:doc:`MachineLearning <../../machinelearning>` / Client / create_data_source_from_rds

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create_data_source_from_rds
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.. py:method:: MachineLearning.Client.create_data_source_from_rds(**kwargs)

  

  Creates a ``DataSource`` object from an `Amazon Relational Database Service <http://aws.amazon.com/rds/>`__ (Amazon RDS). A ``DataSource`` references data that can be used to perform ``CreateMLModel``, ``CreateEvaluation``, or ``CreateBatchPrediction`` operations.

   

  ``CreateDataSourceFromRDS`` is an asynchronous operation. In response to ``CreateDataSourceFromRDS``, Amazon Machine Learning (Amazon ML) immediately returns and sets the ``DataSource`` status to ``PENDING``. After the ``DataSource`` is created and ready for use, Amazon ML sets the ``Status`` parameter to ``COMPLETED``. ``DataSource`` in the ``COMPLETED`` or ``PENDING`` state can be used only to perform ``>CreateMLModel``>, ``CreateEvaluation``, or ``CreateBatchPrediction`` operations.

   

  If Amazon ML cannot accept the input source, it sets the ``Status`` parameter to ``FAILED`` and includes an error message in the ``Message`` attribute of the ``GetDataSource`` operation response.

  

  See also: `AWS API Documentation <https://docs.aws.amazon.com/goto/WebAPI/machinelearning-2014-12-12/CreateDataSourceFromRDS>`_  


  **Request Syntax**
  ::

    response = client.create_data_source_from_rds(
        DataSourceId='string',
        DataSourceName='string',
        RDSData={
            'DatabaseInformation': {
                'InstanceIdentifier': 'string',
                'DatabaseName': 'string'
            },
            'SelectSqlQuery': 'string',
            'DatabaseCredentials': {
                'Username': 'string',
                'Password': 'string'
            },
            'S3StagingLocation': 'string',
            'DataRearrangement': 'string',
            'DataSchema': 'string',
            'DataSchemaUri': 'string',
            'ResourceRole': 'string',
            'ServiceRole': 'string',
            'SubnetId': 'string',
            'SecurityGroupIds': [
                'string',
            ]
        },
        RoleARN='string',
        ComputeStatistics=True|False
    )
    
  :type DataSourceId: string
  :param DataSourceId: **[REQUIRED]** 

    A user-supplied ID that uniquely identifies the ``DataSource``. Typically, an Amazon Resource Number (ARN) becomes the ID for a ``DataSource``.

    

  
  :type DataSourceName: string
  :param DataSourceName: 

    A user-supplied name or description of the ``DataSource``.

    

  
  :type RDSData: dict
  :param RDSData: **[REQUIRED]** 

    The data specification of an Amazon RDS ``DataSource``:

     

    
    * DatabaseInformation - 

      
      * ``DatabaseName`` - The name of the Amazon RDS database.
       
      * ``InstanceIdentifier`` - A unique identifier for the Amazon RDS database instance.
      

    
     
    * DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.
     
    * ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see `Role templates <https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html>`__ for data pipelines.
     
    * ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see `Role templates <https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html>`__ for data pipelines.
     
    * SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [ ``SubnetId``, ``SecurityGroupIds``] pair for a VPC-based RDS DB instance.
     
    * SelectSqlQuery - A query that is used to retrieve the observation data for the ``Datasource``.
     
    * S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using ``SelectSqlQuery`` is stored in this location.
     
    * DataSchemaUri - The Amazon S3 location of the ``DataSchema``.
     
    * DataSchema - A JSON string representing the schema. This is not required if ``DataSchemaUri`` is specified.
     
    * DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the ``Datasource``. Sample - ``"{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"``
    

    

  
    - **DatabaseInformation** *(dict) --* **[REQUIRED]** 

      Describes the ``DatabaseName`` and ``InstanceIdentifier`` of an Amazon RDS database.

      

    
      - **InstanceIdentifier** *(string) --* **[REQUIRED]** 

        The ID of an RDS DB instance.

        

      
      - **DatabaseName** *(string) --* **[REQUIRED]** 

        The name of a database hosted on an RDS DB instance.

        

      
    
    - **SelectSqlQuery** *(string) --* **[REQUIRED]** 

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

      

    
    - **DatabaseCredentials** *(dict) --* **[REQUIRED]** 

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

      

    
      - **Username** *(string) --* **[REQUIRED]** 

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

        

      
      - **Password** *(string) --* **[REQUIRED]** 

        The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the ``RDSSelectQuery`` query.

        

      
    
    - **S3StagingLocation** *(string) --* **[REQUIRED]** 

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

      

    
    - **DataRearrangement** *(string) --* 

      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"}}``
      

      

    
    - **DataSchema** *(string) --* 

      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" ] }

      

    
    - **DataSchemaUri** *(string) --* 

      The Amazon S3 location of the ``DataSchema``.

      

    
    - **ResourceRole** *(string) --* **[REQUIRED]** 

      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 <https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html>`__ for data pipelines.

      

    
    - **ServiceRole** *(string) --* **[REQUIRED]** 

      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 <https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html>`__ for data pipelines.

      

    
    - **SubnetId** *(string) --* **[REQUIRED]** 

      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.

      

    
    - **SecurityGroupIds** *(list) --* **[REQUIRED]** 

      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.

      

    
      - *(string) --* 

      
  
  
  :type RoleARN: string
  :param RoleARN: **[REQUIRED]** 

    The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the ``SelectSqlQuery`` query from Amazon RDS to Amazon S3.

    

  
  :type ComputeStatistics: boolean
  :param ComputeStatistics: 

    The compute statistics for a ``DataSource``. The statistics are generated from the observation data referenced by a ``DataSource``. Amazon ML uses the statistics internally during ``MLModel`` training. This parameter must be set to ``true`` if the DataSource  needs to be used for ``MLModel`` training.

    

  
  
  :rtype: dict
  :returns: 
    
    **Response Syntax**

    
    ::

      {
          'DataSourceId': 'string'
      }
      
    **Response Structure**

    

    - *(dict) --* 

      Represents the output of a ``CreateDataSourceFromRDS`` operation, and is an acknowledgement that Amazon ML received the request.

       

      The ``CreateDataSourceFromRDS``> operation is asynchronous. You can poll for updates by using the ``GetBatchPrediction`` operation and checking the ``Status`` parameter. You can inspect the ``Message`` when ``Status`` shows up as ``FAILED``. You can also check the progress of the copy operation by going to the ``DataPipeline`` console and looking up the pipeline using the ``pipelineId`` from the describe call.

      
      

      - **DataSourceId** *(string) --* 

        A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the ``DataSourceID`` in the request.

        
  
  **Exceptions**
  
  *   :py:class:`MachineLearning.Client.exceptions.InvalidInputException`

  
  *   :py:class:`MachineLearning.Client.exceptions.InternalServerException`

  
  *   :py:class:`MachineLearning.Client.exceptions.IdempotentParameterMismatchException`

  