface BigQueryConfigOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Summary

    Modifier and Type
    Method
    Description
    boolean
    containsCloudFunctionMapping(String key)
    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.
    Map<String,String>
    getCloudFunctionMapping()
    Deprecated.
    int
    getCloudFunctionMappingCount()
    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.
    Map<String,String>
    getCloudFunctionMappingMap()
    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.
    String
    getCloudFunctionMappingOrDefault(String key, String defaultValue)
    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.
    String
    getCloudFunctionMappingOrThrow(String key)
    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.
    boolean
    getCreateDefaultTableIfNotExists()
    If true, App Platform will create the BigQuery DataSet and the BigQuery Table with default schema if the specified table doesn't exist.
    String
    getTable()
    BigQuery table resource for Vision AI Platform to ingest annotations to.
    com.google.protobuf.ByteString
    getTableBytes()
    BigQuery table resource for Vision AI Platform to ingest annotations to.

    Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

    isInitialized

    Methods inherited from interface com.google.protobuf.MessageOrBuilder

    findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

  • Method Details

    • getTable

      String getTable()
       BigQuery table resource for Vision AI Platform to ingest annotations to.
      string table = 1;
      Returns:
      The table.

    • getTableBytes

      com.google.protobuf.ByteString getTableBytes()
       BigQuery table resource for Vision AI Platform to ingest annotations to.
      string table = 1;
      Returns:
      The bytes for table.

    • getCloudFunctionMappingCount

      int getCloudFunctionMappingCount()
       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.
      map<string, string> cloud_function_mapping = 2;

    • containsCloudFunctionMapping

      boolean containsCloudFunctionMapping(String key)
       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.
      map<string, string> cloud_function_mapping = 2;

    • getCloudFunctionMapping

      @Deprecated Map<String,String> getCloudFunctionMapping()
      Deprecated.
      Use getCloudFunctionMappingMap() instead.

    • getCloudFunctionMappingMap

      Map<String,String> getCloudFunctionMappingMap()
       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.
      map<string, string> cloud_function_mapping = 2;

    • getCloudFunctionMappingOrDefault

      String getCloudFunctionMappingOrDefault(String key, String defaultValue)
       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.
      map<string, string> cloud_function_mapping = 2;

    • getCloudFunctionMappingOrThrow

      String getCloudFunctionMappingOrThrow(String key)
       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.
      map<string, string> cloud_function_mapping = 2;

    • getCreateDefaultTableIfNotExists

      boolean getCreateDefaultTableIfNotExists()
       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.
      bool create_default_table_if_not_exists = 3;
      Returns:
      The createDefaultTableIfNotExists.