tDynamoDBInput properties for Apache Spark Batch - 7.3

Amazon DynamoDB

Version
7.3
Language
English
Product
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
Module
Talend Studio
Content
Data Governance > Third-party systems > Amazon services (Integration) > Amazon DynamoDB components
Data Quality and Preparation > Third-party systems > Amazon services (Integration) > Amazon DynamoDB components
Design and Development > Third-party systems > Amazon services (Integration) > Amazon DynamoDB components
Last publication date
2024-02-21

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 Talend products with Big Data and Talend Data Fabric.

Basic settings

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 check box 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.

Note: 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.

Note: 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.

Note: This option is available when Use an existing connection and Inherit credentials from AWS role are cleared.

Region

Specify the AWS region by selecting a region name from the list or entering a region between double quotation marks (e.g. "us-east-1") in the list. For more information about the AWS Region, see Regions and Endpoints.

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

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.

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 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 Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • 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.