tFileInputFullRow 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/Input

 

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.

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

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

 

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. Note that if you make changes, the schema automatically becomes built-in.

  

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/File

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

If the path you set points to a folder, this component will read all of the files stored in that folder, for example, /user/talend/in; if sub-folders exist, the sub-folders are automatically ignored unless you define the property spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive to be true in the Advanced properties table in the Spark configuration tab.

If you want to specify more than one files or directories in this field, separate each path using a comma (,).

If the file to be read is a compressed one, enter the file name with its extension; then tHDFSFullRow automatically decompresses it at runtime. The supported compression formats and their corresponding extensions are:

  • DEFLATE: *.deflate

  • gzip: *.gz

  • bzip2: *.bz2

  • LZO: *.lzo

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.

 

Die on error

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

 

Row separator

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

 

Header

Enter the number of rows to be skipped in the beginning of file.

 

Skip empty rows

Select this check box to skip the empty rows.

Advanced settings

Set minimum partitions

Select this check box to control the number of partitions to be created from the input data over the default partitioning behavior of Spark.

In the displayed field, enter, without quotation marks, the minimum number of partitions you want to obtain.

When you want to control the partition number, you can generally set at least as many partitions as the number of executors for parallelism, while bearing in mind the available memory and the data transfer pressure on your network.

 

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.

Then select the encoding to be used from the list or select Custom and define it manually.

Usage in Spark Streaming Jobs

This component is used as a start component and requires an output link.

This component is only used to provide the lookup flow (the right side of a join operation) to the main flow of a tMap component. In this situation, the lookup model used by this tMap must be Load once.

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.