Github user mateiz commented on a diff in the pull request:
https://github.com/apache/spark/pull/1658#discussion_r17707460
--- Diff: core/src/main/scala/org/apache/spark/SparkContext.scala ---
@@ -511,6 +511,67 @@ class SparkContext(config: SparkConf) extends Logging {
}
/**
+ * Get an RDD for a Hadoop-readable dataset as byte-streams for each file
+ * (useful for binary data)
+ *
+ * @param minPartitions A suggestion value of the minimal splitting
number for input data.
+ *
+ * @note Small files are preferred, large file is also allowable, but
may cause bad performance.
+ */
+ def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions):
+ RDD[(String, Array[Byte])] = {
--- End diff --
What if we just added a toArray to PortableDataStream, and had only one
method for reading these? Then you could do
`sc.binaryFiles(...).map(_.toArray)` if you want to get byte arrays. Or would
this cause a regression?
Basically my suggestion is to have binaryFiles, which will return an RDD of
PortableDataStream, and binaryRecords, which will return an RDD of byte arrays
of the same length (since I imagine there's no point streaming a record).
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