# tDynamoDBInput properties for Apache Spark Batch These properties are used to configure tDynamoDBInput running in the Spark Batch Job framework. The Spark Batch tDynamoDBInput component belongs to the Databases family. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. This component is not supported with Spark 4.0.x. ## Basic settings | Properties | Description | | --- | --- | | Use an existing connection | Select this check box and in the Component
List drop-down list, select the desired connection component to
reuse the connection details you already defined. | | Inherit credentials from AWS
role | Select this checkbox to leverage the instance profile credentials. These
credentials can be used on Amazon EC2 instances, and are delivered through the
Amazon EC2 metadata service. To use this option, your Job must be running within
Amazon EC2 or other services that can leverage IAM Roles for access to resources.
For more information, see [Using an IAM Role to Grant Permissions to
Applications Running on Amazon EC2 Instances](http://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_use_switch-role-ec2.html).

Information noteNote: This option is available when Use an existing
connection is cleared. | | Access
Key | Enter the access
key ID that uniquely identifies an AWS Account. For further information about how to
get your Access Key and Secret Key, see [Getting Your AWS Access
Keys](http://docs.aws.amazon.com/ses/latest/DeveloperGuide/get-aws-keys.html).

Information noteNote: This option is available when Use an existing
connection and Inherit credentials from AWS
role are cleared. | | Secret
Key | Enter the secret access key,
constituting the security credentials in combination with the access Key.

To enter the secret key, click the [...]
button next to the secret key field, and then in the pop-up dialog box enter the password
between double quotes and click OK to save the
settings.

Information noteNote: This option is available when Use an existing
connection and Inherit credentials from AWS
role are cleared. | | Region | Specify the AWS region by selecting or entering a region name between double
quotation marks (e.g. *"us-east-1"*). For more information about the
AWS Region, see [Regions and Endpoints](http://docs.aws.amazon.com/general/latest/gr/rande.html#s3_region). | | Use End Point | Select this check box and in the Server URL
field displayed, specify the Web service URL of the DynamoDB database service. | | Schema and Edit
schema |

A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.
- Built-In: You create and store the schema locally for this component only.
- Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.
Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:
- View schema: choose this option to view the schema only.
- Change to built-in property: choose this option to change the schema to Built-in for local changes.
- Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion.
  
  If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.

| | Table Name | Specify the name of the table from which you need to read data. | ## Advanced settings | Properties | Description | | --- | --- | | Number of scan segments | Enter, without using quotation marks, the number of segments for the parallel scan. | | Number of partitions | Enter, without using quotation marks, the maximum number of partitions into which you want
Spark to split the input data so that the Spark executors can process the data in parallel.
It is recommended to put a number less or equal to the number of segments. | | Throughput read percent | Enter, without using quotation marks, the percentage (expressed in decimal) to be used of
the read capacity pre-defined in Amazon. The rest of this capacity is spared for other
non-*Talend* applications. For further information about this read
capacity, see [Provision throughput for read](http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ProvisionedThroughput.html#HowItWorks.ProvisionedThroughput.Reads). | | Advanced settings | Add properties to define extra operations you need tDynamoDBInput to perform when reading data.

This table is present for future evolution of the component and using it requires the
high-level knowledge of DynamoDB development. Currently, there are no interesting user
configurable properties. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as a start component and requires an
output link.

This component should use a tDynamoDBConfiguration
component present in the same Job to connect to a DynamoDB database. You need to drop a
tDynamoDBConfiguration component alongside this
component and configure the Basic settings of this
component to use tDynamoDBConfiguration.

This component, along with the Spark Batch component Palette it belongs to,
appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a
scenario presents only Standard Jobs, that is to
say traditional
Talend
data integration Jobs. | | Spark Connection | In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

Yarn mode (Yarn client or Yarn cluster):
- When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.
- When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.
- When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
- When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch.

If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis. |