July 30, 2023

tRecommend – Docs for ESB 7.x

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  4. tRecommend – Docs for ESB 7.x

tRecommend

Recommends products to users known to this model, based on the user-product
recommender model generated by tASLModel.

tRecommend uses a given recommender
model to analyse user data incoming from its preceding Spark component
so as to estimate the preferences of these users.

In local mode, Apache Spark 1.3.0 and later versions are supported.

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

tRecommend properties for Apache Spark Batch

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

The Spark Batch
tRecommend component belongs to the Machine Learning family.

The component in this framework is available in all Talend Platform products with Big Data and in Talend Data Fabric.

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.

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.

Note that apart from the columns you can edit by yourself, product_ID and score columns are read-only
and used to carry the data about the user preferences calculated against the recommender
model being used. The score column indicates how strongly
recommended a product is to a given user.

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.

Input parquet model

Enter the directory where the recommender model to be used is
stored. This directory must be in the machine where the Job is
run.

The button for browsing does not work with the Spark
Local mode; if you are
using the other Spark Yarn
modes that the Studio supports with your distribution, ensure that you have properly
configured the connection in a configuration component in the same Job, such as

tHDFSConfiguration
. Use the
configuration component depending on the filesystem to be used.

This model should be generated by a tALSModel component.

Select the User Identity
column

Select the column that is carrying the user ID data from the input
columns.

This tRecommend component needs
the input user IDs to match the users known to the recommender model to be used.

Number of recommendations

Enter the number of the most recommended products to be
outputted.

Note that this is a numeric value and so you cannot use the double
quotation marks around it.

Usage

Usage rule

This component is used as an intermediate step.

This component, along with the Spark Batch component Palette it belongs to,
appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a
scenario presents only Standard Jobs, that is to
say traditional
Talend
 data integration Jobs.

The user IDs processed by this component must be known to the recommender model to be
used. When a user is unknown to the recommender model, the corresponding values returned in
the product_ID and the score columns are null. This allows you to retrieve the records about the
unknown users using a tFilterRow component after tRecommend in the same Job.

MLlib installation

Spark’s machine learning library, MLlib, uses the gfortran runtime library and for this
reason, you need to ensure that this library is already present in
every node of the Spark cluster to be used.

For further information about MLlib and this library, see the
related documentation from Spark.

Spark Connection

In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the
      Google Storage staging bucket
      field in the Spark configuration
      tab.

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      configuration       area in the Spark
      configuration       tab.

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
      configuration       tab.
    • When using Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.

    • When using on-premise
      distributions, use the configuration component corresponding
      to the file system your cluster is using. Typically, this
      system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the
    configuration component corresponding to the file system your cluster is
    using, such as tHDFSConfiguration or
    tS3Configuration.

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks
    Filesystem).

This connection is effective on a per-Job basis.

Related scenarios

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

tRecommend properties for Apache Spark Streaming

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

The Spark Streaming
tRecommend component belongs to the Machine Learning family.

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

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.

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.

Note that apart from the columns you can edit by yourself, product_ID and score columns are read-only
and used to carry the data about the user preferences calculated against the recommender
model being used. The score column indicates how strongly
recommended a product is to a given user.

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.

Input parquet model

Enter the directory where the recommender model to be used is
stored. This directory must be in the machine where the Job is
run.

The button for browsing does not work with the Spark
Local mode; if you are
using the other Spark Yarn
modes that the Studio supports with your distribution, ensure that you have properly
configured the connection in a configuration component in the same Job, such as

tHDFSConfiguration
. Use the
configuration component depending on the filesystem to be used.

This model should be generated by a tALSModel component.

Select the User Identity
column

Select the column that is carrying the user ID data from the input
columns.

This tRecommend component needs
the input user IDs to match the users known to the recommender model to be used.

Number of recommendations

Enter the number of the most recommended products to be
outputted.

Note that this is a numeric value and so you cannot use the double
quotation marks around it.

Usage

Usage rule

This component is used as an intermediate step.

This component, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents
only Standard Jobs, that is to say traditional
Talend
 data
integration Jobs.

The user IDs processed by this component must be known to the recommender model to be
used. When a user is unknown to the recommender model, the corresponding values returned in
the product_ID and the score columns are null. This allows you to retrieve the records about the
unknown users using a tFilterRow component after tRecommend in the same Job.

MLlib installation

Spark’s machine learning library, MLlib, uses the gfortran runtime library and for this
reason, you need to ensure that this library is already present in
every node of the Spark cluster to be used.

For further information about MLlib and this library, see the
related documentation from Spark.

Spark Connection

In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the
      Google Storage staging bucket
      field in the Spark configuration
      tab.

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      configuration       area in the Spark
      configuration       tab.

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
      configuration       tab.
    • When using Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.

    • When using on-premise
      distributions, use the configuration component corresponding
      to the file system your cluster is using. Typically, this
      system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the
    configuration component corresponding to the file system your cluster is
    using, such as tHDFSConfiguration or
    tS3Configuration.

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks
    Filesystem).

This connection is effective on a per-Job basis.

Related scenarios

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

Document get from Talend https://help.talend.com
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Docs 7.x
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