tRecommend properties for Apache Spark Streaming

Machine Learning

author
Talend Documentation Team
EnrichVersion
6.5
EnrichProdName
Talend Big Data Platform
Talend Data Fabric
Talend Real-Time Big Data Platform
Talend Big Data
task
Data Quality and Preparation > Third-party systems > Machine Learning components
Data Governance > Third-party systems > Machine Learning components
Design and Development > Third-party systems > Machine Learning components
EnrichPlatform
Talend Studio

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.

The component in this framework 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 Repository. 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 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.

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

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.