July 30, 2023

tFileInputFullRow – Docs for ESB 7.x

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tFileInputFullRow

Reads a file row by row and sends complete rows of data as defined in the schema to
the next component via a Row link.

Depending on the Talend
product you are using, this component can be used in one, some or all of the following
Job frameworks:

tFileInputFullRow Standard properties

These properties are used to configure tFileInputFullRow running in the Standard Job framework.

The Standard
tFileInputFullRow component belongs to the File family.

The component in this framework is available in all Talend
products.

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. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

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.

 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

File Name

Specify the path to the file to be processed.

Warning: Use absolute path (instead of relative path) for
this field to avoid possible errors.

Row separator

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. The
supported encodings depend on the JVM that you are using. For more information, see
https://docs.oracle.com.

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

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

Usage rule

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

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:

Reading full rows in a delimited file

  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.

    tFileInputFullRow_1.png

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

    tFileInputFullRow_2.png

  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.

    tFileInputFullRow_3.png

  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

    .
  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.

    tFileInputFullRow_4.png

    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.

    tFileInputFullRow_5.png

    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 MapReduce properties (deprecated)

These properties are used to configure tFileInputFullRow running in the MapReduce Job framework.

The MapReduce
tFileInputFullRow component belongs to the MapReduce family.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.

The MapReduce framework is deprecated from Talend 7.3 onwards. Use Talend Jobs for Apache Spark to accomplish your integration tasks.

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. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Click Edit
schema to make changes to the schema.

Note: If you
make changes, the schema automatically becomes built-in.
 

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.

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 mapreduce.input.fileinputformat.input.dir.recursive to be true in the Hadoop properties table in the Hadoop 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

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

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

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

Usage rule

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 for Apache Spark Batch

These properties are used to configure tFileInputFullRow running in the Spark Batch Job framework.

The Spark Batch
tFileInputFullRow component belongs to the File family.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.

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.

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. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Click Edit
schema to make changes to the schema.

Note: If you
make changes, the schema automatically becomes built-in.
 

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.

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.

  • Depending on the filesystem to be used, properly configure the corresponding
    configuration component placed in your Job, for example, a
    tHDFSConfiguration component for HDFS, a
    tS3Configuration component for S3 and a
    tAzureFSConfiguration for Azure Storage and Azure Data Lake
    Storage.

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 other Spark Yarn
modes that the Studio supports with your distribution, ensure that you have properly
configured the connection in a configuration component in the same Job, such as

tHDFSConfiguration
. Use the
configuration component depending on the filesystem to be used.

Die on error

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

Row separator

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

Usage rule

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

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-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.

Related scenarios

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

tFileInputFullRow properties for Apache Spark Streaming

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

The Spark Streaming
tFileInputFullRow component belongs to the File family.

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

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.

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. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Click Edit
schema to make changes to the schema.

Note: If you
make changes, the schema automatically becomes built-in.
 

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.

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.

  • Depending on the filesystem to be used, properly configure the corresponding
    configuration component placed in your Job, for example, a
    tHDFSConfiguration component for HDFS, a
    tS3Configuration component for S3 and a
    tAzureFSConfiguration for Azure Storage and Azure Data Lake
    Storage.

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 other Spark Yarn
modes that the Studio supports with your distribution, ensure that you have properly
configured the connection in a configuration component in the same Job, such as

tHDFSConfiguration
. Use the
configuration component depending on the filesystem to be used.

Die on error

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

Row separator

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

Usage rule

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.

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.

Related scenarios

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

Document get from Talend https://help.talend.com
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