tFileInputDelimited - 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

tFileInputDelimited reads a given file row by row with simple separated fields.

Purpose

Opens a file and reads it row by row to split them up into fields then sends fields 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:

tFileInputDelimited properties

Component family

File/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.

 

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

 

Row separator

Enter 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 include CSV specific parameters such as Escape char and Text enclosure.

 

Header

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

Note

When using the dynamic schema feature, the first row of the input file will be read as the header row whether the Header value is set to 0 or to 1. If you want to use another row as the header row, set the Header value accordingly.

For further information about dynamic schemas, 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. 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.

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.

Warning

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. 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.

 

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 this 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.

 

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

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 displays, 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 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

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 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 Scenario 2: Extracting correct and erroneous data from an XML field in a delimited file.

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.

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. For details, see https://help.talend.com/display/KB/How+to+install+external+modules+in+the+Talend+products or the section describing how to configure the Studio in the Talend Installation Guide.

Scenario: Delimited file content display

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.

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:

  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. For more information regarding the procedures to follow when the support of accented characters is missing, see the Talend Installation Guide.

  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.

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

Scenario 2: 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.

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.

  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.

  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.

  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.

  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.

    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 Scenario 4: Writing dynamic columns from a MySQL database to an output file.

tFileInputDelimited 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, tFileInputDelimited, as well as the whole Map/Reduce Job using it, generates native Map/Reduce code. This section presents the specific properties of tFileInputDelimited 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.

 

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 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

Enter 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

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, 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 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.

 

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 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 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.

 

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.

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.

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.

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.

Related scenarios

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

tFileInputDelimited 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.

 

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 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 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.

 

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.

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.

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.

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.

Related scenarios

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