tFileInputFullRow - 6.1

Talend Components Reference Guide

EnrichVersion
6.1
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

Function

tFileInputFullRow reads a given file row by row.

Purpose

tFileInputFullRow reads a file row by row and sends complete rows as defined in the schema to the next Job component via a Row link.

If you have subscribed to one of the Talend solutions with Big Data, this component is available in the following types of Jobs:

tFileInputFullRow properties

Component family

File/Input

Basic settings

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.

 

File Name

Specify the path to the file to be processed.

 

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.

 

Footer

Enter the number of rows to be skipped at the end of the file.

 

Limit

Enter the maximum number of rows to be processed. If the value is set to 0, no row is read or processed.

 

Skip empty rows

Select this check box to skip the empty rows.

Advanced settings

Encoding

Select the encoding from the list or select Custom and define it manually. This field is compulsory for database data handling.

 

Extract lines at random

Select this check box to set the number of lines to be extracted randomly.

 

tStatCatcher Statistics

Select this check box to gather the Job processing metadata at a Job level as well as at each component level.

Global Variables

NB_LINE: the number of rows processed. This is an After variable and it returns an integer.

ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it.

For further information about variables, see Talend Studio User Guide.

Usage

Use this component to read full rows in delimited files that can get very large.

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.

Scenario: Reading full rows in a delimited file

The following scenario creates a two-component Job that aims at reading complete rows in the delimited file states.csv and displaying the rows on the console.

The content of the file states.csv that holds ten rows of data is as follows:

StateID;StateName
1;Alabama
2;Alaska
3;Arizona
4;Arkansas
5;California
6;Colorado
7;Connecticut
8;Delaware
9;Florida
10;Georgia
  1. Create a new Job and add a tFileInputFullRow component and a tLogRow component by typing their names in the design workspace or dropping them from the Palette.

  2. Link the tFileInputFullRow component to the tLogRow component using a Row > Main connection.

  3. Double-click the tFileInputFullRow component to open its Basic settings view on the Component tab.

  4. Click the [...] button next to Edit schema to view the data to be passed onto the tLogRow component. Note that the schema is read-only and it consists of only one column line.

  5. In the File Name field, browse to or enter the path to the file to be processed. In this scenario, it is E:/states.csv.

  6. In the Row Separator field, enter the separator used to identify the end of a row. In this example, it is the default value \n.

  7. In the Header field, enter 1 to skip the header row at the beginning of the file.

  8. Double-click the tLogRow component to open its Basic settings view on the Component tab.

    In the Mode area, select Table (print values in cells of a table) for better readability of the result.

  9. Press Ctrl+S to save your Job and then F6 to execute it.

    As shown above, ten rows of data in the delimited file states.csv are read one by one, ignoring field separators, and the complete rows of data are displayed on the console.

    To extract fields from rows, you must use tExtractDelimitedFields, tExtractPositionalFields, or tExtractRegexFields. For more information, see tExtractDelimitedFields, tExtractPositionalFields and tExtractRegexFields.

tFileInputFullRow in Talend Map/Reduce Jobs

Warning

The information in this section is only for users that have subscribed to one of the Talend solutions with Big Data and is not applicable to Talend Open Studio for Big Data users.

In a Talend Map/Reduce Job, tFileInputFullRow, as well as the whole Map/Reduce Job using it, generates native Map/Reduce code. This section presents the specific properties of tFileInputFullRow when it is used in that situation. For further information about a Talend Map/Reduce Job, see the Talend Big Data Getting Started Guide.

Component family

MapReduce / Input

 

Basic settings

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 path like /user/talend/in/*.

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

Note that you need to ensure you have properly configured the connection to the Hadoop distribution to be used in the Hadoop configuration tab in the Run view.

 

Die on error

Clear the check box to skip any rows on error and complete the process for error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link.

 

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

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.

Global Variables

ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl + Space to access the variable list and choose the variable to use from it.

For further information about variables, see Talend Studio User Guide.

Usage in Map/Reduce Jobs

In a Talend Map/Reduce Job, it is used as a start component and requires a transformation component as output link. The other components used along with it must be Map/Reduce components, too. They generate native Map/Reduce code that can be executed directly in Hadoop.

Once a Map/Reduce Job is opened in the workspace, tFileInputFullRow as well as the MapReduce family appears in the Palette of the Studio.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs, and non Map/Reduce Jobs.

Hadoop Connection

You need to use the Hadoop Configuration tab in the Run view to define the connection to a given Hadoop distribution for the whole Job.

This connection is effective on a per-Job basis.

Related scenarios

No scenario is available for the Map/Reduce version of this component yet.

tFileInputFullRow properties in Spark Batch Jobs

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 or S3.

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 path like /user/talend/in/*.

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

Note that you need to ensure you have properly configured the connection in the configuration component you have selected from the configuration component list.

 

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

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 Batch Jobs

In a Talend Spark Batch Job, it is used as a start component and requires an output link. The other components used along with it must be Spark Batch components, too. They generate native Spark code that can be executed directly in a Spark cluster.

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.

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.

Related scenarios

No scenario is available for the Spark Batch version of this component yet.

tFileInputFullRow properties in Spark Streaming Jobs

Warning

The streaming version of this component is available in the Palette of the studio on the condition that 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 or S3.

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 path like /user/talend/in/*.

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

Note that you need to ensure you have properly configured the connection in the configuration component you have selected from the configuration component list.

 

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

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

In a Talend Spark Streaming Job, it is used as a start component and requires an output link. The other components used along with it must be Spark Streaming components, too. They generate native Spark code that can be executed directly in a Spark cluster.

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.

Related scenarios

No scenario is available for the Spark Streaming version of this component yet.