July 31, 2023

tFileInputDelimited – Docs for ESB File Delimited 7.x

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tFileInputDelimited

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

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

tFileInputDelimited Standard properties

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

The Standard
tFileInputDelimited 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 and path
of the file to be processed.

Stream: The data flow to
be processed. The data must be added to the flow in order for tFileInputDelimited to fetch
these data via the corresponding representative variable.

This variable could be already pre-defined in
your Studio or provided by the context or the components you are using along
with this component; otherwise, you could define it manually and use it
according to the design of your Job, for example, using tJava or tJavaFlex.

In order to avoid the inconvenience of hand
writing, you could select the variable of interest from the auto-completion
list (Ctrl+Space) to
fill the current field on condition that this variable has been properly
defined.

Related topic to the available variables: see

Talend Studio
User Guide

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.

Field separator

Enter character, string or regular expression to separate fields for the transferred
data.

CSV options

Select this check box to specify the following CSV parameters:

  • Escape char: enter the escape character between double
    quotation marks.

  • Text enclosure: enter the enclosure character (only one
    character) between double quotation marks. For example, """
    needs to be entered when double quotation marks (“) are used as the enclosure
    character.

It is recommended to use standard escape character, that is "".
Otherwise, you should set the same character for Escape char and
Text enclosure. For example, if the escape character is set to
"", the text enclosure can be set to any other character. On
the other hand, if the escape character is set to other character rather than
"", the text enclosure can be set to any other characters.
However, the escape character will be changed to the same character as the text
enclosure. For instance, if the escape character is set to "#"
and the text enclosure is set to "@", the escape character will
be changed to "@", not "#".

Header

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

Note that when dynamic schema is used, the first row of the input
data is always treated as the header row no matter whether the Header field value is set or not. For more information about dynamic
schema, see
Talend Studio

User Guide.

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

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.

Note that if the input value of any
non-nullable primitive field is null, the row of data including that field will
be rejected.

This
component offers the advantage of the dynamic schema feature. This allows you to
retrieve unknown columns from source files or to copy batches of columns from a source
without mapping each column individually. For further information about dynamic schemas,
see
Talend Studio

User Guide.

This
dynamic schema feature is designed for the purpose of retrieving unknown columns of a
table and is recommended to be used for this purpose only; it is not recommended for the
use of creating tables.

When using the dynamic schema feature, the dynamic
column does not contain the actual column names of the input file. If you want
your output flow to include the actual column names, make sure that your input
file has a header row and the Header value is set properly.

 

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.

Uncompress as zip file

Select this check box to uncompress the input
file.

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.

To catch the FileNotFoundException, you also need to select
this check box.

Advanced settings

Advanced separator (for
numbers)

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Extract lines at random

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

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.

Trim all column

Select this check box to remove the leading and trailing whitespaces from all
columns. When this check box is cleared, the Check column to
trim table is displayed, which lets you select particular columns to
trim.

Check each row structure against
schema

Select this check box to check whether the total number of columns
in each row is consistent with the schema. If not consistent, an
error message will be displayed on the console.

Check date

Select this check box to check the date format strictly against the input schema.

Check columns to trim

This table is filled automatically with the schema being used. Select the check
box(es) corresponding to the column(s) to be trimmed.

Split row before field

Select this check box to split rows before
splitting fields.

Permit hexadecimal (0xNNN) or octal
(0NNNN) for numeric types – it will act the opposite for Byte

Select this check box if any of your numeric types (long, integer, short, or byte type), will
be parsed from a hexadecimal or octal string.

In the table that appears, select the check
box next to the column or columns of interest to transform the input string of
each selected column to the type defined in the schema.

Select the Permit hexadecimal or octal check box to select
all the columns.

This table appears only when the Permit hexadecimal
(0xNNN) or octal (0NNNN) for numeric types – it will act the opposite for
Byte check box is selected.

tStatCatcher Statistics

Select this check box to gather the processing
metadata at the 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 using a defined separator. It allows you to
create a data flow using a Row >
Main link or via a Row > Reject link in which case the data is filtered by data
that does not correspond to the type defined. For further information, please
see Procedure.
Limitation

Due to license incompatibility, one or more JARs required to use
this component are not provided. You can install the missing JARs for this particular
component by clicking the Install button
on the Component tab view. You can also
find out and add all missing JARs easily on the Modules tab in the
Integration
 perspective of your studio. You can find more details about how to install external modules in
Talend Help Center (https://help.talend.com).

Reading data from a Delimited file and display the output

The following scenario creates a two-component Job, which aims at reading each
row of a file, selecting delimited data and displaying the output in the Run log console.

tFileInputDelimited_1.png

Dropping and linking components

  1. Drop a tFileInputDelimited component and
    a tLogRow component from the Palette to the design workspace.
  2. Right-click on the tFileInputDelimited
    component and select Row > Main. Then drag it onto the tLogRow component and release when the plug symbol shows
    up.

Configuring the components

  1. Select the tFileInputDelimited component
    again, and define its Basic settings:

    tFileInputDelimited_2.png

  2. Fill in a path to the file in the File
    Name     field. This field is mandatory.

    Warning:

    If the path of the file contains some accented characters, you
    will get an error message when executing your Job.

  3. Define the Row separator allowing to
    identify the end of a row. Then define the Field
    separator     used to delimit fields in a row.
  4. In this scenario, the header and footer limits are not set. And the
    Limit number of processed rows is set
    on 50.
  5. Set the Schema as either a local
    (Built-in) or a remotely managed
    (Repository) to define the data to pass
    on to the tLogRow component.
  6. You can load and/or edit the schema via the Edit
    Schema     function.

    Related topics: see
    Talend Studio User
    Guide    .
  7. Enter the encoding standard the input file is encoded in. This setting is
    meant to ensure encoding consistency throughout all input and output
    files.
  8. Select the tLogRow and define the
    Field separator to use for the output
    display. Related topic: tLogRow.
  9. Select the Print schema column name in front of each
    value     check box to retrieve the column labels in the output
    displayed.

Saving and executing the Job

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

    The file is read row by row and the extracted fields are displayed on the
    Run log as defined in both components
    Basic settings.
    tFileInputDelimited_3.png

    The Log sums up all parameters in a header followed by the result of the
    Job.

Reading data from a remote file in streaming mode

This scenario describes a four component Job used to fetch data from a
voluminous file almost as soon as it has been read. The data is displayed in the Run view. The advantage of this technique is that you do not have
to wait for the entire file to be downloaded, before viewing the data.

tFileInputDelimited_4.png

Dropping and linking components

  1. Drop the following components onto the workspace: tFileFetch, tSleep,
    tFileInputDelimited, and tLogRow.
  2. Connect tSleep and tFileInputDelimited using a Trigger > OnComponentOk
    link and connect tFileInputDelimited to
    tLogRow using a Row > Main link.

Configuring the components

  1. Double-click tFileFetch to display the
    Basic settings tab in the Component view and set the properties.

    tFileInputDelimited_5.png

  2. From the Protocol list, select the
    appropriate protocol to access the server on which your data is
    stored.
  3. In the URI field, enter the URI required
    to access the server on which your file is stored.
  4. Select the Use cache to save the resource
    check box to add your file data to the cache memory. This option allows you
    to use the streaming mode to transfer the data.
  5. In the workspace, click tSleep to display
    the Basic settings tab in the Component view and set the properties.

    By default, tSleep‘s Pause field is set to 1
    second. Do not change this setting. It pauses the second Job in order to
    give the first Job, containing tFileFetch,
    the time to read the file data.
  6. In the workspace, double-click tFileInputDelimited to display its Basic settings tab in the Component view and set the properties.

    tFileInputDelimited_6.png

  7. In the File name/Stream field:

    – Delete the default content.
    – Press Ctrl+Space to view the variables
    available for this component.
    – Select tFileFetch_1_INPUT_STREAM from the
    auto-completion list, to add the following variable to the Filename field:
    ((java.io.InputStream)globalMap.get("tFileFetch_1_INPUT_STREAM")).
  8. From the Schema list, select Built-in and click […] next to the Edit
    schema     field to describe the structure of the file that you
    want to fetch. The US_Employees file is composed of six
    columns: ID, Employee,
    Age, Address,
    State, EntryDate.

    Click [+] to add the six columns and set
    them as indicated in the above screenshot. Click OK.
    tFileInputDelimited_7.png

  9. In the workspace, double-click tLogRow to
    display its Basic settings in the Component view and click Sync Columns to ensure that the schema structure is properly
    retrieved from the preceding component.

Configuring Job execution and executing the Job

  1. Click the Job tab and then on the
    Extra view.

    tFileInputDelimited_8.png

  2. Select the Multi thread execution check
    box in order to run the two Jobs at the same time. Bear in mind that the
    second Job has a one second delay according to the properties set in
    tSleep. This option allows you to fetch
    the data almost as soon as it is read by tFileFetch, thanks to the tFileDelimited component.
  3. Save the Job and press F6 to run it.

    tFileInputDelimited_9.png

    The data is displayed in the console as almost as soon as it is
    read.

    For a scenario concerning the use of dynamic schemas in
    tFileInputDelimited, see Writing dynamic columns from a database to an output file.

tFileInputDelimited MapReduce properties (deprecated)

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

The MapReduce
tFileInputDelimited 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.

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 this component 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.

Field separator

Enter character, string or regular expression to separate fields for the transferred
data.

Header

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

CSV options

Select this check box to include CSV specific parameters such as Escape char and Text
enclosure.

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.

Advanced separator (for number)

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Trim all columns

Select this check box to remove the leading and trailing whitespaces from all
columns. When this check box is cleared, the Check column to
trim table is displayed, which lets you select particular columns to
trim.

Check column to trim

This table is filled automatically with the schema being used. Select the check
box(es) corresponding to the column(s) to be trimmed.

Check each row structure against
schema

Select this check box to check whether the total number of columns
in each row is consistent with the schema. If not consistent, an
error message will be displayed on the console.

Check date

Select this check box to check the date format strictly against the input schema.

Decode String for long, int, short, byte
Types

Select this check box if any of your numeric types (long, integer, short, or byte type), will
be parsed from a hexadecimal or octal string.

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, tFileInputDelimited 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.

tFileInputDelimited properties for Apache Spark Batch

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

The Spark Batch
tFileInputDelimited 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.

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 this component 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.

Field separator

Enter character, string or regular expression to separate fields for the transferred
data.

Header

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

CSV options

Select this check box to include CSV specific parameters such as Escape char and Text
enclosure.
Splittable Select this check box to enable your Spark cluster to use multiple executors to read large files in parallel.

This check box is available when you have select the CSV option check box.

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.

Advanced separator (for number)

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Trim all columns

Select this check box to remove the leading and trailing whitespaces from all
columns. When this check box is cleared, the Check column to
trim table is displayed, which lets you select particular columns to
trim.

Check column to trim

This table is filled automatically with the schema being used. Select the check
box(es) corresponding to the column(s) to be trimmed.

Check each row structure against
schema

Select this check box to check whether the total number of columns
in each row is consistent with the schema. If not consistent, an
error message will be displayed on the console.

Check date

Select this check box to check the date format strictly against the input schema.

Decode String for long, int, short, byte
Types

Select this check box if any of your numeric types (long, integer, short, or byte type), will
be parsed from a hexadecimal or octal string.

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.

tFileInputDelimited properties for Apache Spark Streaming

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

The Spark Streaming
tFileInputDelimited 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.

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 this component 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.

Field separator

Enter character, string or regular expression to separate fields for the transferred
data.

Header

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

CSV options

Select this check box to include CSV specific parameters such as Escape char and Text
enclosure.

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.

Advanced separator (for number)

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Trim all columns

Select this check box to remove the leading and trailing whitespaces from all
columns. When this check box is cleared, the Check column to
trim table is displayed, which lets you select particular columns to
trim.

Check column to trim

This table is filled automatically with the schema being used. Select the check
box(es) corresponding to the column(s) to be trimmed.

Check each row structure against
schema

Select this check box to check whether the total number of columns
in each row is consistent with the schema. If not consistent, an
error message will be displayed on the console.

Check date

Select this check box to check the date format strictly against the input schema.

Decode String for long, int, short, byte
Types

Select this check box if any of your numeric types (long, integer, short, or byte type), will
be parsed from a hexadecimal or octal string.

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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