On 10 Aug 2015, at 20:17, Akshat Aranya <aara...@gmail.com<mailto:aara...@gmail.com>> wrote:
Hi Jerry, Akhil, Thanks your your help. With s3n, the entire file is downloaded even while just creating the RDD with sqlContext.read.parquet(). It seems like even just opening and closing the InputStream causes the entire data to get fetched. As it turned out, I was able to use s3a and avoid this problem. I was under the impression that s3a was only meant for using EMRFS, where the metadata of the FS is kept separately. This is not true; s3a maps object keys directly to file names and directories. There's a bug with close() under the httpclient code which was fixed in s3a; sounds like the same issue has arisen in s3n S3a has had some bugs which surfaced after Hadoop 2.6 shipped; it's ready for use in Hadoop 2.7.1 On Sun, Aug 9, 2015 at 6:01 AM, Jerry Lam <chiling...@gmail.com<mailto: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. It's from some client-side optimisation that "for socket reuse" reads through the entire incoming HTTP stream on close(). Best Regards, Jerry Sent from my iPhone On 9 Aug, 2015, at 5:48 am, Akhil Das <ak...@sigmoidanalytics.com<mailto: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<mailto: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
