July 30, 2023

tFileInputRegex – Docs for ESB 7.x

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tFileInputRegex

Reads a file row by row to split them up into fields using regular expressions and
sends the fields as defined in the schema to the next component.

Powerful feature which can replace number of other
components of the File family. Requires some advanced knowledge on regular expression
syntax.

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

tFileInputRegex Standard properties

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

The Standard
tFileInputRegex component belongs to the File
family.

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

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.

File name/Stream

File name: Name
of the file and/or the variable to be processed.

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

Stream: Data flow
to be processed. The data must be added to the flow so that it can be
collected by the tFileInputRegex via the INPUT_STREAM variable in the autocompletion list
(Ctrl+Space).

For further information about how to define and use a
variable in a Job, see
Talend Studio

User Guide.

Row separator

The separator used to identify the end of a row.

Regex

Type in your Java regular expression including the
subpattern matching the fields to be extracted. This field can contain
multiple lines.

Note: Antislashes need to be
doubled in regexp

Warning:

  • The regular expression needs to be in double quotes.
  • To extract all the desired strings, make sure the regular
    expression contains the corresponding subpatterns that match
    the strings. Also, each subpattern in the regular expression
    needs to be in a pair of brackets.

Header

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

Footer

Number of rows to be skipped at the end of the file.

Limit

Maximum number of rows to be processed. If Limit = 0, no
row is read or processed.

Ignore error message for the unmatched
record

Select this check box to avoid outputing error messages
for records that do not match the specified regex. This check box is
cleared by default.

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.

Skip empty rows

Select this check box to skip the empty rows.

Die on error

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

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.

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.

In the Map/Reduce version of tFileInputRegex, you need to select the Custom encoding check box to display
this list.

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 a file and separate fields
contained in this file according to the defined Regex. You can also
create a rejection flow using a Row >
Reject link to filter the data which doesn’t correspond
to the type defined. For an example of how to use these two links, see
Procedure.

Reading data using a Regex and outputting the result to Positional file

The following scenario creates a two-component Job, reading data from an Input
file using regular expression and outputting delimited data into a positional file.

tFileInputRegex_1.png

Dropping and linking the components

  1. Drop a tFileInputRegex component from the
    Palette to the design workspace.
  2. Drop a tFileOutputPositional component the
    same way.
  3. Right-click on the tFileInputRegex component
    and select Row
    >

    Main. Drag this main row link
    onto the tFileOutputPositional component and
    release when the plug symbol displays.

Configuring the components

  1. Select the tFileInputRegex again so the
    Component view shows up, and define the
    properties:

    tFileInputRegex_2.png

  2. The Job is built-in for this scenario. Hence, the Properties are set for this
    station only.
  3. Fill in a path to the file in File Name
    field. This field is mandatory.
  4. Define the Row separator identifying the end
    of a row.
  5. Then define the Regular expression in order
    to delimit fields of a row, which are to be passed on to the next component. You
    can type in a regular expression using Java code, and on mutiple lines if
    needed.

    Warning:

    Regex syntax requires double quotes.

  6. In this expression, make sure you include all subpatterns matching the fields
    to be extracted.
  7. In this scenario, ignore the header, footer and limit fields.
  8. Select a local (Built-in) Schema to define the data to pass on to the tFileOutputPositional component.
  9. You can load or create the schema through the Edit
    Schema     function.
  10. Then define the second component properties:

    tFileInputRegex_3.png

  11. Enter the Positional file output path.
  12. Enter the Encoding standard, the output file
    is encoded in. Note that, for the time being, the encoding consistency
    verification is not supported.
  13. Select the Schema type. Click on Sync columns to automatically synchronize the schema
    with the Input file schema.

Saving and executing the Job

  1. Press Ctrl+S to save your Job.
  2. Now go to the Run tab, and click on Run to execute the Job.

    The file is read row by row and split up into fields based on the Regular Expression definition. You can open it using
    any standard file editor.
    tFileInputRegex_4.png

tFileInputRegex MapReduce properties (deprecated)

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

The MapReduce
tFileInputRegex 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.

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.

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 ttFileInputRegex 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.

Row separator

The separator used to identify the end of a row.

Regex

This field can contain multiple lines. Type in your
regular expressions including the subpattern matching the fields to be
extracted.

Note: Antislashes
need to be doubled in regexp

Warning:

Regex syntax requires double quotes.

Header

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

Footer

Number of rows to be skipped at the end of the
file.

Limit

Maximum number of rows to be processed. If Limit = 0,
no row is read or processed.

Schema and Edit
Schema

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.

Skip empty rows

Select this check box to skip the empty rows.

Die on error

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

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.

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.

In the Map/Reduce version of tFileInputRegex, you need to select the Custom encoding check box to display this list.

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

Use this component to read a
file and separate fields contained in this file according to the defined
Regex. You can also create a rejection flow using a Row > Reject link to filter the data which doesn’t correspond
to the type defined.

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.

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.

For further information about a
Talend
 Map/Reduce Job, see the sections
describing how to create, convert and configure a
Talend
 Map/Reduce Job of the

Talend Open Studio for Big Data Getting Started Guide
.

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.

Related scenarios

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

tFileInputRegex properties for Apache Spark Batch

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

The Spark Batch
tFileInputRegex 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.

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 ttFileInputRegex 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.

Row separator

The separator used to identify the end of a row.

Regex

This field can contain multiple lines. Type in your
regular expressions including the subpattern matching the fields to be
extracted.

Note: Antislashes
need to be doubled in regexp

Warning:

Regex syntax requires double quotes.

Header

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

Schema and Edit
Schema

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.

Skip empty rows

Select this check box to skip the empty rows.

Die on error

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

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.

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.

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.

tFileInputRegex properties for Apache Spark Streaming

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

The Spark Streaming
tFileInputRegex 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.

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 ttFileInputRegex 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.

Row separator

The separator used to identify the end of a row.

Regex

This field can contain multiple lines. Type in your
regular expressions including the subpattern matching the fields to be
extracted.

Note: Antislashes
need to be doubled in regexp

Warning:

Regex syntax requires double quotes.

Header

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

Schema and Edit
Schema

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.

Skip empty rows

Select this check box to skip the empty rows.

Die on error

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

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.

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.

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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