July 30, 2023

tBigQueryOutput – Docs for ESB 7.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 available in all Talend
products.

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. 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.

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.
 
  • The Record type of BigQuery is not supported.
  • The columns for table metadata such as the Description column or the Mode column cannot be retrieved.
  • The Timestamp data from your BigQuery system is formated to be
    String data.
  • The numeric data of BigQuery is converted to BigDecimal.

Property type

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.

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.
Authentication mode Select the mode to be used to authenticate to your project.

  • OAuth 2.0: authenticate the access using
    OAuth credentials. When selecting this mode, the parameters to be
    defined in the Basic settings view are
    Client ID, Client
    secret     and Authorization
    code    .
  • Service account: authenticate using a Google
    account that is associated with your Google Cloud Platform project.
    When selecting this mode, the parameter to be defined in the
    Basic settings view is Service
    account credentials file    .
Service account credentials file Enter the path to the credentials file created for the service account
to be used. This file must be stored in the machine in which your Talend Job is actually launched and
executed.

For further information about how to create a Google service
account and obtain the credentials file, see Getting Started with
Authentication from the Google documentation.

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 Google 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 Google 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 Google BigQuery. This data must be stored directly
under the bucket root. For example, enter
gs://my_bucket/my_file.csv.

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

Note that this file name 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.
Drop table if exists

Select the Drop table if exists check box to remove the table specified in the Table field, if this table already exists.
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. 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 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.

This component automatically detects and
supports both multi-regional locations and regional locations. When using the
regional locations, the buckets and the datasets to be used must be in the same
locations.

Writing data in Google BigQuery

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

tBigQueryOutput_1.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.

    tBigQueryOutput_2.png

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

    tBigQueryOutput_3.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 Cloud Storage

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

    tBigQueryOutput_4.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 Google Cloud Storage > Interoperable Access to open its
    view.
  6. In Google storage configuration area of the
    Component view, paste Access key, Access
    secret from the Interoperable Access tab view to the corresponding fields,
    respectively.
  7. 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
    tBigQueryOutput_5.png

  8. 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.

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

Building access to BigQuery

  1. In the Dataset field of the
    Component view, 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.
    tBigQueryOutput_6.png

  2. In the Table field, enter the
    name of the table you need to write data in, for example, UScustomer.
  3. 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.
  4. In the Authentication area, add the authentication
    information. In most cases, the Service account mode is more
    straight-forward and easy to handle.

    Authentication mode Description
    Service account Authenticate using a Google account that is associated with your Google
    Cloud Platform project.

    When selecting this mode, the Service
    account credentials file     field is displayed. In this field,
    enter the path to the credentials file created for the service account to be
    used. This file must be stored in the machine in which your Talend Job is actually
    launched and executed.

    For further information about how to create a
    Google service account and obtain the credentials file, see Getting Started with Authentication
    from the Google documentation.
    OAuth 2.0 Authenticate the access using OAuth credentials. When selecting this mode,
    the parameters to be defined in the Basic settings view
    are Client ID, Client secret and
    Authorization code.

    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 the API Access tab to open its view.
    3. 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.

      In the Advanced
      settings       tab, see the file path in the token
      properties File Name       field. The Studio automatically
      generates this file during the first successful login and stores all
      future successful logins in it.

    4. In the Run view of the 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.
    5. Navigate to this address in your web browser and copy the authorization
      code displayed.
    6. In the Component view of tBigQueryOutput, paste the authorization
      code in the Authorization Code
      field.
  5. If you have been using the OAuth 2.0 authentication mode, in
    the Action on data field, select the action to be performed on
    your data. In this example, select Truncate to empty the
    contents, if there are any, of target table and to repopulate it with the transferred
    data. If your are using Service account, ignore this
    step.

    If the table to be used does not exist in BigQuery, select Create the table if it doesn’t exist.

Executing the Job

Press F6.

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

tBigQueryOutput_7.png

The data is transferred to Google BigQuery.

tBigQueryOutput_8.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 in all subscription-based Talend products with Big Data
and 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 Google 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. 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.

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

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.

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.

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

Usage

Usage rule

This component is used as an end component and
requires an input link. It writes data to a given Google BigQuery
table.

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

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.

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