com.google.cloud.visionai.v1.BigQueryConfigOrBuilder Maven / Gradle / Ivy
/*
* Copyright 2024 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: google/cloud/visionai/v1/platform.proto
// Protobuf Java Version: 3.25.3
package com.google.cloud.visionai.v1;
public interface BigQueryConfigOrBuilder
extends
// @@protoc_insertion_point(interface_extends:google.cloud.visionai.v1.BigQueryConfig)
com.google.protobuf.MessageOrBuilder {
/**
*
*
*
* BigQuery table resource for Vision AI Platform to ingest annotations to.
*
*
* string table = 1;
*
* @return The table.
*/
java.lang.String getTable();
/**
*
*
*
* BigQuery table resource for Vision AI Platform to ingest annotations to.
*
*
* string table = 1;
*
* @return The bytes for table.
*/
com.google.protobuf.ByteString getTableBytes();
/**
*
*
*
* 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;
*/
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;
*/
boolean containsCloudFunctionMapping(java.lang.String key);
/** Use {@link #getCloudFunctionMappingMap()} instead. */
@java.lang.Deprecated
java.util.Map getCloudFunctionMapping();
/**
*
*
*
* 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;
*/
java.util.Map 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;
*/
/* nullable */
java.lang.String getCloudFunctionMappingOrDefault(
java.lang.String key,
/* nullable */
java.lang.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;
*/
java.lang.String getCloudFunctionMappingOrThrow(java.lang.String key);
/**
*
*
*
* 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;
*
* @return The createDefaultTableIfNotExists.
*/
boolean getCreateDefaultTableIfNotExists();
}
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