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.
This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric.
Basic settings
Properties | Description |
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Schema and Edit schema |
|
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 guidance | Description |
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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 |
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:
This connection is effective on a per-Job basis. |