Class BigQueryConfig.Builder (0.14.1)

Version 0.14.1

• 0.15.0 (latest)
• 0.14.1

public static final class BigQueryConfig.Builder extends GeneratedMessageV3.Builder<BigQueryConfig.Builder> implements BigQueryConfigOrBuilder

Message of configurations for BigQuery processor.

Protobuf type google.events.cloud.visionai.v1.BigQueryConfig

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > BigQueryConfig.Builder

Implements

BigQueryConfigOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
Descriptor

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public BigQueryConfig.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public BigQueryConfig build()

Returns
Type	Description
BigQueryConfig

buildPartial()

public BigQueryConfig buildPartial()

Returns
Type	Description
BigQueryConfig

clear()

public BigQueryConfig.Builder clear()

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearCloudFunctionMapping()

public BigQueryConfig.Builder clearCloudFunctionMapping()

Returns
Type	Description
BigQueryConfig.Builder

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
Type	Description
BigQueryConfig.Builder	This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public BigQueryConfig.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
field	FieldDescriptor

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearOneof(Descriptors.OneofDescriptor oneof)

public BigQueryConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
oneof	OneofDescriptor

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearTable()

public BigQueryConfig.Builder clearTable()

BigQuery table resource for Vision AI Platform to ingest annotations to.

string table = 1;

Returns
Type	Description
BigQueryConfig.Builder	This builder for chaining.

clone()

public BigQueryConfig.Builder clone()

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

containsCloudFunctionMapping(String key)

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;

Parameter
Name	Description
key	String

Returns
Type	Description
boolean

getCloudFunctionMapping()

public Map<String,String> getCloudFunctionMapping()

Use #getCloudFunctionMappingMap() instead.

Returns
Type	Description
Map<String,String>

getCloudFunctionMappingCount()

public 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;

Returns
Type	Description
int

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;

Returns
Type	Description
Map<String,String>

getCloudFunctionMappingOrDefault(String key, String defaultValue)

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;

Parameters
Name	Description
key	String
defaultValue	String

Returns
Type	Description
String

getCloudFunctionMappingOrThrow(String key)

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;

Parameter
Name	Description
key	String

Returns
Type	Description
String

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;

Returns
Type	Description
boolean	The createDefaultTableIfNotExists.

getDefaultInstanceForType()

public BigQueryConfig getDefaultInstanceForType()

Returns
Type	Description
BigQueryConfig

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getMutableCloudFunctionMapping()

public Map<String,String> getMutableCloudFunctionMapping()

Use alternate mutation accessors instead.

Returns
Type	Description
Map<String,String>

getTable()

public String getTable()

BigQuery table resource for Vision AI Platform to ingest annotations to.

string table = 1;

Returns
Type	Description
String	The table.

getTableBytes()

public ByteString getTableBytes()

BigQuery table resource for Vision AI Platform to ingest annotations to.

string table = 1;

Returns
Type	Description
ByteString	The bytes for table.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

internalGetMapField(int number)

protected MapField internalGetMapField(int number)

Parameter
Name	Description
number	int

Returns
Type	Description
MapField

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetMapField(int fieldNumber)

internalGetMutableMapField(int number)

protected MapField internalGetMutableMapField(int number)

Parameter
Name	Description
number	int

Returns
Type	Description
MapField

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetMutableMapField(int fieldNumber)

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeFrom(BigQueryConfig other)

public BigQueryConfig.Builder mergeFrom(BigQueryConfig other)

Parameter
Name	Description
other	BigQueryConfig

Returns
Type	Description
BigQueryConfig.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public BigQueryConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
BigQueryConfig.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public BigQueryConfig.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
BigQueryConfig.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final BigQueryConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

putAllCloudFunctionMapping(Map<String,String> values)

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;

Parameter
Name	Description
values	Map<String,String>

Returns
Type	Description
BigQueryConfig.Builder

putCloudFunctionMapping(String key, String value)

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;

Parameters
Name	Description
key	String
value	String

Returns
Type	Description
BigQueryConfig.Builder

removeCloudFunctionMapping(String key)

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;

Parameter
Name	Description
key	String

Returns
Type	Description
BigQueryConfig.Builder

setCreateDefaultTableIfNotExists(boolean value)

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;

Parameter
Name	Description
value	boolean The createDefaultTableIfNotExists to set.

Returns
Type	Description
BigQueryConfig.Builder	This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

public BigQueryConfig.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public BigQueryConfig.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
field	FieldDescriptor
index	int
value	Object

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setTable(String value)

public BigQueryConfig.Builder setTable(String value)

BigQuery table resource for Vision AI Platform to ingest annotations to.

string table = 1;

Parameter
Name	Description
value	String The table to set.

Returns
Type	Description
BigQueryConfig.Builder	This builder for chaining.

setTableBytes(ByteString value)

public BigQueryConfig.Builder setTableBytes(ByteString value)

BigQuery table resource for Vision AI Platform to ingest annotations to.

string table = 1;

Parameter
Name	Description
value	ByteString The bytes for table to set.

Returns
Type	Description
BigQueryConfig.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final BigQueryConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
BigQueryConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)