Class BigQueryConfig.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
com.google.cloud.visionai.v1.BigQueryConfig.Builder
All Implemented Interfaces:
BigQueryConfigOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
BigQueryConfig

public static final class BigQueryConfig.Builder extends com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder> implements BigQueryConfigOrBuilder
 Message of configurations for BigQuery processor.
 
Protobuf type google.cloud.visionai.v1.BigQueryConfig
  • Method Summary

    Modifier and Type
    Method
    Description
     
     
     
     
    If true, App Platform will create the BigQuery DataSet and the BigQuery Table with default schema if the specified table doesn't exist.
    BigQuery table resource for Vision AI Platform to ingest annotations to.
    boolean
    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.
    Deprecated.
    int
    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.
    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.
    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.
    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
    If true, App Platform will create the BigQuery DataSet and the BigQuery Table with default schema if the specified table doesn't exist.
     
    static final com.google.protobuf.Descriptors.Descriptor
     
    com.google.protobuf.Descriptors.Descriptor
     
    Deprecated.
    BigQuery table resource for Vision AI Platform to ingest annotations to.
    com.google.protobuf.ByteString
    BigQuery table resource for Vision AI Platform to ingest annotations to.
    protected com.google.protobuf.GeneratedMessage.FieldAccessorTable
     
    protected com.google.protobuf.MapFieldReflectionAccessor
     
    protected com.google.protobuf.MapFieldReflectionAccessor
     
    final boolean
     
     
    mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
     
    mergeFrom(com.google.protobuf.Message other)
     
    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.
    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.
    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.
    If true, App Platform will create the BigQuery DataSet and the BigQuery Table with default schema if the specified table doesn't exist.
    BigQuery table resource for Vision AI Platform to ingest annotations to.
    setTableBytes(com.google.protobuf.ByteString value)
    BigQuery table resource for Vision AI Platform to ingest annotations to.

    Methods inherited from class com.google.protobuf.GeneratedMessage.Builder

    addRepeatedField, clearField, clearOneof, clone, getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMutableMapField, isClean, markClean, mergeUnknownFields, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setField, setRepeatedField, setUnknownFields, setUnknownFieldSetBuilder, setUnknownFieldsProto3

    Methods inherited from class com.google.protobuf.AbstractMessage.Builder

    findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

    Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder

    addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, mergeFrom, newUninitializedMessageException

    Methods inherited from class java.lang.Object

    equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

    Methods inherited from interface com.google.protobuf.Message.Builder

    mergeDelimitedFrom, mergeDelimitedFrom

    Methods inherited from interface com.google.protobuf.MessageLite.Builder

    mergeFrom

    Methods inherited from interface com.google.protobuf.MessageOrBuilder

    findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()
    • internalGetMapFieldReflection

      protected com.google.protobuf.MapFieldReflectionAccessor internalGetMapFieldReflection(int number)
      Overrides:
      internalGetMapFieldReflection in class com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
    • internalGetMutableMapFieldReflection

      protected com.google.protobuf.MapFieldReflectionAccessor internalGetMutableMapFieldReflection(int number)
      Overrides:
      internalGetMutableMapFieldReflection in class com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
    • clear

      public BigQueryConfig.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
    • getDefaultInstanceForType

      public BigQueryConfig getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder
    • build

      public BigQueryConfig build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder
    • buildPartial

      public BigQueryConfig buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder
    • mergeFrom

      public BigQueryConfig.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<BigQueryConfig.Builder>
    • mergeFrom

      public BigQueryConfig.Builder mergeFrom(BigQueryConfig other)
    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
    • mergeFrom

      public BigQueryConfig.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<BigQueryConfig.Builder>
      Throws:
      IOException
    • getTable

      public String getTable()
       BigQuery table resource for Vision AI Platform to ingest annotations to.
       
      string table = 1;
      Specified by:
      getTable in interface BigQueryConfigOrBuilder
      Returns:
      The table.
    • getTableBytes

      public com.google.protobuf.ByteString getTableBytes()
       BigQuery table resource for Vision AI Platform to ingest annotations to.
       
      string table = 1;
      Specified by:
      getTableBytes in interface BigQueryConfigOrBuilder
      Returns:
      The bytes for table.
    • setTable

      public BigQueryConfig.Builder setTable(String value)
       BigQuery table resource for Vision AI Platform to ingest annotations to.
       
      string table = 1;
      Parameters:
      value - The table to set.
      Returns:
      This builder for chaining.
    • clearTable

      public BigQueryConfig.Builder clearTable()
       BigQuery table resource for Vision AI Platform to ingest annotations to.
       
      string table = 1;
      Returns:
      This builder for chaining.
    • setTableBytes

      public BigQueryConfig.Builder setTableBytes(com.google.protobuf.ByteString value)
       BigQuery table resource for Vision AI Platform to ingest annotations to.
       
      string table = 1;
      Parameters:
      value - The bytes for table to set.
      Returns:
      This builder for chaining.
    • getCloudFunctionMappingCount

      public int getCloudFunctionMappingCount()
      Description copied from interface: BigQueryConfigOrBuilder
       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;
      Specified by:
      getCloudFunctionMappingCount in interface BigQueryConfigOrBuilder
    • containsCloudFunctionMapping

      public 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;
      Specified by:
      containsCloudFunctionMapping in interface BigQueryConfigOrBuilder
    • getCloudFunctionMapping

      @Deprecated public Map<String,String> getCloudFunctionMapping()
      Deprecated.
      Specified by:
      getCloudFunctionMapping in interface BigQueryConfigOrBuilder
    • getCloudFunctionMappingMap

      public 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;
      Specified by:
      getCloudFunctionMappingMap in interface BigQueryConfigOrBuilder
    • getCloudFunctionMappingOrDefault

      public 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;
      Specified by:
      getCloudFunctionMappingOrDefault in interface BigQueryConfigOrBuilder
    • getCloudFunctionMappingOrThrow

      public 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;
      Specified by:
      getCloudFunctionMappingOrThrow in interface BigQueryConfigOrBuilder
    • clearCloudFunctionMapping

      public BigQueryConfig.Builder clearCloudFunctionMapping()
    • removeCloudFunctionMapping

      public BigQueryConfig.Builder removeCloudFunctionMapping(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;
    • getMutableCloudFunctionMapping

      @Deprecated public Map<String,String> getMutableCloudFunctionMapping()
      Deprecated.
      Use alternate mutation accessors instead.
    • putCloudFunctionMapping

      public BigQueryConfig.Builder putCloudFunctionMapping(String key, String value)
       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;
    • putAllCloudFunctionMapping

      public BigQueryConfig.Builder putAllCloudFunctionMapping(Map<String,String> values)
       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

      public 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;
      Specified by:
      getCreateDefaultTableIfNotExists in interface BigQueryConfigOrBuilder
      Returns:
      The createDefaultTableIfNotExists.
    • setCreateDefaultTableIfNotExists

      public BigQueryConfig.Builder setCreateDefaultTableIfNotExists(boolean value)
       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;
      Parameters:
      value - The createDefaultTableIfNotExists to set.
      Returns:
      This builder for chaining.
    • clearCreateDefaultTableIfNotExists

      public BigQueryConfig.Builder clearCreateDefaultTableIfNotExists()
       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:
      This builder for chaining.