Develop your own HadoopFileFormat and use
https://spark.apache.org/docs/2.0.2/api/java/org/apache/spark/SparkContext.html#newAPIHadoopRDD(org.apache.hadoop.conf.Configuration,%20java.lang.Class,%20java.lang.Class,%20java.lang.Class)
to load. The Spark datasource API will be relevant for you in the upcoming
version 2 as an alternative.
> On 16. Dec 2017, at 03:33, Christopher Piggott <cpigg...@gmail.com> wrote:
>
> I'm looking to run a job that involves a zillion files in a format called
> CDF, a nasa standard. There are a number of libraries out there that can
> read CDFs but most of them are not high quality compared to the official NASA
> one, which has java bindings (via JNI). It's a little clumsy but I have it
> working fairly well in Scala.
>
> The way I was planning on distributing work was with
> SparkContext.binaryFIles("hdfs://somepath/*) but that's really sending in an
> RDD of byte[] and unfortunately the CDF library doesn't support any kind of
> array or stream as input. The reason is that CDF is really looking for a
> random-access file, for performance reasons.
>
> Whats worse, all this code is implemented down at the native layer, in C.
>
> I think my best choice here is to distribute the job using .binaryFiles() but
> then have the first task of the worker be to write all those bytes to a
> ramdisk file (or maybe a real file, we'll see)... then have the CDF library
> open it as if it were a local file. This seems clumsy and awful but I
> haven't come up with any other good ideas.
>
> Has anybody else worked with these files and have a better idea? Some info
> on the library that parses all this:
>
> https://cdf.gsfc.nasa.gov/html/cdf_docs.html
>
>
> --Chris
>
