tFileOutputPositional Properties in Spark Streaming Jobs - 6.3

Talend Components Reference Guide

EnrichVersion
6.3
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for Data Quality
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
task
Data Governance
Data Quality and Preparation
Design and Development
EnrichPlatform
Talend Studio

Warning

The streaming version of this component is available in the Palette of the Studio only if you have subscribed to Talend Real-time Big Data Platform or Talend Data Fabric.

Component Family

File/Output

 

Basic settings

Define a storage configuration component

Select the configuration component to be used to provide the configuration information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local system.

Note that the configuration component to be used must be present in the same Job. For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write the result in a given HDFS system.

 

Property type

Either Built-In or Repository.

 

 

Built-In: No property data stored centrally.

 

 

Repository: Select the repository file where the properties are stored.

The properties are stored centrally under the Hadoop Cluster node of the Repository tree.

The fields that come after are pre-filled in using the fetched data.

For further information about the Hadoop Cluster node, see the Getting Started Guide.

 

Click this icon to open a database connection wizard and store the database connection parameters you set in the component Basic settings view.

For more information about setting up and storing database connection parameters, see Talend Studio User Guide.

 

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. The schema is either Built-In or stored remotely in the Repository.

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.

 

 

Built-In: You create and store the schema locally for this component only. Related topic: see Talend Studio User Guide.

 

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

 

Folder

Browse to, or enter the path pointing to the data to be used in the file system.

This path must point to a folder rather than a file.

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode, ensure that you have properly configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

 

Action

Select an operation for writing data:

Create: Creates a file and write data in it.

Overwrite: Overwrites the file existing in the directory specified in the Folder field.

 

Compress the data

Select the Compress the data check box to compress the output data.

Hadoop provides different compression formats that help reduce the space needed for storing files and speed up data transfer. When reading a compressed file, the Studio needs to uncompress it before being able to feed it to the input flow.

 

Row separator

Enter the separator used to identify the end of a row.

 

Include header

Select this check box to include the column header to the file.

 

Custom encoding

You may encounter encoding issues when you process the stored data. In that situation, select this check box to display the Encoding list.

Select the encoding from the list or select Custom and define it manually.

 

Formats

Customize the data format of the positional file for each column from the input schema.

  • Column: Select the column you want to customize.

  • Size: Enter the column size.

  • Padding char: Type in between quotes the padding characters used. A space by default.

  • Alignment: Select the appropriate alignment parameter.

  • Keep: If the data in the column or in the field are too long, select the part you want to keep.

Advanced settings

Advanced separator (for numbers)

Select this check box to modify the separators used for numbers:

Thousands separator: define separators for thousands.

Decimal separator: define separators for decimals.

Usage in Spark Streaming Jobs

This component is used as an end component and requires an input link.

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.

Log4j

If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.

Spark Connection

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.