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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/11/11 17:03:00 UTC
[jira] [Commented] (SPARK-29830)
PySpark.context.Sparkcontext.binaryfiles improved memory with buffer
[ https://issues.apache.org/jira/browse/SPARK-29830?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16971727#comment-16971727 ]
Hyukjin Kwon commented on SPARK-29830:
--------------------------------------
(Please avoid to set target version which is usually reserved for committers)
> PySpark.context.Sparkcontext.binaryfiles improved memory with buffer
> --------------------------------------------------------------------
>
> Key: SPARK-29830
> URL: https://issues.apache.org/jira/browse/SPARK-29830
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 2.4.4
> Reporter: Jörn Franke
> Priority: Major
>
> At the moment, Pyspark reads binary files into a byte array directly. This means it reads the full binary file immediately into memory, which is 1) memory in-efficient 2) differs from the Scala implementation (see pyspark here: [https://spark.apache.org/docs/2.4.0/api/python/_modules/pyspark/context.html#SparkContext.binaryFiles). |https://spark.apache.org/docs/2.4.0/api/python/_modules/pyspark/context.html#SparkContext.binaryFiles]
> In Scala, Spark returns a PortableDataStream, which means the application does not need to read the full content of the stream in memory to work on it (see [https://spark.apache.org/docs/2.4.0/api/scala/index.html#org.apache.spark.SparkContext).]
>
> Hence, it is proposed to adapt the Pyspark implementation to return something similar to a PortableDataStream in Scala (e.g. [BytesIO|[https://docs.python.org/3/library/io.html#io.BytesIO].]
>
> Reading binary files in an efficient manner is crucial for many IoT applications, but potentially also other fields (e.g. disk image analysis in forensics).
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