July 30, 2023

tWriteJSONField – Docs for ESB 7.x

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tWriteJSONField

Transforms the incoming data into JSON fields and transfers them to a file, a
database table, etc.

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

Configuring a JSON Tree

When configuring a JSON tree, the default type of an element is string. If an
element is not of type string, you need to add an
attribute for the element to set its type.

  • For an integer, double, float, or
    boolean element, you need to add an
    attribute named type and set its static
    value to integer, number, float, or boolean respectively.

  • For an array element, you need to add
    an attribute named class and set its static
    value to array, and then add a sub-element
    named element set as a loop.

  • For an object element, you need to add
    an attribute named class and set its static
    value to object.

The following figure shows an example of JSON tree configuration:

tWriteJSONField_1.png

tWriteJSONField Standard properties

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

The Standard
tWriteJSONField component belongs to the Processing family.

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

Basic settings

Output Column

Select the column from the list for holding the JSON string
generated. The columns in the list are defined in the output schema.

Configure JSON Tree

Opens the interface to create the JSON data
structure. For more information, see Configuring a JSON Tree.

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.

Note: In the output schema, a column is null by default; only the column selected
from Output Column holds the JSON string generated. You
can add rows in the Group by table and specify other
output columns to hold Group-by values.

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.

Sync columns

Click to synchronize the output file schema
with the input file schema. The Sync function only displays once the Row
connection is linked with the output component.

Group by

Define the aggregation set, the columns you
want to use to regroup the data.

Warning:

Make sure that the data to be grouped is in
sequential order.

Remove root node

Select this check box to remove the root node
from the JSON field generated.

Advanced settings

Quote all not-null
values

Select this check box to surround all values including number
and boolean values in the generated JSON field with double quotation marks.

Clear this check box to surround only string values in the
generated JSON field with double quotation marks.

Quote also null values

Select this check box to surround all the strings with the value of null
in the generated JSON field with double quotation marks.

This option is available when Quote all not-null values is
selected.

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

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.

NB_LINE: the number of rows read by an input component or
transferred to an output component. This is an After variable and it returns an
integer.

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

Preceded by an input component, this component
wraps the incoming data into a JSON field.

Note: For any field with the null value, this component writes a pair of square brackets
([]). If needed, you can remove or replace them using a regular expression in a tJavaRow
component next to the tWriteJSONField component. For
exmaple:

Writing flat data into JSON fields

In this scenario, flat data is wrapped into JSON fields via tWriteJSONField.

Linking the components

  1. Drop the following components from the Palette onto the design workspace: tFixedFlowInput, tWriteJSONField and tLogRow.
  2. Link tFixedFlowInput and tWriteJSONField using a Row > Main connection.
  3. Link tWriteJSONField and tLogRow using a Row > Main connection.

    tWriteJSONField_2.png

Configuring the components

  1. Double-click tFixedFlowInput to display
    its Basic settings view.

    tWriteJSONField_3.png

  2. Click Edit schema to open the schema
    editor.

    tWriteJSONField_4.png

    Click the [+] button to add three
    columns, namely firstname, lastname and dept, with the type of string.
    Click OK to close the editor.
  3. Select the Use Inline Content option and
    enter the data below in the Content box:

  4. Click tWriteJSONField to display its
    Basic settings view.

    tWriteJSONField_5.png

    Select the Remove root node option to
    remove the root node setting from the JSON fields generated.
  5. Click Configure JSON Tree to open the XML
    tree editor.

    tWriteJSONField_6.png

    The schema of tFixedFlowInput appears in
    the Linker source panel.
  6. In the Linker target panel, click the
    default rootTag and type in staff, which is the root node of the JSON field
    to be generated.
  7. Right-click staff and select Add Sub-element from the context menu.
  8. In the pop-up box, enter the sub-node name, namely firstname.

    tWriteJSONField_7.png

    Repeat the steps to add two more sub-nodes, namely lastname and dept.
  9. Right-click firstname and select
    Set As Loop Element from the context
    menu.
  10. Drop firstname from the Linker source panel to its counterpart in the
    Linker target panel.

    In the pop-up dialog box, select Add linker to
    target node    .
    tWriteJSONField_8.png

    Click OK to close the dialog box.
    Repeat the steps to link the two other items.
    Click OK to close the XML tree
    editor.
  11. Click Edit schema to open the schema
    editor.

    tWriteJSONField_9.png

  12. Click the [+] button in the right panel
    to add one column, namely staff, which
    will hold the JSON data generated.

    Click OK to close the editor.
  13. Double-click tLogRow to display its
    Basic settings view.

    tWriteJSONField_10.png

    Select Table (print values in cells of a
    table)     for a better display of the results.

Executing the Job

  1. Press Ctrl + S to save the Job.
  2. Click F6 to execute the Job.

    tWriteJSONField_11.png

    As shown above, the JSON fields have been generated correctly, with the
    root node settings removed.

tWriteJSONField MapReduce properties (deprecated)

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

The MapReduce
tWriteJSONField component belongs to the Processing 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

Output Column

List of the columns defined in the output schema to hold the JSON
field generated.

Configure JSON Tree

Opens the interface to create the JSON data structure. For more
information, see Configuring a JSON Tree.

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.

Sync columns

Click to synchronize the output file schema with the input file
schema. The Sync function only displays once the Row connection is
linked with the output component.

Group by

Define the aggregation set, the columns you want to use to regroup
the data.

Warning:

Make sure that the data to be grouped is in sequential
order.

Remove root node

Select this check box to remove the root node from the JSON field
generated.

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, this component is used as an intermediate
step and 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.

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.

tWriteJSONField properties for Apache Spark Batch

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

The Spark Batch
tWriteJSONField component belongs to the Processing family.

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

Basic settings

Editor

Opens the interface to create the JSON data structure. For more
information, see Configuring a JSON Tree.

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.

Sync columns

Click to synchronize the output file schema with the input file
schema. The Sync function only displays once the Row connection is
linked with the output component.

Group by

Define the aggregation set, the columns you want to use to regroup the
data.

Warning:

Make sure that the data to be grouped is in sequential
order.

Remove root node

Select this check box to remove the root node from the JSON field
generated.

Usage

Usage rule

This component is used as an intermediate step.

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.

tWriteJSONField properties for Apache Spark Streaming

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

The Spark Streaming
tWriteJSONField component belongs to the Processing family.

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

Basic settings

Output type

Select the type of the data to be outputted into the target file. The data is
byte arrays if you select byte.

Editor

Opens the interface to create the JSON data structure. For more
information, see Configuring a JSON Tree.

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.

Sync columns

Click to synchronize the output file schema with the input file
schema. The Sync function only displays once the Row connection is
linked with the output component.

Group by

Define the aggregation set, the columns you want to use to regroup
the data.

Warning:

Make sure that the data to be grouped is in sequential
order.

Remove root node

Select this check box to remove the root node from the JSON field
generated.

Usage

Usage rule

This component is used as an intermediate step.

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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