Github user freeman-lab commented on a diff in the pull request:
https://github.com/apache/spark/pull/1658#discussion_r17709043
--- 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])] = {
+ val job = new NewHadoopJob(hadoopConfiguration)
+ NewFileInputFormat.addInputPath(job, new Path(path))
+ val updateConf = job.getConfiguration
+ new BinaryFileRDD(
+ this,
+ classOf[ByteInputFormat],
+ classOf[String],
+ classOf[Array[Byte]],
+ updateConf,
+ minPartitions).setName(path)
+ }
+
+ /**
+ * Get an RDD for a Hadoop-readable dataset as PortableDataStream for
each file
+ * (useful for binary data)
+ *
+ *
+ * @param minPartitions A suggestion value of the minimal splitting
number for input data.
+ *
+ * @note Care must be taken to close the files afterwards
+ * @note Small files are preferred, large file is also allowable, but
may cause bad performance.
+ */
+ @DeveloperApi
+ def dataStreamFiles(path: String, minPartitions: Int =
defaultMinPartitions):
+ RDD[(String, PortableDataStream)] = {
+ val job = new NewHadoopJob(hadoopConfiguration)
+ NewFileInputFormat.addInputPath(job, new Path(path))
+ val updateConf = job.getConfiguration
+ new BinaryFileRDD(
+ this,
+ classOf[StreamInputFormat],
+ classOf[String],
+ classOf[PortableDataStream],
+ updateConf,
+ minPartitions).setName(path)
+ }
+
+ /**
+ * Load data from a flat binary file, assuming each record is a set of
numbers
+ * with the specified numerical format (see ByteBuffer), and the number
of
+ * bytes per record is constant (see FixedLengthBinaryInputFormat)
+ *
+ * @param path Directory to the input data files
+ * @return An RDD of data with values, RDD[(Array[Byte])]
+ */
+ def binaryRecords(path: String): RDD[Array[Byte]] = {
--- End diff --
@mateiz I agree that makes sense. Originally I used the global
configuration file so I could call it from PySpark, as in
``sc.newAPIHadoopRDD('FixedLengthBinaryInputFormat',..., conf={'recordLength':
'100'})``. But we can just add the new ``binaryRecords`` to PySpark directly
(probably in a separate PR), and have record length an argument as you suggest.
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