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https://issues.apache.org/jira/browse/SPARK-29830?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16971727#comment-16971727
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Hyukjin Kwon commented on SPARK-29830:
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(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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