tExtractJSONFields

Extracts the desired data from JSON fields based on the JSONPath or XPath
query.

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

Standard: see tExtractJSONFields Standard properties.

The component in this framework is available in all Talend
products.
MapReduce: see tExtractJSONFields MapReduce properties (deprecated).

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.
Spark Batch:
see tExtractJSONFields properties for Apache Spark Batch.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.
Spark Streaming:
see tExtractJSONFields properties for Apache Spark Streaming.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.
Storm: see tExtractJSONFields Storm properties (deprecated).

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

tExtractJSONFields Standard properties

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

The Standard
tExtractJSONFields component belongs to the Processing 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.
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. 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. Click Edit schema to make changes to the schema. Note: If you make changes, the schema automatically becomes built-in. 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.
Read By	Select a way of extracting JSON data in the file. JsonPath: Extracts JSON data based on the JSONPath query. With this option selected, you need to select a JSONPath API version from the API version drop-down list. It is recommended to read data by JSONPath in order to gain better performance. Xpath: Extracts JSON data based on the XPath query.
JSON field	List of the JSON fields to be extracted.
Loop Jasonpath query	Enter the path pointing to the node within the JSON field, on which the loop is based. Note if you have selected Xpath from the Read by drop-down list, the Loop Xpath query field is displayed instead.
Mapping	Complete this table to map the columns defined in the schema to the corresponding JSON nodes. Column: The Column cells are automatically filled with the defined schema column names. Json query/JSONPath query: Specify the JSONPath node that holds the desired data. For more information about JSONPath expressions, see http://goessner.net/articles/JsonPath/. This column is available only when JsonPath is selected from the Read By list. XPath query: Specify the XPath node that holds the desired data. This column is available only when Xpath is selected from the Read By list. Get Nodes: Select this check box to extract the JSON data of all the nodes or select the check box next to a specific node to extract the data of that node. This column is available only when Xpath is selected from the Read By list. Is Array: select this check box when the JSON field to be extracted is an array instead of an object. This column is available only when Xpath is selected from the Read By list.
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

Use the loop node as root	Select this check box to use the loop node as the root for querying the file. The loop node is set in the Loop Json query text frame in the Basic Settings view. If this option is checked, only the child elements of the loop node are available for querying; otherwise, both the parent elements and the child elements of the loop node can be queried. You can specify a parent element through JSON path syntax. This check box is available only when JsonPath is selected in the Read By drop-down list of the Basic settings view.
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.
tStatCatcher Statistics	Select this check box to gather the Job processing metadata at a Job level as well as at each component level.

Use the loop node as root

Select this check box to use the loop node as the root for querying the file.

The loop node is set in the Loop Json query text frame
in the Basic Settings view. If this option is checked,
only the child elements of the loop node are available for querying; otherwise,
both the parent elements and the child elements of the loop node can be
queried. You can specify a parent element through JSON path syntax.

This check box is available only when JsonPath is
selected in the Read By drop-down list of the
Basic settings view.

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.

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

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 selected.

Usage

Usage rule	This component is an intermediate component. It needs an input and an output components.
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).

Usage rule

This component is an intermediate component. It needs an input and an
output components.

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).

Retrieving error messages while extracting data from JSON fields

In this scenario, tWriteJSONField wraps the incoming
data into JSON fields, data of which is then extracted by tExtractJSONFields. Meanwhile, the error messages generated due to
extraction failure, which include the concerned JSON fields and errors, are retrieved
via a Row > Reject
link.

Linking the components

Drop the following components from the Palette onto the design workspace: tFixedFlowInput, tWriteJSONField, tExtractJSONFields, and tLogRow (X2). The two tLogRow components are renamed as data_extracted and reject_info.
Link tFixedFlowInput and tWriteJSONField using a Row > Main connection.
Link tWriteJSONField and tExtractJSONFields using a Row > Main connection.
Link tExtractJSONFields and data_extracted using a Row > Main connection.
Link tExtractJSONFields and reject_info using a Row > Reject connection.

Configuring the components

Setting up the tFixedFlowInput

Double-click tFixedFlowInput to display
its Basic settings view.
Click Edit schema to open the schema
editor.

Click the [+] button to add three
columns, namely firstname, lastname and dept, with the type of string.

Click OK to close the editor.
Select Use Inline Content and enter the
data below in the Content box:

Andrew;Wallace;Doc John;Smith;R&D Christian;Dior;Sales

1
2
3

Andrew;Wallace;Doc
John;Smith;R&D
Christian;Dior;Sales

Setting up the tWriteJSONField

Click tWriteJSONField to display its
Basic settings view.
Click Configure JSON Tree to open the XML
tree editor.

The schema of tFixedFlowInput appears in
the Linker source panel.
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.
Right-click staff and select Add Sub-element from the context menu.
In the pop-up box, enter the sub-node name, namely firstname.

Repeat the steps to add two more sub-nodes, namely lastname and dept.
Right-click firstname and select
Set As Loop Element from the context
menu.
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.

Click OK to close the dialog box.
Repeat the steps to link the two other items.

Click OK to close the XML tree
editor.
Click Edit schema to open the schema
editor.
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.

Setting up the tExtractJSONFields

Double-click tExtractJSONFields to
display its Basic settings view.
Click Edit schema to open the schema
editor.
Click the [+] button in the right panel
to add three columns, namely firstname,
lastname and dept, which will hold the data of their counterpart nodes in
the JSON field staff.

Click OK to close the editor.
In the pop-up Propagate box, click
Yes to propagate the schema to the
subsequent components.
In the Loop XPath query field, enter
“/staff”, which is the root node of
the JSON data.
In the Mapping area, type in the node
name of the JSON data under the XPath query
part. The data of those nodes will be extracted and passed to their
counterpart columns defined in the output schema.
Specifically, define the XPath query “firstname” for the column firstname, “lastname” for
the column lastname, and “” for the column dept. Note that “” is not
a valid XPath query and will lead to execution errors.

Setting up the tLogRow components

Double-click data_extracted to display
its Basic settings view.
Select Table (print values in cells of a
table) for a better display of the results.
Perform the same setup on the other tLogRow component, namely reject_info.

Executing the Job

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

As shown above, the reject row offers such details as the data extracted,
the JSON fields whose data is not extracted and the cause of the extraction
failure.

Collecting data from your favorite online social network

In this scenario, tFileInputJSON retrieves the
friends node from a JSON file that contains the
data of a Facebook user and tExtractJSONFields extracts
the data from the friends node for flat data
output.

Linking the components

Drop the following components from the Palette onto the design workspace: tFileInputJSON, tExtractJSONFields and tLogRow.
Link tFileInputJSON and tExtractJSONFields using a Row > Main connection.
Link tExtractJSONFields and tLogRow using a Row > Main connection.

Configuring the components

Double-click tFileInputJSON to display
its Basic settings view.
Click Edit schema to open the schema
editor.

Click the [+] button to add one column,
namely friends, of the String
type.

Click OK to close the editor.

Click the […] button to browse for the
JSON file, facebook.json in this
case:

{ "user": { "id": "9999912398",
            "name": "Kelly Clarkson",
            "friends": [
                 { "name": "Tom Cruise",
                   "id": "55555555555555",
                   "likes": {
                       "data": [
                            { "category": "Movie",
                              "name": "The Shawshank Redemption",
                              "id": "103636093053996",
                              "created_time": "2012-11-20T15:52:07+0000"
                            },
                            { "category": "Community",
                              "name": "Positiveretribution",
                              "id": "471389562899413",
                              "created_time": "2012-12-16T21:13:26+0000"
                            }
                                ]
                            }
                 },
                 { "name": "Tom Hanks",
                   "id": "88888888888888"
                   "likes": {
                        "data": [
                            { "category": "Journalist",
                              "name": "Janelle Wang",
                              "id": "136009823148851",
                              "created_time": "2013-01-01T08:22:17+0000"
                            },
                            { "category": "Tv show",
                              "name": "Now With Alex Wagner",
                              "id": "305948749433410",
                              "created_time": "2012-11-20T06:14:10+0000"
                            }
                            ]
                           }
                  }
                        ]
          }
}

{ "user": { "id": "9999912398",

"name": "Kelly Clarkson",

"friends": [

{ "name": "Tom Cruise",

"id": "55555555555555",

"likes": {

"data": [

{ "category": "Movie",

"name": "The Shawshank Redemption",

"id": "103636093053996",

"created_time": "2012-11-20T15:52:07+0000"

{ "category": "Community",

"name": "Positiveretribution",

"id": "471389562899413",

"created_time": "2012-12-16T21:13:26+0000"

}

]

}

{ "name": "Tom Hanks",

"id": "88888888888888"

"likes": {

"data": [

{ "category": "Journalist",

"name": "Janelle Wang",

"id": "136009823148851",

"created_time": "2013-01-01T08:22:17+0000"

{ "category": "Tv show",

"name": "Now With Alex Wagner",

"id": "305948749433410",

"created_time": "2012-11-20T06:14:10+0000"

}

]

}

]

}

Clear the Read by XPath check box.

In the Mapping table, enter the JSONPath
query “$.user.friends[*]” next to the
friends column, retrieving the entire
friends node from the source
file.
Double-click tExtractJSONFields to
display its Basic settings view.
Click Edit schema to open the schema
editor.
Click the [+] button in the right panel
to add five columns, namely id, name, like_id, like_name and
like_category, which will hold the
data of relevant nodes in the JSON field friends.

Click OK to close the editor.
In the pop-up Propagate box, click
Yes to propagate the schema to the
subsequent components.
In the Loop XPath query field, enter
“/likes/data”.
In the Mapping area, type in the queries
of the JSON nodes in the XPath query
column. The data of those nodes will be extracted and passed to their
counterpart columns defined in the output schema.
Specifically, define the XPath query “../../id” (querying the “/friends/id” node) for the column id, “../../name”
(querying the “/friends/name” node) for
the column name, “id” for the column like_id, “name” for the
column like_name, and “category” for the column like_category.
Double-click tLogRow to display its
Basic settings view.
Select Table (print values in cells of a
table) for a better display of the results.

Executing the Job

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

As shown above, the friends data of the Facebook user Kelly Clarkson is
extracted correctly.

Extracting data from a JSON file through looping

This scenario describes a Job that extracts data from a JSON file through
multiple loops and displays the data on the console.

The following lists the content of the JSON file,
sample.json.

{
    "Guid": "a2hdge9-5517-4e12-b9j6-887ft29e1711",
    "Transactions": [
        {
            "TransactionId": 1,
            "Products": [
                {
                    "ProductId": "A1",
                    "Packs": [
                        {
                            "Quantity": 20,
                            "Price": 40.00,
                            "Due_Date": "2019/03/01"
                        }
                    ]
                }
            ]
        },
        {
            "TransactionId": 2,
            "Products": [
                {
                    "ProductId": "B1",
                    "Packs": [
                        {
                            "Quantity": 1,
                            "Price": 15.00,
                            "Due_Date": "2019/01/01"
                        },
                        {
                            "Quantity": 21,
                            "Price": 315.00,
                            "Due_Date": "2019/02/14"
                        }
                    ]
                }
            ]
        },
        {
            "TransactionId": 3,
            "Products": [
                {
                    "ProductId": "C1",
                    "Packs": [
                        {
                            "Quantity": 2,
                            "Price": 5.00,
                            "Due_Date": "2019/02/19"
                        },
                        {
                            "Quantity": 3,
                            "Price": 7.50,
                            "Due_Date": "2019/05/21"
                        }
                    ]
                }
            ]
        }
    ]
    }

{

"Guid": "a2hdge9-5517-4e12-b9j6-887ft29e1711",

"Transactions": [

{

"TransactionId": 1,

"Products": [

{

"ProductId": "A1",

"Packs": [

{

"Quantity": 20,

"Price": 40.00,

"Due_Date": "2019/03/01"

}

]

}

]

{

"TransactionId": 2,

"Products": [

{

"ProductId": "B1",

"Packs": [

{

"Quantity": 1,

"Price": 15.00,

"Due_Date": "2019/01/01"

{

"Quantity": 21,

"Price": 315.00,

"Due_Date": "2019/02/14"

}

]

}

]

{

"TransactionId": 3,

"Products": [

{

"ProductId": "C1",

"Packs": [

{

"Quantity": 2,

"Price": 5.00,

"Due_Date": "2019/02/19"

{

"Quantity": 3,

"Price": 7.50,

"Due_Date": "2019/05/21"

}

]

}

]

}

]

}

This Job extracts the values of the following elements.

Guid
TransactionId
ProductId
Quantity
Price
Due-Date

Establishing the tExtractJSONFields looping Job

Create a Job and add a tFileInputJSON component, three tExtractJsonFields
components, and a tLogRow component.
Connect the components using Row > Main connections.

Configuring tExtractJSONFields looping input

This task assumes that you know the structure of the JSON file.

In the Basic settings view
of the tFileInputJSON component, select
JsonPath from the Read By drop-down list.
In the filename field, specified the input JSON file,
sample.json in this example.
In the schema editor, add two columns,
Guid (type String) and
Transactions (type Object).
Click Yes in the subsequent dialog box
to propagate the schema to the next component.

The columns just added appear in the Mapping table of the
Basic settings view.
In the Basic settings view, enter
"$" in the Loop Json
query text box to loop the elements within the root elements.
In the Json query column of the Mapping table, enter the
following Json query expressions in double quotation marks.
- $.Guid to extract the value of the Guid
  element;
- $.Transactions to extract the content of the
  Transactions element.

Configuring the tExtractJSONFields components for looping

In the schema editor of the first
tExtractJSONFileds component, add the following
columns in the output table.
- Guid, type String;
- TransactionId, type Integer;
- Products, type Object
Close the schema editor and click
Yes in the subsequent dialog box to propagate the
schema to the next component.

The columns just added appear in the Mapping table of the
Basic settings view.
Set the other options in the Basic
settings view as follows.
- JSON field: Transactions;
- Loop Jsonpath query: "*" (in double quotation marks);
- Guid: empty, for receiving the Guid value from the previous
  component;
- TransactionId: "TransactionId" (in double quotation marks);
- Products: "Products" (in double quotation marks);
- Others: unchanged
The settings loop all the elements within the Transactions element and extract
the values of the TransactionId and the Products elements.
In the schema editor of the second tExtractJSONFileds
component, add the following columns in the output table.
- Guid, type String;
- TransactionId, type Integer;
- ProductId, type String;
- Packs, type Object
Close the schema editor and click
Yes in the subsequent dialog box to propagate the
schema to the next component.

The columns just added appear in the Mapping table of the
Basic settings view.
Set the other options in the Basic
settings view as follows.
- JSON field: Products;
- Loop Jsonpath query: "*" (in double quotation
  marks);
- Guid: empty, for receiving the Guid value from the previous
  component;
- TransactionId: empty, for receiving the TransactionId from the previous
  component;
- ProductId: "ProductId" (in double quotation
  marks);
- Packs: "Packs" (in double quotation marks);
- Others: unchanged
The settings in the above figure loop all the elements within the Products
element and extract the values of the ProductId and the Packs elements.
In the schema editor of the third tExtractJSONFileds
component, add the following columns in the output table.
- Guid, type String;
- TransactionId, type Integer;
- ProductId, type String;
- Quantity, type Integer;
- Price, type Float;
- Due_Date, type Date
Close the schema editor and click
Yes in the subsequent dialog box to propagate the
schema to the next component.

The columns just added appear in the Mapping table of the
Basic settings view.
Set the other options in the Basic
settings view as follows.
- JSON field: Packs;
- Loop Jsonpath query: "*" (in double quotation
  marks);
- Guid: empty, for receiving the Guid value from the previous
  component;
- TransactionId: empty, for receiving the TransactionId value from the
  previous component;
- ProductId: empty, for receiving the ProductId value from the previous
  component;
- Quantity: "Quantity" (in double quotation
  marks);
- Price: "Price" (in double quotation marks);
- Due_Date: "Due_Date" (in double quotation
  marks);
- Others: unchanged
The settings in the above figure loop all the elements within the Packs
element and extract the values of the Quantity, the Price, and the Due_Date
elements.

Setting the display for tExtractJSONFields values

Open the Basic settings view of the
tLogRow component.
Select the preferred option in the Mode section.

Executing tExtractJSONFields loop Job

Press Ctrl+S to save the Job.
Press F6 to execute the Job.
The following figure shows the result.

The values of the Guid element, the TransactionId element, the
ProductId element, the Quantity element, the Price element, and the Due_date element
are extracted from the source JSON file and displayed.

tExtractJSONFields MapReduce properties (deprecated)

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

The MapReduce
tExtractJSONFields 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

Property type	Either Built-In or Repository.
	Built-In: No property data stored centrally.
	Repository: Select the repository file where the properties are stored.
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.
Read by	Select a way of extracting the JSON data in the file. Xpath: Extracts the JSON data based on the XPath query. JsonPath: Extracts the JSON data based on the JSONPath query. Note that it is recommended to read the data by JSONPath in order to gain better performance.
JSON field	List of the JSON fields to be extracted.
Loop Jasonpath query	Enter the path pointing to the node within the JSON field, on which the loop is based. Note if you have selected Xpath from the Read by drop-down list, the Loop Xpath query field is displayed instead.
Mapping	Complete this table to map the columns defined in the schema to the corresponding JSON nodes. Column: The Column cells are automatically filled with the defined schema column names. Json query/JSONPath query: Specify the JSONPath node that holds the desired data. For more information about JSONPath expressions, see http://goessner.net/articles/JsonPath/. This column is available only when JsonPath is selected from the Read By list. XPath query: Specify the XPath node that holds the desired data. This column is available only when Xpath is selected from the Read By list. Get Nodes: Select this check box to extract the JSON data of all the nodes or select the check box next to a specific node to extract the data of that node. This column is available only when Xpath is selected from the Read By list. Is Array: select this check box when the JSON field to be extracted is an array instead of an object. This column is available only when Xpath is selected from the Read By list.
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.

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.

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	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.

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.

tExtractJSONFields properties for Apache Spark Batch

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

The Spark Batch
tExtractJSONFields 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

Property type	Either Built-In or Repository.
	Built-In: No property data stored centrally.
	Repository: Select the repository file where the properties are stored.
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.
Read by	Select a way of extracting the JSON data in the file. Xpath: Extracts the JSON data based on the XPath query. JsonPath: Extracts the JSON data based on the JSONPath query. Note that it is recommended to read the data by JSONPath in order to gain better performance.
JSON field	List of the JSON fields to be extracted.
Loop Jasonpath query	Enter the path pointing to the node within the JSON field, on which the loop is based. Note if you have selected Xpath from the Read by drop-down list, the Loop Xpath query field is displayed instead.
Mapping	Complete this table to map the columns defined in the schema to the corresponding JSON nodes. Column: The Column cells are automatically filled with the defined schema column names. Json query/JSONPath query: Specify the JSONPath node that holds the desired data. For more information about JSONPath expressions, see http://goessner.net/articles/JsonPath/. This column is available only when JsonPath is selected from the Read By list. XPath query: Specify the XPath node that holds the desired data. This column is available only when Xpath is selected from the Read By list. Get Nodes: Select this check box to extract the JSON data of all the nodes or select the check box next to a specific node to extract the data of that node. This column is available only when Xpath is selected from the Read By list. Is Array: select this check box when the JSON field to be extracted is an array instead of an object. This column is available only when Xpath is selected from the Read By list.
Die on error	Select the check box to stop the execution of the Job when an error occurs.

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.

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.

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.

tExtractJSONFields properties for Apache Spark Streaming

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

The Spark Streaming
tExtractJSONFields component belongs to the Processing family.

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

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.
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.
Read by	Select a way of extracting the JSON data in the file. Xpath: Extracts the JSON data based on the XPath query. JsonPath: Extracts the JSON data based on the JSONPath query. Note that it is recommended to read the data by JSONPath in order to gain better performance.
JSON field	List of the JSON fields to be extracted.
Loop Jasonpath query	Enter the path pointing to the node within the JSON field, on which the loop is based. Note if you have selected Xpath from the Read by drop-down list, the Loop Xpath query field is displayed instead.
Mapping	Complete this table to map the columns defined in the schema to the corresponding JSON nodes. Column: The Column cells are automatically filled with the defined schema column names. Json query/JSONPath query: Specify the JSONPath node that holds the desired data. For more information about JSONPath expressions, see http://goessner.net/articles/JsonPath/. This column is available only when JsonPath is selected from the Read By list. XPath query: Specify the XPath node that holds the desired data. This column is available only when Xpath is selected from the Read By list. Get Nodes: Select this check box to extract the JSON data of all the nodes or select the check box next to a specific node to extract the data of that node. This column is available only when Xpath is selected from the Read By list. Is Array: select this check box when the JSON field to be extracted is an array instead of an object. This column is available only when Xpath is selected from the Read By list.
Die on error	Select the check box to stop the execution of the Job when an error occurs.

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.

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.

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

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

For a related scenario, see Analyzing a Twitter flow in near real-time.

tExtractJSONFields Storm properties (deprecated)

These properties are used to configure tExtractJSONFields running in the Storm Job framework.

The Storm
tExtractJSONFields component belongs to the Processing family.

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

The Storm framework is deprecated from Talend 7.1 onwards. Use Talend Jobs for Apache Spark Streaming to accomplish your Streaming related 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.
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.
JSON field	List of the JSON fields to be extracted.
Loop XPath query	Node within the JSON field, on which the loop is based.
Mapping	Column: schema defined to hold the data extracted from the JSON field. XPath Query: XPath Query to specify the node within the JSON field. Get nodes: select this check box to extract the JSON data of all the nodes specified in the XPath query list or select the check box next to a specific node to extract its JSON data only. Is Array: select this check box when the JSON field to be extracted is an array instead of an object.

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.

Usage

Usage rule	If you have subscribed to one of the Talend solutions with Big Data, you can also use this component as a Storm component. In a Talend Storm Job, this component is used as an intermediate step and other components used along with it must be Storm components, too. They generate native Storm code that can be executed directly in a Storm system. The Storm version does not support the use of the global variables. You need to use the Storm Configuration tab in the Run view to define the connection to a given Storm system for the whole Job. This connection is effective on a per-Job basis. For further information about a Talend Storm Job, see the sections describing how to create and configure a Talend Storm 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.

Usage rule

If you have subscribed to one of the
Talend
solutions with Big Data, you can also
use this component as a Storm component. In a
Talend
Storm Job, this component is used as
an intermediate step and other components used along with it must be Storm components, too.
They generate native Storm code that can be executed directly in a Storm system.

The Storm version does not support the use of the global variables.

You need to use the Storm Configuration tab in the
Run view to define the connection to a given Storm
system for the whole Job.

This connection is effective on a per-Job basis.

For further information about a
Talend
Storm Job, see the sections
describing how to create and configure a
Talend
Storm 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.

Related scenarios

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

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

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