public final class BigQueryConfig extends GeneratedMessageV3 implements BigQueryConfigOrBuilder
Message of configurations for BigQuery processor.
Protobuf type google.events.cloud.visionai.v1.BigQueryConfig
Inherited Members
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT)
com.google.protobuf.GeneratedMessageV3.<ListT>makeMutableCopy(ListT,int)
com.google.protobuf.GeneratedMessageV3.<T>emptyList(java.lang.Class<T>)
com.google.protobuf.GeneratedMessageV3.internalGetMapFieldReflection(int)
Static Fields
CLOUD_FUNCTION_MAPPING_FIELD_NUMBER
public static final int CLOUD_FUNCTION_MAPPING_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
CREATE_DEFAULT_TABLE_IF_NOT_EXISTS_FIELD_NUMBER
public static final int CREATE_DEFAULT_TABLE_IF_NOT_EXISTS_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
TABLE_FIELD_NUMBER
public static final int TABLE_FIELD_NUMBER
| Field Value |
| Type |
Description |
int |
|
Static Methods
getDefaultInstance()
public static BigQueryConfig getDefaultInstance()
getDescriptor()
public static final Descriptors.Descriptor getDescriptor()
newBuilder()
public static BigQueryConfig.Builder newBuilder()
newBuilder(BigQueryConfig prototype)
public static BigQueryConfig.Builder newBuilder(BigQueryConfig prototype)
public static BigQueryConfig parseDelimitedFrom(InputStream input)
public static BigQueryConfig parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
parseFrom(byte[] data)
public static BigQueryConfig parseFrom(byte[] data)
| Parameter |
| Name |
Description |
data |
byte[]
|
parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
public static BigQueryConfig parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
parseFrom(ByteString data)
public static BigQueryConfig parseFrom(ByteString data)
parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static BigQueryConfig parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
public static BigQueryConfig parseFrom(CodedInputStream input)
public static BigQueryConfig parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
public static BigQueryConfig parseFrom(InputStream input)
public static BigQueryConfig parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
parseFrom(ByteBuffer data)
public static BigQueryConfig parseFrom(ByteBuffer data)
parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
public static BigQueryConfig parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
parser()
public static Parser<BigQueryConfig> parser()
Methods
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
|
equals(Object obj)
public boolean equals(Object obj)
| Parameter |
| Name |
Description |
obj |
Object
|
Overrides
getCloudFunctionMapping()
public Map<String,String> getCloudFunctionMapping()
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;
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;
| 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()
getParserForType()
public Parser<BigQueryConfig> getParserForType()
Overrides
getSerializedSize()
public int getSerializedSize()
| Returns |
| Type |
Description |
int |
|
Overrides
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.
|
getUnknownFields()
public final UnknownFieldSet getUnknownFields()
Overrides
hashCode()
| Returns |
| Type |
Description |
int |
|
Overrides
internalGetFieldAccessorTable()
protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Overrides
internalGetMapField(int number)
protected MapField internalGetMapField(int number)
| Parameter |
| Name |
Description |
number |
int
|
Overrides
isInitialized()
public final boolean isInitialized()
Overrides
newBuilderForType()
public BigQueryConfig.Builder newBuilderForType()
newBuilderForType(GeneratedMessageV3.BuilderParent parent)
protected BigQueryConfig.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Overrides
newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
| Returns |
| Type |
Description |
Object |
|
Overrides
toBuilder()
public BigQueryConfig.Builder toBuilder()
writeTo(CodedOutputStream output)
public void writeTo(CodedOutputStream output)
Overrides
