Struct aws_sdk_machinelearning::types::RdsMetadata

#[non_exhaustive]
pub struct RdsMetadata {
    pub database: Option<RdsDatabase>,
    pub database_user_name: Option<String>,
    pub select_sql_query: Option<String>,
    pub resource_role: Option<String>,
    pub service_role: Option<String>,
    pub data_pipeline_id: Option<String>,
}

The datasource details that are specific to Amazon RDS.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

database: Option<RdsDatabase>

The database details required to connect to an Amazon RDS.

database_user_name: Option<String>

The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.

select_sql_query: Option<String>

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

resource_role: Option<String>

The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

service_role: Option<String>

The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

data_pipeline_id: Option<String>

The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

Implementations

impl RdsMetadata

pub fn database(&self) -> Option<&RdsDatabase>

The database details required to connect to an Amazon RDS.

pub fn database_user_name(&self) -> Option<&str>

The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query.

pub fn select_sql_query(&self) -> Option<&str>

The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

pub fn resource_role(&self) -> Option<&str>

The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

pub fn service_role(&self) -> Option<&str>

The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

pub fn data_pipeline_id(&self) -> Option<&str>

The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

impl RdsMetadata

pub fn builder() -> RdsMetadataBuilder

Creates a new builder-style object to manufacture RdsMetadata.

Trait Implementations

impl Clone for RdsMetadata

fn clone(&self) -> RdsMetadata

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for RdsMetadata

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for RdsMetadata

fn eq(&self, other: &RdsMetadata) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for RdsMetadata