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https://issues.apache.org/jira/browse/JENA-1233?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15647325#comment-15647325
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ASF GitHub Bot commented on JENA-1233:
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GitHub user afs opened a pull request:
https://github.com/apache/jena/pull/191
JENA-1233: Serializable for Node/Triple/Quad.
Note: this works if and only if both jena-core and jena-arq are used.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/afs/jena serializable
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/jena/pull/191.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #191
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commit 079d9388e6a27bab95aaf30cb30d2b2621465836
Author: Andy Seaborne <[email protected]>
Date: 2016-11-07T18:07:04Z
JENA-1233: Serializable for Node/Triple/Quad.
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> Make RDF primitives Serializable
> --------------------------------
>
> Key: JENA-1233
> URL: https://issues.apache.org/jira/browse/JENA-1233
> Project: Apache Jena
> Issue Type: Improvement
> Components: Elephas
> Affects Versions: Jena 3.1.0
> Reporter: Itsuki Toyota
>
> I always use Jena when I handle RDF data with Apache Spark.
> However, when I want to store resulting RDD data (ex. RDD[Triple]) in binary
> format, I can't call RDD.saveAsObjectFile method.
> It's because RDD.saveAsObjectFile requires java.io.Serializable interface.
> See the following code.
> https://github.com/apache/spark/blob/v1.6.0/core/src/main/scala/org/apache/spark/rdd/RDD.scala#L1469
> https://github.com/apache/spark/blob/v1.6.0/core/src/main/scala/org/apache/spark/util/Utils.scala#L79-L86
> You can see that
> 1) RDD.saveAsObjectFile calls Util.serialize method
> 2) Util.serialize method requires the RDD-wrapped object implementing
> java.io.Serializable interface. For example, if you want to save a
> RDD[Triple] object, Triple must implements java.io.Serializable.
> So why not implement java.io.Serializable ?
> I think it will improve the usability in Apache Spark.
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