tDynamoDBOutput properties for Apache Spark Streaming - 7.1

Amazon DynamoDB

author
Talend Documentation Team
EnrichVersion
7.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Open Studio for Big Data
Talend Real-Time Big Data Platform
task
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
EnrichPlatform
Talend Studio

These properties are used to configure tDynamoDBOutput running in the Spark Streaming Job framework.

The Spark Streaming tDynamoDBOutput component belongs to the Databases family.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

Basic settings

Use an existing connection

Select this check box and in the Component List click the relevant connection component to reuse the connection details you already defined.

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.

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.

Use End Point

Select this check box and in the Server Url field displayed, specify the Web service URL of the DynamoDB database service.

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.

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 in which you need to write data. This table must already exist.

Die on error

Select the check box to stop the execution of the Job when an error occurs.

Advanced settings

Throughput write percent

Enter, without using quotation marks, the percentage (expressed in decimal) to be used of the write capacity pre-defined in Amazon. For further information about this write capacity, see Provision throughput for write.

Advanced properties

Add properties to define extra operations you need tDynamoDBOutput to perform when writing 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 an end component and requires an input 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 Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming 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-premise 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 or tS3Configuration.

    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.