Thanks, sounds interesting! How do you load files to Spark? Did you consider having multiple files instead of file lines?
From: Hector Yee [mailto:hector....@gmail.com] Sent: Wednesday, April 01, 2015 11:36 AM To: Ulanov, Alexander Cc: Evan R. Sparks; Stephen Boesch; dev@spark.apache.org Subject: Re: Storing large data for MLlib machine learning I use Thrift and then base64 encode the binary and save it as text file lines that are snappy or gzip encoded. It makes it very easy to copy small chunks locally and play with subsets of the data and not have dependencies on HDFS / hadoop for server stuff for example. On Thu, Mar 26, 2015 at 2:51 PM, Ulanov, Alexander <alexander.ula...@hp.com<mailto:alexander.ula...@hp.com>> wrote: Thanks, Evan. What do you think about Protobuf? Twitter has a library to manage protobuf files in hdfs https://github.com/twitter/elephant-bird From: Evan R. Sparks [mailto:evan.spa...@gmail.com<mailto:evan.spa...@gmail.com>] Sent: Thursday, March 26, 2015 2:34 PM To: Stephen Boesch Cc: Ulanov, Alexander; dev@spark.apache.org<mailto:dev@spark.apache.org> Subject: Re: Storing large data for MLlib machine learning On binary file formats - I looked at HDF5+Spark a couple of years ago and found it barely JVM-friendly and very Hadoop-unfriendly (e.g. the APIs needed filenames as input, you couldn't pass it anything like an InputStream). I don't know if it has gotten any better. Parquet plays much more nicely and there are lots of spark-related projects using it already. Keep in mind that it's column-oriented which might impact performance - but basically you're going to want your features in a byte array and deser should be pretty straightforward. On Thu, Mar 26, 2015 at 2:26 PM, Stephen Boesch <java...@gmail.com<mailto:java...@gmail.com><mailto:java...@gmail.com<mailto:java...@gmail.com>>> wrote: There are some convenience methods you might consider including: MLUtils.loadLibSVMFile and MLUtils.loadLabeledPoint 2015-03-26 14:16 GMT-07:00 Ulanov, Alexander <alexander.ula...@hp.com<mailto:alexander.ula...@hp.com><mailto:alexander.ula...@hp.com<mailto:alexander.ula...@hp.com>>>: > Hi, > > Could you suggest what would be the reasonable file format to store > feature vector data for machine learning in Spark MLlib? Are there any best > practices for Spark? > > My data is dense feature vectors with labels. Some of the requirements are > that the format should be easy loaded/serialized, randomly accessible, with > a small footprint (binary). I am considering Parquet, hdf5, protocol buffer > (protobuf), but I have little to no experience with them, so any > suggestions would be really appreciated. > > Best regards, Alexander > -- Yee Yang Li Hector<http://google.com/+HectorYee> google.com/+HectorYee<http://google.com/+HectorYee>
