# tRecommend properties for Apache Spark Streaming (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. These properties are used to configure tRecommend running in the Spark Streaming Job framework. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. 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 | Properties | Description | | --- | --- | | 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 Talend Studio
supports with your distribution, ensure that you have properly configured the connection
in a configuration component in the same Job. 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 guidance | Description | | --- | --- | | **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 on-premises 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 Apache Spark Batch or tS3Configuration Apache Spark Batch.

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. |