You can enable rdd compression (*spark.rdd.compress*) also you can
use MEMORY_ONLY_SER (
*sc.sequenceFile[String,String]("s3n://somebucket/part-00000").persist(StorageLevel.MEMORY_ONLY_SER*
*)* ) to reduce the rdd size in memory.
Thanks
Best Regards
On Wed, Oct 22, 2014 at 7:51 PM, Darin McBeath <ddmcbe...@yahoo.com.invalid>
wrote:
> I have a PairRDD of type <String,String> which I persist to S3 (using the
> following code).
>
> JavaPairRDD<Text, Text> aRDDWritable = aRDD.mapToPair(new
> ConvertToWritableTypes());
> aRDDWritable.saveAsHadoopFile(outputFile, Text.class, Text.class,
> SequenceFileOutputFormat.class);
>
> class ConvertToWritableTypes implements PairFunction<Tuple2<String,
> String>, Text, Text> {
> public Tuple2<Text, Text> call(Tuple2<String, String> record) {
> return new Tuple2(new Text(record._1), new Text(record._2));
>
> }
> }
>
> When I look at the S3 reported size for say one of the parts (part-00000)
> it indicates the size is 156MB.
>
> I then bring up a spark-shell and load this part-00000 and cache it.
>
> scala> val keyPair =
> sc.sequenceFile[String,String]("s3n://somebucket/part-00000").cache()
>
> After execution an action for the above RDD to force the cache, I look at
> the storage (using the Application UI) and it show that I'm using 297MB for
> this RDD (when it was only 156MB in S3). I get that there could be some
> differences between the serialized storage format and what is then used in
> memory, but I'm curious as to whether I'm missing something and/or should
> be doing things differently.
>
> Thanks.
>
> Darin.
>
