August 15, 2023

tBigQueryOutput – Docs for ESB 6.x

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tBigQueryOutput

Transfers the data provided by its preceding component to Google
BigQuery.

This component writes the data it receives in a user-specified
directory and transfers the data to Google BigQuery via Google Cloud
Storage.

tBigQueryOutput Standard properties

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

The Standard
tBigQueryOutput component belongs to the Big Data family.

The component in this framework is generally available.

Basic settings

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.

Property type

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.

Local filename

Browse to, or enter the path to the file you want to write the
received data in.

Append

Select this check box to add rows to the existing data in the file
specified in Local filename.

Client ID and Client
secret

Paste the client ID and the client secret, both created and viewable on the
API Access tab view of the project hosting the BigQuery service and the Cloud
Storage service you need to use.

To enter the client secret, click the […] button next
to the client secret field, and then in the pop-up dialog box enter the client secret
between double quotes and click OK to save the
settings.

Project ID

Paste the ID of the project hosting the BigQuery service you
need to use.

The ID of your project can be found in the URL of the Google
API Console, or by hovering your mouse pointer over the name of the
project in the BigQuery Browser Tool.

Authorization code

Paste the authorization code provided by Google for the access you are
building.

To obtain the authorization code, you need to execute the Job using this
component and when this Job pauses execution to print out an URL address, you
navigate to this address to copy the authorization code displayed.

Dataset

Enter the name of the dataset you need to transfer data to.

Table

Enter the name of the table you need to transfer data to.

If this table does not exist, select the Create the table if it doesn’t exist check
box.

Action on data

Select the action to be performed from the drop-down list when
transferring data to the target table. The action may be:

  • Truncate: it empties the
    contents of the table and repopulates it with the
    transferred data.

  • Append: it adds rows to
    the existing data in the table.

  • Empty: it populates the
    empty table.

Access key and Secret key

Paste the authentication information obtained from Google for making
requests to Google Cloud Storage.

To enter the secret key, click the […] button next to
the secret key field, and then in the pop-up dialog box enter the password between double
quotes and click OK to save the settings.

These keys can be consulted on the Interoperable Access tab view under the
Google Cloud Storage tab of the project.

Bucket

Enter the name of the bucket, the Google Cloud Storage
container, which holds the data to be transferred to Google
BigQuery.

File

Enter the directory of the data stored on Google Cloud Storage
and to be transferred to BigQuery.

If the data is not on Google Cloud Storage, this directory is
used as the intermediate destination before the data is transferred to
BigQuery.

Note that the name of the directory must be
identical with the name of the file specified in the Local
filename field.
Header

Set values to ignore the header of the transferred data. For
example, enter 0 to ignore no
rows for the data without header and set 1 for the data with header at the first row.

Die on error

This check box is cleared by default, meaning to skip the row on
error and to complete the process for error-free rows.

Advanced settings

token properties File Name

Enter the path to, or browse to the refresh token file you need to use.

At the first Job execution using the Authorization
code you have obtained from Google BigQuery, the value in this field
is the directory and the name of that refresh token file to be created and used; if
that token file has been created and you need to reuse it, you have to specify its
directory and file name in this field.

With only the token file name entered,
Talend Studio
 considers the directory of that token file to be the root of the Studio
folder.

For further information about the refresh token, see the manual of Google
BigQuery.

Field Separator

Enter character, string or regular expression to separate fields for the transferred
data.
Create directory if not exists

Select this check box to create the directory you defined in the
File field for Google Cloud Storage, if it
does not exist.

Custom the flush buffer size

Enter the number of rows to be processed before the memory is freed.

Check disk space

Select this check box to throw an exception during execution if
the disk is full.

Encoding

Select the encoding from the list or select Custom and define it manually. This field is compulsory for database
data handling.

tStatCatcher Statistics

Select this check box to collect the log data at the 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.

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

This is an output component used at the end of a Job. It receives
data from its preceding component such as tFileInputDelimited, tMap or tMysqlInput.

Scenario: Writing data in BigQuery

This scenario uses two components to write data in Google BigQuery.

use_case-tbigqueryoutput1.png

Linking the components

  1. In the
    Integration
     perspective
    of
    Talend Studio
    ,
    create an empty Job, named WriteBigQuery for example, from the Job Designs node in the Repository tree view.

    For further information about how to create a Job, see the
    Talend Studio

    User
    Guide    .
  2. Drop tRowGenerator and tBigQueryOutput onto the workspace.

    The tRowGenerator component generates the
    data to be transferred to Google BigQuery in this scenario. In the
    real-world case, you can use other components such as tMysqlInput or tMap in the
    place of tRowGenerator to design a
    sophisticated process to prepare your data to be transferred.
  3. Connect them using the Row > Main
    link.

Preparing the data to be transferred

  1. Double-click tRowGenerator to open its
    Component view.

    use_case-tbigqueryoutput2.png

  2. Click RowGenerator Editor to open the
    editor.
  3. Click

    Button_Plus.png

    three times to add three rows in the Schema table.

  4. In the Column column, enter the name of
    your choice for each of the new rows. For example, fname, lname and
    States.
  5. In the Functions column, select TalendDataGenerator.getFirstName for the
    fname row, TalendDataGenerator.getLastName for the lname row and TalendDataGenerator.getUsState for the States row.
  6. In the Number of Rows for RowGenerator
    field, enter, for example, 100 to define
    the number of rows to be generated.
  7. Click OK to validate these
    changes.

Configuring the access to BigQuery and Cloud Storage

Building access to BigQuery

  1. Double-click tBigQueryOutput to open its
    Component view.

    use_case-tbigqueryoutput3.png

  2. Click Sync columns to retrieve the schema
    from its preceding component.
  3. In the Local filename field, enter the
    directory where you need to create the file to be transferred to
    BigQuery.
  4. Navigate to the Google APIs Console in your web browser to access the
    Google project hosting the BigQuery and the Cloud Storage services you need
    to use.
  5. Click the API Access tab to open its view.
  6. In the Component view of the Studio,
    paste Client ID, Client secret and Project ID from the API Access tab view
    to the corresponding fields, respectively.
  7. In the Dataset field, enter the dataset
    you need to transfer data in. In this scenario, it is documentation.

    This dataset must exist in BigQuery. The following figure shows the
    dataset used by this scenario.
    use_case-tbigqueryoutput4.png

  8. In the Table field, enter the name of the
    table you need to write data in, for example, UScustomer.

    If this table does not exist in BigQuery you are using, select Create the table if it doesn’t exist.
  9. In the Action on data field, select the
    action. In this example, select Truncate to
    empty the contents, if there are any, of target table and to repopulate it
    with the transferred data.

Building access to Cloud Storage

  1. Navigate to the Google APIs Console in your web browser to access the
    Google project hosting the BigQuery and the Cloud Storage services you need
    to use.
  2. Click Google Cloud Storage > Interoperable Access to open its view.
  3. In the Component view of the Studio,
    paste Access key, Access secret from the Interoperable Access tab view to
    the corresponding fields, respectively.
  4. In the Bucket field, enter the path to
    the bucket you want to store the transferred data in. In this example, it is
    talend/documentation

    This bucket must exist in the directory in Cloud Storage
    use_case-tbigqueryoutput5.png

  5. In the File field, enter the directory
    where in Google Clould Storage you receive and create the file to be
    transferred to BigQuery. In this example, it is gs://talend/documentation/biquery_UScustomer.csv. The file
    name must be the same as the one you defined in the Local filename field.

    Troubleshooting: if you encounter issues such as Unable to read source URI of the file
    stored in Google Cloud Storage, check whether you put the same file name
    in these two fields.

  6. Enter 0 in the Header field to ignore no rows in the transferred
    data.

Getting Authorization code

  1. In the Run view of
    Talend Studio
    , click Run to execute this Job. The execution will pause at a given
    moment to print out in the console the URL address used to get the
    authorization code.
  2. Navigate to this address in your web browser and copy the authorization
    code displayed.
  3. In the Component view of tBigQueryOutput, paste the authorization code in
    the Authorization Code field.

Executing the Job

Press F6.

Once done, the Run view is opened automatically,
where you can check the execution result.

use_case-tbigqueryoutput-result.png

The data is transferred to Google BigQuery.

use_case-tbigqueryoutput-result2.png

tBigQueryOutput properties for Apache Spark Batch

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

The Spark Batch
tBigQueryOutput component belongs to the Databases family.

The component in this framework is available only if you have subscribed to one
of the
Talend
 solutions with Big Data.

Basic settings

Dataset

Enter the name of the dataset to which the table to be created or updated
belongs.

When you use BigQuery with Dataproc, in
Google Cloud Platform, select the same region for your BigQuery dataset
as for the Dataproc cluster to be run.

Table

Enter the name of the table to be created or updated.

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.

 

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.

Table operations
Select the operation to be performed on the defined table:

  • Create table if does not exist: The table is
    created if it does not exist.

  • Truncate: The table content is
    deleted.

Data operation
Select the operation to be performed on the incoming data:

  • Append: Append data to the table, whether
    the table is empty or not.

Usage

Usage rule

This is an input component. It sends data extracted from
BigQuery to the component that follows it.

Place a
tBigQueryConfiguration component in the same Job
because it needs to use the BigQuery configuration information provided
by tBigQueryConfiguration.

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, 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: when using Google
    Dataproc, specify a bucket in the Google Storage staging
    bucket     field in the Spark
    configuration     tab; when using other distributions, use a
    tHDFSConfiguration
    component to specify the directory.

  • Standalone mode: you need to choose
    the configuration component depending on the file system you are using, such
    as tHDFSConfiguration
    or tS3Configuration.

This connection is effective on a per-Job basis.

tBigQueryOutput properties for Apache Spark Streaming

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

The Spark Streaming
tBigQueryOutput component belongs to the Databases family.

The component in this framework is available only if you have subscribed to Talend Real-time Big Data Platform or Talend Data
Fabric.

Basic settings

Dataset

Enter the name of the dataset to which the table to be created or updated
belongs.

When you use BigQuery with Dataproc, in
Google Cloud Platform, select the same region for your BigQuery dataset
as for the Dataproc cluster to be run.

Table

Enter the name of the table to be created or updated.

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.

 

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.

Table operations
Select the operation to be performed on the defined table:

  • Create table if does not exist: The table is
    created if it does not exist.

  • Truncate: The table content is
    deleted.

Data operation
Select the operation to be performed on the incoming data:

  • Append: Append data to the table, whether
    the table is empty or not.

Usage

Usage rule

This is an input component. It sends data extracted from
BigQuery to the component that follows it.

Place a
tBigQueryConfiguration component in the same Job
because it needs to use the BigQuery configuration information provided
by tBigQueryConfiguration.

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, 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: when using Google
    Dataproc, specify a bucket in the Google Storage staging
    bucket     field in the Spark
    configuration     tab; when using other distributions, use a
    tHDFSConfiguration
    component to specify the directory.

  • Standalone mode: you need to choose
    the configuration component depending on the file system you are using, such
    as tHDFSConfiguration
    or tS3Configuration.

This connection is effective on a per-Job basis.

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