July 31, 2023

tJDBCOutput – Docs for ESB Jdbc 7.x

tJDBCOutput

Executes the action defined on the data contained in the table, based on the flow
incoming from the preceding component in the Job.

tJDBCOutput writes, updates, makes changes or suppresses
entries in any type of database connected to a JDBC API.

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

tJDBCOutput Standard properties

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

The Standard
tJDBCOutput component belongs to the Databases family.

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

Note: This component is a specific version of a dynamic database
connector. The properties related to database settings vary depending on your database
type selection. For more information about dynamic database connectors, see Dynamic database components.

Basic settings

Database

Select a type of database from the list and click
Apply.

Property Type

Select the way the connection details
will be set.

  • Built-In: The connection details will be set
    locally for this component. You need to specify the values for all
    related connection properties manually.

  • Repository: The connection details stored
    centrally in Repository > Metadata will be reused by this component. You need to click
    the […] button next to it and in the pop-up
    Repository Content dialog box, select the
    connection details to be reused, and all related connection
    properties will be automatically filled in.

This property is not available when other connection component is selected
from the Connection Component drop-down list.

tJDBCOutput_1.png

Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings
view.

For more information about setting up and storing database
connection parameters, see Talend Studio User Guide.

Connection Component

Select the component that opens the database connection to be reused by this
component.

JDBC URL

The JDBC URL of the database to be used. For
example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database.

Drivers

Complete this table to load the driver JARs needed. To do
this, click the [+] button under the table to add
as many rows as needed, each row for a driver JAR, then select the cell and click the
[…] button at the right side of the cell to
open the Module dialog box from which you can select the driver JAR
to be used. For example, the driver jar RedshiftJDBC41-1.1.13.1013.jar for the Redshift database.

For more information, see Importing a database driver.

Driver Class

Enter the class name for the specified driver between double
quotation marks. For example, for the RedshiftJDBC41-1.1.13.1013.jar driver, the name to be entered is
com.amazon.redshift.jdbc41.Driver.

Use Id and Password

The database user authentication data.

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

Table Name

The name of the table into
which data will be written.

Data Action

Select an action to be performed on data of the table defined.

  • Insert: Add new entries to the table. If
    duplicates are found, job stops.

  • Update: Make changes to existing
    entries.

  • Insert or update: Insert a new record in the
    index pool. If the record with the given reference already exists, an update would be
    made.

  • Update or insert: Update the record with the
    given reference. If the record does not exist in the index pool, a new record would be
    inserted.

  • Delete: Remove entries corresponding to
    the input flow.

Warning:

It is necessary to specify at least one column as a
primary key on which the Update and Delete operations are based. You
can do that by clicking Edit Schema and selecting the check box(es)
next to the column(s) you want to set as primary key(s). For an
advanced use, click the Advanced settings view where you can
simultaneously define primary keys for the Update and Delete
operations. To do that: Select the Use field options check box and
then in the Key in update column, select the check boxes next to the
column names you want to use as a base for the Update operation. Do
the same in the Key in delete column for the Delete operation.

Clear data in table

Select this check box to clear data in the table before performing the
action defined.

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.

When the schema to be reused has default values that are
integers or functions, ensure that these default values are not enclosed within
quotation marks. If they are, you must remove the quotation marks manually.

You can find more details about how to
verify default values in retrieved schema in Talend Help Center (https://help.talend.com).

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.

Guess Schema

Click this button to generate schema columns based on the settings of database table
columns.

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 connection.

Specify a data source alias

Select this check box and in the
Data source alias field displayed, specify the alias of
a data source created on Talend Runtime side to use
the shared connection pool defined in the data source configuration. This option
works only when you deploy and run your Job in Talend Runtime.

If you use the component’s own DB configuration, your data source
connection will be closed at the end of the component. To prevent this from
happening, use a shared DB connection with the data source alias specified.

This property is not available when other connection component is selected from
the Connection Component drop-down list.

Advanced settings

Commit every

Specify the number of rows to be processed before committing
batches of rows together into the database.

This option ensures transaction quality (but not rollback) and, above all, better
performance at executions.

Additional Columns

This option allows you to call SQL functions to perform actions on columns, which are
not insert, update or delete actions, or actions that require particular preprocessing.
It is not offered if you create (with or without drop) the database table.

  • Name: The name of the schema column to be inserted, or the
    name of the schema column used to replace an existing column.

  • SQL expression: The SQL statement to be executed in order to
    insert or replace relevant column.

  • Position: Select Before,
    After, or Replace according to the
    action to be performed on the reference column.

  • Reference column: The name of the reference column that can
    be used to locate the new column to be inserted or that will be replaced.

Use field options

Select this check box and in the Fields options table
displayed, select the check box for the corresponding column to customize a
request, particularly if multiple actions are being carried out on the
data.

  • Key in update: Select the check box for the
    corresponding column based on which data is updated.
  • Key in delete: Select the check box for the
    corresponding column based on which data is deleted.
  • Updatable: Select the check box if data in the
    corresponding column can be updated.

  • Insertable: Select the check box if data in
    the corresponding column can be inserted.

Debug query mode

Select this check box to display each step during processing entries
in a database.

Use Batch

Select this check box to activate the batch mode for data
processing, and in the Batch Size field displayed, specify the
number of records to be processed in each batch.

tStatCatcher Statistics

Select this check box to gather the Job processing metadata at the Job level
as well as at each component level.

Enable parallel execution

Select this check box to perform high-speed data processing by treating
multiple data flows simultaneously. This feature depends on the database or
the application ability to handle multiple inserts in parallel as well as
the number of CPU affected. With this check box selected, you need to
specify the number of parallel executions desired in the Number
of parallel executions
field displayed.

Note: When parallel execution is enabled, it is not possible to use global
variables to retrieve return values.

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.

NB_LINE

The number of rows processed. This is an After variable and it returns an integer.

NB_LINE_INSERTED

The number of rows inserted. This is an After variable and it returns an integer.

NB_LINE_UPDATED

The number of rows updated. This is an After variable and it returns an integer.

NB_LINE_DELETED

The number of rows deleted. This is an After variable and it returns an integer.

NB_LINE_REJECTED

The number of rows rejected. This is an After variable and it returns an integer.

QUERY

The query statement being processed. This is a Flow variable and it returns a
string.

Usage

Usage rule

This component offers the flexibility benefit of the
database query and covers all of the SQL queries possible.

This component must be used as an output component. It
allows you to carry out actions on a table or on the data of a table in
a JDBC database. It also allows you to create a reject flow using a
Row > Rejects link to
filter data in error. For an example of tMySqlOutput in use, see Retrieving data in error with a Reject link.

Dynamic settings

Click the [+] button to add a row in the table
and fill the Code field with a context
variable to choose your database connection dynamically from multiple
connections planned in your Job. This feature is useful when you need to
access database tables having the same data structure but in different
databases, especially when you are working in an environment where you
cannot change your Job settings, for example, when your Job has to be
deployed and executed independent of Talend Studio.

For examples on using dynamic parameters, see Reading data from databases through context-based dynamic connections and Reading data from different MySQL databases using dynamically loaded connection parameters. For more information on Dynamic
settings
and context variables, see Talend Studio
User Guide.

Related scenarios

For tJDBCOutput related topics, see:

tJDBCOutput MapReduce properties (deprecated)

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

The MapReduce
tJDBCOutput component belongs to the MapReduce and the Databases families.

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.

tJDBCOutput_1.png

Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings
view.

For more information about setting up and storing database
connection parameters, see Talend Studio User Guide.

JDBC URL

The JDBC URL of the database to be used. For
example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database.

Driver JAR

Complete this table to load the driver JARs needed. To do
this, click the [+] button under the table to add
as many rows as needed, each row for a driver JAR, then select the cell and click the
[…] button at the right side of the cell to
open the Module dialog box from which you can select the driver JAR
to be used. For example, the driver jar RedshiftJDBC41-1.1.13.1013.jar for the Redshift database.

For more information, see Importing a database driver.

Class Name

Enter the class name for the specified driver between double
quotation marks. For example, for the RedshiftJDBC41-1.1.13.1013.jar driver, the name to be entered is
com.amazon.redshift.jdbc41.Driver.

Username and Password

Enter the authentication information to the database you need
to connect to.

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

Table name

Name of the table to be written. Note that only one table can
be written at a time.

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.

Advanced settings

Use Batch Size

When selected, enables you to define the number
of lines in each processed batch.

Usage

Usage rule

In a
Talend
Map/Reduce Job, it is used as an end component and requires
a transformation component as input link. The other components used along with it must be
Map/Reduce components, too. They generate native Map/Reduce code that can be executed
directly in Hadoop.

This component, along with the MapReduce family it belongs to, appears only when you are
creating a Map/Reduce 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, and non Map/Reduce Jobs.

Hadoop Connection

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

This connection is effective on a per-Job basis.

Limitation

We recommend using the following databases with
the Map/Reduce version of this component: DB2, Informix, MSSQL, MySQL, Netezza,
Oracle, Postgres, Teradata and Vertica.

It may work with other databases as well, but
these may not necessarily have been tested.

Related scenarios

If you are a subscription-based Big Data user, you can consult a
Talend
Map/Reduce Job using the Map/Reduce version of tJDBCOutput:

tJDBCOutput properties for Apache Spark Batch

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

The Spark Batch
tJDBCOutput component belongs to the Databases family.

This component can be used to write data to a RDS MariaDB, a
RDS PostgreSQL or a RDS SQLServer database.

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.

tJDBCOutput_1.png

Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings
view.

For more information about setting up and storing database
connection parameters, see Talend Studio User Guide.

Use an existing
connection

Select this check box and in the Component List click the relevant connection component to
reuse the connection details you already defined.

JDBC URL

The JDBC URL of the database to be used. For
example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database.

If you are using Spark V1.3, this URL should contain the
authentication information, such
as:

Driver JAR

Complete this table to load the driver JARs needed. To do
this, click the [+] button under the table to add
as many rows as needed, each row for a driver JAR, then select the cell and click the
[…] button at the right side of the cell to
open the Module dialog box from which you can select the driver JAR
to be used. For example, the driver jar RedshiftJDBC41-1.1.13.1013.jar for the Redshift database.

For more information, see Importing a database driver.

Class Name

Enter the class name for the specified driver between double
quotation marks. For example, for the RedshiftJDBC41-1.1.13.1013.jar driver, the name to be entered is
com.amazon.redshift.jdbc41.Driver.

Username and
Password

Enter the authentication information to the database you need
to connect to.

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

Available only for Spark V1.4. and onwards.

Table name

Name of the table to be written. Note that only one table can
be written at a time.

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.

Action on data

Select an action to be performed on data of the table defined.

  • Insert:
    Add new entries to the table.

  • Update: Make changes to existing
    entries.

  • Insert or update: Insert a new record. If
    the record with the given reference already exists, an update would be made.

  • Update or insert: Update the record with the
    given reference. If the record does not exist in the index pool, a new record would be
    inserted.

  • Delete: Remove entries corresponding to
    the input flow.

Die on error

Select the check box to stop the execution of the Job when an error
occurs.

Advanced settings

Additional JDBC
parameters

Specify additional connection properties for the database connection you are
creating. The properties are separated by semicolon and each property is a key-value
pair, for example, encryption=1;clientname=Talend.

This field is not available if the Use an existing
connection
check box is selected.

Left protected
char
and Right protected char

Enter the symbol reserved by the database you are using, the left part in Left protected char and the right part in Right
protected char
, so that tJDBCOutput can generate
SQL expressions with this reserved symbol properly placed.

For example, if you are using Oracle, double quotation marks (“) are reserved for object
names and so you need to enter the left and the right marks in these fields, respectively.
Then at runtime, tJDBCOutput places double quotations marks
around object names such as a table name.

Additional
Columns

This option allows you to call SQL functions to perform actions on columns,
provided that these are not insert, update or delete actions, or actions that require
pre-processing. This option is not available if you have just created the database table (even
if you delete it beforehand). Click the [+] button under
the table to add column(s), and set the following parameters for each column.

 

Name: Type in the name of the schema column to be altered
or inserted.

 

SQL expression: Type in the SQL statement to be executed in
order to alter or insert the data in the corresponding column.

 

Position: Select Before,
Replace or After,
depending on the action to be performed on the reference column.

 

Reference column: Type in a reference column that the
current component can use to locate or replace the new column, or the column to be
modified.

Use field
options

Select the check box for the corresponding column to customize a request,
particularly if multiple actions are being carried out on the data.

  • Key in update: Select the check box for
    the corresponding column based on which the data is updated.

  • Key in delete: Select the check box for
    the corresponding column based on which the data is deleted.

  • Updatable: Select the check box if the
    data in the corresponding column can be updated.

  • Insertable: Select the check box if the
    data in the corresponding column can be inserted.

Use Batch

Select this check box to activate the batch mode for data processing.

This check box is available only when
the Insert, the
Update or
the Delete
option is selected from the Action on data list in the Basic settings view.

Batch Size

Specify the number of records to be processed in each batch.

This field appears only when the Use batch mode
check box is selected.

Connection pool

In this area, you configure, for each Spark executor, the connection pool used to control
the number of connections that stay open simultaneously. The default values given to the
following connection pool parameters are good enough for most use cases.

  • Max total number of connections: enter the maximum number
    of connections (idle or active) that are allowed to stay open simultaneously.

    The default number is 8. If you enter -1, you allow unlimited number of open connections at the same
    time.

  • Max waiting time (ms): enter the maximum amount of time
    at the end of which the response to a demand for using a connection should be returned by
    the connection pool. By default, it is -1, that is to say, infinite.

  • Min number of idle connections: enter the minimum number
    of idle connections (connections not used) maintained in the connection pool.

  • Max number of idle connections: enter the maximum number
    of idle connections (connections not used) maintained in the connection pool.

Evict
connections

Select this check box to define criteria to destroy connections in the connection pool. The
following fields are displayed once you have selected it.

  • Time between two eviction runs: enter the time interval
    (in milliseconds) at the end of which the component checks the status of the connections and
    destroys the idle ones.

  • Min idle time for a connection to be eligible to
    eviction
    : enter the time interval (in milliseconds) at the end of which the idle
    connections are destroyed.

  • Soft min idle time for a connection to be eligible to
    eviction
    : this parameter works the same way as Min idle
    time for a connection to be eligible to eviction
    but it keeps the minimum number
    of idle connections, the number you define in the Min number of idle
    connections
    field.

Usage

Usage rule

This component is used as an end component and requires an input link.

This component should use a tJDBCConfiguration component present in the same Job to
connect to a database. You need to drop a tJDBCConfiguration component alongside this component and configure the
Basic settings of this component
to use tJDBCConfiguration.

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

For a scenario about how to use the same type of component in a Spark Batch Job, see Writing and reading data from MongoDB using a Spark Batch Job.

tJDBCOutput properties for Apache Spark Streaming

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

The Spark Streaming
tJDBCOutput component belongs to the Databases family.

This component can be used to write data to a RDS MariaDB, a
RDS PostgreSQL or a RDS SQLServer database.

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.

tJDBCOutput_1.png

Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings
view.

For more information about setting up and storing database
connection parameters, see Talend Studio User Guide.

Use an existing
connection

Select this check box and in the Component List click the relevant connection component to
reuse the connection details you already defined.

JDBC URL

The JDBC URL of the database to be used. For
example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database.

If you are using Spark V1.3, this URL should contain the
authentication information, such
as:

Driver JAR

Complete this table to load the driver JARs needed. To do
this, click the [+] button under the table to add
as many rows as needed, each row for a driver JAR, then select the cell and click the
[…] button at the right side of the cell to
open the Module dialog box from which you can select the driver JAR
to be used. For example, the driver jar RedshiftJDBC41-1.1.13.1013.jar for the Redshift database.

For more information, see Importing a database driver.

Class Name

Enter the class name for the specified driver between double
quotation marks. For example, for the RedshiftJDBC41-1.1.13.1013.jar driver, the name to be entered is
com.amazon.redshift.jdbc41.Driver.

Username and Password

Enter the authentication information to the database you need
to connect to.

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

Available only for Spark V1.4. and onwards.

Table

Name of the table to be written. Note that only one table can
be written at a time.

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.

Action on data

Select an action to be performed on data of the table defined.

  • Insert: Add new
    entries to the table.

  • Update: Make changes to existing
    entries.

  • Insert or update: Insert a new record. If
    the record with the given reference already exists, an update would be made.

  • Update or insert: Update the record with the
    given reference. If the record does not exist in the index pool, a new record would be
    inserted.

  • Delete: Remove entries corresponding to
    the input flow.

Die on error

Select the check box to stop the execution of the Job when an error
occurs.

Advanced settings

Additional JDBC
parameters

Specify additional connection properties for the database connection you are
creating. The properties are separated by semicolon and each property is a key-value
pair, for example, encryption=1;clientname=Talend.

This field is not available if the Use an existing
connection
check box is selected.

Left protected char and
Right protected char

Enter the symbol reserved by the database you are using, the left part in Left protected char and the right part in Right
protected char
, so that tJDBCOutput can generate
SQL expressions with this reserved symbol properly placed.

For example, if you are using Oracle, double quotation marks (“) are reserved for object
names and so you need to enter the left and the right marks in these fields, respectively.
Then at runtime, tJDBCOutput places double quotations marks
around object names such as a table name.

Additional Columns

This option allows you to call SQL functions to perform actions on columns,
provided that these are not insert, update or delete actions, or actions that require
pre-processing. This option is not available if you have just created the database table (even
if you delete it beforehand). Click the [+] button under
the table to add column(s), and set the following parameters for each column.

 

Name: Type in the name of the schema column to be altered
or inserted.

 

SQL expression: Type in the SQL statement to be executed in
order to alter or insert the data in the corresponding column.

 

Position: Select Before,
Replace or After,
depending on the action to be performed on the reference column.

 

Reference column: Type in a reference column that the
current component can use to locate or replace the new column, or the column to be
modified.

Use field options

Select the check box for the corresponding column to customize a request,
particularly if multiple actions are being carried out on the data.

  • Key in update: Select the check box for
    the corresponding column based on which the data is updated.

  • Key in delete: Select the check box for
    the corresponding column based on which the data is deleted.

  • Updatable: Select the check box if the
    data in the corresponding column can be updated.

  • Insertable: Select the check box if the
    data in the corresponding column can be inserted.

Use Batch

Select this check box to activate the batch mode for data processing.

This check box is available only when the
Insert, the
Update or the
Delete option is
selected from the Action on
data
list in the Basic settings view.

Batch Size

Specify the number of records to be processed in each batch.

This field appears only when the Use batch mode
check box is selected.

Connection pool

In this area, you configure, for each Spark executor, the connection pool used to control
the number of connections that stay open simultaneously. The default values given to the
following connection pool parameters are good enough for most use cases.

  • Max total number of connections: enter the maximum number
    of connections (idle or active) that are allowed to stay open simultaneously.

    The default number is 8. If you enter -1, you allow unlimited number of open connections at the same
    time.

  • Max waiting time (ms): enter the maximum amount of time
    at the end of which the response to a demand for using a connection should be returned by
    the connection pool. By default, it is -1, that is to say, infinite.

  • Min number of idle connections: enter the minimum number
    of idle connections (connections not used) maintained in the connection pool.

  • Max number of idle connections: enter the maximum number
    of idle connections (connections not used) maintained in the connection pool.

Evict connections

Select this check box to define criteria to destroy connections in the connection pool. The
following fields are displayed once you have selected it.

  • Time between two eviction runs: enter the time interval
    (in milliseconds) at the end of which the component checks the status of the connections and
    destroys the idle ones.

  • Min idle time for a connection to be eligible to
    eviction
    : enter the time interval (in milliseconds) at the end of which the idle
    connections are destroyed.

  • Soft min idle time for a connection to be eligible to
    eviction
    : this parameter works the same way as Min idle
    time for a connection to be eligible to eviction
    but it keeps the minimum number
    of idle connections, the number you define in the Min number of idle
    connections
    field.

Usage

Usage rule

This component is used as an end component and requires an input link.

This component should use a tJDBCConfiguration component present in the same Job to
connect to a database. You need to drop a tJDBCConfiguration component alongside this component and configure the
Basic settings of this component
to use tJDBCConfiguration.

Although tJDBCOutput is
flexible to connect to as various target databases as possible, some databases
are not made to directly receive streaming data. Before using tJDBCOutput, ensure that your target system fits
streaming operations and validate your architecture design based on this
verification.

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

For a scenario about how to use the same type of component in a Spark Streaming Job, see
Reading and writing data in MongoDB using a Spark Streaming Job.

tJDBCOutput Storm properties (deprecated)

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

The Storm
tJDBCOutput component belongs to the Storm and the Databases families.

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.

tJDBCOutput_1.png

Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings
view.

For more information about setting up and storing database
connection parameters, see Talend Studio User Guide.

JDBC URL

The JDBC URL of the database to be used. For
example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database.

Driver JAR

Complete this table to load the driver JARs needed. To do
this, click the [+] button under the table to add
as many rows as needed, each row for a driver JAR, then select the cell and click the
[…] button at the right side of the cell to
open the Module dialog box from which you can select the driver JAR
to be used. For example, the driver jar RedshiftJDBC41-1.1.13.1013.jar for the Redshift database.

For more information, see Importing a database driver.

Class Name

Enter the class name for the specified driver between double
quotation marks. For example, for the RedshiftJDBC41-1.1.13.1013.jar driver, the name to be entered is
com.amazon.redshift.jdbc41.Driver.

Username and
Password

The JDBC URL of the database to be used. For
example, the JDBC URL for the Amazon Redshift database is jdbc:redshift://endpoint:port/database.

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

Table name

Name of the table to be written. Note that only one table can
be written at a time.

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.

Usage

Usage rule

In a
Talend
Storm Job, it is used as an end component. The 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.

This component, along with the Storm family it belongs to, appears only when you are
creating a Storm 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.

Storm Connection

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.

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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