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/*
* Copyright 2011-2016 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not
* use this file except in compliance with the License. A copy of the License is
* located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on
* an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
* express or implied. See the License for the specific language governing
* permissions and limitations under the License.
*/
package com.amazonaws.services.machinelearning.model;
import java.io.Serializable;
/**
*
* The data specification of an Amazon Relational Database Service (Amazon RDS)
* DataSource.
*
*/
public class RDSDataSpec implements Serializable, Cloneable {
/**
*
* Describes the DatabaseName and
* InstanceIdentifier of an an Amazon RDS database.
*
*/
private RDSDatabase databaseInformation;
/**
*
* The query that is used to retrieve the observation data for the
* DataSource.
*
*/
private String selectSqlQuery;
/**
*
* The AWS Identity and Access Management (IAM) credentials that are used
* connect to the Amazon RDS database.
*
*/
private RDSDatabaseCredentials databaseCredentials;
/**
*
* The Amazon S3 location for staging Amazon RDS data. The data retrieved
* from Amazon RDS using SelectSqlQuery is stored in this
* location.
*
*/
private String s3StagingLocation;
/**
*
* 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"}}
*
*
*
*/
private String dataRearrangement;
/**
*
* 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" ] }
*
*
*/
private String dataSchema;
/**
*
* The Amazon S3 location of the DataSchema.
*
*/
private String dataSchemaUri;
/**
*
* 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.
*
*/
private String resourceRole;
/**
*
* 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.
*
*/
private String serviceRole;
/**
*
* 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.
*
*/
private String subnetId;
/**
*
* 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.
*
*/
private com.amazonaws.internal.SdkInternalList securityGroupIds;
/**
*
* Describes the DatabaseName and
* InstanceIdentifier of an an Amazon RDS database.
*
*
* @param databaseInformation
* Describes the DatabaseName and
* InstanceIdentifier of an an Amazon RDS database.
*/
public void setDatabaseInformation(RDSDatabase databaseInformation) {
this.databaseInformation = databaseInformation;
}
/**
*
* Describes the DatabaseName and
* InstanceIdentifier of an an Amazon RDS database.
*
*
* @return Describes the DatabaseName and
* InstanceIdentifier of an an Amazon RDS database.
*/
public RDSDatabase getDatabaseInformation() {
return this.databaseInformation;
}
/**
*
* Describes the DatabaseName and
* InstanceIdentifier of an an Amazon RDS database.
*
*
* @param databaseInformation
* Describes the DatabaseName and
* InstanceIdentifier of an an Amazon RDS database.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withDatabaseInformation(RDSDatabase databaseInformation) {
setDatabaseInformation(databaseInformation);
return this;
}
/**
*
* The query that is used to retrieve the observation data for the
* DataSource.
*
*
* @param selectSqlQuery
* The query that is used to retrieve the observation data for the
* DataSource.
*/
public void setSelectSqlQuery(String selectSqlQuery) {
this.selectSqlQuery = selectSqlQuery;
}
/**
*
* The query that is used to retrieve the observation data for the
* DataSource.
*
*
* @return The query that is used to retrieve the observation data for the
* DataSource.
*/
public String getSelectSqlQuery() {
return this.selectSqlQuery;
}
/**
*
* The query that is used to retrieve the observation data for the
* DataSource.
*
*
* @param selectSqlQuery
* The query that is used to retrieve the observation data for the
* DataSource.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withSelectSqlQuery(String selectSqlQuery) {
setSelectSqlQuery(selectSqlQuery);
return this;
}
/**
*
* The AWS Identity and Access Management (IAM) credentials that are used
* connect to the Amazon RDS database.
*
*
* @param databaseCredentials
* The AWS Identity and Access Management (IAM) credentials that are
* used connect to the Amazon RDS database.
*/
public void setDatabaseCredentials(
RDSDatabaseCredentials databaseCredentials) {
this.databaseCredentials = databaseCredentials;
}
/**
*
* The AWS Identity and Access Management (IAM) credentials that are used
* connect to the Amazon RDS database.
*
*
* @return The AWS Identity and Access Management (IAM) credentials that are
* used connect to the Amazon RDS database.
*/
public RDSDatabaseCredentials getDatabaseCredentials() {
return this.databaseCredentials;
}
/**
*
* The AWS Identity and Access Management (IAM) credentials that are used
* connect to the Amazon RDS database.
*
*
* @param databaseCredentials
* The AWS Identity and Access Management (IAM) credentials that are
* used connect to the Amazon RDS database.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withDatabaseCredentials(
RDSDatabaseCredentials databaseCredentials) {
setDatabaseCredentials(databaseCredentials);
return this;
}
/**
*
* The Amazon S3 location for staging Amazon RDS data. The data retrieved
* from Amazon RDS using SelectSqlQuery is stored in this
* location.
*
*
* @param s3StagingLocation
* The Amazon S3 location for staging Amazon RDS data. The data
* retrieved from Amazon RDS using SelectSqlQuery is
* stored in this location.
*/
public void setS3StagingLocation(String s3StagingLocation) {
this.s3StagingLocation = s3StagingLocation;
}
/**
*
* The Amazon S3 location for staging Amazon RDS data. The data retrieved
* from Amazon RDS using SelectSqlQuery is stored in this
* location.
*
*
* @return The Amazon S3 location for staging Amazon RDS data. The data
* retrieved from Amazon RDS using SelectSqlQuery is
* stored in this location.
*/
public String getS3StagingLocation() {
return this.s3StagingLocation;
}
/**
*
* The Amazon S3 location for staging Amazon RDS data. The data retrieved
* from Amazon RDS using SelectSqlQuery is stored in this
* location.
*
*
* @param s3StagingLocation
* The Amazon S3 location for staging Amazon RDS data. The data
* retrieved from Amazon RDS using SelectSqlQuery is
* stored in this location.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withS3StagingLocation(String s3StagingLocation) {
setS3StagingLocation(s3StagingLocation);
return this;
}
/**
*
* 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"}}
*
*
*
*
* @param 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"}}
*
*
*/
public void setDataRearrangement(String dataRearrangement) {
this.dataRearrangement = 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"}}
*
*
*
*
* @return 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"}}
*
*
*/
public String getDataRearrangement() {
return this.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"}}
*
*
*
*
* @param 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"}}
*
*
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withDataRearrangement(String dataRearrangement) {
setDataRearrangement(dataRearrangement);
return this;
}
/**
*
* 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" ] }
*
*
*
* @param 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" ] }
*
*/
public void setDataSchema(String dataSchema) {
this.dataSchema = 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" ] }
*
*
*
* @return 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" ] }
*
*/
public String getDataSchema() {
return this.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" ] }
*
*
*
* @param 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" ] }
*
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withDataSchema(String dataSchema) {
setDataSchema(dataSchema);
return this;
}
/**
*
* The Amazon S3 location of the DataSchema.
*
*
* @param dataSchemaUri
* The Amazon S3 location of the DataSchema.
*/
public void setDataSchemaUri(String dataSchemaUri) {
this.dataSchemaUri = dataSchemaUri;
}
/**
*
* The Amazon S3 location of the DataSchema.
*
*
* @return The Amazon S3 location of the DataSchema.
*/
public String getDataSchemaUri() {
return this.dataSchemaUri;
}
/**
*
* The Amazon S3 location of the DataSchema.
*
*
* @param dataSchemaUri
* The Amazon S3 location of the DataSchema.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withDataSchemaUri(String dataSchemaUri) {
setDataSchemaUri(dataSchemaUri);
return this;
}
/**
*
* 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.
*
*
* @param 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.
*/
public void setResourceRole(String resourceRole) {
this.resourceRole = 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.
*
*
* @return 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.
*/
public String getResourceRole() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withResourceRole(String resourceRole) {
setResourceRole(resourceRole);
return this;
}
/**
*
* 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.
*
*
* @param 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.
*/
public void setServiceRole(String serviceRole) {
this.serviceRole = 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.
*
*
* @return 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.
*/
public String getServiceRole() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withServiceRole(String serviceRole) {
setServiceRole(serviceRole);
return this;
}
/**
*
* 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.
*
*
* @param 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.
*/
public void setSubnetId(String subnetId) {
this.subnetId = 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.
*
*
* @return 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.
*/
public String getSubnetId() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withSubnetId(String subnetId) {
setSubnetId(subnetId);
return this;
}
/**
*
* 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.
*
*
* @return 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.
*/
public java.util.List getSecurityGroupIds() {
if (securityGroupIds == null) {
securityGroupIds = new com.amazonaws.internal.SdkInternalList();
}
return 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.
*
*
* @param 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.
*/
public void setSecurityGroupIds(
java.util.Collection securityGroupIds) {
if (securityGroupIds == null) {
this.securityGroupIds = null;
return;
}
this.securityGroupIds = new com.amazonaws.internal.SdkInternalList(
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 {@link #setSecurityGroupIds(java.util.Collection)} or
* {@link #withSecurityGroupIds(java.util.Collection)} if you want to
* override the existing values.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withSecurityGroupIds(String... securityGroupIds) {
if (this.securityGroupIds == null) {
setSecurityGroupIds(new com.amazonaws.internal.SdkInternalList(
securityGroupIds.length));
}
for (String ele : securityGroupIds) {
this.securityGroupIds.add(ele);
}
return this;
}
/**
*
* 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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be
* chained together.
*/
public RDSDataSpec withSecurityGroupIds(
java.util.Collection securityGroupIds) {
setSecurityGroupIds(securityGroupIds);
return this;
}
/**
* Returns a string representation of this object; useful for testing and
* debugging.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getDatabaseInformation() != null)
sb.append("DatabaseInformation: " + getDatabaseInformation() + ",");
if (getSelectSqlQuery() != null)
sb.append("SelectSqlQuery: " + getSelectSqlQuery() + ",");
if (getDatabaseCredentials() != null)
sb.append("DatabaseCredentials: " + getDatabaseCredentials() + ",");
if (getS3StagingLocation() != null)
sb.append("S3StagingLocation: " + getS3StagingLocation() + ",");
if (getDataRearrangement() != null)
sb.append("DataRearrangement: " + getDataRearrangement() + ",");
if (getDataSchema() != null)
sb.append("DataSchema: " + getDataSchema() + ",");
if (getDataSchemaUri() != null)
sb.append("DataSchemaUri: " + getDataSchemaUri() + ",");
if (getResourceRole() != null)
sb.append("ResourceRole: " + getResourceRole() + ",");
if (getServiceRole() != null)
sb.append("ServiceRole: " + getServiceRole() + ",");
if (getSubnetId() != null)
sb.append("SubnetId: " + getSubnetId() + ",");
if (getSecurityGroupIds() != null)
sb.append("SecurityGroupIds: " + getSecurityGroupIds());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof RDSDataSpec == false)
return false;
RDSDataSpec other = (RDSDataSpec) obj;
if (other.getDatabaseInformation() == null
^ this.getDatabaseInformation() == null)
return false;
if (other.getDatabaseInformation() != null
&& other.getDatabaseInformation().equals(
this.getDatabaseInformation()) == false)
return false;
if (other.getSelectSqlQuery() == null
^ this.getSelectSqlQuery() == null)
return false;
if (other.getSelectSqlQuery() != null
&& other.getSelectSqlQuery().equals(this.getSelectSqlQuery()) == false)
return false;
if (other.getDatabaseCredentials() == null
^ this.getDatabaseCredentials() == null)
return false;
if (other.getDatabaseCredentials() != null
&& other.getDatabaseCredentials().equals(
this.getDatabaseCredentials()) == false)
return false;
if (other.getS3StagingLocation() == null
^ this.getS3StagingLocation() == null)
return false;
if (other.getS3StagingLocation() != null
&& other.getS3StagingLocation().equals(
this.getS3StagingLocation()) == false)
return false;
if (other.getDataRearrangement() == null
^ this.getDataRearrangement() == null)
return false;
if (other.getDataRearrangement() != null
&& other.getDataRearrangement().equals(
this.getDataRearrangement()) == false)
return false;
if (other.getDataSchema() == null ^ this.getDataSchema() == null)
return false;
if (other.getDataSchema() != null
&& other.getDataSchema().equals(this.getDataSchema()) == false)
return false;
if (other.getDataSchemaUri() == null ^ this.getDataSchemaUri() == null)
return false;
if (other.getDataSchemaUri() != null
&& other.getDataSchemaUri().equals(this.getDataSchemaUri()) == false)
return false;
if (other.getResourceRole() == null ^ this.getResourceRole() == null)
return false;
if (other.getResourceRole() != null
&& other.getResourceRole().equals(this.getResourceRole()) == false)
return false;
if (other.getServiceRole() == null ^ this.getServiceRole() == null)
return false;
if (other.getServiceRole() != null
&& other.getServiceRole().equals(this.getServiceRole()) == false)
return false;
if (other.getSubnetId() == null ^ this.getSubnetId() == null)
return false;
if (other.getSubnetId() != null
&& other.getSubnetId().equals(this.getSubnetId()) == false)
return false;
if (other.getSecurityGroupIds() == null
^ this.getSecurityGroupIds() == null)
return false;
if (other.getSecurityGroupIds() != null
&& other.getSecurityGroupIds().equals(
this.getSecurityGroupIds()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime
* hashCode
+ ((getDatabaseInformation() == null) ? 0
: getDatabaseInformation().hashCode());
hashCode = prime
* hashCode
+ ((getSelectSqlQuery() == null) ? 0 : getSelectSqlQuery()
.hashCode());
hashCode = prime
* hashCode
+ ((getDatabaseCredentials() == null) ? 0
: getDatabaseCredentials().hashCode());
hashCode = prime
* hashCode
+ ((getS3StagingLocation() == null) ? 0
: getS3StagingLocation().hashCode());
hashCode = prime
* hashCode
+ ((getDataRearrangement() == null) ? 0
: getDataRearrangement().hashCode());
hashCode = prime * hashCode
+ ((getDataSchema() == null) ? 0 : getDataSchema().hashCode());
hashCode = prime
* hashCode
+ ((getDataSchemaUri() == null) ? 0 : getDataSchemaUri()
.hashCode());
hashCode = prime
* hashCode
+ ((getResourceRole() == null) ? 0 : getResourceRole()
.hashCode());
hashCode = prime
* hashCode
+ ((getServiceRole() == null) ? 0 : getServiceRole().hashCode());
hashCode = prime * hashCode
+ ((getSubnetId() == null) ? 0 : getSubnetId().hashCode());
hashCode = prime
* hashCode
+ ((getSecurityGroupIds() == null) ? 0 : getSecurityGroupIds()
.hashCode());
return hashCode;
}
@Override
public RDSDataSpec clone() {
try {
return (RDSDataSpec) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException(
"Got a CloneNotSupportedException from Object.clone() "
+ "even though we're Cloneable!", e);
}
}
}