August 15, 2023

tCacheOut – Docs for ESB 6.x

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  4. tCacheOut – Docs for ESB 6.x

tCacheOut

Persists the input RDDs depending on the specific storage level you define in order
to offer faster access to these datasets later.

tCacheOut writes RDDs (Resilient
Distributed Datasets) to the cache for later use in the same Job.

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

tCacheOut properties for Apache Spark Batch

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

The Spark Batch
tCacheOut component belongs to the Processing family.

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

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. 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.

Storage level

From the Storage level drop-down list that is displayed, select how the cached RDDs are
stored, such as in memory only or in memory and on disk.

For further information about each of the storage level, see https://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence.

Usage

Usage rule

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

This component makes datasets persist and is closely related to tCacheIn. Iteratively, tCacheOut stores input
data as cache so that tCacheIn can reads the cache without having to calculate again all of the
Spark DAG (Directed Acyclic Graph, the model used by Spark for scheduling Spark
actions).

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

tCacheOut properties for Apache Spark Streaming

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

The Spark Streaming
tCacheOut component belongs to the Processing 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

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.

Storage level

From the Storage level drop-down list that is displayed, select how the cached RDDs are
stored, such as in memory only or in memory and on disk.

For further information about each of the storage level, see https://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence.

Usage

Usage rule

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

This component makes datasets persist and is closely related to tCacheIn. Iteratively, tCacheOut stores input
data as cache so that tCacheIn can reads the cache without having to calculate again all of the
Spark DAG (Directed Acyclic Graph, the model used by Spark for scheduling Spark
actions).

At any given moment, tCacheOut stores only one micro-batch in memory.

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 Streaming version of this component
yet.

Document get from Talend https://help.talend.com
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Docs 6.x
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