Package com.google.cloud.visionai.v1
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 TypeMethodDescriptionbuild()clear()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.booleanData 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.intData 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.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.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.booleanIf 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.Descriptorcom.google.protobuf.Descriptors.DescriptorDeprecated.getTable()BigQuery table resource for Vision AI Platform to ingest annotations to.com.google.protobuf.ByteStringBigQuery table resource for Vision AI Platform to ingest annotations to.protected com.google.protobuf.GeneratedMessage.FieldAccessorTableprotected com.google.protobuf.MapFieldReflectionAccessorinternalGetMapFieldReflection(int number) protected com.google.protobuf.MapFieldReflectionAccessorinternalGetMutableMapFieldReflection(int number) final booleanmergeFrom(BigQueryConfig other) mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) mergeFrom(com.google.protobuf.Message other) 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.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.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.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.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, setUnknownFieldsProto3Methods inherited from class com.google.protobuf.AbstractMessage.Builder
findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toStringMethods inherited from class com.google.protobuf.AbstractMessageLite.Builder
addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, mergeFrom, newUninitializedMessageExceptionMethods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface com.google.protobuf.Message.Builder
mergeDelimitedFrom, mergeDelimitedFromMethods inherited from interface com.google.protobuf.MessageLite.Builder
mergeFromMethods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
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Method Details
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getDescriptor
public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() -
internalGetMapFieldReflection
protected com.google.protobuf.MapFieldReflectionAccessor internalGetMapFieldReflection(int number) - Overrides:
internalGetMapFieldReflectionin classcom.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
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internalGetMutableMapFieldReflection
protected com.google.protobuf.MapFieldReflectionAccessor internalGetMutableMapFieldReflection(int number) - Overrides:
internalGetMutableMapFieldReflectionin classcom.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
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internalGetFieldAccessorTable
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()- Specified by:
internalGetFieldAccessorTablein classcom.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
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clear
- Specified by:
clearin interfacecom.google.protobuf.Message.Builder- Specified by:
clearin interfacecom.google.protobuf.MessageLite.Builder- Overrides:
clearin classcom.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
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getDescriptorForType
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()- Specified by:
getDescriptorForTypein interfacecom.google.protobuf.Message.Builder- Specified by:
getDescriptorForTypein interfacecom.google.protobuf.MessageOrBuilder- Overrides:
getDescriptorForTypein classcom.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
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getDefaultInstanceForType
- Specified by:
getDefaultInstanceForTypein interfacecom.google.protobuf.MessageLiteOrBuilder- Specified by:
getDefaultInstanceForTypein interfacecom.google.protobuf.MessageOrBuilder
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build
- Specified by:
buildin interfacecom.google.protobuf.Message.Builder- Specified by:
buildin interfacecom.google.protobuf.MessageLite.Builder
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buildPartial
- Specified by:
buildPartialin interfacecom.google.protobuf.Message.Builder- Specified by:
buildPartialin interfacecom.google.protobuf.MessageLite.Builder
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mergeFrom
- Specified by:
mergeFromin interfacecom.google.protobuf.Message.Builder- Overrides:
mergeFromin classcom.google.protobuf.AbstractMessage.Builder<BigQueryConfig.Builder>
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mergeFrom
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isInitialized
public final boolean isInitialized()- Specified by:
isInitializedin interfacecom.google.protobuf.MessageLiteOrBuilder- Overrides:
isInitializedin classcom.google.protobuf.GeneratedMessage.Builder<BigQueryConfig.Builder>
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mergeFrom
public BigQueryConfig.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException - Specified by:
mergeFromin interfacecom.google.protobuf.Message.Builder- Specified by:
mergeFromin interfacecom.google.protobuf.MessageLite.Builder- Overrides:
mergeFromin classcom.google.protobuf.AbstractMessage.Builder<BigQueryConfig.Builder>- Throws:
IOException
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getTable
BigQuery table resource for Vision AI Platform to ingest annotations to.
string table = 1;- Specified by:
getTablein interfaceBigQueryConfigOrBuilder- Returns:
- The table.
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getTableBytes
public com.google.protobuf.ByteString getTableBytes()BigQuery table resource for Vision AI Platform to ingest annotations to.
string table = 1;- Specified by:
getTableBytesin interfaceBigQueryConfigOrBuilder- Returns:
- The bytes for table.
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setTable
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.
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clearTable
BigQuery table resource for Vision AI Platform to ingest annotations to.
string table = 1;- Returns:
- This builder for chaining.
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setTableBytes
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.
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getCloudFunctionMappingCount
public int getCloudFunctionMappingCount()Description copied from interface:BigQueryConfigOrBuilderData 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:
getCloudFunctionMappingCountin interfaceBigQueryConfigOrBuilder
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containsCloudFunctionMapping
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:
containsCloudFunctionMappingin interfaceBigQueryConfigOrBuilder
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getCloudFunctionMapping
Deprecated.UsegetCloudFunctionMappingMap()instead.- Specified by:
getCloudFunctionMappingin interfaceBigQueryConfigOrBuilder
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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:
getCloudFunctionMappingMapin interfaceBigQueryConfigOrBuilder
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getCloudFunctionMappingOrDefault
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:
getCloudFunctionMappingOrDefaultin interfaceBigQueryConfigOrBuilder
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getCloudFunctionMappingOrThrow
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:
getCloudFunctionMappingOrThrowin interfaceBigQueryConfigOrBuilder
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clearCloudFunctionMapping
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removeCloudFunctionMapping
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.Use alternate mutation accessors instead. -
putCloudFunctionMapping
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
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:
getCreateDefaultTableIfNotExistsin interfaceBigQueryConfigOrBuilder- Returns:
- The createDefaultTableIfNotExists.
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setCreateDefaultTableIfNotExists
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.
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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.
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