Interface RDSDataSpec.Builder

All Superinterfaces:
Buildable, CopyableBuilder<RDSDataSpec.Builder,RDSDataSpec>, SdkBuilder<RDSDataSpec.Builder,RDSDataSpec>, SdkPojo
Enclosing class:
RDSDataSpec
@Mutable @NotThreadSafe public static interface RDSDataSpec.Builder extends SdkPojo, CopyableBuilder<RDSDataSpec.Builder,RDSDataSpec>

  • Method Details

    • databaseInformation

      RDSDataSpec.Builder databaseInformation(RDSDatabase databaseInformation)

      Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

      Parameters:
      databaseInformation - Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • databaseInformation

      default RDSDataSpec.Builder databaseInformation(Consumer<RDSDatabase.Builder> databaseInformation)

      Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

      This is a convenience method that creates an instance of the RDSDatabase.Builder avoiding the need to create one manually via RDSDatabase.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to databaseInformation(RDSDatabase).

      Parameters:
      databaseInformation - a consumer that will call methods on RDSDatabase.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • selectSqlQuery

      RDSDataSpec.Builder selectSqlQuery(String selectSqlQuery)

      The query that is used to retrieve the observation data for the DataSource.

      Parameters:
      selectSqlQuery - The query that is used to retrieve the observation data for the DataSource.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • databaseCredentials

      RDSDataSpec.Builder databaseCredentials(RDSDatabaseCredentials databaseCredentials)

      The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

      Parameters:
      databaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • databaseCredentials

      default RDSDataSpec.Builder databaseCredentials(Consumer<RDSDatabaseCredentials.Builder> databaseCredentials)

      The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

      This is a convenience method that creates an instance of the RDSDatabaseCredentials.Builder avoiding the need to create one manually via RDSDatabaseCredentials.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to databaseCredentials(RDSDatabaseCredentials).

      Parameters:
      databaseCredentials - a consumer that will call methods on RDSDatabaseCredentials.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • s3StagingLocation

      RDSDataSpec.Builder s3StagingLocation(String s3StagingLocation)

      The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

      Parameters:
      s3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • dataRearrangement

      RDSDataSpec.Builder dataRearrangement(String dataRearrangement)

      A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

      There are multiple parameters that control what data is used to create a datasource:

      • percentBegin

        Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

      • percentEnd

        Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

      • complement

        The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

        For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

        Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}

        Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}

      • strategy

        To change how Amazon ML splits the data for a datasource, use the strategy parameter.

        The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

        The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

        Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}

        Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}

        To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

        The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

        Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

        Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

      Parameters:
      dataRearrangement - A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

      There are multiple parameters that control what data is used to create a datasource:

      • percentBegin

        Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

      • percentEnd

        Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.

      • complement

        The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.

        For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.

        Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}

        Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}

      • strategy

        To change how Amazon ML splits the data for a datasource, use the strategy parameter.

        The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.

        The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:

        Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}

        Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}

        To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.

        The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:

        Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}

        Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • dataSchema

      RDSDataSpec.Builder dataSchema(String dataSchema)

      A JSON string that represents the schema for an Amazon RDS DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

      A DataSchema is not required if you specify a DataSchemaUri

      Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

      { "version": "1.0",

      "recordAnnotationFieldName": "F1",

      "recordWeightFieldName": "F2",

      "targetFieldName": "F3",

      "dataFormat": "CSV",

      "dataFileContainsHeader": true,

      "attributes": [

      { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

      "excludedVariableNames": [ "F6" ] }

      Parameters:
      dataSchema - A JSON string that represents the schema for an Amazon RDS DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

      A DataSchema is not required if you specify a DataSchemaUri

      Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

      { "version": "1.0",

      "recordAnnotationFieldName": "F1",

      "recordWeightFieldName": "F2",

      "targetFieldName": "F3",

      "dataFormat": "CSV",

      "dataFileContainsHeader": true,

      "attributes": [

      { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

      "excludedVariableNames": [ "F6" ] }

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • dataSchemaUri

      RDSDataSpec.Builder dataSchemaUri(String dataSchemaUri)

      The Amazon S3 location of the DataSchema.

      Parameters:
      dataSchemaUri - The Amazon S3 location of the DataSchema.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • resourceRole

      RDSDataSpec.Builder resourceRole(String resourceRole)

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

      Parameters:
      resourceRole - The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • serviceRole

      RDSDataSpec.Builder serviceRole(String serviceRole)

      The role (DataPipelineDefaultRole) assumed by AWS 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.

      Parameters:
      serviceRole - The role (DataPipelineDefaultRole) assumed by AWS 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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • subnetId

      RDSDataSpec.Builder subnetId(String subnetId)

      The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.

      Parameters:
      subnetId - The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • securityGroupIds

      RDSDataSpec.Builder securityGroupIds(Collection<String> securityGroupIds)

      The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

      Parameters:
      securityGroupIds - The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • securityGroupIds

      RDSDataSpec.Builder securityGroupIds(String... securityGroupIds)

      The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

      Parameters:
      securityGroupIds - The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
      Returns:
      Returns a reference to this object so that method calls can be chained together.