August 15, 2023

tAvroOutput – Docs for ESB 6.x

tAvroOutput

Receives data flows from the processing component placed ahead of it and writes
the data into Avro format files in a given distributed file system.

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

tAvroOutput MapReduce properties

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

The MapReduce
tAvroOutput component belongs to the MapReduce family.

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

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.

The properties are stored centrally under the Hadoop
Cluster
node of the Repository
tree.

The fields that come after are pre-filled in using the fetched
data.

For further information about the Hadoop
Cluster
node, see the Getting Started Guide.

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.

Folder

Browse to, or enter the path pointing to the data to be used in the file system.

This path must point to a folder rather than a file, because a

Talend
Map/Reduce
Job need to write in its target folder not only the final result but
also multiple part- files
generated in performing Map/Reduce computations.

Note that you need
to ensure you have properly configured the connection to the Hadoop
distribution to be used in the Hadoop
configuration
tab in the Run view.

Action

Select an operation for writing data:

Create: Creates a file and write
data in it.

Overwrite: Overwrites the file
existing in the directory specified in the Folder field.

Compression

Select the Compress data blocks
check box to compress the output data.

Hadoop provides different compression formats that help reduce the space needed for
storing files and speed up data transfer. When reading a compressed file, the Studio needs
to uncompress it before being able to feed it to the input flow.

Advanced settings

tStatCatcher Statistics

Select this check box to collect log data at the component level.
Note that this check box is not available in the Map/Reduce version
of the component.

Global Variables

Global Variables

ERROR_MESSAGE: the error message generated by the
component when an error occurs. This is an After variable and it returns a string. This
variable functions only if the Die on error check box is
cleared, if the component has this check box.

A Flow variable functions during the execution of a component while an After variable
functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl +
Space
to access the variable list and choose the variable to use from it.

For further information about variables, see
Talend Studio

User Guide.

Usage

Usage rule

In a
Talend
Map/Reduce Job, 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.

Once a Map/Reduce Job is opened in the workspace, tAvroOutput as well as the MapReduce
family appears in the Palette of
the Studio.

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.

Related scenario

tAvroOutput properties for Apache Spark Batch

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

The Spark Batch
tAvroOutput component belongs to the File family.

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

Basic settings

Define a storage configuration
component

Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job. For
example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system.

Property type

Either Built-In or Repository.

 

Built-In: No property data stored centrally.

 

Repository: Select the repository file where the
properties are stored.

The properties are stored centrally under the Hadoop
Cluster
node of the Repository
tree.

The fields that come after are pre-filled in using the fetched
data.

For further information about the Hadoop
Cluster
node, see the Getting Started Guide.

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.

Folder

Browse to, or enter the path pointing to the data to be used in the file system.

This path must point to a folder rather than a file, because a

Talend
Map/Reduce
Job need to write in its target folder not only the final result but
also multiple part- files
generated in performing Map/Reduce computations.

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode,
ensure that you have properly configured the connection in a configuration component in
the same Job, such as tHDFSConfiguration.

Action

Select an operation for writing data:

Create: Creates a file and write
data in it.

Overwrite: Overwrites the file
existing in the directory specified in the Folder field.

Compression

Select the Compress data blocks
check box to compress the output data.

Hadoop provides different compression formats that help reduce the space needed for
storing files and speed up data transfer. When reading a compressed file, the Studio needs
to uncompress it before being able to feed it to the input flow.

Advanced settings

Use output schema compatible with
AvroKeyValueOutputFormat

Select this check box to define a hierarchical key/value schema for the Avro file to be
written. According to this schema, the current component embeds the columns of the input row
into an output record with two fields, named key and
value.

For this purpose, you need to specify the columns of the output data to be used as key
fields in the table that is displayed.

For example, the following data is an Avro record written with this key/value structure:

Usage

Usage rule

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

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

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.

Related scenarios

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

tAvroOutput properties for Apache Spark Streaming

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

The Spark Streaming
tAvroOutput component belongs to the File 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

Define a storage configuration
component

Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job. For
example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system.

Property type

Either Built-In or Repository.

 

Built-In: No property data stored centrally.

 

Repository: Select the repository file where the
properties are stored.

The properties are stored centrally under the Hadoop
Cluster
node of the Repository
tree.

The fields that come after are pre-filled in using the fetched
data.

For further information about the Hadoop
Cluster
node, see the Getting Started Guide.

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.

Folder

Browse to, or enter the path pointing to the data to be used in the file system.

This path must point to a folder rather than a file, because a

Talend
Map/Reduce
Job need to write in its target folder not only the final result but
also multiple part- files
generated in performing Map/Reduce computations.

The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn or the Spark Standalone mode,
ensure that you have properly configured the connection in a configuration component in
the same Job, such as tHDFSConfiguration.

Action

Select an operation for writing data:

Create: Creates a file and write
data in it.

Overwrite: Overwrites the file
existing in the directory specified in the Folder field.

Compression

Select the Compress data blocks
check box to compress the output data.

Hadoop provides different compression formats that help reduce the space needed for
storing files and speed up data transfer. When reading a compressed file, the Studio needs
to uncompress it before being able to feed it to the input flow.

Usage

Usage rule

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

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

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.

Related scenarios

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


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