RDSDataSpec (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.machinelearning.model.RDSDataSpec

All Implemented Interfaces:

Serializable, Cloneable
```
public class RDSDataSpec
extends Object
implements Serializable, Cloneable
```
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

RDSDataSpec()

Constructors
Constructor and Description
`RDSDataSpec()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`RDSDataSpec`	`clone()`
`boolean`	`equals(Object obj)`
`RDSDatabaseCredentials`	`getDatabaseCredentials()` The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
`RDSDatabase`	`getDatabaseInformation()` Describes the `DatabaseName` and `InstanceIdentifier` of an an Amazon RDS database.
`String`	`getDataRearrangement()` A JSON string that represents the splitting and rearrangement processing to be applied to a `DataSource`.
`String`	`getDataSchema()` A JSON string that represents the schema for an Amazon RDS `DataSource`.
`String`	`getDataSchemaUri()` The Amazon S3 location of the `DataSchema`.
`String`	`getResourceRole()` 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.
`String`	`getS3StagingLocation()` The Amazon S3 location for staging Amazon RDS data.
`List<String>`	`getSecurityGroupIds()` The security group IDs to be used to access a VPC-based RDS DB instance.
`String`	`getSelectSqlQuery()` The query that is used to retrieve the observation data for the `DataSource`.
`String`	`getServiceRole()` The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3.
`String`	`getSubnetId()` The subnet ID to be used to access a VPC-based RDS DB instance.
`int`	`hashCode()`
`void`	`setDatabaseCredentials(RDSDatabaseCredentials databaseCredentials)` The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
`void`	`setDatabaseInformation(RDSDatabase databaseInformation)` Describes the `DatabaseName` and `InstanceIdentifier` of an an Amazon RDS database.
`void`	`setDataRearrangement(String dataRearrangement)` A JSON string that represents the splitting and rearrangement processing to be applied to a `DataSource`.
`void`	`setDataSchema(String dataSchema)` A JSON string that represents the schema for an Amazon RDS `DataSource`.
`void`	`setDataSchemaUri(String dataSchemaUri)` The Amazon S3 location of the `DataSchema`.
`void`	`setResourceRole(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.
`void`	`setS3StagingLocation(String s3StagingLocation)` The Amazon S3 location for staging Amazon RDS data.
`void`	`setSecurityGroupIds(Collection<String> securityGroupIds)` The security group IDs to be used to access a VPC-based RDS DB instance.
`void`	`setSelectSqlQuery(String selectSqlQuery)` The query that is used to retrieve the observation data for the `DataSource`.
`void`	`setServiceRole(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.
`void`	`setSubnetId(String subnetId)` The subnet ID to be used to access a VPC-based RDS DB instance.
`String`	`toString()` Returns a string representation of this object; useful for testing and debugging.
`RDSDataSpec`	`withDatabaseCredentials(RDSDatabaseCredentials databaseCredentials)` The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
`RDSDataSpec`	`withDatabaseInformation(RDSDatabase databaseInformation)` Describes the `DatabaseName` and `InstanceIdentifier` of an an Amazon RDS database.
`RDSDataSpec`	`withDataRearrangement(String dataRearrangement)` A JSON string that represents the splitting and rearrangement processing to be applied to a `DataSource`.
`RDSDataSpec`	`withDataSchema(String dataSchema)` A JSON string that represents the schema for an Amazon RDS `DataSource`.
`RDSDataSpec`	`withDataSchemaUri(String dataSchemaUri)` The Amazon S3 location of the `DataSchema`.
`RDSDataSpec`	`withResourceRole(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.
`RDSDataSpec`	`withS3StagingLocation(String s3StagingLocation)` The Amazon S3 location for staging Amazon RDS data.
`RDSDataSpec`	`withSecurityGroupIds(Collection<String> securityGroupIds)` The security group IDs to be used to access a VPC-based RDS DB instance.
`RDSDataSpec`	`withSecurityGroupIds(String... securityGroupIds)` The security group IDs to be used to access a VPC-based RDS DB instance.
`RDSDataSpec`	`withSelectSqlQuery(String selectSqlQuery)` The query that is used to retrieve the observation data for the `DataSource`.
`RDSDataSpec`	`withServiceRole(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.
`RDSDataSpec`	`withSubnetId(String subnetId)` The subnet ID to be used to access a VPC-based RDS DB instance.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - RDSDataSpec
```
public RDSDataSpec()
```
- Method Detail
  - setDatabaseInformation
```
public void setDatabaseInformation(RDSDatabase databaseInformation)
```
    Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
    
    Parameters:
    
    databaseInformation - Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
  - getDatabaseInformation
```
public RDSDatabase getDatabaseInformation()
```
    Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
    
    Returns:
    
    Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
  - withDatabaseInformation
```
public RDSDataSpec withDatabaseInformation(RDSDatabase databaseInformation)
```
    Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
    
    Parameters:
    
    databaseInformation - Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setSelectSqlQuery
```
public void setSelectSqlQuery(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.
  - getSelectSqlQuery
```
public String getSelectSqlQuery()
```
    The query that is used to retrieve the observation data for the DataSource.
    
    Returns:
    
    The query that is used to retrieve the observation data for the DataSource.
  - withSelectSqlQuery
```
public RDSDataSpec withSelectSqlQuery(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.
  - setDatabaseCredentials
```
public void setDatabaseCredentials(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.
  - getDatabaseCredentials
```
public RDSDatabaseCredentials getDatabaseCredentials()
```
    The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
    
    Returns:
    
    The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
  - withDatabaseCredentials
```
public RDSDataSpec withDatabaseCredentials(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.
  - setS3StagingLocation
```
public void setS3StagingLocation(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.
  - getS3StagingLocation
```
public String getS3StagingLocation()
```
    The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
    
    Returns:
    
    The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
  - withS3StagingLocation
```
public RDSDataSpec withS3StagingLocation(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.
  - setDataRearrangement
```
public void setDataRearrangement(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"}}
  - getDataRearrangement
```
public String getDataRearrangement()
```
    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:
    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"}}
  - withDataRearrangement
```
public RDSDataSpec withDataRearrangement(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.
  - setDataSchema
```
public void setDataSchema(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" ] }
  - getDataSchema
```
public String getDataSchema()
```
    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:
    
    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" ] }
  - withDataSchema
```
public RDSDataSpec withDataSchema(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.
  - setDataSchemaUri
```
public void setDataSchemaUri(String dataSchemaUri)
```
    The Amazon S3 location of the DataSchema.
    
    Parameters:
    
    dataSchemaUri - The Amazon S3 location of the DataSchema.
  - getDataSchemaUri
```
public String getDataSchemaUri()
```
    The Amazon S3 location of the DataSchema.
    
    Returns:
    
    The Amazon S3 location of the DataSchema.
  - withDataSchemaUri
```
public RDSDataSpec withDataSchemaUri(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.
  - setResourceRole
```
public void setResourceRole(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.
  - getResourceRole
```
public String getResourceRole()
```
    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:
    
    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.
  - withResourceRole
```
public RDSDataSpec withResourceRole(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.
  - setServiceRole
```
public void setServiceRole(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.
  - getServiceRole
```
public String getServiceRole()
```
    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:
    
    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.
  - withServiceRole
```
public RDSDataSpec withServiceRole(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.
  - setSubnetId
```
public void setSubnetId(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.
  - getSubnetId
```
public String getSubnetId()
```
    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:
    
    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.
  - withSubnetId
```
public RDSDataSpec withSubnetId(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.
  - getSecurityGroupIds
```
public List<String> getSecurityGroupIds()
```
    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:
    
    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.
  - setSecurityGroupIds
```
public void setSecurityGroupIds(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.
  - withSecurityGroupIds
```
public RDSDataSpec withSecurityGroupIds(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.
    
    NOTE: This method appends the values to the existing list (if any). Use setSecurityGroupIds(java.util.Collection) or withSecurityGroupIds(java.util.Collection) if you want to override the existing values.
    
    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.
  - withSecurityGroupIds
```
public RDSDataSpec withSecurityGroupIds(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.
  - toString
```
public String toString()
```
    Returns a string representation of this object; useful for testing and debugging.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    A string representation of this object.
    
    See Also:
    
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public RDSDataSpec clone()
```
    Overrides:
    
    clone in class Object

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Class RDSDataSpec

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

RDSDataSpec

Method Detail

setDatabaseInformation

getDatabaseInformation

withDatabaseInformation

setSelectSqlQuery

getSelectSqlQuery

withSelectSqlQuery

setDatabaseCredentials

getDatabaseCredentials

withDatabaseCredentials

setS3StagingLocation

getS3StagingLocation

withS3StagingLocation

setDataRearrangement

getDataRearrangement

withDataRearrangement

setDataSchema

getDataSchema

withDataSchema

setDataSchemaUri

getDataSchemaUri

withDataSchemaUri

setResourceRole

getResourceRole

withResourceRole

setServiceRole

getServiceRole

withServiceRole

setSubnetId

getSubnetId

withSubnetId

getSecurityGroupIds

setSecurityGroupIds

withSecurityGroupIds

withSecurityGroupIds

toString

equals

hashCode

clone