Hi Akshat, I find some open source library which implements S3 InputFormat for Hadoop. Then I use Spark newAPIHadoopRDD to load data via that S3 InputFormat.
The open source library is https://github.com/ATLANTBH/emr-s3-io. It is a little old. I look inside it and make some changes. Then it works, and I have been using it for more than half year with Spark. It sill work great so far with latest Spark 1.4.0. You may need to modify it to avoid reading the whole file. Please feel free to let me know if you hit any questions. Best, Bo On Sun, Aug 9, 2015 at 6:01 AM, Jerry Lam <chiling...@gmail.com> wrote: > Hi Akshat, > > Is there a particular reason you don't use s3a? From my experience,s3a > performs much better than the rest. I believe the inefficiency is from the > implementation of the s3 interface. > > Best Regards, > > Jerry > > Sent from my iPhone > > On 9 Aug, 2015, at 5:48 am, Akhil Das <ak...@sigmoidanalytics.com> wrote: > > Depends on which operation you are doing, If you are doing a .count() on a > parquet, it might not download the entire file i think, but if you do a > .count() on a normal text file it might pull the entire file. > > Thanks > Best Regards > > On Sat, Aug 8, 2015 at 3:12 AM, Akshat Aranya <aara...@gmail.com> wrote: > >> Hi, >> >> I've been trying to track down some problems with Spark reads being very >> slow with s3n:// URIs (NativeS3FileSystem). After some digging around, I >> realized that this file system implementation fetches the entire file, >> which isn't really a Spark problem, but it really slows down things when >> trying to just read headers from a Parquet file or just creating partitions >> in the RDD. Is this something that others have observed before, or am I >> doing something wrong? >> >> Thanks, >> Akshat >> > >
