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https://issues.apache.org/jira/browse/SPARK-6112?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14596297#comment-14596297
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Bogdan Ghit commented on SPARK-6112:
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I'd rather use tmpfs, because ramdisk needs root. However, I don't think the
last parameter is the issue.
More details:
I set LAZY_PERSIST policy on /tmp/spark-dfs and the following parameters for
Spark:
spark.externalBlockStore.baseDir /tmp/spark-dfs
spark.externalBlockStore.folderName external
spark.externalBlockStore.blockManager
org.apache.spark.storage.HDFSBlockManager
spark.externalBlockStore.url
hdfs://master.ib.cluster:54321
When I import data to HDFS with hadoop -copyFromLocal data /tmp/spark-dfs/, the
data goes in tmpfs.
When I write the output of an application in Spark with
saveAsTextFile("/tmp/spark-dfs/output"), the data goes to disk.
> Provide external block store support through HDFS RAM_DISK
> ----------------------------------------------------------
>
> Key: SPARK-6112
> URL: https://issues.apache.org/jira/browse/SPARK-6112
> Project: Spark
> Issue Type: New Feature
> Components: Block Manager
> Reporter: Zhan Zhang
> Attachments: SparkOffheapsupportbyHDFS.pdf
>
>
> HDFS Lazy_Persist policy provide possibility to cache the RDD off_heap in
> hdfs. We may want to provide similar capacity to Tachyon by leveraging hdfs
> RAM_DISK feature, if the user environment does not have tachyon deployed.
> With this feature, it potentially provides possibility to share RDD in memory
> across different jobs and even share with jobs other than spark, and avoid
> the RDD recomputation if executors crash.
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